From 51c4c4102aa4d9b2ad66b8b0edad03ffd6bcdf42 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sun, 29 Dec 2024 16:42:53 -0700 Subject: [PATCH 01/50] intermediate work --- benchmarks/stateful_paths.py | 0 diffrax/_adjoint.py | 58 +++++-- diffrax/_brownian/base.py | 42 ++++- diffrax/_brownian/path.py | 173 +++++++++++++++++++- diffrax/_brownian/tree.py | 30 +++- diffrax/_integrate.py | 38 ++++- diffrax/_path.py | 65 +++++++- diffrax/_saveat.py | 6 + diffrax/_solution.py | 2 + diffrax/_solver/base.py | 54 ++++-- diffrax/_solver/euler.py | 9 +- diffrax/_term.py | 99 +++++++---- examples/underdamped_langevin_example.ipynb | 43 ++++- 13 files changed, 522 insertions(+), 97 deletions(-) create mode 100644 benchmarks/stateful_paths.py diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py new file mode 100644 index 00000000..e69de29b diff --git a/diffrax/_adjoint.py b/diffrax/_adjoint.py index 4ff2dd2c..cd9b6e34 100644 --- a/diffrax/_adjoint.py +++ b/diffrax/_adjoint.py @@ -32,7 +32,7 @@ def _is_subsaveat(x: Any) -> bool: def _nondiff_solver_controller_state( - adjoint, init_state, passed_solver_state, passed_controller_state + adjoint, init_state, passed_solver_state, passed_controller_state, passed_path_state ): if passed_solver_state: name = ( @@ -55,6 +55,16 @@ def _nondiff_solver_controller_state( ) else: controller_fn = lax.stop_gradient + if passed_path_state: + name = ( + f"When using `adjoint={adjoint.__class__.__name__}()`, then `path_state`" + ) + path_fn = ft.partial( + eqxi.nondifferentiable, + name=name, + ) + else: + path_fn = lax.stop_gradient init_state = eqx.tree_at( lambda s: s.solver_state, init_state, @@ -67,6 +77,12 @@ def _nondiff_solver_controller_state( replace_fn=controller_fn, is_leaf=_is_none, ) + init_state = eqx.tree_at( + lambda s: s.path_state, + init_state, + replace_fn=path_fn, + is_leaf=_is_none, + ) return init_state @@ -131,6 +147,7 @@ def loop( init_state, passed_solver_state, passed_controller_state, + passed_path_state, progress_meter, ) -> Any: """Runs the main solve loop. Subclasses can override this to provide custom @@ -264,15 +281,16 @@ def loop( throw, passed_solver_state, passed_controller_state, + passed_path_state, **kwargs, ): - del throw, passed_solver_state, passed_controller_state - if is_unsafe_sde(terms): - raise ValueError( - "`adjoint=RecursiveCheckpointAdjoint()` does not support " - "`UnsafeBrownianPath`. Consider using `adjoint=DirectAdjoint()` " - "instead." - ) + del throw, passed_solver_state, passed_controller_state, passed_path_state + # if is_unsafe_sde(terms): + # raise ValueError( + # "`adjoint=RecursiveCheckpointAdjoint()` does not support " + # "`UnsafeBrownianPath`. Consider using `adjoint=DirectAdjoint()` " + # "instead." + # ) if self.checkpoints is None and max_steps is None: inner_while_loop = ft.partial(_inner_loop, kind="lax") outer_while_loop = ft.partial(_outer_loop, kind="lax") @@ -344,18 +362,19 @@ def loop( throw, passed_solver_state, passed_controller_state, + passed_path_state, **kwargs, ): - del throw, passed_solver_state, passed_controller_state + del throw, passed_solver_state, passed_controller_state, passed_path_state # TODO: remove the `is_unsafe_sde` guard. # We need JAX to release bloops, so that we can deprecate `kind="bounded"`. - if is_unsafe_sde(terms): - kind = "lax" - msg = ( - "Cannot reverse-mode autodifferentiate when using " - "`UnsafeBrownianPath`." - ) - elif max_steps is None: + # if is_unsafe_sde(terms): + # kind = "lax" + # msg = ( + # "Cannot reverse-mode autodifferentiate when using " + # "`UnsafeBrownianPath`." + # ) + if max_steps is None: kind = "lax" msg = ( "Cannot reverse-mode autodifferentiate when using " @@ -478,6 +497,7 @@ def loop( init_state, passed_solver_state, passed_controller_state, + passed_path_state, **kwargs, ): del throw @@ -489,7 +509,7 @@ def loop( "`saveat=SaveAt(t1=True)`." ) init_state = _nondiff_solver_controller_state( - self, init_state, passed_solver_state, passed_controller_state + self, init_state, passed_solver_state, passed_controller_state, passed_path_state ) inputs = (args, terms, self, kwargs, solver, saveat, init_state) ys, residual = optxi.implicit_jvp( @@ -788,6 +808,7 @@ def loop( init_state, passed_solver_state, passed_controller_state, + passed_path_state, event, **kwargs, ): @@ -806,6 +827,7 @@ def loop( raise NotImplementedError( "Cannot use `adjoint=BacksolveAdjoint()` with `saveat=SaveAt(fn=...)`." ) + # is this still true with DirectAdjoint? if is_unsafe_sde(terms): raise ValueError( "`adjoint=BacksolveAdjoint()` does not support `UnsafeBrownianPath`. " @@ -838,7 +860,7 @@ def loop( y = init_state.y init_state = eqx.tree_at(lambda s: s.y, init_state, object()) init_state = _nondiff_solver_controller_state( - self, init_state, passed_solver_state, passed_controller_state + self, init_state, passed_solver_state, passed_controller_state, passed_path_state ) final_state, aux_stats = _loop_backsolve( diff --git a/diffrax/_brownian/base.py b/diffrax/_brownian/base.py index 21618b76..e9496960 100644 --- a/diffrax/_brownian/base.py +++ b/diffrax/_brownian/base.py @@ -14,13 +14,53 @@ _Control = TypeVar("_Control", bound=Union[PyTree[Array], AbstractBrownianIncrement]) +_BrownianState = TypeVar("_BrownianState") -class AbstractBrownianPath(AbstractPath[_Control]): +class AbstractBrownianPath(AbstractPath[_Control, _BrownianState]): """Abstract base class for all Brownian paths.""" levy_area: AbstractVar[type[Union[BrownianIncrement, SpaceTimeLevyArea]]] + @abc.abstractmethod + def __call__( + self, + t0: RealScalarLike, + brownian_state: _BrownianState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + use_levy: bool = False, + ) -> tuple[_Control, _BrownianState]: + r"""Samples a Brownian increment $w(t_1) - w(t_0)$. + + Each increment has distribution $\mathcal{N}(0, t_1 - t_0)$. + + This is equivalent to `evaluate` but enables stateful evaluation. + + **Arguments:** + + - `t0`: Any point in $[t_0, t_1]$ to evaluate the path at. + - `brownian_state`: The current state of the path. + - `t1`: If passed, then the increment from `t1` to `t0` is evaluated instead. + - `left`: Ignored. (This determines whether to treat the path as + left-continuous or right-continuous at any jump points, but Brownian + motion has no jump points.) + - `use_levy`: If True, the return type will be a `LevyVal`, which contains + PyTrees of Brownian increments and their Lévy areas. + + **Returns:** + + If `t1` is not passed: + + The value of the Brownian motion at `t0`. + + If `t1` is passed: + + The increment of the Brownian motion between `t0` and `t1`. + + In both cases, the updated state is also returned. + """ + @abc.abstractmethod def evaluate( self, diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 0333caa5..7c3c45b3 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -1,5 +1,5 @@ import math -from typing import cast, Optional, Union +from typing import cast, Optional, TypeAlias, Union import equinox as eqx import equinox.internal as eqxi @@ -8,16 +8,18 @@ import jax.random as jr import jax.tree_util as jtu import lineax.internal as lxi -from jaxtyping import Array, PRNGKeyArray, PyTree +from jaxtyping import Array, Float, PRNGKeyArray, PyTree from lineax.internal import complex_to_real_dtype from .._custom_types import ( AbstractBrownianIncrement, + Args, BrownianIncrement, levy_tree_transpose, RealScalarLike, SpaceTimeLevyArea, SpaceTimeTimeLevyArea, + Y, ) from .._misc import ( force_bitcast_convert_type, @@ -27,13 +29,22 @@ from .base import AbstractBrownianPath -class UnsafeBrownianPath(AbstractBrownianPath): +_Control = Union[PyTree[Array], AbstractBrownianIncrement] +_BrownianState: TypeAlias = Union[ + tuple[None, PyTree[Array], int], tuple[PRNGKeyArray, None, None] +] + + +class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): """Brownian simulation that is only suitable for certain cases. - This is a very quick way to simulate Brownian motion, but can only be used when all - of the following are true: + This is a very quick way to simulate Brownian motion (faster than VBT), but can only be + used if you are not using an adaptive scheme that rejects steps (pre-visible adaptive + methods are valid). + + If using the stateless `evaluate` method, stricter requirements are imposed, namely: - 1. You are using a fixed step size controller. (Not an adaptive one.) + 1. You are not using an adaptive solver that rejects steps. 2. You do not need to backpropagate through the differential equation. @@ -66,6 +77,7 @@ class UnsafeBrownianPath(AbstractBrownianPath): Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = eqx.field(static=True) key: PRNGKeyArray + precompute: bool = eqx.field(static=True) def __init__( self, @@ -74,6 +86,7 @@ def __init__( levy_area: type[ Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = BrownianIncrement, + precompute: bool = True, ): self.shape = ( jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) @@ -82,12 +95,13 @@ def __init__( ) self.key = key self.levy_area = levy_area + self.precompute = precompute if any( not jnp.issubdtype(x.dtype, jnp.inexact) for x in jtu.tree_leaves(self.shape) ): - raise ValueError("UnsafeBrownianPath dtypes all have to be floating-point.") + raise ValueError("DirectBrownianPath dtypes all have to be floating-point.") @property def t0(self): @@ -97,6 +111,106 @@ def t0(self): def t1(self): return jnp.inf + def _generate_noise( + self, + key: PRNGKeyArray, + shape: jax.ShapeDtypeStruct, + ) -> Float[Array, "levy_dims shape"]: + if self.levy_area is SpaceTimeTimeLevyArea: + key_w, key_hh, key_kk = jr.split(key, 3) + w = jr.normal(key_w, shape.shape, shape.dtype) + hh = jr.normal(key_hh, shape.shape, shape.dtype) + kk = jr.normal(key_kk, shape.shape, shape.dtype) + noise = jnp.stack([w, hh, kk]) + elif self.levy_area is SpaceTimeLevyArea: + key_w, key_hh = jr.split(key, 2) + w = jr.normal(key_w, shape.shape, shape.dtype) + hh = jr.normal(key_hh, shape.shape, shape.dtype) + noise = jnp.stack([w, hh]) + elif self.levy_area is BrownianIncrement: + noise = jr.normal(key, shape.shape, shape.dtype) + else: + assert False + + return noise + + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> _BrownianState: + if max_steps is not None: + subkey = split_by_tree(self.key, self.shape) + noise = jtu.tree_map( + lambda subkey, shape: self._generate_noise(subkey, shape), + subkey, + self.shape, + ) + counter = 0 + key = None + else: + noise = None + counter = None + key = self.key + + return key, noise, counter + + def __call__( + self, + t0: RealScalarLike, + brownian_state: _BrownianState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + use_levy: bool = False, + ) -> tuple[_Control, _BrownianState]: + del left + if t1 is None: + dtype = jnp.result_type(t0) + t1 = t0 + t0 = jnp.array(0, dtype) + else: + with jax.numpy_dtype_promotion("standard"): + dtype = jnp.result_type(t0, t1) + t0 = jnp.astype(t0, dtype) + t1 = jnp.astype(t1, dtype) + t0 = eqxi.nondifferentiable(t0, name="t0") + t1 = eqxi.nondifferentiable(t1, name="t1") + t1 = cast(RealScalarLike, t1) + + key, noises, counter = brownian_state + if key is None: # precomputed noise + out = jtu.tree_map( + lambda shape, noise: self._evaluate_leaf_precomputed( + t0, t1, shape, self.levy_area, use_levy, noise + ), + self.shape, + jax.tree.map(lambda x: x[counter], noises), + ) + if use_levy: + out = levy_tree_transpose(self.shape, out) + assert isinstance(out, self.levy_area) + # if a solver needs to call .evaluate twice, but wants access to the same + # brownian motion, the solver could just decrease the counter + return out, (None, noises, counter + 1) + else: + assert noises is None and counter is None + new_key, key = jr.split(key) + key = split_by_tree(key, self.shape) + out = jtu.tree_map( + lambda key, shape: self._evaluate_leaf( + t0, t1, key, shape, self.levy_area, use_levy + ), + key, + self.shape, + ) + if use_levy: + out = levy_tree_transpose(self.shape, out) + assert isinstance(out, self.levy_area) + return out, (new_key, None, None) + @eqx.filter_jit def evaluate( self, @@ -135,11 +249,48 @@ def evaluate( assert isinstance(out, self.levy_area) return out + @staticmethod + def _evaluate_leaf_precomputed( + t0: RealScalarLike, + t1: RealScalarLike, + shape: jax.ShapeDtypeStruct, + levy_area: type[ + Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] + ], + use_levy: bool, + noises: Float[Array, "levy_dims shape"], + ): + w_std = jnp.sqrt(t1 - t0).astype(shape.dtype) + dt = jnp.asarray(t1 - t0, dtype=complex_to_real_dtype(shape.dtype)) + + if levy_area is SpaceTimeTimeLevyArea: + w = noises[0] * w_std + hh_std = w_std / math.sqrt(12) + hh = noises[1] * hh_std + kk_std = w_std / math.sqrt(720) + kk = noises[2] * kk_std + levy_val = SpaceTimeTimeLevyArea(dt=dt, W=w, H=hh, K=kk) + + elif levy_area is SpaceTimeLevyArea: + w = noises[0] * w_std + hh_std = w_std / math.sqrt(12) + hh = noises[1] * hh_std + levy_val = SpaceTimeLevyArea(dt=dt, W=w, H=hh) + elif levy_area is BrownianIncrement: + w = noises * w_std + levy_val = BrownianIncrement(dt=dt, W=w) + else: + assert False + + if use_levy: + return levy_val + return w + @staticmethod def _evaluate_leaf( t0: RealScalarLike, t1: RealScalarLike, - key, + key: PRNGKeyArray, shape: jax.ShapeDtypeStruct, levy_area: type[ Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] @@ -175,7 +326,7 @@ def _evaluate_leaf( return w -UnsafeBrownianPath.__init__.__doc__ = """ +DirectBrownianPath.__init__.__doc__ = """ **Arguments:** - `shape`: Should be a PyTree of `jax.ShapeDtypeStruct`s, representing the shape, @@ -185,4 +336,8 @@ def _evaluate_leaf( - `key`: A random key. - `levy_area`: Whether to additionally generate Lévy area. This is required by some SDE solvers. +- `precompute`: Whether or not to precompute the brownian motion (if possible). Precomputing + requires additional memory at initialization time, but can result in faster integrations. """ + +UnsafeBrownianPath = DirectBrownianPath diff --git a/diffrax/_brownian/tree.py b/diffrax/_brownian/tree.py index 83259567..306956b0 100644 --- a/diffrax/_brownian/tree.py +++ b/diffrax/_brownian/tree.py @@ -15,6 +15,7 @@ from .._custom_types import ( AbstractBrownianIncrement, + Args, BoolScalarLike, BrownianIncrement, IntScalarLike, @@ -22,6 +23,7 @@ RealScalarLike, SpaceTimeLevyArea, SpaceTimeTimeLevyArea, + Y, ) from .._misc import ( is_tuple_of_ints, @@ -62,6 +64,8 @@ ] _Spline: TypeAlias = Literal["sqrt", "quad", "zero"] _BrownianReturn = TypeVar("_BrownianReturn", bound=AbstractBrownianIncrement) +_Control = Union[PyTree[Array], AbstractBrownianIncrement] +_BrownianState: TypeAlias = None # An internal dataclass that holds the rescaled Lévy areas @@ -175,7 +179,7 @@ def _split_interval( return x_s, x_u, x_su -class VirtualBrownianTree(AbstractBrownianPath): +class VirtualBrownianTree(AbstractBrownianPath[_Control, _BrownianState]): """Brownian simulation that discretises the interval `[t0, t1]` to tolerance `tol`. !!! info "Lévy Area" @@ -299,6 +303,26 @@ def is_dt(z): other_normalized = jtu.tree_map(sqrt_mult, other) return eqx.combine(dt_normalized, other_normalized) + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> _BrownianState: + return None + + def __call__( + self, + t0: RealScalarLike, + brownian_state: _BrownianState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + use_levy: bool = False, + ) -> tuple[_Control, _BrownianState]: + return self.evaluate(t0, t1, left, use_levy), brownian_state + @eqx.filter_jit def evaluate( self, @@ -306,7 +330,7 @@ def evaluate( t1: Optional[RealScalarLike] = None, left: bool = True, use_levy: bool = False, - ) -> Union[PyTree[Array], AbstractBrownianIncrement]: + ) -> _Control: t0 = eqxi.nondifferentiable(t0, name="t0") # map the interval [self.t0, self.t1] onto [0,1] t0 = linear_rescale(self.t0, t0, self.t1) @@ -326,7 +350,7 @@ def evaluate( # now map [0,1] back onto [self.t0, self.t1] levy_out = self._denormalise_bm_inc(levy_out) assert isinstance(levy_out, self.levy_area) - return levy_out if use_levy else levy_out.W + return (levy_out if use_levy else levy_out.W, None) def _evaluate(self, r: RealScalarLike) -> PyTree: """Maps the _evaluate_leaf function at time r using self.key onto self.shape""" diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 6a31fe59..a1fdec53 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -62,9 +62,10 @@ AbstractAdaptiveStepSizeController, AbstractStepSizeController, ConstantStepSize, + PIDController, StepTo, ) -from ._term import AbstractTerm, MultiTerm, ODETerm, WrapTerm +from ._term import AbstractTerm, MultiTerm, ODETerm, WrapTerm, _AbstractControlTerm from ._typing import better_isinstance, get_args_of, get_origin_no_specials @@ -85,6 +86,7 @@ class State(eqx.Module): made_jump: BoolScalarLike solver_state: PyTree[ArrayLike] controller_state: PyTree[ArrayLike] + path_state: PyTree progress_meter_state: PyTree[Array] result: RESULTS # @@ -334,13 +336,14 @@ def body_fun_aux(state): # step sizes, all that jazz. # - (y, y_error, dense_info, solver_state, solver_result) = solver.step( + (y, y_error, dense_info, solver_state, path_state, solver_result) = solver.step( terms, state.tprev, state.tnext, state.y, args, state.solver_state, + state.path_state, state.made_jump, ) @@ -387,6 +390,7 @@ def body_fun_aux(state): y = jtu.tree_map(keep, y, state.y) solver_state = jtu.tree_map(keep, solver_state, state.solver_state) made_jump = static_select(keep_step, made_jump, state.made_jump) + path_state = jtu.tree_map(keep, path_state, state.path_state) solver_result = RESULTS.where(keep_step, solver_result, RESULTS.successful) # TODO: if we ever support non-terminating events, then they should go in here. @@ -580,6 +584,7 @@ def _outer_cond_fn(cond_fn_i, old_event_value_i): made_jump=made_jump, # pyright: ignore solver_state=solver_state, controller_state=controller_state, + path_state=path_state, result=result, num_steps=num_steps, num_accepted_steps=num_accepted_steps, @@ -869,6 +874,7 @@ def diffeqsolve( solver_state: Optional[PyTree[ArrayLike]] = None, controller_state: Optional[PyTree[ArrayLike]] = None, made_jump: Optional[BoolScalarLike] = None, + path_state: Optional[PyTree] = None, # Exists for backward compatibility discrete_terminating_event: Optional[AbstractDiscreteTerminatingEvent] = None, ) -> Solution: @@ -951,6 +957,9 @@ def diffeqsolve( - `controller_state`: Some initial state for the step size controller. Generally obtained by `SaveAt(controller_state=True)` from a previous solve. + + - `path_state`: Some initial state for the path. Generally obtained by + `SaveAt(path_state=True)` from a previous solve. - `made_jump`: Whether a jump has just been made at `t0`. Used to update `solver_state` (if passed). Generally obtained by `SaveAt(made_jump=True)` @@ -1109,9 +1118,9 @@ def _promote(yi): "method, as it may not converge to the correct solution." ) if is_unsafe_sde(terms): - if isinstance(stepsize_controller, AbstractAdaptiveStepSizeController): + if isinstance(stepsize_controller, PIDController): raise ValueError( - "`UnsafeBrownianPath` cannot be used with adaptive step sizes." + "`DirecBrownianPath` cannot be used with PIDController as it may reject steps." ) # Normalises time: if t0 > t1 then flip things around. @@ -1221,6 +1230,18 @@ def _subsaveat_direction_fn(x): else: tnext = t0 + dt0 tnext = jnp.minimum(tnext, t1) + + def _path_init(term): + if isinstance(term, _AbstractControlTerm): + return term.control.init(t0, tnext, y0, args, max_steps) + return None + + if path_state is None: + passed_path_state = False + path_state = jtu.tree_map(_path_init, terms) + else: + passed_path_state = True + if solver_state is None: passed_solver_state = False solver_state = solver.init(terms, t0, tnext, y0, args) @@ -1264,7 +1285,7 @@ def _allocate_output(subsaveat: SubSaveAt) -> SaveState: result = RESULTS.successful if saveat.dense or event is not None: _, _, dense_info_struct, _, _ = eqx.filter_eval_shape( - solver.step, terms, tprev, tnext, y0, args, solver_state, made_jump + solver.step, terms, tprev, tnext, y0, args, solver_state, made_jump, path_state ) if saveat.dense: if max_steps is None: @@ -1378,6 +1399,7 @@ def _outer_cond_fn(cond_fn_i): made_jump=made_jump, solver_state=solver_state, controller_state=controller_state, + path_state=path_state, result=result, num_steps=num_steps, num_accepted_steps=num_accepted_steps, @@ -1413,6 +1435,7 @@ def _outer_cond_fn(cond_fn_i): throw=throw, passed_solver_state=passed_solver_state, passed_controller_state=passed_controller_state, + passed_path_state=passed_path_state, progress_meter=progress_meter, ) @@ -1439,6 +1462,10 @@ def _outer_cond_fn(cond_fn_i): solver_state = final_state.solver_state else: solver_state = None + if saveat.path_state: + path_state = final_state.path_state + else: + path_state = None if saveat.made_jump: made_jump = final_state.made_jump else: @@ -1479,6 +1506,7 @@ def _outer_cond_fn(cond_fn_i): result=result, solver_state=solver_state, controller_state=controller_state, + path_state=path_state, made_jump=made_jump, event_mask=event_mask, ) diff --git a/diffrax/_path.py b/diffrax/_path.py index e78b8d8b..c73909c4 100644 --- a/diffrax/_path.py +++ b/diffrax/_path.py @@ -4,6 +4,7 @@ import equinox as eqx import jax import jax.numpy as jnp +from jaxtyping import PyTree if TYPE_CHECKING: @@ -11,13 +12,14 @@ else: from equinox import AbstractVar -from ._custom_types import Control, RealScalarLike +from ._custom_types import Args, Control, RealScalarLike, Y _Control = TypeVar("_Control", bound=Control) +_PathState = TypeVar("_PathState") -class AbstractPath(eqx.Module, Generic[_Control]): +class AbstractPath(eqx.Module, Generic[_Control, _PathState]): """Abstract base class for all paths. Every path has a start point `t0` and an end point `t1`. In between these values @@ -47,6 +49,65 @@ def evaluate(self, t0, t1=None, left=True): t0: AbstractVar[RealScalarLike] t1: AbstractVar[RealScalarLike] + @abc.abstractmethod + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> _PathState: + """Initialises any hidden state for the path. + + **Arguments** as [`diffrax.diffeqsolve`][]. + + **Returns:** + + The initial path state. + """ + + @abc.abstractmethod + def __call__( + self, + t0: RealScalarLike, + path_state: _PathState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + ) -> tuple[_Control, _PathState]: + r"""Evaluate the path at any point in the interval $[t_0, t_1]$. + + This is equivalent to `evaluate` but enables stateful evaluation. + + **Arguments:** + + - `t0`: Any point in $[t_0, t_1]$ to evaluate the path at. + - `path_state`: The current state for the path. + - `t1`: If passed, then the increment from `t1` to `t0` is evaluated instead. + - `left`: Across jump points: whether to treat the path as left-continuous + or right-continuous. + + !!! faq "FAQ" + + Note that we use $t_0$ and $t_1$ to refer to the overall interval, as + obtained via `instance.t0` and `instance.t1`. We use `t0` and `t1` to refer + to some subinterval of $[t_0, t_1]$. This is an API that is used for + consistency with the rest of the package, and just happens to be a little + confusing here. + + **Returns:** + + If `t1` is not passed: + + The value of the path at `t0`. + + If `t1` is passed: + + The increment of the path between `t0` and `t1`. + + In both cases, the updated state is also returned. + """ + @abc.abstractmethod def evaluate( self, t0: RealScalarLike, t1: Optional[RealScalarLike] = None, left: bool = True diff --git a/diffrax/_saveat.py b/diffrax/_saveat.py index 6ee373de..aee5d75f 100644 --- a/diffrax/_saveat.py +++ b/diffrax/_saveat.py @@ -64,6 +64,7 @@ class SaveAt(eqx.Module): dense: bool = False solver_state: bool = False controller_state: bool = False + path_state: bool = False made_jump: bool = False def __init__( @@ -78,6 +79,7 @@ def __init__( dense: bool = False, solver_state: bool = False, controller_state: bool = False, + path_state: bool = False, made_jump: bool = False, ): if subs is None: @@ -93,6 +95,7 @@ def __init__( self.dense = dense self.solver_state = solver_state self.controller_state = controller_state + self.path_state = path_state self.made_jump = made_jump @@ -131,6 +134,9 @@ def __init__( - `controller_state`: If `True`, save the internal state of the step size controller at `t1`; accessible as `sol.controller_state`. +- `path_state`: If `True`, save the internal state of the path at `t1`; accessible as + `sol.path_state`. + - `made_jump`: If `True`, save the internal state of the jump tracker at `t1`; accessible as `sol.made_jump`. diff --git a/diffrax/_solution.py b/diffrax/_solution.py index f1b8d21b..351dec23 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -89,6 +89,7 @@ class Solution(AbstractPath): - `solver_state`: If saved, the final internal state of the numerical solver. - `controller_state`: If saved, the final internal state for the step size controller. + - `path_state`: If saved, the final internal state for the path. - `made_jump`: If saved, the final internal state for the jump tracker. - `event_mask`: If using [events](../events), a boolean mask indicating which event triggered. This is a PyTree of bools, with the same PyTree stucture as the event @@ -119,6 +120,7 @@ class Solution(AbstractPath): result: RESULTS solver_state: Optional[PyTree] controller_state: Optional[PyTree] + path_state: Optional[PyTree] made_jump: Optional[BoolScalarLike] event_mask: Optional[PyTree[BoolScalarLike]] diff --git a/diffrax/_solver/base.py b/diffrax/_solver/base.py index 42f19e4c..56a33230 100644 --- a/diffrax/_solver/base.py +++ b/diffrax/_solver/base.py @@ -34,6 +34,7 @@ _SolverState = TypeVar("_SolverState") +_PathState = TypeVar("_PathState") def vector_tree_dot(a, b): @@ -71,7 +72,7 @@ def _term_compatible_contr_kwargs(term_structure): return jtu.tree_map(_term_compatible_contr_kwargs, term_structure) -class AbstractSolver(eqx.Module, Generic[_SolverState], **_set_metaclass): +class AbstractSolver(eqx.Module, Generic[_SolverState, _PathState], **_set_metaclass): """Abstract base class for all differential equation solvers. Subclasses should have a class-level attribute `terms`, specifying the PyTree @@ -149,7 +150,8 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, Optional[Y], DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, Optional[Y], DenseInfo, _SolverState, _PathState, RESULTS]: """Make a single step of the solver. Each step is made over the specified interval $[t_0, t_1]$. @@ -166,6 +168,7 @@ def step( Some solvers (notably FSAL Runge--Kutta solvers) usually assume that there are no jumps and for efficiency re-use information between steps; this indicates that a jump has just occurred and this assumption is not true. + - `path_state`: Any evolving state for any path being used. **Returns:** @@ -179,6 +182,7 @@ def step( routine to calculate dense output. (Used with `SaveAt(ts=...)` or `SaveAt(dense=...)`.) - The value of the solver state at `t1`. + - The value of the path state at `t1`. - An integer (corresponding to `diffrax.RESULTS`) indicating whether the step happened successfully, or if (unusually) it failed for some reason. """ @@ -206,7 +210,7 @@ def func( """ -class AbstractImplicitSolver(AbstractSolver[_SolverState]): +class AbstractImplicitSolver(AbstractSolver[_SolverState, _PathState]): """Indicates that this is an implicit differential equation solver, and as such that it should take a root finder as an argument. """ @@ -215,25 +219,25 @@ class AbstractImplicitSolver(AbstractSolver[_SolverState]): root_find_max_steps: AbstractVar[int] -class AbstractItoSolver(AbstractSolver[_SolverState]): +class AbstractItoSolver(AbstractSolver[_SolverState, _PathState]): """Indicates that when used as an SDE solver that this solver will converge to the Itô solution. """ -class AbstractStratonovichSolver(AbstractSolver[_SolverState]): +class AbstractStratonovichSolver(AbstractSolver[_SolverState, _PathState]): """Indicates that when used as an SDE solver that this solver will converge to the Stratonovich solution. """ -class AbstractAdaptiveSolver(AbstractSolver[_SolverState]): +class AbstractAdaptiveSolver(AbstractSolver[_SolverState, _PathState]): """Indicates that this solver provides error estimates, and that as such it may be used with an adaptive step size controller. """ -class AbstractWrappedSolver(AbstractSolver[_SolverState]): +class AbstractWrappedSolver(AbstractSolver[_SolverState, _PathState]): """Wraps another solver "transparently", in the sense that all `isinstance` checks will be forwarded on to the wrapped solver, e.g. when testing whether the solver is implicit/adaptive/SDE-compatible/etc. @@ -246,7 +250,8 @@ class if that is not desired behaviour.) class HalfSolver( - AbstractAdaptiveSolver[_SolverState], AbstractWrappedSolver[_SolverState] + AbstractAdaptiveSolver[_SolverState, _PathState], + AbstractWrappedSolver[_SolverState, _PathState], ): """Wraps another solver, trading cost in order to provide error estimates. (That is, it means the solver can be used with an adaptive step size controller, @@ -317,26 +322,43 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, Optional[Y], DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, Optional[Y], DenseInfo, _SolverState, _PathState, RESULTS]: original_solver_state = solver_state + original_path_state = path_state thalf = t0 + 0.5 * (t1 - t0) - yhalf, _, _, solver_state, result1 = self.solver.step( - terms, t0, thalf, y0, args, solver_state, made_jump + yhalf, _, _, solver_state, path_state, result1 = self.solver.step( + terms, t0, thalf, y0, args, solver_state, made_jump, path_state ) - y1, _, _, solver_state, result2 = self.solver.step( - terms, thalf, t1, yhalf, args, solver_state, made_jump=False + y1, _, _, solver_state, path_state, result2 = self.solver.step( + terms, + thalf, + t1, + yhalf, + args, + solver_state, + made_jump=False, + path_state=path_state, ) # TODO: use dense_info from the pair of half-steps instead - y1_alt, _, dense_info, _, result3 = self.solver.step( - terms, t0, t1, y0, args, original_solver_state, made_jump + # this potentially reuses the same brownian increment, is this right? + y1_alt, _, dense_info, _, _, result3 = self.solver.step( + terms, + t0, + t1, + y0, + args, + original_solver_state, + made_jump, + original_path_state, ) y_error = (y1**ω - y1_alt**ω).call(jnp.abs).ω result = update_result(result1, update_result(result2, result3)) - return y1, y_error, dense_info, solver_state, result + return y1, y_error, dense_info, solver_state, path_state, result def func( self, terms: PyTree[AbstractTerm], t0: RealScalarLike, y0: Y, args: Args diff --git a/diffrax/_solver/euler.py b/diffrax/_solver/euler.py index c38642e9..aa37aec8 100644 --- a/diffrax/_solver/euler.py +++ b/diffrax/_solver/euler.py @@ -8,7 +8,7 @@ from .._local_interpolation import LocalLinearInterpolation from .._solution import RESULTS from .._term import AbstractTerm -from .base import AbstractItoSolver +from .base import _PathState, AbstractItoSolver _ErrorEstimate: TypeAlias = None @@ -54,12 +54,13 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump - control = terms.contr(t0, t1) + control, path_state = terms.contr(t0, t1, path_state) y1 = (y0**ω + terms.vf_prod(t0, y0, args, control) ** ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, RESULTS.successful + return y1, None, dense_info, None, path_state, RESULTS.successful def func( self, diff --git a/diffrax/_term.py b/diffrax/_term.py index bacaef9d..896f9d6d 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -30,9 +30,10 @@ _VF = TypeVar("_VF", bound=VF) _Control = TypeVar("_Control", bound=Control) +_ControlState = TypeVar("_ControlState") -class AbstractTerm(eqx.Module, Generic[_VF, _Control]): +class AbstractTerm(eqx.Module, Generic[_VF, _Control, _ControlState]): r"""Abstract base class for all terms. Let $y$ solve some differential equation with vector field $f$ and control $x$. @@ -62,7 +63,13 @@ def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: pass @abc.abstractmethod - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _ControlState, + **kwargs, + ) -> tuple[_Control, _ControlState]: r"""The control. Represents the $\mathrm{d}t$ in an ODE, or the $\mathrm{d}w(t)$ in an SDE, etc. @@ -171,7 +178,7 @@ def is_vf_expensive( return False -class ODETerm(AbstractTerm[_VF, RealScalarLike]): +class ODETerm(AbstractTerm[_VF, RealScalarLike, None]): r"""A term representing $f(t, y(t), args) \mathrm{d}t$. That is to say, the term appearing on the right hand side of an ODE, in which the control is time. @@ -210,8 +217,14 @@ def _broadcast_and_upcast(oi, yi): return jtu.tree_map(_broadcast_and_upcast, out, y) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> RealScalarLike: - return t1 - t0 + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: None = None, + **kwargs, + ) -> tuple[RealScalarLike, None]: + return t1 - t0, None def prod(self, vf: _VF, control: RealScalarLike) -> Y: def _mul(v): @@ -235,7 +248,7 @@ def _mul(v): """ -class _CallableToPath(AbstractPath[_Control]): +class _CallableToPath(AbstractPath[_Control, _ControlState]): fn: Callable @property @@ -254,9 +267,9 @@ def evaluate( def _callable_to_path( x: Union[ - AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] + AbstractPath[_Control, _ControlState], Callable[[RealScalarLike, RealScalarLike], _Control] ], -) -> AbstractPath[_Control]: +) -> AbstractPath[_Control, _ControlState]: if isinstance(x, AbstractPath): return x else: @@ -272,17 +285,23 @@ def _prod(vf, control): # This class exists for backward compatibility with `WeaklyDiagonalControlTerm`. If we # were writing things again today it would be folded into just `ControlTerm`. -class _AbstractControlTerm(AbstractTerm[_VF, _Control]): +class _AbstractControlTerm(AbstractTerm[_VF, _Control, _ControlState]): vector_field: Callable[[RealScalarLike, Y, Args], _VF] control: Union[ - AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] + AbstractPath[_Control, _ControlState], Callable[[RealScalarLike, RealScalarLike], _Control] ] = eqx.field(converter=_callable_to_path) # pyright: ignore def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: return self.vector_field(t, y, args) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: - return self.control.evaluate(t0, t1, **kwargs) # pyright: ignore + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _ControlState, + **kwargs, + ) -> tuple[_Control, _ControlState]: + return self.control(t0, control_state, t1, **kwargs) # pyright: ignore def to_ode(self) -> ODETerm: r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ @@ -311,14 +330,14 @@ def to_ode(self) -> ODETerm: - `control`: The control. Should either be - 1. a [`diffrax.AbstractPath`][], in which case its `.evaluate(t0, t1)` method + 1. a [`diffrax.AbstractPath`][], in which case its `.__call__(t0, path_state, t1)` method will be used to give the increment of the control over a time interval `[t0, t1]`, or 2. a callable `(t0, t1) -> increment`, which returns the increment directly. """ -class ControlTerm(_AbstractControlTerm[_VF, _Control]): +class ControlTerm(_AbstractControlTerm[_VF, _Control, _ControlState]): r"""A term representing the general case of $f(t, y(t), args) \mathrm{d}x(t)$, in which the vector field ($f$) - control ($\mathrm{d}x$) interaction is a matrix-vector product. @@ -458,7 +477,7 @@ def prod(self, vf: _VF, control: _Control) -> Y: return jtu.tree_map(_prod, vf, control) -class WeaklyDiagonalControlTerm(_AbstractControlTerm[_VF, _Control]): +class WeaklyDiagonalControlTerm(_AbstractControlTerm[_VF, _Control, _ControlState]): r""" DEPRECATED. Prefer: @@ -539,6 +558,7 @@ def _sum(*x): _Terms = TypeVar("_Terms", bound=tuple[AbstractTerm, ...]) +_MultiControlState = TypeVar("_MultiControlState", bound=tuple) class MultiTerm(AbstractTerm, Generic[_Terms]): @@ -573,9 +593,17 @@ def vf(self, t: RealScalarLike, y: Y, args: Args) -> tuple[PyTree[ArrayLike], .. return tuple(term.vf(t, y, args) for term in self.terms) def contr( - self, t0: RealScalarLike, t1: RealScalarLike, **kwargs - ) -> tuple[PyTree[ArrayLike], ...]: - return tuple(term.contr(t0, t1, **kwargs) for term in self.terms) + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _MultiControlState, + **kwargs, + ) -> tuple[tuple[PyTree[ArrayLike], ...], _MultiControlState]: + contrs = [ + term.contr(t0, t1, state, **kwargs) + for term, state in zip(self.terms, control_state) + ] + return (tuple(i[0] for i in contrs), tuple(i[1] for i in contrs)) def prod( self, vf: tuple[PyTree[ArrayLike], ...], control: tuple[PyTree[ArrayLike], ...] @@ -609,18 +637,19 @@ def is_vf_expensive( return any(term.is_vf_expensive(t0, t1, y, args) for term in self.terms) -class WrapTerm(AbstractTerm[_VF, _Control]): - term: AbstractTerm[_VF, _Control] +class WrapTerm(AbstractTerm[_VF, _Control, _ControlState]): + term: AbstractTerm[_VF, _Control, _ControlState] direction: IntScalarLike def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: t = t * self.direction return self.term.vf(t, y, args) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: + def contr(self, t0: RealScalarLike, t1: RealScalarLike, control_state: _ControlState, **kwargs) -> tuple[_Control, _ControlState]: _t0 = jnp.where(self.direction == 1, t0, -t1) _t1 = jnp.where(self.direction == 1, t1, -t0) - return (self.direction * self.term.contr(_t0, _t1, **kwargs) ** ω).ω + contrs = self.term.contr(_t0, _t1, control_state, **kwargs) + return (self.direction * contrs[0]** ω).ω, contrs[1] def prod(self, vf: _VF, control: _Control) -> Y: with jax.numpy_dtype_promotion("standard"): @@ -642,8 +671,8 @@ def is_vf_expensive( return self.term.is_vf_expensive(_t0, _t1, y, args) -class AdjointTerm(AbstractTerm[_VF, _Control]): - term: AbstractTerm[_VF, _Control] +class AdjointTerm(AbstractTerm[_VF, _Control, _ControlState]): + term: AbstractTerm[_VF, _Control, _ControlState] def is_vf_expensive( self, @@ -721,8 +750,14 @@ def _fn(_control): ) return jtu.tree_transpose(vf_prod_tree, control_tree, jac) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: - return self.term.contr(t0, t1, **kwargs) + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _ControlState, + **kwargs, + ) -> tuple[_Control, _ControlState]: + return self.term.contr(t0, t1, control_state, **kwargs) def prod( self, vf: PyTree[ArrayLike], control: _Control @@ -832,7 +867,7 @@ def broadcast_underdamped_langevin_arg( class UnderdampedLangevinDiffusionTerm( AbstractTerm[ - UnderdampedLangevinX, Union[UnderdampedLangevinX, AbstractBrownianIncrement] + UnderdampedLangevinX, Union[UnderdampedLangevinX, AbstractBrownianIncrement], _ControlState ] ): r"""Represents the diffusion term in the Underdamped Langevin Diffusion (ULD). @@ -891,9 +926,13 @@ def _fun(_gamma, _u): return vf_v def contr( - self, t0: RealScalarLike, t1: RealScalarLike, **kwargs - ) -> Union[UnderdampedLangevinX, AbstractBrownianIncrement]: - return self.control.evaluate(t0, t1, **kwargs) + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _ControlState, + **kwargs, + ) -> tuple[Union[UnderdampedLangevinX, AbstractBrownianIncrement], _ControlState]: + return self.control(t0, control_state, t1, **kwargs) def prod( self, vf: UnderdampedLangevinX, control: UnderdampedLangevinX diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index 61309563..b2ee7791 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "9deba250066ddc39", "metadata": { "ExecuteTime": { @@ -46,7 +46,26 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'AbstractBrownianPath' from partially initialized module 'diffrax._brownian' (most likely due to a circular import) (/Users/owenlockwood/Documents/diffrax_extensions/diffrax/_brownian/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mwarnings\u001b[39;00m \u001b[39mimport\u001b[39;00m simplefilter\n\u001b[1;32m 4\u001b[0m simplefilter(action\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m, category\u001b[39m=\u001b[39m\u001b[39mFutureWarning\u001b[39;00m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mdiffrax\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjnp\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrandom\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjr\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/__init__.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mimportlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetadata\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_adjoint\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 4\u001b[0m AbstractAdjoint \u001b[39mas\u001b[39;00m AbstractAdjoint,\n\u001b[1;32m 5\u001b[0m BacksolveAdjoint \u001b[39mas\u001b[39;00m BacksolveAdjoint,\n\u001b[1;32m 6\u001b[0m DirectAdjoint \u001b[39mas\u001b[39;00m DirectAdjoint,\n\u001b[1;32m 7\u001b[0m ImplicitAdjoint \u001b[39mas\u001b[39;00m ImplicitAdjoint,\n\u001b[1;32m 8\u001b[0m RecursiveCheckpointAdjoint \u001b[39mas\u001b[39;00m RecursiveCheckpointAdjoint,\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_autocitation\u001b[39;00m \u001b[39mimport\u001b[39;00m citation \u001b[39mas\u001b[39;00m citation, citation_rules \u001b[39mas\u001b[39;00m citation_rules\n\u001b[1;32m 11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_brownian\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 12\u001b[0m AbstractBrownianPath \u001b[39mas\u001b[39;00m AbstractBrownianPath,\n\u001b[1;32m 13\u001b[0m UnsafeBrownianPath \u001b[39mas\u001b[39;00m UnsafeBrownianPath,\n\u001b[1;32m 14\u001b[0m VirtualBrownianTree \u001b[39mas\u001b[39;00m VirtualBrownianTree,\n\u001b[1;32m 15\u001b[0m )\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_adjoint.py:17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39moptimistix\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39minternal\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39moptxi\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mequinox\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39minternal\u001b[39;00m \u001b[39mimport\u001b[39;00m ω\n\u001b[0;32m---> 17\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_heuristics\u001b[39;00m \u001b[39mimport\u001b[39;00m is_sde, is_unsafe_sde\n\u001b[1;32m 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_saveat\u001b[39;00m \u001b[39mimport\u001b[39;00m save_y, SaveAt, SubSaveAt\n\u001b[1;32m 19\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_solver\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractItoSolver, AbstractRungeKutta, AbstractStratonovichSolver\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_heuristics.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtree_util\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjtu\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mjaxtyping\u001b[39;00m \u001b[39mimport\u001b[39;00m PyTree\n\u001b[0;32m----> 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_brownian\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractBrownianPath, UnsafeBrownianPath\n\u001b[1;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_path\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractPath\n\u001b[1;32m 6\u001b[0m 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TypeVar(\u001b[39m\"\u001b[39m\u001b[39m_BrownianState\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_path.py:16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mequinox\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractVar\n\u001b[1;32m 15\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_custom_types\u001b[39;00m \u001b[39mimport\u001b[39;00m Args, Control, RealScalarLike, Y\n\u001b[0;32m---> 16\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_term\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractTerm\n\u001b[1;32m 19\u001b[0m _Control \u001b[39m=\u001b[39m TypeVar(\u001b[39m\"\u001b[39m\u001b[39m_Control\u001b[39m\u001b[39m\"\u001b[39m, bound\u001b[39m=\u001b[39mControl)\n\u001b[1;32m 20\u001b[0m _PathState \u001b[39m=\u001b[39m TypeVar(\u001b[39m\"\u001b[39m\u001b[39m_PathState\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mequinox\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39minternal\u001b[39;00m \u001b[39mimport\u001b[39;00m ω\n\u001b[1;32m 15\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mjaxtyping\u001b[39;00m \u001b[39mimport\u001b[39;00m Array, ArrayLike, PyTree, PyTreeDef, Shaped\n\u001b[0;32m---> 17\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_brownian\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractBrownianPath\n\u001b[1;32m 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_custom_types\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 19\u001b[0m AbstractBrownianIncrement,\n\u001b[1;32m 20\u001b[0m Args,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 25\u001b[0m Y,\n\u001b[1;32m 26\u001b[0m )\n\u001b[1;32m 27\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_misc\u001b[39;00m \u001b[39mimport\u001b[39;00m upcast_or_raise\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'AbstractBrownianPath' from partially initialized module 'diffrax._brownian' (most likely due to a circular import) (/Users/owenlockwood/Documents/diffrax_extensions/diffrax/_brownian/__init__.py)" + ] + } + ], "source": [ "from warnings import simplefilter\n", "\n", @@ -70,9 +89,10 @@ "y0 = (x0, v0)\n", "\n", "# Brownian motion\n", - "bm = diffrax.VirtualBrownianTree(\n", - " t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", - ")\n", + "# bm = diffrax.VirtualBrownianTree(\n", + "# t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", + "# )\n", + "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea)\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", @@ -130,21 +150,26 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.14 ('dev_diffrax')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "01761703e8e304055600d311574f89f8a646f73edac04b8bff1580ad2d98581f" + } } }, "nbformat": 4, From 382d171b4d096ef1b34903d50d746a0ba5846016 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 30 Dec 2024 21:43:02 -0700 Subject: [PATCH 02/50] solver work --- diffrax/_adjoint.py | 16 ++- diffrax/_brownian/path.py | 9 +- diffrax/_global_interpolation.py | 27 ++++- diffrax/_integrate.py | 71 ++++++++--- diffrax/_local_interpolation.py | 28 ++++- diffrax/_path.py | 1 - diffrax/_solution.py | 21 +++- diffrax/_solver/align.py | 3 +- diffrax/_solver/base.py | 9 +- diffrax/_solver/euler.py | 1 + diffrax/_solver/euler_heun.py | 22 ++-- diffrax/_solver/foster_langevin_srk.py | 43 +++++-- diffrax/_solver/implicit_euler.py | 11 +- diffrax/_solver/leapfrog_midpoint.py | 11 +- diffrax/_solver/milstein.py | 49 +++++--- diffrax/_solver/quicsort.py | 3 +- diffrax/_solver/reversible_heun.py | 18 ++- diffrax/_solver/runge_kutta.py | 30 ++++- diffrax/_solver/semi_implicit_euler.py | 15 ++- diffrax/_solver/should.py | 3 +- diffrax/_solver/srk.py | 23 ++-- diffrax/_term.py | 31 +++-- examples/neural_sde.ipynb | 123 +++++++++----------- examples/underdamped_langevin_example.ipynb | 59 ++++++---- 24 files changed, 425 insertions(+), 202 deletions(-) diff --git a/diffrax/_adjoint.py b/diffrax/_adjoint.py index cd9b6e34..8b1b739b 100644 --- a/diffrax/_adjoint.py +++ b/diffrax/_adjoint.py @@ -56,9 +56,7 @@ def _nondiff_solver_controller_state( else: controller_fn = lax.stop_gradient if passed_path_state: - name = ( - f"When using `adjoint={adjoint.__class__.__name__}()`, then `path_state`" - ) + name = f"When using `adjoint={adjoint.__class__.__name__}()`, then `path_state`" path_fn = ft.partial( eqxi.nondifferentiable, name=name, @@ -509,7 +507,11 @@ def loop( "`saveat=SaveAt(t1=True)`." ) init_state = _nondiff_solver_controller_state( - self, init_state, passed_solver_state, passed_controller_state, passed_path_state + self, + init_state, + passed_solver_state, + passed_controller_state, + passed_path_state, ) inputs = (args, terms, self, kwargs, solver, saveat, init_state) ys, residual = optxi.implicit_jvp( @@ -860,7 +862,11 @@ def loop( y = init_state.y init_state = eqx.tree_at(lambda s: s.y, init_state, object()) init_state = _nondiff_solver_controller_state( - self, init_state, passed_solver_state, passed_controller_state, passed_path_state + self, + init_state, + passed_solver_state, + passed_controller_state, + passed_path_state, ) final_state, aux_stats = _loop_backsolve( diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 7c3c45b3..b027a426 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -86,7 +86,7 @@ def __init__( levy_area: type[ Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = BrownianIncrement, - precompute: bool = True, + precompute: bool = False, ): self.shape = ( jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) @@ -142,7 +142,7 @@ def init( args: Args, max_steps: Optional[int], ) -> _BrownianState: - if max_steps is not None: + if max_steps is not None and self.precompute: subkey = split_by_tree(self.key, self.shape) noise = jtu.tree_map( lambda subkey, shape: self._generate_noise(subkey, shape), @@ -181,7 +181,7 @@ def __call__( t1 = cast(RealScalarLike, t1) key, noises, counter = brownian_state - if key is None: # precomputed noise + if self.precompute: # precomputed noise out = jtu.tree_map( lambda shape, noise: self._evaluate_leaf_precomputed( t0, t1, shape, self.levy_area, use_levy, noise @@ -338,6 +338,9 @@ def _evaluate_leaf( solvers. - `precompute`: Whether or not to precompute the brownian motion (if possible). Precomputing requires additional memory at initialization time, but can result in faster integrations. + Some thought may be required before enabling this, as solvers which require multiple + brownian increments may result in index out of bounds causing silent errors as the size + of the precomputed brownian motion is derived from the maximum steps. """ UnsafeBrownianPath = DirectBrownianPath diff --git a/diffrax/_global_interpolation.py b/diffrax/_global_interpolation.py index 15d13681..3eebafbc 100644 --- a/diffrax/_global_interpolation.py +++ b/diffrax/_global_interpolation.py @@ -1,6 +1,7 @@ import functools as ft from collections.abc import Callable from typing import cast, Optional, TYPE_CHECKING +from typing_extensions import TypeAlias import equinox as eqx import equinox.internal as eqxi @@ -18,16 +19,17 @@ from equinox.internal import ω from jaxtyping import Array, ArrayLike, PyTree, Real, Shaped -from ._custom_types import DenseInfos, IntScalarLike, RealScalarLike, Y +from ._custom_types import DenseInfos, IntScalarLike, RealScalarLike, Y, Args from ._local_interpolation import AbstractLocalInterpolation from ._misc import fill_forward, left_broadcast_to -from ._path import AbstractPath +from ._path import AbstractPath, _Control ω = cast(Callable, ω) +_PathState: TypeAlias = None -class AbstractGlobalInterpolation(AbstractPath): +class AbstractGlobalInterpolation(AbstractPath[_Control, _PathState]): ts: AbstractVar[Real[Array, " times"]] ts_size: AbstractVar[IntScalarLike] @@ -55,6 +57,25 @@ def t1(self): """The end of the interval over which the interpolation is defined.""" return self.ts[-1] + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> _PathState: + return None + + def __call__( + self, + t0: RealScalarLike, + path_state: _PathState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + ) -> tuple[_Control, _PathState]: + return self.evaluate(t0, t1, left), path_state + class LinearInterpolation(AbstractGlobalInterpolation): """Linearly interpolates some data `ys` over the interval $[t_0, t_1]$ with knots diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index a1fdec53..099a7104 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -65,7 +65,14 @@ PIDController, StepTo, ) -from ._term import AbstractTerm, MultiTerm, ODETerm, WrapTerm, _AbstractControlTerm +from ._term import ( + _AbstractControlTerm, + AbstractTerm, + MultiTerm, + ODETerm, + UnderdampedLangevinDiffusionTerm, + WrapTerm, +) from ._typing import better_isinstance, get_args_of, get_origin_no_specials @@ -158,14 +165,14 @@ def _check(term_cls, term, term_contr_kwargs, yi): # `term_cls` | `term_args` # --------------------------|-------------- # AbstractTerm | () - # AbstractTerm[VF, Control] | (VF, Control) + # AbstractTerm[VF, Control] | (VF, Control, Path) # ----------------------------------------- term_args = get_args_of(AbstractTerm, term_cls, error_msg) n_term_args = len(term_args) if n_term_args == 0: pass - elif n_term_args == 2: - vf_type_expected, control_type_expected = term_args + elif n_term_args == 3: + vf_type_expected, control_type_expected, path_type_expected = term_args try: vf_type = eqx.filter_eval_shape(term.vf, 0.0, yi, args) except Exception as e: @@ -179,7 +186,7 @@ def _check(term_cls, term, term_contr_kwargs, yi): contr = ft.partial(term.contr, **term_contr_kwargs) # Work around https://github.com/google/jax/issues/21825 try: - control_type = eqx.filter_eval_shape(contr, 0.0, 0.0) + control_type, path_type = eqx.filter_eval_shape(contr, 0.0, 0.0) except Exception as e: raise ValueError(f"Error while tracing {term}.contr: " + str(e)) control_type_compatible = eqx.filter_eval_shape( @@ -187,6 +194,11 @@ def _check(term_cls, term, term_contr_kwargs, yi): ) if not control_type_compatible: raise ValueError(f"Control term {term} is incompatible.") + path_type_compatible = eqx.filter_eval_shape( + better_isinstance, path_type, path_type_expected + ) + if not path_type_compatible: + raise ValueError(f"Control term {term} path state is incompatible.") else: assert False, "Malformed term structure" # If we've got to this point then the term is compatible @@ -343,8 +355,8 @@ def body_fun_aux(state): state.y, args, state.solver_state, - state.path_state, state.made_jump, + state.path_state, ) # e.g. if someone has a sqrt(y) in the vector field, and dt0 is so large that @@ -853,7 +865,7 @@ class SaveAt(eqx.Module): # noqa: F811 t1: bool -@eqx.filter_jit +# @eqx.filter_jit @eqxi.doc_remove_args("discrete_terminating_event") def diffeqsolve( terms: PyTree[AbstractTerm], @@ -957,8 +969,8 @@ def diffeqsolve( - `controller_state`: Some initial state for the step size controller. Generally obtained by `SaveAt(controller_state=True)` from a previous solve. - - - `path_state`: Some initial state for the path. Generally obtained by + + - `path_state`: Some initial state for the path. Generally obtained by `SaveAt(path_state=True)` from a previous solve. - `made_jump`: Whether a jump has just been made at `t0`. Used to update @@ -1094,13 +1106,27 @@ def _promote(yi): ) terms = MultiTerm(*terms) + def _path_init(term): + if isinstance(term, _AbstractControlTerm) or isinstance( + term, UnderdampedLangevinDiffusionTerm + ): + return term.control.init(t0, t1, y0, args, max_steps) + elif isinstance(term, MultiTerm): + return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, AbstractTerm)) + return None + + if path_state is None: + path_state = jtu.tree_map( + _path_init, terms, is_leaf=lambda x: isinstance(x, AbstractTerm) + ) + # Error checking for term compatibility _assert_term_compatible( y0, args, terms, solver.term_structure, - solver.term_compatible_contr_kwargs, + jtu.tree_map(lambda x, y: x | {"control_state": y}, solver.term_compatible_contr_kwargs, path_state, is_leaf=lambda x: isinstance(x, dict)), ) if is_sde(terms): @@ -1231,20 +1257,27 @@ def _subsaveat_direction_fn(x): tnext = t0 + dt0 tnext = jnp.minimum(tnext, t1) + # reinit for tnext def _path_init(term): - if isinstance(term, _AbstractControlTerm): + if isinstance(term, _AbstractControlTerm) or isinstance( + term, UnderdampedLangevinDiffusionTerm + ): return term.control.init(t0, tnext, y0, args, max_steps) + elif isinstance(term, MultiTerm): + return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, AbstractTerm)) return None - + if path_state is None: passed_path_state = False - path_state = jtu.tree_map(_path_init, terms) + path_state = jtu.tree_map( + _path_init, terms, is_leaf=lambda x: isinstance(x, AbstractTerm) + ) else: passed_path_state = True if solver_state is None: passed_solver_state = False - solver_state = solver.init(terms, t0, tnext, y0, args) + solver_state = solver.init(terms, t0, tnext, y0, args, path_state) else: passed_solver_state = True @@ -1285,7 +1318,15 @@ def _allocate_output(subsaveat: SubSaveAt) -> SaveState: result = RESULTS.successful if saveat.dense or event is not None: _, _, dense_info_struct, _, _ = eqx.filter_eval_shape( - solver.step, terms, tprev, tnext, y0, args, solver_state, made_jump, path_state + solver.step, + terms, + tprev, + tnext, + y0, + args, + solver_state, + made_jump, + path_state, ) if saveat.dense: if max_steps is None: diff --git a/diffrax/_local_interpolation.py b/diffrax/_local_interpolation.py index 29a8eb9e..390f07eb 100644 --- a/diffrax/_local_interpolation.py +++ b/diffrax/_local_interpolation.py @@ -1,5 +1,6 @@ from collections.abc import Callable from typing import cast, Optional, TYPE_CHECKING +from typing_extensions import TypeAlias import jax import jax.numpy as jnp @@ -14,17 +15,36 @@ from equinox.internal import ω from jaxtyping import Array, ArrayLike, PyTree, Shaped -from ._custom_types import RealScalarLike, Y +from ._custom_types import RealScalarLike, Y, Args from ._misc import linear_rescale -from ._path import AbstractPath +from ._path import AbstractPath, _Control +_PathState: TypeAlias = None + ω = cast(Callable, ω) -class AbstractLocalInterpolation(AbstractPath): - pass +class AbstractLocalInterpolation(AbstractPath[_Control, _PathState]): + + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> _PathState: + return None + def __call__( + self, + t0: RealScalarLike, + path_state: _PathState, + t1: Optional[RealScalarLike] = None, + left: bool = True, + ) -> tuple[_Control, _PathState]: + return self.evaluate(t0, t1, left), path_state class LocalLinearInterpolation(AbstractLocalInterpolation): t0: RealScalarLike diff --git a/diffrax/_path.py b/diffrax/_path.py index c73909c4..d9e4a2bc 100644 --- a/diffrax/_path.py +++ b/diffrax/_path.py @@ -4,7 +4,6 @@ import equinox as eqx import jax import jax.numpy as jnp -from jaxtyping import PyTree if TYPE_CHECKING: diff --git a/diffrax/_solution.py b/diffrax/_solution.py index 351dec23..8c3d06b1 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -5,7 +5,7 @@ import optimistix as optx from jaxtyping import Array, Bool, PyTree, Real, Shaped -from ._custom_types import BoolScalarLike, RealScalarLike +from ._custom_types import BoolScalarLike, RealScalarLike, Args, Y from ._global_interpolation import DenseInterpolation from ._path import AbstractPath @@ -124,6 +124,25 @@ class Solution(AbstractPath): made_jump: Optional[BoolScalarLike] event_mask: Optional[PyTree[BoolScalarLike]] + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> None: + return None + + def __call__( + self, + t0: RealScalarLike, + path_state: None, + t1: Optional[RealScalarLike] = None, + left: bool = True, + ) -> tuple[PyTree[Shaped[Array, "?*shape"], " Y"], None]: + return self.evaluate(t0, t1, left), path_state + def evaluate( self, t0: RealScalarLike, t1: Optional[RealScalarLike] = None, left: bool = True ) -> PyTree[Shaped[Array, "?*shape"], " Y"]: diff --git a/diffrax/_solver/align.py b/diffrax/_solver/align.py index c6bc6105..45422105 100644 --- a/diffrax/_solver/align.py +++ b/diffrax/_solver/align.py @@ -14,6 +14,7 @@ UnderdampedLangevinTuple, UnderdampedLangevinX, ) +from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -43,7 +44,7 @@ def __init__(self, beta, a1, b1, aa, chh): _ErrorEstimate = UnderdampedLangevinTuple -class ALIGN(AbstractFosterLangevinSRK[_ALIGNCoeffs, _ErrorEstimate]): +class ALIGN(AbstractFosterLangevinSRK[_ALIGNCoeffs, _ErrorEstimate, _PathState]): r"""The Adaptive Langevin via Interpolated Gradients and Noise method designed by James Foster. This is a second order solver for the Underdamped Langevin Diffusion, and accepts terms of the form diff --git a/diffrax/_solver/base.py b/diffrax/_solver/base.py index 56a33230..dc5767ce 100644 --- a/diffrax/_solver/base.py +++ b/diffrax/_solver/base.py @@ -130,7 +130,11 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: + # does this need to return a path state as well?, or it is fine just to + # have it consume it? AbstractFosterLangevinSRK is the only one that + # uses rn I think, so can this brownian increment be reused? """Initialises any hidden state for the solver. **Arguments** as [`diffrax.diffeqsolve`][]. @@ -272,7 +276,7 @@ class HalfSolver( [`diffrax.Euler`][]. Such solvers are most common when solving SDEs. """ - solver: AbstractSolver[_SolverState] + solver: AbstractSolver[_SolverState, _PathState] @property def term_structure(self): @@ -310,8 +314,9 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: - return self.solver.init(terms, t0, t1, y0, args) + return self.solver.init(terms, t0, t1, y0, args, path_state) def step( self, diff --git a/diffrax/_solver/euler.py b/diffrax/_solver/euler.py index aa37aec8..52b333f2 100644 --- a/diffrax/_solver/euler.py +++ b/diffrax/_solver/euler.py @@ -42,6 +42,7 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: return None diff --git a/diffrax/_solver/euler_heun.py b/diffrax/_solver/euler_heun.py index c8338c88..dc78fe13 100644 --- a/diffrax/_solver/euler_heun.py +++ b/diffrax/_solver/euler_heun.py @@ -8,7 +8,7 @@ from .._local_interpolation import LocalLinearInterpolation from .._solution import RESULTS from .._term import AbstractTerm, MultiTerm -from .base import AbstractStratonovichSolver +from .base import _PathState, AbstractStratonovichSolver _ErrorEstimate: TypeAlias = None @@ -27,7 +27,7 @@ class EulerHeun(AbstractStratonovichSolver): """ term_structure: ClassVar = MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm] + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] ] interpolation_cls: ClassVar[ Callable[..., LocalLinearInterpolation] @@ -41,29 +41,35 @@ def strong_order(self, terms): def init( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: return None def step( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump drift, diffusion = terms.terms - dt = drift.contr(t0, t1) - dW = diffusion.contr(t0, t1) + dt, path_state = drift.contr(t0, t1, path_state) + dW, path_state = diffusion.contr(t0, t1, path_state) f0 = drift.vf_prod(t0, y0, args, dt) g0 = diffusion.vf_prod(t0, y0, args, dW) @@ -74,7 +80,7 @@ def step( y1 = (y0**ω + f0**ω + 0.5 * (g0**ω + g_prime**ω)).ω dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, RESULTS.successful + return y1, None, dense_info, None, path_state, RESULTS.successful def func( self, diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index dbdf3939..19c43ba5 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -30,7 +30,7 @@ UnderdampedLangevinX, WrapTerm, ) -from .base import AbstractStratonovichSolver +from .base import _PathState, AbstractStratonovichSolver _ErrorEstimate = TypeVar("_ErrorEstimate", None, UnderdampedLangevinTuple) @@ -42,7 +42,9 @@ def _get_args_from_terms( - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], ) -> tuple[ PyTree, PyTree, @@ -98,8 +100,8 @@ class SolverState(eqx.Module, Generic[_Coeffs]): class AbstractFosterLangevinSRK( - AbstractStratonovichSolver[SolverState], - Generic[_Coeffs, _ErrorEstimate], + AbstractStratonovichSolver[SolverState, _PathState], + Generic[_Coeffs, _ErrorEstimate, _PathState], ): r"""Abstract class for Stochastic Runge Kutta methods specifically designed for Underdamped Langevin Diffusion of the form @@ -243,11 +245,14 @@ def _choose(tay_leaf, direct_leaf): def init( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: UnderdampedLangevinTuple, args: PyTree, + path_state: _PathState, ) -> SolverState: """Precompute _SolverState which carries the Taylor coefficients and the SRK coefficients (which can be computed from h and the Taylor coefficients). @@ -263,7 +268,10 @@ def init( grad_f, ) = _get_args_from_terms(terms) - h = drift.contr(t0, t1) + # is this the only solver class that has `init` depend on the path state? + # feels irksome to change everything for one class, but I'm going to make + # `init` now depend on path state for the sake of generality + h, _ = drift.contr(t0, t1, path_state) x0, v0 = y0 gamma = broadcast_underdamped_langevin_arg(gamma_drift, x0, "gamma") @@ -359,21 +367,30 @@ def _compute_step( def step( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: UnderdampedLangevinTuple, args: PyTree, solver_state: SolverState, made_jump: BoolScalarLike, + path_state: _PathState, ) -> tuple[ - UnderdampedLangevinTuple, _ErrorEstimate, DenseInfo, SolverState, RESULTS + UnderdampedLangevinTuple, + _ErrorEstimate, + DenseInfo, + SolverState, + _PathState, + RESULTS, ]: del args st = solver_state drift, diffusion = terms.terms + drift_path, diffusion_path = path_state - h = drift.contr(t0, t1) + h, drift_path = drift.contr(t0, t1, drift_path) h_prev = st.h tay: PyTree[_Coeffs] = st.taylor_coeffs old_coeffs: _Coeffs = st.coeffs @@ -392,7 +409,7 @@ def step( ) # compute the Brownian increment and space-time(-time) Levy area - levy = diffusion.contr(t0, t1, use_levy=True) + levy, diffusion_path = diffusion.contr(t0, t1, diffusion_path, use_levy=True) if not isinstance(levy, self.minimal_levy_area): raise ValueError( f"The Brownian motion must have" @@ -436,11 +453,13 @@ def check_shapes_dtypes(arg, *args): rho=st.rho, prev_f=f_fsal, ) - return y1, error, dense_info, st, RESULTS.successful + return y1, error, dense_info, st, (drift_path, diffusion_path), RESULTS.successful def func( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, y0: UnderdampedLangevinTuple, args: PyTree, diff --git a/diffrax/_solver/implicit_euler.py b/diffrax/_solver/implicit_euler.py index eb3bdb00..c2f434d1 100644 --- a/diffrax/_solver/implicit_euler.py +++ b/diffrax/_solver/implicit_euler.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import ClassVar +from typing import ClassVar, TypeVar from typing_extensions import TypeAlias import optimistix as optx @@ -15,6 +15,7 @@ _SolverState: TypeAlias = None +_PathState = TypeVar("_PathState") def _implicit_relation(z1, nonlinear_solve_args): @@ -59,6 +60,7 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: return None @@ -71,9 +73,10 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, Y, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, Y, DenseInfo, _SolverState, _PathState, RESULTS]: del made_jump - control = terms.contr(t0, t1) + control, path_state = terms.contr(t0, t1, path_state) # Could use FSAL here but that would mean we'd need to switch to working with # `f0 = terms.vf(t0, y0, args)`, and that gets quite hairy quite quickly. # (C.f. `AbstractRungeKutta.step`.) @@ -96,7 +99,7 @@ def step( dense_info = dict(y0=y0, y1=y1) solver_state = None result = RESULTS.promote(nonlinear_sol.result) - return y1, y_error, dense_info, solver_state, result + return y1, y_error, dense_info, solver_state, path_state, result def func( self, diff --git a/diffrax/_solver/leapfrog_midpoint.py b/diffrax/_solver/leapfrog_midpoint.py index 00ba11da..e43ca2b8 100644 --- a/diffrax/_solver/leapfrog_midpoint.py +++ b/diffrax/_solver/leapfrog_midpoint.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import ClassVar +from typing import ClassVar, TypeVar from typing_extensions import TypeAlias from equinox.internal import ω @@ -14,6 +14,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = tuple[RealScalarLike, PyTree] +_PathState = TypeVar("_PathState") # TODO: support arbitrary linear multistep methods @@ -59,6 +60,7 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: del terms, t1, args # Corresponds to making an explicit Euler step on the first step. @@ -73,14 +75,15 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del made_jump tm1, ym1 = solver_state - control = terms.contr(tm1, t1) + control, path_state = terms.contr(tm1, t1, path_state) y1 = (ym1**ω + terms.vf_prod(t0, y0, args, control) ** ω).ω dense_info = dict(y0=y0, y1=y1) solver_state = (t0, y0) - return y1, None, dense_info, solver_state, RESULTS.successful + return y1, None, dense_info, solver_state, path_state, RESULTS.successful def func(self, terms: AbstractTerm, t0: RealScalarLike, y0: Y, args: Args) -> VF: return terms.vf(t0, y0, args) diff --git a/diffrax/_solver/milstein.py b/diffrax/_solver/milstein.py index ce59d83b..0d4872ce 100644 --- a/diffrax/_solver/milstein.py +++ b/diffrax/_solver/milstein.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import Any, ClassVar +from typing import Any, ClassVar, TypeVar from typing_extensions import TypeAlias import jax @@ -16,7 +16,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = None - +_PathState = TypeVar("_PathState") # # The best online reference I've found for commutative-noise Milstein is @@ -43,7 +43,7 @@ class StratonovichMilstein(AbstractStratonovichSolver): """ # noqa: E501 term_structure: ClassVar = MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm] + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] ] interpolation_cls: ClassVar[ Callable[..., LocalLinearInterpolation] @@ -57,28 +57,35 @@ def strong_order(self, terms): def init( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: return None def step( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump drift, diffusion = terms.terms - dt = drift.contr(t0, t1) - dw = diffusion.contr(t0, t1) + # should these be same path state? + dt, _ = drift.contr(t0, t1, path_state) + dw, path_state = diffusion.contr(t0, t1, path_state) f0_prod = drift.vf_prod(t0, y0, args, dt) g0_prod = diffusion.vf_prod(t0, y0, args, dw) @@ -90,7 +97,7 @@ def _to_jvp(_y0): y1 = (y0**ω + f0_prod**ω + g0_prod**ω + 0.5 * v0_prod**ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, RESULTS.successful + return y1, None, dense_info, None, path_state, RESULTS.successful def func( self, @@ -119,7 +126,7 @@ class ItoMilstein(AbstractItoSolver): """ # noqa: E501 term_structure: ClassVar = MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm] + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] ] interpolation_cls: ClassVar[ Callable[..., LocalLinearInterpolation] @@ -133,28 +140,34 @@ def strong_order(self, terms): def init( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: return None def step( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump drift, diffusion = terms.terms - Δt = drift.contr(t0, t1) - Δw = diffusion.contr(t0, t1) + Δt, path_state = drift.contr(t0, t1, path_state) + Δw, path_state = diffusion.contr(t0, t1, path_state) # # So this is a bit involved, largely because of the generality that the rest of @@ -365,11 +378,13 @@ def _dot(_, _v0): # dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, RESULTS.successful + return y1, None, dense_info, None, path_state, RESULTS.successful def func( self, - terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike], AbstractTerm]], + terms: MultiTerm[ + tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + ], t0: RealScalarLike, y0: Y, args: Args, diff --git a/diffrax/_solver/quicsort.py b/diffrax/_solver/quicsort.py index 4f21bd6f..a05955e7 100644 --- a/diffrax/_solver/quicsort.py +++ b/diffrax/_solver/quicsort.py @@ -14,6 +14,7 @@ ) from .._local_interpolation import LocalLinearInterpolation from .._term import UnderdampedLangevinLeaf, UnderdampedLangevinX +from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -44,7 +45,7 @@ def __init__(self, beta_lr1, a_lr1, b_lr1, a_third, a_div_h): self.dtype = jnp.result_type(*all_leaves) -class QUICSORT(AbstractFosterLangevinSRK[_QUICSORTCoeffs, None]): +class QUICSORT(AbstractFosterLangevinSRK[_QUICSORTCoeffs, None, _PathState]): r"""The QUadrature Inspired and Contractive Shifted ODE with Runge-Kutta Three method by James Foster and Daire O'Kane. This is a third order solver for the Underdamped Langevin Diffusion, and accepts terms of the form diff --git a/diffrax/_solver/reversible_heun.py b/diffrax/_solver/reversible_heun.py index 0f0a9fe9..4393b867 100644 --- a/diffrax/_solver/reversible_heun.py +++ b/diffrax/_solver/reversible_heun.py @@ -1,6 +1,6 @@ from collections.abc import Callable from typing import ClassVar -from typing_extensions import TypeAlias +from typing_extensions import TypeAlias, TypeVar import jax.lax as lax from equinox.internal import ω @@ -14,6 +14,7 @@ _SolverState: TypeAlias = tuple[PyTree, PyTree] +_PathState = TypeVar("_PathState") class ReversibleHeun(AbstractAdaptiveSolver, AbstractStratonovichSolver): @@ -54,6 +55,7 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: del t1 vf0 = terms.vf(t0, y0, args) @@ -68,12 +70,13 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, Y, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, Y, DenseInfo, _SolverState, _PathState, RESULTS]: yhat0, vf0 = solver_state vf0 = lax.cond(made_jump, lambda _: terms.vf(t0, y0, args), lambda _: vf0, None) - control = terms.contr(t0, t1) + control, new_path_state = terms.contr(t0, t1, path_state) yhat1 = (2 * y0**ω - yhat0**ω + terms.prod(vf0, control) ** ω).ω vf1 = terms.vf(t1, yhat1, args) y1 = (y0**ω + 0.5 * terms.prod((vf0**ω + vf1**ω).ω, control) ** ω).ω @@ -81,7 +84,14 @@ def step( dense_info = dict(y0=y0, y1=y1) solver_state = (yhat1, vf1) - return y1, y1_error, dense_info, solver_state, RESULTS.successful + return ( + y1, + y1_error, + dense_info, + solver_state, + new_path_state, + RESULTS.successful, + ) def func(self, terms: AbstractTerm, t0: RealScalarLike, y0: Y, args: Args) -> VF: return terms.vf(t0, y0, args) diff --git a/diffrax/_solver/runge_kutta.py b/diffrax/_solver/runge_kutta.py index 11a9f6c8..9bd7340a 100644 --- a/diffrax/_solver/runge_kutta.py +++ b/diffrax/_solver/runge_kutta.py @@ -44,7 +44,12 @@ ) from .._solution import is_okay, RESULTS, update_result from .._term import AbstractTerm, MultiTerm, ODETerm, WrapTerm -from .base import AbstractAdaptiveSolver, AbstractImplicitSolver, vector_tree_dot +from .base import ( + _PathState, + AbstractAdaptiveSolver, + AbstractImplicitSolver, + vector_tree_dot, +) # Not a pytree node! @@ -342,7 +347,7 @@ def _assert_same_structure(x, y): return eqx.tree_equal(x, y) is True -class AbstractRungeKutta(AbstractAdaptiveSolver[_SolverState]): +class AbstractRungeKutta(AbstractAdaptiveSolver[_SolverState, _PathState]): """Abstract base class for all Runge--Kutta solvers. (Other than fully-implicit Runge--Kutta methods, which have a different computational structure.) @@ -417,6 +422,7 @@ def init( t1: RealScalarLike, y0: Y, args: Args, + path_state: _PathState, ) -> _SolverState: _, fsal = self._common(terms, t0, t1, y0, args) if fsal: @@ -450,7 +456,8 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, Y, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, Y, DenseInfo, _SolverState, _PathState, RESULTS]: # # Alright, settle in for what is probably the most advanced Runge-Kutta # implementation on the planet. @@ -603,6 +610,15 @@ def _fn(tableau, *_trees): return jtu.tree_map(_fn, tableaus, *trees) + def t_map_contr(fn, *trees, control, implicit_val=sentinel): + def _fn(tableau, *_trees): + if tableau.implicit and implicit_val is not sentinel: + return implicit_val + else: + return fn(*_trees, control) + + return jtu.tree_map(_fn, tableaus, *trees) + # Structure of `y` and `k`. def y_map(fn, *trees): def _fn(_, *_trees): @@ -639,7 +655,11 @@ def _get_implicit_impl(term, x): return value dt = t1 - t0 - control = t_map(lambda term_i: term_i.contr(t0, t1), terms) + control, new_path_state = t_map_contr( + lambda term_i, path_i: term_i.contr(t0, t1, path_i), + terms, + control=path_state, + ) if implicit_tableau is None: implicit_control = _unused else: @@ -1198,7 +1218,7 @@ def _increment(tab_i, k_i): new_solver_state = False, f1_for_fsal else: new_solver_state = None - return y1, y_error, dense_info, new_solver_state, result + return y1, y_error, dense_info, new_solver_state, new_path_state, result class AbstractERK(AbstractRungeKutta): diff --git a/diffrax/_solver/semi_implicit_euler.py b/diffrax/_solver/semi_implicit_euler.py index 00b9e1db..f5067c4d 100644 --- a/diffrax/_solver/semi_implicit_euler.py +++ b/diffrax/_solver/semi_implicit_euler.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import ClassVar +from typing import ClassVar, TypeVar from typing_extensions import TypeAlias from equinox.internal import ω @@ -14,6 +14,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = None +_PathState = TypeVar("_PathState") Ya: TypeAlias = PyTree[Float[ArrayLike, "?*y"], " Y"] Yb: TypeAlias = PyTree[Float[ArrayLike, "?*y"], " Y"] @@ -41,6 +42,7 @@ def init( t1: RealScalarLike, y0: tuple[Ya, Yb], args: Args, + path_state: _PathState, ) -> _SolverState: return None @@ -53,20 +55,23 @@ def step( args: Args, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[tuple[Ya, Yb], _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[ + tuple[Ya, Yb], _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS + ]: del solver_state, made_jump term_1, term_2 = terms y0_1, y0_2 = y0 - control1 = term_1.contr(t0, t1) - control2 = term_2.contr(t0, t1) + control1, path_state = term_1.contr(t0, t1, path_state) + control2, path_state = term_2.contr(t0, t1, path_state) y1_1 = (y0_1**ω + term_1.vf_prod(t0, y0_2, args, control1) ** ω).ω y1_2 = (y0_2**ω + term_2.vf_prod(t0, y1_1, args, control2) ** ω).ω y1 = (y1_1, y1_2) dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, RESULTS.successful + return y1, None, dense_info, None, path_state, RESULTS.successful def func( self, diff --git a/diffrax/_solver/should.py b/diffrax/_solver/should.py index caab54d3..d4819c67 100644 --- a/diffrax/_solver/should.py +++ b/diffrax/_solver/should.py @@ -10,6 +10,7 @@ ) from .._local_interpolation import LocalLinearInterpolation from .._term import UnderdampedLangevinLeaf, UnderdampedLangevinX +from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -56,7 +57,7 @@ def __init__(self, beta_half, a_half, b_half, beta1, a1, b1, aa, chh, ckk): self.dtype = jnp.result_type(*all_leaves) -class ShOULD(AbstractFosterLangevinSRK[_ShOULDCoeffs, None]): +class ShOULD(AbstractFosterLangevinSRK[_ShOULDCoeffs, None, _PathState]): r"""The Shifted-ODE Runge-Kutta Three method designed by James Foster. This is a third order solver for the Underdamped Langevin Diffusion, the terms of the form diff --git a/diffrax/_solver/srk.py b/diffrax/_solver/srk.py index fba6120d..e39630fc 100644 --- a/diffrax/_solver/srk.py +++ b/diffrax/_solver/srk.py @@ -39,6 +39,7 @@ _ErrorEstimate: TypeAlias = Optional[Y] _SolverState: TypeAlias = None +_PathState = TypeVar("_PathState") _CarryType: TypeAlias = tuple[PyTree[Array], PyTree[Array], PyTree[Array]] @@ -199,7 +200,7 @@ def __post_init__(self): """ -class AbstractSRK(AbstractSolver[_SolverState]): +class AbstractSRK(AbstractSolver[_SolverState, _PathState]): r"""A general Stochastic Runge-Kutta method. This accepts `terms` of the form @@ -287,14 +288,15 @@ def init( self, terms: MultiTerm[ tuple[ - AbstractTerm[Any, RealScalarLike], - AbstractTerm[Any, AbstractBrownianIncrement], + AbstractTerm[Any, RealScalarLike, None], # ODE Term + AbstractTerm[Any, AbstractBrownianIncrement, _PathState], ] ], t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: PyTree, + path_state: _PathState, ) -> _SolverState: del t1 # Check that the diffusion has the correct Lévy area @@ -326,8 +328,8 @@ def step( self, terms: MultiTerm[ tuple[ - AbstractTerm[Any, RealScalarLike], - AbstractTerm[Any, AbstractBrownianIncrement], + AbstractTerm[Any, RealScalarLike, None], + AbstractTerm[Any, AbstractBrownianIncrement, _PathState], ] ], t0: RealScalarLike, @@ -336,7 +338,8 @@ def step( args: PyTree, solver_state: _SolverState, made_jump: BoolScalarLike, - ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, RESULTS]: + path_state: _PathState, + ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump dtype = jnp.result_type(*jtu.tree_leaves(y0)) @@ -377,7 +380,7 @@ def make_zeros_aux(leaf): # Now the diffusion related stuff # Brownian increment (and space-time Lévy area) - bm_inc = diffusion.contr(t0, t1, use_levy=True) + bm_inc, path_state = diffusion.contr(t0, t1, path_state, use_levy=True) if not isinstance(bm_inc, self.minimal_levy_area): raise ValueError( f"The Brownian increment {bm_inc} does not have the " @@ -658,14 +661,14 @@ def compute_and_insert_kg_j(_w_kgs_in, _levylist_kgs_in): y1 = (y0**ω + drift_result**ω + diffusion_result**ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, error, dense_info, None, RESULTS.successful + return y1, error, dense_info, None, path_state, RESULTS.successful def func( self, terms: MultiTerm[ tuple[ - AbstractTerm[Any, RealScalarLike], - AbstractTerm[Any, AbstractBrownianIncrement], + AbstractTerm[Any, RealScalarLike, None], + AbstractTerm[Any, AbstractBrownianIncrement, _PathState], ] ], t0: RealScalarLike, diff --git a/diffrax/_term.py b/diffrax/_term.py index 896f9d6d..56e202b1 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -267,7 +267,8 @@ def evaluate( def _callable_to_path( x: Union[ - AbstractPath[_Control, _ControlState], Callable[[RealScalarLike, RealScalarLike], _Control] + AbstractPath[_Control, _ControlState], + Callable[[RealScalarLike, RealScalarLike], _Control], ], ) -> AbstractPath[_Control, _ControlState]: if isinstance(x, AbstractPath): @@ -288,7 +289,8 @@ def _prod(vf, control): class _AbstractControlTerm(AbstractTerm[_VF, _Control, _ControlState]): vector_field: Callable[[RealScalarLike, Y, Args], _VF] control: Union[ - AbstractPath[_Control, _ControlState], Callable[[RealScalarLike, RealScalarLike], _Control] + AbstractPath[_Control, _ControlState], + Callable[[RealScalarLike, RealScalarLike], _Control], ] = eqx.field(converter=_callable_to_path) # pyright: ignore def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: @@ -599,6 +601,7 @@ def contr( control_state: _MultiControlState, **kwargs, ) -> tuple[tuple[PyTree[ArrayLike], ...], _MultiControlState]: + # print(self.terms, control_state) contrs = [ term.contr(t0, t1, state, **kwargs) for term, state in zip(self.terms, control_state) @@ -645,11 +648,17 @@ def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: t = t * self.direction return self.term.vf(t, y, args) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, control_state: _ControlState, **kwargs) -> tuple[_Control, _ControlState]: + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _ControlState, + **kwargs, + ) -> tuple[_Control, _ControlState]: _t0 = jnp.where(self.direction == 1, t0, -t1) _t1 = jnp.where(self.direction == 1, t1, -t0) contrs = self.term.contr(_t0, _t1, control_state, **kwargs) - return (self.direction * contrs[0]** ω).ω, contrs[1] + return (self.direction * contrs[0] ** ω).ω, contrs[1] def prod(self, vf: _VF, control: _Control) -> Y: with jax.numpy_dtype_promotion("standard"): @@ -867,7 +876,9 @@ def broadcast_underdamped_langevin_arg( class UnderdampedLangevinDiffusionTerm( AbstractTerm[ - UnderdampedLangevinX, Union[UnderdampedLangevinX, AbstractBrownianIncrement], _ControlState + UnderdampedLangevinX, + Union[UnderdampedLangevinX, AbstractBrownianIncrement], + _ControlState, ] ): r"""Represents the diffusion term in the Underdamped Langevin Diffusion (ULD). @@ -1013,8 +1024,14 @@ def fun(_gamma, _u, _v, _f_x): vf_y = (vf_x, vf_v) return vf_y - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> RealScalarLike: - return t1 - t0 + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: None = None, + **kwargs, + ) -> tuple[RealScalarLike, None]: + return t1 - t0, None def prod( self, vf: UnderdampedLangevinTuple, control: RealScalarLike diff --git a/examples/neural_sde.ipynb b/examples/neural_sde.ipynb index a4624cad..ac641b33 100644 --- a/examples/neural_sde.ipynb +++ b/examples/neural_sde.ipynb @@ -575,83 +575,67 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Step: 0, Loss: 0.13390611750738962\n", - "Step: 200, Loss: 4.786926678248814\n", - "Step: 400, Loss: 7.736175605228969\n", - "Step: 600, Loss: 10.103722981044225\n", - "Step: 800, Loss: 11.831081799098424\n", - "Step: 1000, Loss: 7.418417045048305\n", - "Step: 1200, Loss: 6.938951356070382\n", - "Step: 1400, Loss: 2.881302390779768\n", - "Step: 1600, Loss: 1.5363099915640694\n", - "Step: 1800, Loss: 1.0079529796327864\n", - "Step: 2000, Loss: 0.936917781829834\n", - "Step: 2200, Loss: 0.9594544768333435\n", - "Step: 2400, Loss: 1.247592806816101\n", - "Step: 2600, Loss: 0.9021680951118469\n", - "Step: 2800, Loss: 0.861811808177403\n", - "Step: 3000, Loss: 1.1381437267575945\n", - "Step: 3200, Loss: 1.5369644505637032\n", - "Step: 3400, Loss: 1.3387839964457922\n", - "Step: 3600, Loss: 1.0477747491427831\n", - "Step: 3800, Loss: 1.7565655538014002\n", - "Step: 4000, Loss: 1.8188678196498327\n", - "Step: 4200, Loss: 1.4719816957201277\n", - "Step: 4400, Loss: 1.4189972026007516\n", - "Step: 4600, Loss: 0.6867345826966422\n", - "Step: 4800, Loss: 0.6138326355389186\n", - "Step: 5000, Loss: 0.5908999613353184\n", - "Step: 5200, Loss: 0.579599814755576\n", - "Step: 5400, Loss: -0.8964726499148777\n", - "Step: 5600, Loss: -4.22784035546439\n", - "Step: 5800, Loss: 1.8623723132269723\n", - "Step: 6000, Loss: -0.17913252328123366\n", - "Step: 6200, Loss: 1.2232166869299752\n", - "Step: 6400, Loss: 1.1680303982325964\n", - "Step: 6600, Loss: -0.5765694592680249\n", - "Step: 6800, Loss: 0.5931433950151715\n", - "Step: 7000, Loss: 0.12497492773192269\n", - "Step: 7200, Loss: 0.5957097922052655\n", - "Step: 7400, Loss: 0.33551327671323505\n", - "Step: 7600, Loss: 0.5243289640971592\n", - "Step: 7800, Loss: 0.797236042363303\n", - "Step: 8000, Loss: 0.5341930559703282\n", - "Step: 8200, Loss: 1.1995042221886771\n", - "Step: 8400, Loss: -0.5231874521289553\n", - "Step: 8600, Loss: -0.42040516648973736\n", - "Step: 8800, Loss: 1.384656548500061\n", - "Step: 9000, Loss: 1.4223246574401855\n", - "Step: 9200, Loss: 0.2646511915538992\n", - "Step: 9400, Loss: -0.046253203813518794\n", - "Step: 9600, Loss: 0.738983656678881\n", - "Step: 9800, Loss: 1.1247712458883012\n", - "Step: 9999, Loss: -0.44179755449295044\n" + "ename": "TracerArrayConversionError", + "evalue": "The numpy.ndarray conversion method __array__() was called on traced array with shape float32[3]\nThe error occurred while tracing the function _fn at /Users/owenlockwood/miniforge3/envs/dev_diffrax/lib/python3.10/site-packages/equinox/_eval_shape.py:31 for jit. This concrete value was not available in Python because it depends on the values of the arguments _dynamic[1][0].tprev and _dynamic[1][0].tnext.\nSee https://jax.readthedocs.io/en/latest/errors.html#jax.errors.TracerArrayConversionError", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniforge3/envs/dev_diffrax/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3209\u001b[0m, in \u001b[0;36mndim\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 3208\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3209\u001b[0m \u001b[39mreturn\u001b[39;00m a\u001b[39m.\u001b[39;49mndim\n\u001b[1;32m 3210\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'ndim'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mTracerArrayConversionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m main()\n", + "Cell \u001b[0;32mIn[7], line 54\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(initial_noise_size, noise_size, hidden_size, width_size, depth, generator_lr, discriminator_lr, batch_size, steps, steps_per_print, dataset_size, seed)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[39mfor\u001b[39;00m step, (ts_i, ys_i) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mrange\u001b[39m(steps), infinite_dataloader):\n\u001b[1;32m 53\u001b[0m step \u001b[39m=\u001b[39m jnp\u001b[39m.\u001b[39masarray(step)\n\u001b[0;32m---> 54\u001b[0m generator, discriminator, g_opt_state, d_opt_state \u001b[39m=\u001b[39m make_step(\n\u001b[1;32m 55\u001b[0m generator,\n\u001b[1;32m 56\u001b[0m discriminator,\n\u001b[1;32m 57\u001b[0m g_opt_state,\n\u001b[1;32m 58\u001b[0m d_opt_state,\n\u001b[1;32m 59\u001b[0m g_optim,\n\u001b[1;32m 60\u001b[0m d_optim,\n\u001b[1;32m 61\u001b[0m ts_i,\n\u001b[1;32m 62\u001b[0m ys_i,\n\u001b[1;32m 63\u001b[0m key,\n\u001b[1;32m 64\u001b[0m step,\n\u001b[1;32m 65\u001b[0m )\n\u001b[1;32m 66\u001b[0m \u001b[39mif\u001b[39;00m (step \u001b[39m%\u001b[39m steps_per_print) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m step \u001b[39m==\u001b[39m steps \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 67\u001b[0m total_score \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n", + " \u001b[0;31m[... skipping hidden 15 frame]\u001b[0m\n", + "Cell \u001b[0;32mIn[6], line 36\u001b[0m, in \u001b[0;36mmake_step\u001b[0;34m(generator, discriminator, g_opt_state, d_opt_state, g_optim, d_optim, ts_i, ys_i, key, step)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[39m@eqx\u001b[39m\u001b[39m.\u001b[39mfilter_jit\n\u001b[1;32m 24\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmake_step\u001b[39m(\n\u001b[1;32m 25\u001b[0m generator,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 34\u001b[0m step,\n\u001b[1;32m 35\u001b[0m ):\n\u001b[0;32m---> 36\u001b[0m g_grad, d_grad \u001b[39m=\u001b[39m grad_loss((generator, discriminator), ts_i, ys_i, key, step)\n\u001b[1;32m 37\u001b[0m g_updates, g_opt_state \u001b[39m=\u001b[39m g_optim\u001b[39m.\u001b[39mupdate(g_grad, g_opt_state)\n\u001b[1;32m 38\u001b[0m d_updates, d_opt_state \u001b[39m=\u001b[39m d_optim\u001b[39m.\u001b[39mupdate(d_grad, d_opt_state)\n", + " \u001b[0;31m[... skipping hidden 11 frame]\u001b[0m\n", + "Cell \u001b[0;32mIn[6], line 15\u001b[0m, in \u001b[0;36mgrad_loss\u001b[0;34m(g_d, ts_i, ys_i, key, step)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[39m@eqx\u001b[39m\u001b[39m.\u001b[39mfilter_grad\n\u001b[1;32m 13\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgrad_loss\u001b[39m(g_d, ts_i, ys_i, key, step):\n\u001b[1;32m 14\u001b[0m generator, discriminator \u001b[39m=\u001b[39m g_d\n\u001b[0;32m---> 15\u001b[0m \u001b[39mreturn\u001b[39;00m loss(generator, discriminator, ts_i, ys_i, key, step)\n", + " \u001b[0;31m[... skipping hidden 15 frame]\u001b[0m\n", + "Cell \u001b[0;32mIn[6], line 6\u001b[0m, in \u001b[0;36mloss\u001b[0;34m(generator, discriminator, ts_i, ys_i, key, step)\u001b[0m\n\u001b[1;32m 4\u001b[0m key \u001b[39m=\u001b[39m jr\u001b[39m.\u001b[39mfold_in(key, step)\n\u001b[1;32m 5\u001b[0m key \u001b[39m=\u001b[39m jr\u001b[39m.\u001b[39msplit(key, batch_size)\n\u001b[0;32m----> 6\u001b[0m fake_ys_i \u001b[39m=\u001b[39m jax\u001b[39m.\u001b[39;49mvmap(generator)(ts_i, key\u001b[39m=\u001b[39;49mkey)\n\u001b[1;32m 7\u001b[0m real_score \u001b[39m=\u001b[39m jax\u001b[39m.\u001b[39mvmap(discriminator)(ts_i, ys_i)\n\u001b[1;32m 8\u001b[0m fake_score \u001b[39m=\u001b[39m jax\u001b[39m.\u001b[39mvmap(discriminator)(ts_i, fake_ys_i)\n", + " \u001b[0;31m[... skipping hidden 3 frame]\u001b[0m\n", + "Cell \u001b[0;32mIn[4], line 53\u001b[0m, in \u001b[0;36mNeuralSDE.__call__\u001b[0;34m(self, ts, key)\u001b[0m\n\u001b[1;32m 51\u001b[0m y0 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minitial(init)\n\u001b[1;32m 52\u001b[0m saveat \u001b[39m=\u001b[39m diffrax\u001b[39m.\u001b[39mSaveAt(ts\u001b[39m=\u001b[39mts)\n\u001b[0;32m---> 53\u001b[0m sol \u001b[39m=\u001b[39m diffrax\u001b[39m.\u001b[39;49mdiffeqsolve(terms, solver, t0, t1, dt0, y0, saveat\u001b[39m=\u001b[39;49msaveat)\n\u001b[1;32m 54\u001b[0m \u001b[39mreturn\u001b[39;00m jax\u001b[39m.\u001b[39mvmap(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreadout)(sol\u001b[39m.\u001b[39mys)\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_integrate.py:1464\u001b[0m, in \u001b[0;36mdiffeqsolve\u001b[0;34m(terms, solver, t0, t1, dt0, y0, args, saveat, stepsize_controller, adjoint, event, max_steps, throw, progress_meter, solver_state, controller_state, made_jump, path_state, discrete_terminating_event)\u001b[0m\n\u001b[1;32m 1436\u001b[0m init_state \u001b[39m=\u001b[39m State(\n\u001b[1;32m 1437\u001b[0m y\u001b[39m=\u001b[39my0,\n\u001b[1;32m 1438\u001b[0m tprev\u001b[39m=\u001b[39mtprev,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1457\u001b[0m event_mask\u001b[39m=\u001b[39mevent_mask,\n\u001b[1;32m 1458\u001b[0m )\n\u001b[1;32m 1460\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[1;32m 1461\u001b[0m \u001b[39m# Main loop\u001b[39;00m\n\u001b[1;32m 1462\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m-> 1464\u001b[0m final_state, aux_stats \u001b[39m=\u001b[39m adjoint\u001b[39m.\u001b[39;49mloop(\n\u001b[1;32m 1465\u001b[0m args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 1466\u001b[0m terms\u001b[39m=\u001b[39;49mterms,\n\u001b[1;32m 1467\u001b[0m solver\u001b[39m=\u001b[39;49msolver,\n\u001b[1;32m 1468\u001b[0m stepsize_controller\u001b[39m=\u001b[39;49mstepsize_controller,\n\u001b[1;32m 1469\u001b[0m event\u001b[39m=\u001b[39;49mevent,\n\u001b[1;32m 1470\u001b[0m saveat\u001b[39m=\u001b[39;49msaveat,\n\u001b[1;32m 1471\u001b[0m t0\u001b[39m=\u001b[39;49mt0,\n\u001b[1;32m 1472\u001b[0m t1\u001b[39m=\u001b[39;49mt1,\n\u001b[1;32m 1473\u001b[0m dt0\u001b[39m=\u001b[39;49mdt0,\n\u001b[1;32m 1474\u001b[0m max_steps\u001b[39m=\u001b[39;49mmax_steps,\n\u001b[1;32m 1475\u001b[0m init_state\u001b[39m=\u001b[39;49minit_state,\n\u001b[1;32m 1476\u001b[0m throw\u001b[39m=\u001b[39;49mthrow,\n\u001b[1;32m 1477\u001b[0m passed_solver_state\u001b[39m=\u001b[39;49mpassed_solver_state,\n\u001b[1;32m 1478\u001b[0m passed_controller_state\u001b[39m=\u001b[39;49mpassed_controller_state,\n\u001b[1;32m 1479\u001b[0m passed_path_state\u001b[39m=\u001b[39;49mpassed_path_state,\n\u001b[1;32m 1480\u001b[0m progress_meter\u001b[39m=\u001b[39;49mprogress_meter,\n\u001b[1;32m 1481\u001b[0m )\n\u001b[1;32m 1483\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[1;32m 1484\u001b[0m \u001b[39m# Finish up\u001b[39;00m\n\u001b[1;32m 1485\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[1;32m 1487\u001b[0m progress_meter\u001b[39m.\u001b[39mclose(final_state\u001b[39m.\u001b[39mprogress_meter_state)\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_adjoint.py:308\u001b[0m, in \u001b[0;36mRecursiveCheckpointAdjoint.loop\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 304\u001b[0m outer_while_loop \u001b[39m=\u001b[39m ft\u001b[39m.\u001b[39mpartial(\n\u001b[1;32m 305\u001b[0m _outer_loop, kind\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcheckpointed\u001b[39m\u001b[39m\"\u001b[39m, checkpoints\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheckpoints\n\u001b[1;32m 306\u001b[0m )\n\u001b[1;32m 307\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 308\u001b[0m final_state \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_loop(\n\u001b[1;32m 309\u001b[0m terms\u001b[39m=\u001b[39;49mterms,\n\u001b[1;32m 310\u001b[0m saveat\u001b[39m=\u001b[39;49msaveat,\n\u001b[1;32m 311\u001b[0m init_state\u001b[39m=\u001b[39;49minit_state,\n\u001b[1;32m 312\u001b[0m max_steps\u001b[39m=\u001b[39;49mmax_steps,\n\u001b[1;32m 313\u001b[0m inner_while_loop\u001b[39m=\u001b[39;49minner_while_loop,\n\u001b[1;32m 314\u001b[0m outer_while_loop\u001b[39m=\u001b[39;49mouter_while_loop,\n\u001b[1;32m 315\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m 316\u001b[0m )\n\u001b[1;32m 317\u001b[0m \u001b[39mif\u001b[39;00m msg \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 318\u001b[0m final_state \u001b[39m=\u001b[39m eqxi\u001b[39m.\u001b[39mnondifferentiable_backward(\n\u001b[1;32m 319\u001b[0m final_state, msg\u001b[39m=\u001b[39mmsg, symbolic\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 320\u001b[0m )\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_integrate.py:624\u001b[0m, in \u001b[0;36mloop\u001b[0;34m(solver, stepsize_controller, event, saveat, t0, t1, dt0, max_steps, terms, args, init_state, inner_while_loop, outer_while_loop, progress_meter)\u001b[0m\n\u001b[1;32m 622\u001b[0m static_made_jump \u001b[39m=\u001b[39m init_state\u001b[39m.\u001b[39mmade_jump\n\u001b[1;32m 623\u001b[0m static_result \u001b[39m=\u001b[39m init_state\u001b[39m.\u001b[39mresult\n\u001b[0;32m--> 624\u001b[0m _, traced_jump, traced_result \u001b[39m=\u001b[39m eqx\u001b[39m.\u001b[39;49mfilter_eval_shape(body_fun_aux, init_state)\n\u001b[1;32m 625\u001b[0m \u001b[39mif\u001b[39;00m traced_jump:\n\u001b[1;32m 626\u001b[0m static_made_jump \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", + " \u001b[0;31m[... skipping hidden 14 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_integrate.py:351\u001b[0m, in \u001b[0;36mloop..body_fun_aux\u001b[0;34m(state)\u001b[0m\n\u001b[1;32m 344\u001b[0m state \u001b[39m=\u001b[39m _handle_static(state)\n\u001b[1;32m 346\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[1;32m 347\u001b[0m \u001b[39m# Actually do some differential equation solving! Make numerical steps, adapt\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[39m# step sizes, all that jazz.\u001b[39;00m\n\u001b[1;32m 349\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m--> 351\u001b[0m (y, y_error, dense_info, solver_state, path_state, solver_result) \u001b[39m=\u001b[39m solver\u001b[39m.\u001b[39;49mstep(\n\u001b[1;32m 352\u001b[0m terms,\n\u001b[1;32m 353\u001b[0m state\u001b[39m.\u001b[39;49mtprev,\n\u001b[1;32m 354\u001b[0m state\u001b[39m.\u001b[39;49mtnext,\n\u001b[1;32m 355\u001b[0m state\u001b[39m.\u001b[39;49my,\n\u001b[1;32m 356\u001b[0m args,\n\u001b[1;32m 357\u001b[0m state\u001b[39m.\u001b[39;49msolver_state,\n\u001b[1;32m 358\u001b[0m state\u001b[39m.\u001b[39;49mmade_jump,\n\u001b[1;32m 359\u001b[0m state\u001b[39m.\u001b[39;49mpath_state,\n\u001b[1;32m 360\u001b[0m )\n\u001b[1;32m 362\u001b[0m \u001b[39m# e.g. if someone has a sqrt(y) in the vector field, and dt0 is so large that\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[39m# we get a negative value for y, and then get a NaN vector field. (And then\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[39m# everything breaks.) See #143.\u001b[39;00m\n\u001b[1;32m 365\u001b[0m y_error \u001b[39m=\u001b[39m jtu\u001b[39m.\u001b[39mtree_map(\u001b[39mlambda\u001b[39;00m x: jnp\u001b[39m.\u001b[39mwhere(jnp\u001b[39m.\u001b[39misnan(x), jnp\u001b[39m.\u001b[39minf, x), y_error)\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_solver/reversible_heun.py:80\u001b[0m, in \u001b[0;36mReversibleHeun.step\u001b[0;34m(self, terms, t0, t1, y0, args, solver_state, made_jump, path_state)\u001b[0m\n\u001b[1;32m 77\u001b[0m vf0 \u001b[39m=\u001b[39m lax\u001b[39m.\u001b[39mcond(made_jump, \u001b[39mlambda\u001b[39;00m _: terms\u001b[39m.\u001b[39mvf(t0, y0, args), \u001b[39mlambda\u001b[39;00m _: vf0, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 79\u001b[0m control, new_path_state \u001b[39m=\u001b[39m terms\u001b[39m.\u001b[39mcontr(t0, t1, path_state)\n\u001b[0;32m---> 80\u001b[0m yhat1 \u001b[39m=\u001b[39m (\u001b[39m2\u001b[39m \u001b[39m*\u001b[39m y0\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mω \u001b[39m-\u001b[39m yhat0\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mω \u001b[39m+\u001b[39m terms\u001b[39m.\u001b[39;49mprod(vf0, control) \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m ω)\u001b[39m.\u001b[39mω\n\u001b[1;32m 81\u001b[0m vf1 \u001b[39m=\u001b[39m terms\u001b[39m.\u001b[39mvf(t1, yhat1, args)\n\u001b[1;32m 82\u001b[0m y1 \u001b[39m=\u001b[39m (y0\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mω \u001b[39m+\u001b[39m \u001b[39m0.5\u001b[39m \u001b[39m*\u001b[39m terms\u001b[39m.\u001b[39mprod((vf0\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mω \u001b[39m+\u001b[39m vf1\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mω)\u001b[39m.\u001b[39mω, control) \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m ω)\u001b[39m.\u001b[39mω\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:614\u001b[0m, in \u001b[0;36mMultiTerm.prod\u001b[0;34m(self, vf, control)\u001b[0m\n\u001b[1;32m 611\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprod\u001b[39m(\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m, vf: \u001b[39mtuple\u001b[39m[PyTree[ArrayLike], \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m], control: \u001b[39mtuple\u001b[39m[PyTree[ArrayLike], \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m]\n\u001b[1;32m 613\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Y:\n\u001b[0;32m--> 614\u001b[0m out \u001b[39m=\u001b[39m [\n\u001b[1;32m 615\u001b[0m term\u001b[39m.\u001b[39mprod(vf_, control_)\n\u001b[1;32m 616\u001b[0m \u001b[39mfor\u001b[39;00m term, vf_, control_ \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mterms, vf, control)\n\u001b[1;32m 617\u001b[0m ]\n\u001b[1;32m 618\u001b[0m \u001b[39mreturn\u001b[39;00m jtu\u001b[39m.\u001b[39mtree_map(_sum, \u001b[39m*\u001b[39mout)\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:615\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 611\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprod\u001b[39m(\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m, vf: \u001b[39mtuple\u001b[39m[PyTree[ArrayLike], \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m], control: \u001b[39mtuple\u001b[39m[PyTree[ArrayLike], \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m]\n\u001b[1;32m 613\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Y:\n\u001b[1;32m 614\u001b[0m out \u001b[39m=\u001b[39m [\n\u001b[0;32m--> 615\u001b[0m term\u001b[39m.\u001b[39;49mprod(vf_, control_)\n\u001b[1;32m 616\u001b[0m \u001b[39mfor\u001b[39;00m term, vf_, control_ \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mterms, vf, control)\n\u001b[1;32m 617\u001b[0m ]\n\u001b[1;32m 618\u001b[0m \u001b[39mreturn\u001b[39;00m jtu\u001b[39m.\u001b[39mtree_map(_sum, \u001b[39m*\u001b[39mout)\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:665\u001b[0m, in \u001b[0;36mWrapTerm.prod\u001b[0;34m(self, vf, control)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mprod\u001b[39m(\u001b[39mself\u001b[39m, vf: _VF, control: _Control) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Y:\n\u001b[1;32m 664\u001b[0m \u001b[39mwith\u001b[39;00m jax\u001b[39m.\u001b[39mnumpy_dtype_promotion(\u001b[39m\"\u001b[39m\u001b[39mstandard\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 665\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mterm\u001b[39m.\u001b[39;49mprod(vf, control)\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:479\u001b[0m, in \u001b[0;36mControlTerm.prod\u001b[0;34m(self, vf, control)\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[39mreturn\u001b[39;00m vf\u001b[39m.\u001b[39mmv(control)\n\u001b[1;32m 478\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 479\u001b[0m \u001b[39mreturn\u001b[39;00m jtu\u001b[39m.\u001b[39;49mtree_map(_prod, vf, control)\n", + " \u001b[0;31m[... skipping hidden 2 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/_term.py:284\u001b[0m, in \u001b[0;36m_prod\u001b[0;34m(vf, control)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_prod\u001b[39m(vf, control):\n\u001b[0;32m--> 284\u001b[0m \u001b[39mreturn\u001b[39;00m jnp\u001b[39m.\u001b[39mtensordot(jnp\u001b[39m.\u001b[39mconj(vf), control, axes\u001b[39m=\u001b[39mjnp\u001b[39m.\u001b[39;49mndim(control))\n", + "File \u001b[0;32m~/miniforge3/envs/dev_diffrax/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3211\u001b[0m, in \u001b[0;36mndim\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 3209\u001b[0m \u001b[39mreturn\u001b[39;00m a\u001b[39m.\u001b[39mndim\n\u001b[1;32m 3210\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[0;32m-> 3211\u001b[0m \u001b[39mreturn\u001b[39;00m asarray(a)\u001b[39m.\u001b[39mndim\n", + "File \u001b[0;32m~/miniforge3/envs/dev_diffrax/lib/python3.10/site-packages/jax/_src/core.py:714\u001b[0m, in \u001b[0;36mTracer.__array__\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m 713\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw):\n\u001b[0;32m--> 714\u001b[0m \u001b[39mraise\u001b[39;00m TracerArrayConversionError(\u001b[39mself\u001b[39m)\n", + "\u001b[0;31mTracerArrayConversionError\u001b[0m: The numpy.ndarray conversion method __array__() was called on traced array with shape float32[3]\nThe error occurred while tracing the function _fn at /Users/owenlockwood/miniforge3/envs/dev_diffrax/lib/python3.10/site-packages/equinox/_eval_shape.py:31 for jit. This concrete value was not available in Python because it depends on the values of the arguments _dynamic[1][0].tprev and _dynamic[1][0].tnext.\nSee https://jax.readthedocs.io/en/latest/errors.html#jax.errors.TracerArrayConversionError" ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ "main()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afd29b2c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "py38", + "display_name": "Python 3.10.14 ('dev_diffrax')", "language": "python", - "name": "py38" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -663,7 +647,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "01761703e8e304055600d311574f89f8a646f73edac04b8bff1580ad2d98581f" + } } }, "nbformat": 4, diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index b2ee7791..f9237d11 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -46,26 +46,7 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'AbstractBrownianPath' from partially initialized module 'diffrax._brownian' (most likely due to a circular import) (/Users/owenlockwood/Documents/diffrax_extensions/diffrax/_brownian/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mwarnings\u001b[39;00m \u001b[39mimport\u001b[39;00m simplefilter\n\u001b[1;32m 4\u001b[0m simplefilter(action\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m, category\u001b[39m=\u001b[39m\u001b[39mFutureWarning\u001b[39;00m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mdiffrax\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjnp\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mjax\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrandom\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mjr\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/diffrax_extensions/diffrax/__init__.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mimportlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetadata\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_adjoint\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 4\u001b[0m AbstractAdjoint \u001b[39mas\u001b[39;00m AbstractAdjoint,\n\u001b[1;32m 5\u001b[0m BacksolveAdjoint \u001b[39mas\u001b[39;00m BacksolveAdjoint,\n\u001b[1;32m 6\u001b[0m DirectAdjoint 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PyTreeDef, Shaped\n\u001b[0;32m---> 17\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_brownian\u001b[39;00m \u001b[39mimport\u001b[39;00m AbstractBrownianPath\n\u001b[1;32m 18\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_custom_types\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[1;32m 19\u001b[0m AbstractBrownianIncrement,\n\u001b[1;32m 20\u001b[0m Args,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 25\u001b[0m Y,\n\u001b[1;32m 26\u001b[0m )\n\u001b[1;32m 27\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_misc\u001b[39;00m \u001b[39mimport\u001b[39;00m upcast_or_raise\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'AbstractBrownianPath' from partially initialized module 'diffrax._brownian' (most likely due to a circular import) (/Users/owenlockwood/Documents/diffrax_extensions/diffrax/_brownian/__init__.py)" - ] - } - ], + "outputs": [], "source": [ "from warnings import simplefilter\n", "\n", @@ -75,7 +56,8 @@ "import jax.numpy as jnp\n", "import jax.random as jr\n", "import matplotlib.pyplot as plt\n", - "\n", + "import jax\n", + "import equinox as eqx\n", "\n", "t0, t1 = 0.0, 20.0\n", "dt0 = 0.05\n", @@ -99,6 +81,31 @@ "terms = diffrax.MultiTerm(drift_term, diffusion_term)\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", + "# solver = diffrax.Euler()\n", + "\n", + "def _path_init(term):\n", + " if isinstance(term, diffrax.ControlTerm) or isinstance(term, diffrax.UnderdampedLangevinDiffusionTerm):\n", + " return term.control.init(t0, t1, y0, None, 100)\n", + " elif isinstance(term, diffrax.MultiTerm):\n", + " return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", + " return None\n", + "\n", + "state = jax.tree.map(_path_init, terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", + "# print(state)\n", + "# print(terms.contr(t0, t1, state)[0])\n", + "\n", + "# @eqx.filter_jit\n", + "# def f():\n", + "# return diffrax._integrate._assert_term_compatible(\n", + "# y0,\n", + "# None,\n", + "# terms,\n", + "# solver.term_structure,\n", + "# solver.term_compatible_contr_kwargs | {\"control_state\": state},\n", + "# )\n", + "\n", + "# f()\n", + "\n", "sol = diffrax.diffeqsolve(\n", " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat\n", ")\n", @@ -118,7 +125,7 @@ "outputs": [ { "data": { - "image/png": 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emND0/9euGUFu+Vb2ZZfz6I8HefuGUXaJV5IkydnYv4WQJElST1eWIbZWTLIqPOIwad2grqz5+JLUgvVpRdz8wS50BhPzhkRw77TEdj9Xq1F49rIhuGgUVh4qZO2RQhtGKkmS5LxkkiVJkmRvpdZPskwaF0xh5jVBZMmg1IKTxdU8+PU+bvtoNw16I9MHhPHfK4e32g65NQMj/bj1AjG69era4+etMJEkSeqpnKpcUJIkqVtoSrISzv+4DjJFJUP+XsjbC0Mut+qxJcdUUt3A5uPFlNfqyC2rI62gCh93F0YnBHHxsChCfd2p1xl4de1x/rcpHb1RJESXjYjmP38Yimsn18S6fXJvPtyWyb6cCn47cZoL+oZY88uSJElyejLJkiRJsieTySZzsgBMUSNgz/tyJMsOqup1vLclg8ySGnRGE7OSwrloaBRajX3mwp2ubuDDbad4d3M6tY2Gcz6/4lABr6w5xjVj4/gxNY/8inoApg0I495piV1uwR7i487Vo+NYujWTJetPyCRLkiTpd2SSJUmSZE9VBaCvA0UD/rFWPbQpMlns5O8DowE0tls0tifbcryE//t2P7nldU0f+2V/Pq+vO8ErVyeTFOVns3PX6wz8a9lBlqXmojOIUakBEb70CfMh1MedARG+lNXq+GlfHofzK3l7o0joo/w9ePTiQcweZL2Fqm+f3JtPtp9iW/ppdmWWMjo+qO0nSZLk8GQJsHWugUyyJEmS7MnSlMI/FlzcrHvs4ERw84HGaig+CuFJ1j1+D2Mwmvh5fx4ZJTXU1OuoLFTY9uNhvtiVA0BckBfXjY2jql7PR9syOV5UzZVvb+ON60YwuV+oTWJ6cdVRvt4jzj8sxp8/XtiHuYMjzplXdfukBD74LZOf9+dxyfBorh0bh4erdZPu6ABPrhgVw+c7s3l+5VG+vGNch+d3SZLkOFxdxZIitbW1eHp6qhyNumpra4Hma9IZMsmSJEmyJxuVCgJi5CpyOJzaIlq5yySr02oa9Nz/xV7WHCk646NaSBcJzo3je/F/cwfg5SZeRm+blMCdn+xhe3optyzdxce3jmV8n2CrxrTt5Gne3SKS9CXXjmD+0MhWH+ui1XD75N7cPtkGP2dnuHdaX77dk8vOjFI2Hy+xWXIpSZLtabVaAgICKCoSf/e8vLwc5saJ0WiksbGR+vp6NBrb9e0zmUzU1tZSVFREQEAAWm3nb07JJEuSJMmebNT0okl0skiycvdA8vW2OUc3V9Og55p3trM/pwJ3Fw2XJkfjqlXYdSQT/6Bg7p/Rjwl9zp6DFODlxoe3jOH+z1NZcaiAuz7dww93T6RXsLdVYmrQG/jrN/swmeCaMbHnTbDsKSrAk+vGxfHBb5m8uOook/qGOMybMkmSOi4iQpQUWxItR2Eymairq8PT09Muf2MCAgKarkVnySRLkiTJnmw5kgUQNUJsZfOLTjEaTfz5y1T251QQ5O3GuzeOYkRcIDqdjuVKOvPmjW61fMTdRcvLVw/nqre3sS+ngj9+vIef7r2g0x38zvTV7hxyyuoI93Pnn/Mda4TyT1MS+WxHFvtyKtiVWcaYBDk3S5KclaIoREZGEhYWhk6nUzucJjqdjk2bNjF58uQulfC1h6ura5dGsCxkkiVJkmRPliQr0FYjWeYkq/AQ6BvAxd025+mmXl9/glWHC3HTapoSrI7wcNXyzqJRzH55E2kFVXyy/RQ3T+za97pBb+CN9ScAkdB4uzvWS3eorzuXjYjm853ZvL8lQyZZktQNaLVaqyQa1qLVatHr9Xh4eNg8ybIWuRixJEmSvZhMNlmI+CwBvcArGIw6KDhom3N0U3WNBv63SSTBT106uMMJlkWYnwcPzu4PwEurj3G6uqFLcX21K5v8inoi/Dy4arR1O1JaiyWRXHW4gOzSWpWjkSRJUp9MsiRJkuylrgwaKsR+YLxtzqEozSWDebJksCNWHS6gukFPbJAnl4+I6dKxrh4dR1KkH5X1eh7/6XCn2wHX6wwsWX8SgLun9rF6h0Br6Rfuy6S+IRhN8OHWzE4dQ3aNliSpO5FJliRJkr1YRrF8I8HNy3bniZbzsjrj25RcAC5NjkHTxUWFtRqFJxcOQqtR+HFfXtMI2Zmq6nVtJl9f7sqmoLKeSH8PrnTQUSyLmybEA7AsNRe9wdiu55RUN/Dsr2mMenodj+7R8vD3h8ivqGv7iZIkSQ7OsQq7JUmSujNbN72waGp+sce25+lGCivr2XK8GIA/jIi2yjFH9gri0QVJPPLDIZ5dkcaKQwUkBHuDAofzKkkrqGJCn2CWXDuCQO9z10yr1xl4Y4N5LtbURNxdHHMUy2Jyv1CCvN0oqW5ky4kSpvQPO+/j04vFumIl1Y3mjyh8k5LL8eIavr9rQpcTXUmSJDXJkSxJkiR7sSxEbKumFxaWkaySY9BQZdtzdRPfpuRgNMHo+ECrtV0HuGFcL26eGI/JBHuzyvluby7fpeSSViC+L1tPnuaSJb+x+nAhBmPzqFZlvY6/fL2PwsoGovw9uHJU18oX7cFVq+Eic2v5H1LzzvvYgop6bnhvJyXVjSSG+fDmtcO5c6ABb3ct+7LFdZIkSXJmciRLkiTJXppGsmycZPmEgV8MVOZAXiokTLLt+ZxcXaOB97dkAnDFKOuW5CmKwqMLBrFofDwHcivIL6/DBET6exAd4Mnir/aRVVrL7R/tJibQkxvG9UKrUXh/SwZ5FfVoNQr/mJ/k8KNYFpcMj+ajbadYeaiA2kZ902LNZzIaTfzp0z3klteREOLNF3eMw99dQ2OGibun9Oa5lcf5z4o0Zg8Kx9fDObqISZIk/Z5MsiRJkuzFXkkWiEWJK3NE8wuZZJ3XpztOUVLdQEygJwuHW6dU8PcSQrxJCDl3hOyHuyfy1qaTfLkrm5yyOp75Na3pc3FBXrx89fBOdzlUw4i4AOKCvMgqrWXVoUIWJp97Pb9JySElqxxvNy0f3TKGEB/3pvV4bhzXi6/35JFRUsPD3x3gtWuS5eLGkiQ5JVkuKEmSZC+2bt9+pqhksS04YPtzObG6RgNvbRTJ7z1TE3Fzse/LYqC3Gw/PHcj2h6fz3B+GkhwXwPDYAJ65bAgrH5jsVAkWiJG7y8xz2j7clnnO5ytqdfzHnEjeP6MvsUFnN4Bxc9Hw/OVDcdEo/Lw/n3c3Z9g8ZkmSJFuQSZYkSZI9NFRBTZHYt/WcLICwQWJbeNj253Jir6473jSKdVkX27Z3hYerlitHx/L9nyay7O6JXDMmDk835ygR/L3rxvbCTathb1Y5e06VnfW5x38+xOkaMQ+rtUWaR8UH8a+LkgB4dkUaWafluluSJDkfmWRJkiTZQ1mm2HoGgWeA7c8XLt6kUnIMDDrbn88J7TlVxtsbxRpU/5yfZPdRrO4q1NedS4ZHAfDelubW9V/vzua7lFw0Cjx96RBcta1f70Xje3FBYggGo4kvdmXZPGZJkiRrk68okiRJ9mCv9u0W/rHg5gtGHZw+YZ9zOpHy2kYe/HofRhNclhzNnMERaofUrdw6SYxSrThYwNojhaw5XMi/fjgIwOKZ/RiTEHTe5yuKwnVj4wD4ek8OunauuyVJkuQoZJIlSZJkD03zsexQKgigKBA2UOwXHrLPOZ1EdYOeGz/YRUZJDZH+Hjy6YJDaIXU7AyL8mDs4AqMJbv1wN7d9tJt6nZEL+4XypymJ7TrG9IHhBHu7UVzVwPq0IhtHLEmSZF0yyZIkSbIHe49kQXPJYJGcl2VhMpm4+9MU9mWXE+Dlyoe3jMHfS7YJt4VXrk7mpgnxTf+/9YIE3lk0qt2LDLu5aLh8pJgn98WubFuEKEmSZDOyhbskSZI9WJIsezS9sAizJFlH7HdOB/dDah4bjxXj7qLho1vG0C/cV+2Qui03Fw2PXTyIOYMjcNUqjOx1/hLBllwxKpa3N6Wz6VgxVfU6uW6WJElOQ45kSZIk2YOl8YU9R7IsSZYsFwSgok7HU7+IUb37pvdlaEyAugH1EON6B3cqwQJIDPMhIcQbvdHEbydKrByZJEmS7cgkS5Ikydb0DVCRI/bVSLLKT4kW8j3ckvUnKKlupE+oN7dPsuP3QeqSKf1DAdhwtFjlSCRJktpPJlmSJEm2VnYKMIGbD3iH2O+83sHgEy72i4/a77wOqEFv4KvdYl7P3+cNlO3anciU/mEArD9ahMlkUjkaSZKk9pGvMpIkSbbW1PQiQXT9s6cw2fwCYO2RIsprdUT4eTS9aZecw9iEIDxcNRRWNnAkX47ISpLkHGSSJUmSZGtqNL2wCO0vtj18JOtr8yjWZSOi0bazu53kGDxctUzsI0aANxyTrdwlSXIOMsmSpO5AVw/blsDaJ6BGTg53OGWWNbJUmAckkywKK+vZeEzM57G0BJecy5QBYvRx7RGZZEmS5BxkkiVJzi5rB7wxFlb+HTa/CK8Mh62vgdGgdmSSxZnlgvYWYk6ySnpukvXL/nyMJhjZK5DeoT5qhyN1woyBIslKySqjqKpe5WgkSZLaJpMsSXJmdeXw1SLRHtw3EiKGQGMVrPonLL0IKnLVjlACKFVzJGuA2JZnQ2ON/c/vADYfF6NYs5LCVY5E6qxIf0+GxfhjMsnRLEmSnINMsiTJma19HKoLIDgR7tkFd2yCBa+ILnZZW+Gbm0F241KXQS9aqIM6SZZ3MHgFAyYoOW7/86usUW9kR0YpABf0tWNnR8nqZg2KAGDVoQKVI7GNynodeeV1aochSZKVyCRLkpxV1g7Y/b7Yv+hlcPcFjQZG3gR3bARXL8jeAQe/VTNKqTIHjHrQuoNvlDoxWEazSo6pc34V7c0qo7bRQLC3GwMj/NQOR+qC2YPESORvJ05TVa9TORrrKaio5x/fH2DMv9cw4dl1zH1lM8v2yioESXJ2MsmSJGekb4Sf7hf7yddDwqSzPx+SCBf8WeyvfhR08u6oapo6C8aLJFgNIf3EtjhNnfOraMsJ0QhmYmIIGtlV0Kn1CfWhd4g3jQYj67vJwsQ7M0qZ/+pmPt2RRb3OiKLAkfxKHvgylZ/356kdniRJXSCTLElyRltfgeIj4BUCM59s+THj7wG/GDGSsuMt+8YnNVOz6YWFZSSrB3YY3HxcJFmyVND5KYrCvCGRAHy7J0flaLru1wP5XPvOdk7XNDIw0o8v7hjH3n/N5PpxcQD85at97DlVpnKUkiR1lkyyJMnZlJ2Cjc+L/TnPgldQy49z84Jp/xD7W1+Dhmr7xCedTc2mFxah5pGsHlYuWFGrY39OOQCTZJLVLVwxSrTg33S8mJyyWpWj6bz1R4u474u96I0m5g+J5Lu7JjCudzABXm48fvFgpg8Io0Fv5Ib3drD2SKHa4UqS1AkyyZIkZ7P5RTA0QMJkGHL5+R875EqxAG7tadj1rn3ik87mEEmWeSTr9ElRatpDrDtaiNEEfcN8iPT3VDscyQp6BXszoU8wJhN8vdv5RrMMRhPvbk7njx/vQWcwcdHQSF69JhlPN23TY7QahVevSWZS3xBqGw3c/tFuPt6WqV7QkiR1ikyyJMmZlGdD6mdif+o/QGljjonWBS58SOxvfbXHtvBWlWUh4kAVywV9I8HdH0wGON1zOgwuPyC60M01l5hJ3cNVo2MB+Hp3NqU1znPTwGQyccdHu3nqlyM06o3MHhTOS1cNR9vCXEFvdxfev2k0V42KxWiCf/1wiKd+PoxJdouVJKchkyxJcia/vQxGnRjFihvXvucMuRIC4sRo1tFfbRqe9Dsm0xkjWSomWYoC4Uliv/CQenHYUXWDno3HRHOEeUMiVI5GsqbZgyII8HIlr6Ke8c+s5d+/HKZB7/iLr/96sIC1aUW4u2h45rIhvHX9SFy1rb8Nc9VqePYPQ/jrbLGg+LtbMnhvS4a9wpUkqYucKsnatGkTCxYsICoqCkVRWLZsWZvP2bBhAyNGjMDd3Z3ExESWLl1q8zglySbqyiHlY7E/+aH2P0/rAkOuEPuHvrd6WNJ5VBWAvg4UrUh01RQ+SGwLD6obh52sTyuiUW+kd4g3/cN91Q5HsiIPVy3vLBrF4Gg/GvRG3tmcwZVvbePUaccdqdcZjDy3QnT3vPPCPlwzJg6lrUoERLOPu6cm8tgCcZPk2V/TSM0ut2WokiRZiVMlWTU1NQwbNowlS5a06/EZGRnMnz+fqVOnkpqaygMPPMBtt93GypUrbRypJNlA2i9iLlboAIi/oGPPTVootifWyAYY9mTpLBgQC1pXdWMJHyy2PWQk69eD+QDMHRLRrjezknMZHR/ET/dcwDuLRhHg5cq+nApm/Hcj/1x2gIo6ddbQOpxXyfMr07j9o938c9kBCirqATAaTby27gSZp2sJ8XHj9skdn59544R45g+JRG80cfenKRRV1Vs7fEmSrMxF7QA6Yu7cucydO7fdj3/rrbdISEjgxRdfBGDgwIFs2bKFl156idmzZ9sqTEmyDcuiwoMvb3su1u9FDBGNF0rT4diKthtmSNbRtEaWiqWCFpYkq6D7j2TV6wysTxOlgnMHy/lY3ZWiKMxMCufney/g4e8OsPl4CZ9sz+JQXiWf3DoWb3f7vMXJKatl6W+ZvP9bBsYzpkx9l5LLxMQQCirqOZBbAcD9M/rh04m4FEXhmT8M4XB+JRklNdyydBdf3jHebl+jJEkd161/O7dt28aMGTPO+tjs2bN54IEHWn1OQ0MDDQ0NTf+vrKwEQKfTodOpc3fMcl61zt8TOPw1rinBJX0DCqAbsAA6EadmwCVot76E8eB3GAZcYv0Yz8Phr6+NaEpOogUMAfEYbfi1t+v6BiXiClBdgK48H7y7b0vzjUeLqdMZiPT3oF+op1V+7nrqz7C9dOX6hvu48v6iEWxLP819X+xnb1Y5tyzdyeMLkugT6g3A6ZpG8srr6B/ui5tL14p4qur1/LAvj12ZZRzKq+JUaXMr+ekDQhmbEMSKQ4WkZJWz+rBov+7tpuX+6YlcNSKy0z9Dnlp454ZkrvzfDg7mVvKXr1J57eph7Xqu/Pm1LXl9bc+RrnF7Y1BMTtqqRlEUvv/+exYuXNjqY/r168fNN9/Mww8/3PSx5cuXM3/+fGpra/H0PLel72OPPcbjjz9+zsc/++wzvLy8rBK7JHVUfMk6hmUvpdwzno0DnujUMfxrM5ly9BH0ihvLh72NSdG2/SSpS0ZlvE50+U4ORl/DybD2j8LbyvRDD+LTWMRviX+jxDdJ7XBs5ouTGrYVaZgUbuTy3ka1w5Hs6FQVLDmspcEoRvsD3EzoTVCtE/8P8TBxSS8jQ4M6/tanwQCrczVsKlBoMDRXE2gw0csXZkYbGRQojmsyQVqFwul6MJpgaJCJAHcrfIFAZhW8clCLEYU/DjCQFOiUb+MkyWnV1tZy7bXXUlFRgZ+fX6uP69YjWZ3x8MMPs3jx4qb/V1ZWEhsby6xZs857IW1Jp9OxevVqZs6ciauryvM6uilHv8baj94AwHfCTcwbN69zBzEZMb3wHC6N1cwdnQhhA60Y4fk5+vW1Fe17olR5wPi59O/fye9bO7T3+mprv4KjPzMuwQfjGNvFoyaj0cRTz28EGrl5zigmJVpnxK6n/gzbizWv7+S8Sl5ff5J1R4spb2xOhrzctJTUG3jvqJbHLhrAdWPb34xmz6kyFn99gDzzPKs+od4sHBbJoCg/hsf64+txbszzu/RVnF+l/1He++0Uvxb5cO+VE3B3Pf9NM/nza1vy+tqeI11jS5VbW7p1khUREUFh4dkrpRcWFuLn59fiKBaAu7s77u7n3m5ydXVV/ZvqCDF0dw55jYuPQfZ2UDRoh12FtivxRQ6DU7/hWnwIoodaL8Z2csjraysmE5RlAuAS1g/s8HW3eX0jh8DRn9EWH+naz5EDS80up7i6ER93Fyb2DcPVxbojtj3qZ1gF1ri+w3sF8+5NwRRV1ZNfXo+LViE2yAutovDCqqN88FsmTy0/SlJ0IGMSgto8XlpBJbd/vJeqBj3RAZ7866IkZg8KV7Whyp9nDeCn/QVkldbx0c4c/jQlsV3Pkz+/tiWvr+05wjVu7/mdqrtgR40fP561a9ee9bHVq1czfvx4lSKSpE5I+VBs+84Gv6iuHSvSXL+fv69rx5HaVlsKDWKyO4HxqobSxNLGveCAunHY0BrzHJgL+4XibuUES3IuYb4eDIsNYFCUP34erni7u/DIRUksGBaF3mjiT5/uIb+i7rzHKKys56b3d1HVoGdMfBCrF09mzmD1O1b6uLvwf3MGAPDOpnRqGvSqxmMNxwqr2HqihJSsMoxGWQIpOT+nSrKqq6tJTU0lNTUVEC3aU1NTycrKAkSp36JFi5oef+edd5Kens5DDz1EWloab7zxBl999RV//vOf1QhfkjpO3wCpn4n9kTd1/XgyybKfMvOiob5R4NryyLndhZnnYZUcA6PjL97aGWuOiCRrRlKYypFIjkhRFP7zhyEMiPClpLqROz9JOe9Cxo//dIiCynr6hvnwzqJReLk5TgHQJcOjSAjxpqxWx0fbTqkdTqfpDUYe/eEgs17axLXv7uCyN7by5saTaoclSV3mVEnW7t27SU5OJjk5GYDFixeTnJzMI488AkB+fn5TwgWQkJDAL7/8wurVqxk2bBgvvvgi7777rmzfLjmPtJ+hrlS8UU+c0fbj22JJsgr2g1E2BLCp0+Y3CUEdXxPHZgLjwcUD9PVNpYzdSXZpLWkFVWg1ClP7yyRLapmXmwv/u2EU/p6u7Msu50+fpJB1upaqet1Za2xtOlbM8gMFaDUKr16TjL+XY5WBuWg13DNVlAm+s9k5R7PqdQZuXrqLD81JYnywaDD27uZ0ahud7+uRpDM5zi2ZdpgyZQrna4a4dOnSFp+zd+9eG0YlSTa0Z6nYjrgBtFb4dQ3uCy6e0FgNpSchpG/Xjym17PRxsQ1p31wJu9BoIaSfSLKLjkBwH7Ujsqq15lGsUb0CCfByUzkayZHFBXvx2jXJ3Lx0F2vTilibVgSAi0bhb3MHsDA5mkd/FAt3Lxrfi4GR6jS+asslw6N4bd1xMk/X8q9lB3nxymGqlzK2l95g5L7P97L5eAleblr+e+VwZiaFM+3FDZw6XcuXu7K5eaIDrDEoSZ3kVCNZktSjnD4JGZsABZKvt84xtS4QYV6UVpYM2tbpE2Ib7EBJFjSXDBYfUTcOG1hzRLxRnpkUrnIkkjOY3C+UZX+ayOR+oU0f0xtNPPXLEWa9tImMkhrCfN3588x+KkZ5fi5aDc/+YShajcJ3e3P5ZLvzlA0+9csRVh0uxM1Fw/s3jWbO4Ai0GoU7JovR/3c2paMzyIoLyXnJJEuSHFXKR2KbOAMC2t9quE2Rw8U2P9V6x5TOVWJJshxstDBMTJanKE3dOKyssl7H9vTTAEwfKJMsqX2GxPjz0S1j2Pn36Rx8fHZT+V1pTSMJId58dvtY/Fpoz+5IxvUO5m/mJhhP/HyYPafKVI6obYfyKvhwWyYAr149nHG9g5s+94cRMYT4uJNXUc/yA/kqRShJXSeTLElyRPpGSP1U7I+80brHls0vbM9oFOWY4HgjWaHm9dGKutdI1sajxeiNJhLDfEgI8VY7nM4x6MXo9bFVojulZDdhfh74uLvwl1n9ePziQdw0IZ5ld08kMcxX7dDa5bZJCcwbEoHOILomFlc1qB3SeT37axomEywYFsWcwZFnfc7DVcui8b0AWLo1U4XoJMk6nGpOliT1GMd+hZpi8AmHfnOse+wzkyyTCZykft+pVOWBrhY0LhDYS+1ozmYZyTp9XLypt8ZcPwfQ1FXQGUaxDDqoKwdFA6e2QMZmqMiB3N3i996i10SYcK/4GyB/T+1CURRunBCvdhgdpigKz10+jKMFVZwsruGPH+/mo1vH4uPueL/fm48Xs/l4Ca5ahb/O6t/iY64eE8tr646zN6uc/TnlDI0JsG+QkmQFciRLkhyRpeHF8OtAa+VSldABoHWD+good576fadimY8VGG/9719X+ceBqzcYGqE0Xe1orEJnMLI+zTIfy8G7CualwstD4IVEeL43fLUIdr3TfGPFM6i5xPTUb/D51bDxP6qGLDkHH3cX3r5hJL4eLqRklbPovR1U1evafqKdfbhVvO5cN7YXceZugr8X5uvB/CGRZz1ekpyNTLIkydGUZcLJ9WJ/xKLzPrRTXNyamx/kpVr/+BKUmDsLOtp8LACNBkLNd4+7SfOLXZmlVNbrCfZ2Y3hsoNrhtC5rB3y4AKrOmGcS1AfG/QkuegkW/QgPHoN7d8OfD8PYO8VjNv8XyuQbTaltiWG+fHrbWPzMidY/vj+odkhnadAb2HqyBIDLR8ac97GWEcWf9uVRVFVv69AkyepkkiVJjiblY8AEvadCkI3a18p5WbZlWSPLkdq3nymse83LWnNYjGJNGxCGVuOgZXVGI3x3GzRUijLAv2XBP4vgvhSY8wyMugV6X9g88ukfDXOehYTJYGiANY+qG7/kNIbGBLD0ljEoCvy4L4+dGY4zv293Zhm1jQZCfNxJaqMtfnJcICPiAmg0GFn6W6Z9ApQkK5JJliQ5EqPBdg0vziSTLNuyrJHlaE0vLELN87KKnb/DoMlkYvWRAgBmOHLr9tw9UJ4Fbj5w7Vfg4Q8u7ud/jqLA7KcBBQ59L44hSe0wIi6Qq0eLrrSP/ngIg7H1NUbtacNRcUPkwn6haNpxQ+TOC8Vafh9vP+WQpY+SdD4yyZIkR5KfKkqJ3P2h/3zbnaepjbu5+YVkXY5cLgjNyZ9lxM2JHSusJru0DjcXDZP6hqgdTusOLxPbfnPA3af9z4sYAkOuEPt7P7F6WFL39eCsfvh5uHAkv5LHfj6CJc8qq2nk0x2n+M+KNP617CBL1p9gzyn7jHZtPCYau0zpH9rGI4UZA8NJDPOhql7PZzuybBmaJFmd47WdkaSeLGOz2MZPFHOnbCV8EChaqC2ByjxRmiRZh75BjFiA445kBYu7w5SmO32HyV/M6+hM7huCl5uDvqSZTHD4R7GfdEnHnz/8GjjwlRjNmvMf2/5tkLqNYB93nrp0CPd/sZcvduWw20/DispUNh4voV537iK/b10/kjmDI2wWT155HccKq9EotPuGiMa8OPFD3+zn7U3pXD06Dn8vB2smJEmtkCNZkuRIMreIbfwk257H1aN5Xo4sGbSu0nTABO5+4OOgne4C40X78MZqqC5SO5pOM5lMTYuVzh8a2cajVZSXAhVZoqtj35kdf37ChWI5h7oyOLnO+vFJ3dbFw6J4+arhaDUKJyo1rDxcRL3OSFKkHzdNiOeeqYlckCgSnr9/f8CmDSbWmpdZSI4LJMCr/TcKLk2Opm+YD6U1jby05pitwpMkq5NJliQ5CoMOsraJ/fgLbH8+OS/LNizt24P7OO4IkYs7+Js7e5U6b8ngscJqThRV46bVMN2R18c6tExs+80CV8+OP1+jhcF/EPsHvrJaWFLPcMnwaL66fQwXxxl4eE4/vrxjHL/cdwGPXTyIB2f35/2bRjMw0o/Smkbu/zyVijrbzH1alpoHwNwOjpa5ajU8dvEgQMzNSiuotHpskmQLMsmSJEeRv0+MLHgEQPhg259PJlm24ejzsSya5mWdUDeOLmgqFewXip+Hg5YQmUxw+Aexn7Sw88cZcrnYpi2HhqouhyX1LENj/JkebeKWifGM7R2McsYNIDcXDa9cPRx3Fw3b0k8z75XNHMipsOr5s0tr2XOqDEWBBcOiOvz8iYkhzB0cgcFo4oWVR60amyTZikyyJMlRZGwS2/gLxFpGttaUZKXa/lw9SVP7dgdPsoLM87KctPmFyWTil/3izvj8obabR9Jl+ali0W8Xz86VClpEjRDfM30dpP1itfAkCaBfuC9f/XE8cUFe5JbXcecne6hrNFjt+D+k5gIwoU8w4X4enTrGg7P7oyiw5kgRR/LlaJbk+GSSJUmOwl7zsSzCBwOK6GZYVWifc/YETe3b+6gbR1uaml84Z5J1KK+Sk8U1uLk4eKmgZRSr3yxw8+78cRQFhl4p9g983fW4JOl3hsUG8PN9FxAd4ElueR1vbrDOKLfJZGoqFbxkeOebLPUJ9WHeEDH3csl65x2Bl3oOmWRJkiMwmSBnt9jvNd4+53T3aR5tKdhvn3P2BE1zspxlJCtd3Tg66bsUcWd8ZlK4Y5cKWuZjdaar4O9ZWrmfXA/VxV0/niT9jp+HK/+6SDRFemtjOpklNV0+5vb0UjF30kXT5e6Fd08RZc6/HMgnvbi6y7FJki3JJEuSHEFpOjRUgNYdwpLsd15ZMmhdtaVQe1rsO81IVjoYz23n7Mj0BiM/7hNJ1h9GOPDyAwUHoCwDXDyg7+yuHy+4jygbNBng0HddP54ktWD2oAgm9Q2h0WDkzQ1dG+k2mUy8sErMobpyVEyXb4gkRfkxbUAYJhO8tdE5R+GlnkMmWZLkCCxJTvgg0NrxrvyZixJLXWeZ3+QX3bXSMHsIiAONi5jjU5WndjQdsvl4CSXVjYT4uDGpb/sWNVVF+nqx7TOtYwsQn09TyeA31jmeJP2OoijcN12MxP+wL7dL3QbXHy1iz6kyPFw13DfNOqP7d08Vo1nfpeSSW15nlWNKki3IJEuSHEFeqthGDbfveWWHQetylvlYIJL5gF5i38maX3y3V4xiLRgWhavWgV/G8vaKbcxo6x1zwEVim7sHGmutd1xJOsOoXoH0D/elXmfku5ScTh2jrtHAs7+mAXDjhHjCOtnw4vdG9gpkfO9g9EYT/5OjWZIDc+BXJ0nqQSwjWZaRJXuJGCK25Vmi1E3qGmeZj2XhhM0vdAYjG9LEAsoXd6IVtF1ZkqyoZOsd0z8GfCJEyaAs85VsRFEUrh8XB8An209hMpk69HyTycQ/vj/AscJqgr3duHOydW88WUazvtiVzenqBqseW5KsRSZZkqQ2k6l5JMneI1meARCYIPbzUux77u7IskaWo7dvt3DCNu4pp8qoatAT5O3GsJgAtcNpXW0plGWKfWv+XisKxIwS+5ZmOZJkAwuTo/F203KyuIZPdmSd97FGo4nVhwtZtjeXTceKefDr/Xy3NxeNAq9dm0ygt5tVY5uYGMyQaH8a9Ea+3J1t1WNLkrW4qB2AJPV4ZRlQXwFaNwgdaP/z95ogYsjYBIkz7H/+7qRpJCtR3TjaK9j5kqwNx0RXvcl9Q9BolDYerSLLjZPABPAMtO6xY0ZB2s+QK5MsyXZ8PVy5e1oiz604yqM/HKS2QY9Wo6DVKPh7ujKhTwgR/h7kltfx0Df7+O3E6XOO8fd5A5nQJ8TqsSmKwo0T4nnw6318uj2LOyb1xsWRS4elHkkmWZKkNst8rPBB4GLdu33tknAhpH4K6Rvsf+7uxGhsTlacLclyonLBDUdFkjWlf5jKkbShqVRwuPWPHS1HsiT7uOvCPmQU1/D1nhyeMc+vslAUiPL3bGo+4emqZXC0HwWV9YyMC+TK0bE2SbAsLhoayb9/OUxueR1r04qYPciBFyWXeiSZZEmS2tSaj2XR+0JzHPtFiZNXkDpxOLuKbDA0iBHJgDi1o2kfS7lgWSYYDaDRqhpOWwor6zmSX4miwKS+tnvzZhW2mI9lEZUMigYqc6EyH/wirX8OSUKMGD192RBcXTSkF1cT6uuByWQip6yO1OzypgRrdHwgz/5hKH1CrdRFsx08XLVcPSaONzecZOlvmTLJkhyOTLIkSW255rlQ0SPUOb9vBIQOgOI0yNxsnUVTeyJLqWBQb4dPVpr4x4ik0NAoksTAeLUjOq+N5lGsodH+BPu4qxxNGyw3T2yRZLn7iPX0Cg+KkkG/BdY/hySZuWo1PH3pkHM+nlteR0ZxDYOi/Kw+56q9rh/Xi3c2pbMt/TR7s8pIjrNyaa4kdYEsYJUkNRkNzXe8LSVAakgwj2alb1QvBmfnbPOxQCSDlsYnTjAva1u6mPMxuZ8Dr40FYkS43NwowLJMgrVFjxTbnF22Ob4ktSE6wJML+oaolmBZYrg0WSxI/vq6E6rFIUktkUmWJKmpOA0aq8HNB0L7qxeHpWRQzsvqPGdMsqA5XidIsnafEssMjIp38JLWwkNiGxgPHv62OYclyZJr3Ek93J+mJqJRYG1aEQdzK9QOR5KayCRLktRkmbgelaxuiVn8BaBoRQOEslPqxeHMnK19u0Vwb7F18OYXRZX1ZJfWoSiQHBegdjjnV2xuEBA6wHbniBgstgUHxTIQktRDJYR4s8C8Zt7La46rHI0kNZNJliSpydKCOUbFUkEQd9tjx4j9E2vUjcVZOVtnQQsnWStrz6kyAPqH++Ln4apyNG0oPiq2thydDksSzS9qS6C60HbnkSQncO+0vmgUWHOkkN2ZpWqHI0mATLIkSV05e8RWzflYFonTxfbEWnXjcEa6OtE4AiDY2UaynKON+25zkjUq3gkmtttjJMvVszmhLzhou/NIkhNIDPPhqtGxADz7axomOborOQCZZEmSWhqqoOiw2Fd7JAsgcabYZmwEfaO6sTib0nTABB4BztcCv6mN+ykw6NSN5TwsSdbIXs6QZNlhJAsg3FwyWHjAtueRJCdw//R+eLhq2H2qrGnRcmdQVFXPkvUneOibfRRW1qsdjmRFMsmSJLXk7QVM4Bcj2qirLWIoeIeKRhzZ29WOxrmcOR9LUdSNpaN8I8HVC0wGh52PV9do4JB5QvuoXg6exNaWQk2R2A/pZ9tznTkvS5J6uAh/D64d0wuAr3ZlqxxN+6w+XMjEZ9fx/MqjfLU7h8ve2Ep6cbXaYUlWIpMsSVLLqW1iGzta3TgsNBpInCH25bysjjltTrKcbT4WiO97gHhjQrljJln7c8rRG02E+boTE+ipdjjnV3JMbP1jwd3XtudqGsmSSZYkAVw+MgaAtUeKqKh13JF5gAa9gcd+PITOYGJYjD/xwV7kltdxxVvbyCmrVTs8yQpkkiVJaskwr0mVMFndOM5kSbKOrlA3DmdzOl1sLfObnE1AnNha1nZyMClZ5QCMiAtEcfSRQst8LFuPYkFzklVyHHSyzEiSkqL8GBDhS6PByC8H8tUO57w+3Z5FbnkdEX4efPnH8Xxz1wQGRvpxuqaRP368h7pGg9ohSl0kkyxJUkNjDWTvFPuWhYAdQeIM0LhCyVEoSlM7GudRliG2Qb3VjaOzHDzJ2psl5mON6BWgbiDt0TQfy4ZNLyz8osAzUJR6FsvfV0kCmhYn/n5vjsqRtK6mQc+S9WJtxfum98XDVUuIjzvv3jiKYG83DuVV8vhPh1SOUuoqmWRJkhqytoFRJ0qKHOmNuWcA9Jkm9g8vUzMS51JqTrICE9SNo7McOMkymUzszS4HIDlONr04i6I0j2YVyOYXkgRwyfBoFAV2ZZaRXeqYZXff7MnhdE0jvYK9uGJUTNPHowM8ee2aZAC+TcmhvFY2oXJmMsmSJDWkW0oFL3S8RgmDFort4R9UDcNpNNZAdYHYD5JJlrXlltdRXNWAi0ZhSLS/2uG0zZ4jWQCRw8Q2f599zidJDi7C34OxCaJBzurDjreGnMlk4pPtYv7rLRMTcNWe/VZ8QmIIAyP90BlM/HqwQI0QJSuRSZYkqcEyH6u3A5UKWvSfJ0oGiw5D8TG1o3F8ZZli6xEgSreckQMnWZb5WElRfni4atUNpi2NtVBpLlGyVxOUyOFim59qn/NJkhOYmSQ69q467HhJyo6MUo4XVePpquXSEdEtPuaS4VEA/JCaa8/QJCuTSZYk2VvNacjfL/YdqemFhWcA9Jkq9lM/UTUUp2ApFXTWUSxo7i5YXeBwDRQs87GSYwPUDaQ9mhJuf/utlxY1XGwLDoJBb59zSpKDm5UUDoiSwbIaxyq5s4xiLUyOxs/DtcXHLBgmkqwdGaUUVDjW32Sp/WSSJUn2dug7wCTWpXKE9bFaMuJGsd3+ZvMaUFLLypx8PhaIhMDVW+xXONZk8b3mkSynmI9Vau4yGdTHfmXAQX3AzQf0dc3t4yWph4sN8mJAhC8Go4l1aUVqh9Mkv6KOlYfE6Nr14+JafVx0gCdj4oMwmeCnfXn2Ck+yMqdLspYsWUJ8fDweHh6MHTuWnTt3tvrYpUuXoijKWf88PDzsGK0ktSD1M7Eddo26cZzPgPmQOBMMjfDzn8FkUjsix9UdRrIU5YySQcdZK6tBb+BwXiUg2rc7vNKTYmvPZjYajbhhA7JkUJLOYBnNcqSSwbc3pqMzmBibEMSgqPPPMb1oWCTgWPFLHeNUSdaXX37J4sWLefTRR0lJSWHYsGHMnj2boqLW71L4+fmRn5/f9O/UKcd5AyH1QMVHIS8FNC4w5Aq1o2mdosD8F8DFEzI3w9HlakfkuLrDSBY45LysQ3mVNBqMBHu7ERvk4IsQwxkjWXbuGGopGcxLte95JcmBWeZlbT5egs5gVDkaKK5q4POd4u/rvdP6tvn46QNFkrjnlOOVPErt41RJ1n//+19uv/12br75ZpKSknjrrbfw8vLi/fffb/U5iqIQERHR9C88PNyOEUvS7+z7XGwTZ4JPqLqxtCUwHsbeIfZ3vadqKA6t1MnXyLJwwCQr5ZR5PlZcgOMvQgzqJVlNzS9kh0FJshgU5Ueglyu1jQb251SoHQ7vbcmgQW9keGwAExOD23x8dIAnAyJ8MZpg47FiO0QoWZuL2gG0V2NjI3v27OHhhx9u+phGo2HGjBls27at1edVV1fTq1cvjEYjI0aM4Omnn2bQoEGtPr6hoYGGhoam/1dWilIVnU6HTqezwlfScZbzqnX+nsAu19iox2XfFyiAfvAVmJzh+znsBlx/ewVOrkVXdFwkXp3QbX+GDTpcyrNQAJ1vLDjx3wiNXzRawFiWicFBvk8pp0oBGBrtp/rPTnuuscvpk+L327+XfX+/wwbjCpgK9qNvqAeNg3dhbEG3/RvhIHrq9R0dH8iqw0VsPV7E0Cgfm52nreubX1HP0q3ihtydk+PR69vXpGZKvxDSCqpYc7iA+YPDrBOsk3Kkn+H2xuA0SVZJSQkGg+Gckajw8HDS0lpe6b5///68//77DB06lIqKCl544QUmTJjAoUOHiImJafE5zzzzDI8//vg5H1+1ahVeXl5d/0K6YPXq1aqevyew5TWOqEhhbFU+DS6+rDppwpjhHCV443yHEF51gMxvHuVw9FVdOlZ3+xn2aihkpsmAQXFl+eY9oKhbHNCV6xtZVsIYoDxzP5uXO8bP5rZjWkChIe8oy5e3/Hfe3lq7xhpjIwsqRbvl1XtO0rjfjneeTUbma9xx0dWy+fv3qfJsuS20M+hufyMcTU+7vr61CqDll13HiKux/d+Q1q7vR8c11Os09PE1UX9yN8vT23c8jyoAF9YezuenX3LQOsGAvq05ws9wbW37Frl2miSrM8aPH8/48eOb/j9hwgQGDhzI22+/zZNPPtnicx5++GEWL17c9P/KykpiY2OZNWsWfn5+No+5JTqdjtWrVzNz5kxcXVtu9yl1jT2usfbLjwFwGbWIOdMvsck5bEE5CnyziMTq7cTPehtcOt48prv+DCvp6+EwaIJ7M2/+RarFYY3rq+RFwgevE6hUMW/ePCtH2HGFlfWUbduERoHbLpuJj7u6L1dtXuPiNNgHJndfZlx8ld0XGdeUDIecHUzu549piPrfv47qrn8jHEVPvb69C6r4dsk2supcmTl76jkL/1rL+a7v3qxy9mzbiaLAi9ePZ1BU+99LGowmPkzfQFmtjvBB4xgTb6elIRyQI/0MW6rc2uI0SVZISAharZbCwrNX7y4sLCQion1tsF1dXUlOTubEiROtPsbd3R13d/cWn6v2N9URYujubHaNy7Ph5FoAtKNuQetM38eB88EvBqUyB9cj38OIRZ0+VLf7Ga4SIxdKYLxDfF1dur4hfQBQqgtxxQCu6nZiPZh/GoB+4b4E+jhO04tWr3GlmMumBPXG1c3NzlEB0cmQswOXooPgep39z28l3e5vhIPpadd3UHQgAV6ulNfqSCuqtXmX0pau7/tbxd+Gy0fEMLxX23OxzjoeMKV/GN/vzWXT8VIm9pV9BRzhZ7i953eaxhdubm6MHDmStWvXNn3MaDSydu3as0arzsdgMHDgwAEiIyNtFaYktSzlIzAZIX4ShCSqHU3HaF1g3J1if+vrYFS/S5PDsKwpFRCrbhzW4GBrZTUtQuwMrdvh7DWy1GBpfiE7DEpSE41GYWyCGP3Znn7a7uevbtCz/qjogH3TxPhOHWPaADEXy5HW+5Lax2mSLIDFixfzzjvv8OGHH3LkyBHuuusuampquPnmmwFYtGjRWY0xnnjiCVatWkV6ejopKSlcf/31nDp1ittuu02tL0HqiQx62CtKBRl1s7qxdNaIG8HdD0qOwgn166EdhiUZ8W95jqdTURQI7CX2HWCtrOZFiANUjaPdTquwRtaZLG3cC/bLGyGSdIZxvcXo0dYT9k+y1h4ppEFvJCHEm6TIzk05mdwvFK1G4XhRNVmn2zcXSHIMTlMuCHDVVVdRXFzMI488QkFBAcOHD2fFihVNzTCysrLQaJrzxrKyMm6//XYKCgoIDAxk5MiRbN26laSkJLW+BKknOr4SqvLBKxgGqDdvp0s8/GDkjbD1Ndi2BPrNVjsix2BJsvy6QZIFoo170WHV27jrDEb255YDMMJZkqwylRelDu4r1rVrrIbTJyC0nzpxSPaXlwpHf4Xa02JUfdSt4G67TnrO5oLEEAB2ZpZSrzPg4Wq/7ps/7csH4KKhkZ1ehsLf05XR8YFsTy9lXVohN0108jUZexCnSrIA7rnnHu65554WP7dhw4az/v/SSy/x0ksv2SEqSTqP3R+I7fDrwOXc+X5OY/TtIsnK3Ay1paK8rKer7EYjWeAwa2UdLaiiXmfEz8OF3iFO8maxzDz618llDrpM6wIRQyBnJ+SnyiSrJ6ivhNWPwJ6lgKn549uWwJxnYfBlakXmUBLDfIj09yC/op6dGaVM7mefNSor63VsMq9vddHQqC4da9qAMLanl7I2rUgmWU7EqcoFJcnplGfBiTVif+RNqobSZYG9IHyImFt2fJXa0ajPaIQK0fhCJlnWZZmPNTwuEI3GCXoWGw1QkS321UqyoLlkUC5K7PxMJsjeBXXlLX/eoIcvr4c9HwAmUSVxwZ8hMAGqC+Gbm2HDf8RxejhFUZjUV4xmbbLjor6rDhXSaDDSN8yH/hG+XTrWtAGiYmtHeinVDe1bY0tSn0yyJMmWDnwDmETDi2CVJsRbk6VM8Oiv6sbhCGqKwKgTa2P5dpNmOg6SZKVY5mPFBqgaR7tV5oJRDxpXdX8WZPOL7qHwEHwwF96bAa8MhU3PQ/UZTQ9MJlj5d8jYKJrV3PgTXP0pzHgM7t4JE+4Tj9vwNPxwN+gbVfkyHIll9Grz8RK7nfP7vaLS4ZLhXRvFAugT6k3vUG8aDUZ+2pfX5eNJ9iGTLEmypZPrxHbgxerGYS3954rtyXXyhdsyiuUbKUq1ugMHSbKaOwsGqBpHu1lKBQNiQWO/+R7nOHMky2hQLw6p88qz4d2ZkLVN/L++AtY9BS/2h/fnwK9/g/9dCDvfFp+/7G1ImNz8fBc3mPUkXPQSKFpI/RQ+/QMUHrT/1+JAJvYJQVHgaGEVBRX1Nj9ffkUdW0+KRhuXDO/64uCKonDtGPH3+ZPtpzDJEUqnIJMsSbKVxlrI3iH2+0xVNxZriRoB3mHQUAlZW9WORl2W8rDuUioIEGDuLlhdADrbvxFpSWlNI5nmDlrJsU7Svr0sU2zVLBUECB0gRjYaq6DkmLqxSJ2z7XXQ1UDkMLh/P1z2jvi7azKKxGvHmyKJdvGEuc/BwAUtH2fULXDtl+DmAxmbcH13ChOOPyPmcfVAgd5uDI0JAGDTcduXDP6YmofJBGPig4gN8rLKMf8wIgY3Fw2H8irZn1NhlWNKttWpJKumpoZ//etfTJgwgcTERHr37n3WP0mSgFNbwdAI/rEQ7GRrY7VGo4F+s8T+sZXqxqK2ps6CXb9L6TA8A8WbMlBtrazUbDGK1SfUG38vJ1k01dLy3pKkqkWjhegRYj9nl7qxSB1XUwJ7PhT7Mx4X82CHXgl3rIf7UuHi12HsnTD9EfjzIRj7x/Mfr+9MuHU1DLoUk8aV0OojaH/4Y48d5ZxiLhlcc7jQ5uf6fq+odLh0hPVeHwK93bhoiChH/mS7+stsSG3rVI3LbbfdxsaNG7nhhhuIjOx8W0pJ6tbS14tt7yliDaLuovdU2PsJZG1XOxJ1VXazphcgfk6b2rifUmXh7JRT5YATLUIMZ3QWVDnJAogZJTqA5uyGEYvUjkbqiB1vgb4OopLF68aZghI6tzxAeBJcsRTDqZ0oS+ehPbEa1j8N0/9llZCdyaxB4byy9jibjhdT12jA0802pb0niqpJK6jCTath3mDrztG8blwc3+3NZVlqLn+amkhCiLdVjy9ZV6eSrF9//ZVffvmFiRMnWjseSeo+LPOxukupoEXMaLEt2A+6OnD1VDcetXTHckFQfa2sHRliHsPIXk6UZFlGstQuFwSIHiW2ObvVjUPqmPpK2Pk/sX/BYqvfmDNFJbMv7lZGnnoLtrwEydept3C2SpIi/YgJ9CSnrI6Nx4qZMzjCJuexdDAc2zvI6qPxI+ICubBfKBuPFfPUz4d576bRVj2+ZF2dKhcMDAwkKEiukSNJraoqFG9UUSBhisrBWFlAnJiXZdRD/n61o1FPRTdbI8tCxeYXNQ169po7C07sE2L383eaZU6W2uWCIEayQPz9aahSNxap/fZ8IJpchPSz2aL1OUETMPaeDiYDbHzOJuewi8YayE3pcNmjoijMHiQSq5WHCmwRGdA858vSNt6aFEXhXxcl4aJRWJtWxPqjRW0/SVJNp5KsJ598kkceeYTa2lprxyNJ3cOpLWIbMRi8g9WNxdoUpXk0qyfP++j2SZb9a/53ZpaiN5qICfQkLtg6k8VtTlcn1iUCxxjJ8o0Q80AxQd5etaOR2kNXLxYQBpj4gJj7aiPGC/9P7Oz/EoqdsDlKVQG8Mw3emQqvDIMtL4s1w9rJMnq19kghOoPR6uE16AxsTxej8bZa9DgxzIebJ8YD8ORPh2nUW//rkKyjU7/JL774IitXriQ8PJwhQ4YwYsSIs/5JUo93ytx5r9cF6sZhK5a75T01ydLVQ425Q5VfN0uy/GPFVoXGF9vMLY8n9HGiGxOWET93P9E4xBH09N9PZ7PvM5Go+8XAkCtseipT1AjoN1d0K1z3pE3PZXWVeaKNfXGa+H9FNqx5FD65VDQNaYcRcYGE+LhRWa9vSoasaU9WOfU6I2G+7vQP79oCxOdz7/S+hPi4kV5Sw4dbM212HqlrOjUna+HChVYOQ5K6maYka7y6cdhK00hWD533UWVeDNLFA7y6Wem0ZWTOsg6YHf12QrxRmpjopKWCjtLgJmY0HPq+5/5+OpOGKtjwrNifcK9Y58rWpv8Ljq+EIz9Cxqaz19lyZGseh7IM8bt23dei+dKKh8XX8OZEuOR10VHxPLQahZlJ4Xy+M5uVhwqY1Ne6o02bT4jEbVLfUJs2hfPzcOWhOQN46Jv9vLL2OJckRxHm62Gz80md06kk69FHH7V2HJLUfdSWmudjAXET1I3FVqKSQdFAZY64u+jX9RXtnUqVuZ7fN9Jx3lhbiyXJqsoTZTh2Wmi5rKaRw/liDZ/xzjSS5UidBS3ObH5hMnW/n9HuZNPzYhQrqA+Mutk+5wwfBCNvht3vicWN/7jJ8RdUL8+Gg9+I/SuWQmh/8S92DHy1SKwL9+nlMPmvMPUf5/2ZnzUogs93ZrPqUCFPXDwYjcZ6vx9bjosbRZP72f5G0eUjYvh0+yn25VSw9LdMHpozwObnlDqmS4W/e/bs4ZNPPuGTTz5h715Z+y1JQHNr8+C+4GObmmzVuftA2CCx3xNLkqryxdbXuu15HYJ3GGhcRTlRte0mh//etvTTmEzQL9zHue7Inj4htp1pr20rkUPF97CmSLUukVI7nD4J294Q+3OeARd3+5172j/BIwCKDonW8Y5u+5ui2VLC5Oa14ADCBookceyd4v+bnod1T4mbC62Y0CcYH3cXiqoaSM0pt1qIFY2QVliNomD1EbKWaDQKd0zuA4h1uQzG1r9mSR2dSrKKioqYNm0ao0eP5r777uO+++5j5MiRTJ8+neJi26+kLUkOLctSKthNR7EsYseIraU0sidpGsmyTQtgVWk0zSOTdpyXtT5NdMm6INHJbkyUmJsHhPRXN44zuXpCxBCx3xNvgjiLTc+DUQeJM6DfbPue2ysIZjwm9tc+AUVH7Hv+jqgrgz1Lxf7E+8/9vKsnzP0PzH5G/H/zC7D341YP5+6iZeqAMMC6XQaPVogRscFR/gR526HsE5g+MAx/T1fyK+rZerJ989Ik++lUknXvvfdSVVXFoUOHKC0tpbS0lIMHD1JZWcl9991n7Rglybmc6iFJVrx5nbzM39SNQw3deSQL7N78wmg0scG8tsw085sfp1FyXGxD+qkbx+/FyPWyHFrZKdj/ldif+g91Yhh5EyTOBEMDfHc7GHTqxNGWlI9BVyOqJ/pMb/1x4//UfC1X/bP5ZlgLZg8KFw87VIjpPKNeHZFWLpIse5QKWni4alkwTLwOfbvH/s2KpPPrVJK1YsUK3njjDQYOHNj0saSkJJYsWcKvv/5qteAkyek0VEP+PrHf3ZMsS+fEwoPiTmNP0p1HsgD8o8XWTknWobxKiqsa8HbTMjrBQTr0tUdjjZiXCBDSV91Yfs/SnCZXJlkOaeurYr2q3lPPLn+zJ0URzSI8g6DgAOx6V504zsdogF3viP1xd7Y9v/CCxRA5XKw59utDrT5sSv8w3Fw0ZJTUcLyouuthGk3NSZYdSgXP9IcRYh7tikMFVNU7aKLcQ3UqyTIajbi6nruKtaurK0aj7Ncv9WA5u0TduH9s83pD3ZVvuJh3hglObVM7Gvs6s/FFd9TUYdA+SZZlQc2JiSG4u2jtck6rsMzH8gp2vC6TlpGs/H2gb1A3FulsVYVidAZg0mJ1Y/GNgBnmZmYbnhWNmxzJsRViXqFnYPva22td4OLXQNHC4R8gf3+LD/Nxd+ECcxfTFQe7XjJ4pKCKGr2Ct5uWEb3se6NoeGwAfUK9qdcZWX4g367nls6vU0nWtGnTuP/++8nLy2v6WG5uLn/+85+ZPv08Q7mS1BF5e2HvJ6BvVDuS9ssyJxtx3bR1++9ZSgZP9bCSwaZywW46kuVnHsmqtE8b93Xm+ViyVNCKAhNE8mdoFKMUkuPY/oYo0YsZDfGT1I4Gkm+A8MFQXw4bnlE7mrPteFtsRywSc6/aI3Io9J8r9tN+afVhcwaJv9/WmJe12dxVcFzvIFy1tltMuiWKovCHkeLG2Ld77L/0htS6Tv0kvP7661RWVhIfH0+fPn3o06cPCQkJVFZW8tprr1k7RqknqsiFpQvgh7vh3WlQeFjtiNqnp8zHsrCUDGZuVjcOe+v2I1mWOVnZNj9VSXUD+8wdvqb0d7Yky9L0wsFKBUGUVUXLeVkOp64cdr0n9i9Y7Bjt9TVamP202N+zVJTBOoLjqyFjo1guZPRtHXvugIvE9jxJ1vSBYWgUUa6cXVrbhUBhoznJmpSozvITlyXHoFFgZ2Ypp047yPdP6lySFRsbS0pKCr/88gsPPPAADzzwAMuXLyclJYWYmBhrxyj1NCYT/PIXaKwS/y84AB9eJF6cHJm+sbmTV09JsiwjWQUHRA28SnQGIwdyKqi0Rz16QxU0mmv4fcNtfz41NM3Jsv1d0eUH8jGZYGiMPxH+TtS6Hc5IshxwJAsgarjYypEsx7HrHfHaFpYE/eaoHU2zhMni5oqhsbkiQ00NVfDzn8X+2Ls6Xn7fb7YoGSw80LyW3e8E+7gzOl6U+a46XNjpUE9XN5CSVQ6oNxof4e/RtIj7tylyNMtRdHpMU1EUZs6cyb333su9997LjBkzrBmX1JMd/gGO/SrWeVn0o5j3U3sati1RO7Lzy9sL+npRouOob7qszS8KgnqLNZUs64PZUWZJDf9adpDR/17Dgte3MOGZdTz7axp1jQbbndQyiuXmC+6+tjuPmixzsupKbX5X+4dUUXZ+8TAnXNC6xDwnK9gBR7JAlICBeKMpqa++Qqz3BHDBn8VyCY5CUaD3hWI/fYOqoWAywcp/iJH0gDiY1onui15BzTc7jy5v9WGzrVAyuC6tCKMJYrxNRKp4o+jyppLBHKt1TJS6pt1LfL/66qvccccdeHh48Oqrr573sbKNu9RpJpNYOwTgggfEH/3pj8BXN4g69rF/BG/7tUftEMv6WHHjHaMExF56TYTSdMjcYre1Xoqq6nl2eRrLUnOxrL/o7qKhukHPWxtPUlzVwItXDrPNybv7fCwAD39w94OGSjGaFWqbmwbZpbXsOVWGosACZ0uyjEY4bZmT5aBJVoQ5ySpKA4NeNAWQ1LPlZXHDMLgvDLpM7WjO1XuqmAetdpK16XlI+VDsL3gF3Lw7d5z+80Qpe9ovMO6uFh8ya1A4T/x8mN2ZpZyubiDYp+MLQq85IkbBBgeqm9jMSorA201Lbnkde7PLGRHnRJ1au6l2/8V96aWXuO666/Dw8OCll15q9XGKosgkS+q8rO2iJbiLJ4y/W3xs4ALRkjU/Fba8BLP/rWaErbOsF9VTSgUt4i8QCz/aqfnFmsOF/PnLVKoa9IAoz7h5Yjzjewfz68EC7vtiL9+m5DB/aATTBtignK+7t2+38IuG4krRotxGSdaP+8Qo1vjewYT7OVmpYEW2GLnWukFAL7WjaVlAPLj5iPLW08chbGCbT5FspCJH3CgEmPmEYya8CZPFtuAA1JSoc0Nz/9ew3vwaP+dZ6DOt88fqNxtWPizeV+gbwOXcBCom0IvB0X4czK1kzZFCrhrdsbLEep2BTcfEfKzBgep21/Z00zIjKZwfUvP4eV++TLIcQLvHqjMyMggODm7ab+1fenq6zYKVegDLOh1DLhctW0GMCk39u9jf+4ljtiPWNzQnGZYXqp6il3leVl6qqKO3oZLqBv7y9T6qGvQMi/Hnh7sn8v5No5nUNxQXrYYFw6K4dWICAA9/d8A2c7S6e9MLCxu3cTeZTCzbK+YOXDLcyUaxoLmzYFAfx3zDDKIcLXyQ2C84qG4sPd2qf4mkvNfE5s53jsYnrLnENGOj/c9v0MO6J8X+xAdaHX1qt6De4n2EUQdFrTfPmp1kKRns+Lys306UUKczEOHnTkwnB9ysaf4Q8bq0/EA+RqMsGVRbpwqCn3jiCWprz+3EUldXxxNPPNHloCTr2pddzjPLj/D08iNNd44dUnWRmI8F53YSSpwBvlGixexRB1zwOnsn6GrBO0ysSt+TBMSKO/kmA2TvsOmpnv7lCBV1OpIi/fj2rgkMiw045zEPzu5PfLAXhZUNvL8lw/pB9JSRLBs3v9h4rJjjRdV4u2mZM9gJE9ampheJ6sbRFjkvS337voRD34lGDLP/7djl5L2niO3J9fY/96HvoPyUmNd84f91/XiKIqpgQKwX14rZg8Xf8i3HS6g2V0i01we/ZYpjDAp3iG/rhf1D8XV3oaCynj1ZZWqH0+N1Ksl6/PHHqa4+d4Xs2tpaHn/88S4HJVlPXnkdi97fydub0vnfpnTu+3wvG48Vqx1Wy/Z9Ie44RY9q7oplodHCsKvMj/vc7qG16eQ6se09xbEmM9tLvKWV+xabnWLVoQK+25uLosDTlw3BpZW1SDxctTw4uz8A723OoKLWyqNZTXOynDAx6Agbj2S9vVFUPVw9Jg5/z3MXt3d4jt5Z0MIyL0uOZKmjLFN0ywWY8jeISlY1nDZZyvOOrQSjDRsI/Z7RKKYDgBjBcvOyznEjzXNz81JbfUjfMB8SQrxpNBhZb16zrz1Ss8vZcqIEF43CzRMco2TY3UXLzEGiTP5nR76p3kN06t2gyWRCaSFl37dvH0FBDrbqfQ9mMJp44MtUKup09A/3ZXK/UAD+9u1++7S67qjDy8R22NUtf37YtWJ7fLUY9XIk6ea7fl2pH3dmlpLBTNvMy9pwtIh7PtsLwI3j4xnewgjWmeYNjmRAhC9VDXre3WLlEuaeMpLlZ06yKq2fZO3PKWdb+mlcNAq3XJBg9ePbxWlzZ0FHT7LCh4htoUyyVPHr30TL9rjxMOkvakfTtoTJ4BEANUXN6z7aQ/o6UdLn5gujb7fecS03bM8zkqUoCnPNo1nvbslod2e+N9aLvwGXDI8mOqCdCyXbgaWJ0A/78qjX2TFRls7RoSQrMDCQoKAgFEWhX79+BAUFNf3z9/dn5syZXHnllbaKVeqgdzenszOjFG83LW/fMJK3rh9BfLAX+RX1PP3LEbXDO1t5FuTuARQYeHHLjwntJ0a5TAY4+K1dwzuv2tLmu2SWUoueJmGS2ObugTrrlSg06o28vu44d3y0h0aDkbmDI/jn/LYn72s0Cg/MEG9+392cQXrxuSPvnSZHsrrEaDTx7K9pgGjb7khvTjrEkRciPlN4EqBAdSFUO2gVQ3d1cr15ORIXWPCqqMhwdFrX5oV8LTc+7eHgd2I77GrwDLDecS0jWYWHwND6zeWbJybg5aZlX3Z5u9q5rz5cyKrDhSgK3DWlt7WitYrJfUOJ9PegvFbXpdb0Utd1KMl6+eWX+e9//4vJZOLxxx/npZdeavr31ltvsWXLFpYscfC1jHqI09UNvL5O3GV5ZEES8SHeeLm58Nzl4g/Ol7uzSSuoVDPEsx3+UWx7TTj/Aq9J5gQsY7PtY2qv9A2ASSwu6dfN33i3JiAOQgeKBPj4GqscUmcwcu0723lh1TEaDUbmD4nk1WuSWy0T/L3Zg8IZ1zuIOp2B+77YS6PeCp2fTKYzRrK66ULEFmcmWVZcc+V/m9PZevI0nq5a7p3u4AlKa+rKRdICjrtGloWbNwT3EfvnuZsvWZnRINZ6AjHH2EYdOm1i0EKxPfyjfUoG9Y2Q9rP53Jda99iBCeDuD4YGKGr95nKorzu3mUfVn1t5FL3h7NcLk8nEhqNFPPPrEZasP8E9n6UAcO2YOBLDHGu9RK1G4cpRsQB8sTNb5Wh6tg61RLrxxhsBSEhIYMKECbi6OmEdfQ/x6trjVDXoGRTlxxUjY5s+PiYhiHlDIlh+oIDnVhzl/ZtGqxjlGSwNL5IWnv9xceb26FnbRA23I8x/apqPNVXdONTWfw4UHxF3bode0eXDvbXhJLtPleHr4cJTCwdz8bCoFsuUW6MoCi9dNZy5r2zmYG4lz61I458XJXUtqPoK0NeJfZ/uXi5o7vinrxejtd7BXT7kwdwKXlh5FIBHFySREOIA7bg6w1Iq6BMBHn7qxtIeUSNEzLl7oO8MtaPpGY6thKJDovTOGk0c7CnhQrFWXk2ReK21zLm1lfQN4m+rTzjEjbPusRUFIoeK9bLy94n9Vtw+uTcfbz9FenENj/54iKcWDkZnMLH+aBEf/JbB9vTSsx4/Y2AYj1/smI2urhwdy6vrjrMt/TQZJTXO+7fWybX7HWplZfOoR3JyMnV1dVRWVrb4T1JXenE1n+7IAuDv8wai0Zz9xvTBWf3RahTWpRWxI/20GiGerSIHcnYiSgUXnP+xkcPEGlp1pc3lOmoymZoXbuyp87Es+pnbEh9fc96yjPY4XljFa+aR2CcvGcwlw6M7lGBZRPp78rx59PbdLRmsP9rFuXyWUSwPf+tNzHZULu7iTQ+INaGs4OU1x9EbTcweFM5Vo2PbfoKjcpZSQYuYUWKbu1vdOHoSS0n78OvAy8nmqru4wQDza7E9Gk1ZyhKTLrFNSaWlZDA/9bwP8/Vw5elLh6Ao8OmOLBa+sZWRT63mjx/vYXt6KW4uGi4bEc30AWFcMTKG164Z0e7KCnuLDvBkinke/jub5dJKamn3T0dgYCBFReINSkBAAIGBgef8s3xcUtdzK46iN5qY2j+UiYnnLibYO9Sn6Q3O/zY5wC/fkZ/ENm5c2+V2Lm7Nbxiy7DgptzWnT4g3oFq3nrcI8e/FjBKtdxsqujxh+plf02g0GJk2IKzLayjNTArnxvGi89ODX+2jqLK+8wfrKfOxLPzMbdwru97G/WhBFWuOiDkMD80Z0Kmk2WFY1shy9KYXFtHmv5k5u61a+im1QlfXvNTI4MvUjaWzRtwgtge+teo823PoG5pLBduqZOksS0fHvL1tPnTukEj+vVA0i9mXXU5VvZ4wX3fumNybdX+5kP9eOZz3bhrN81cMw9PNsefY3XmhKBP+YmcWxwptu4al1LJ2lwuuW7euqXPg+vUqrJ8gtcuuzFJWHCpAo8DD81pvEHDzhHg+25HFhmPFFFc1EOp77krodnNomdi29w9s3Hgx9J+1HUbdYquo2seylkjcuO4/stEWjRb6zoZ9n8GxFdD7wk4dpqymkU3mZQb+MX+gVd6MPzxvIDsySkkrqOKpX47w6jWdbKPcUzoLWvjHQF6KVZpfvLXxJABzB0fQJ9Sny8dTlbO0b7eIGCxuBNWVQlmGWKRVsp3jq0BXA/5xED1S7Wg6J3asWGOt8CCkfg7j/2Sb86T9LEoFfaOsXypoET1CbAsOiPlfLm7nffi1Y+MI8nYlp6yOMQlBDIryR6txvptCY3sHM3tQOCsPFfL08iMsvXmM2iH1OO1Osi688MIW9yXHYTSa+Le5a+CVo2LpF976ZMy+4b4Miw1gX3Y5P6TmctsklV50K/Mge7vYb6tU0KLXeLE9tc02MXWEnI91tr4zRZKVsanTh1h1uAC90URSpJ/V3ox7uGp54YphLHh9Cz/uy+PWCxJaXMi4TT1tJKup+UXXygVzy+uaFkL/0xQHX7y3PZpGspykXNDFHSKGiDlZOXtkkmVrh74X20ELHXvh4fNRFBh9K/z8Z9j1Loy90zZzoPcsFdsRN9iu+2JgAngGihG5woPNSdd5OOUC6S3429yBrEsrYsPRYv748W7+dVESMYHNN4S3nihhWWouh/MrCfRy4+WrhhPso+JN926mU78xK1asYMuW5kVHlyxZwvDhw7n22mspK5MrTKvl9fUnSM0ux9NVy+KZbd9hvXykeAP1zZ6cdq8LYXWWUsHYseAf3b7nxIwBRQsVWTZbKLVdDDoxogbQRyZZQPN6WYWHRAe2Tvh5v0hk5g+17ovc4Gh/Lk0WP2P/Xn6kcz/zPXEkC6Cia+WCn+/IwmA0MaFPMIOj/a0QmIoMOig1l1k7S5IFzSWDcl6WbenqRdMLcN5SQYshV4p1q0pPinWsrO30SfMNOQWSr7f+8S0UpXlEMXeP7c7jgBJCvPn7vIFoNQorDxUy+bn1XPO/7Tz6w0Fu/mAn1767g69253Awt5LNx0u4+n/bKexKSb10lk4lWX/961+bGlwcOHCAxYsXM2/ePDIyMli8eLFVA5Ta57cTJby0RpSwPHHJIML8PNp8zoKhkbhpNaQVVHEgt8LWIbasvV0Fz+Tu09whSM3RrOwd0FgNnkEQMUy9OByJb7j5LrlJXJ8OOl3dwNaTohnLRVZOskA0fXF30bAzo5TNx0s6foCeNpJlmZPVhZsZOoORL3eLkbDrx/WyRlTqKj8FRp1owGNZsNkZxJwxL0uynZxdoKsVnScjh6sdTde4+zTPzdr6uvWPn/KR2CbOEMuA2FJTkpVi2/M4oJsnJvDLfRcwoU8wRhNsSz/Nh9tOsf5oMVqNwrVj4/jvlcOI9PfgeFE1tyzdhcEo525aQ6eSrIyMDJKSRCvkb7/9lgULFvD000+zZMkSfv31V6sGKLWttlHPg1/vw2SCK0fFcMWo9nXtCvByY+4QcUf+uRVH7T+aVVcu2sNC+0sFLeLMJYNqNr+wJIj9ZjtGK3lHYWmz34nmF6sOF2IwmhgS7U+vYOu3nI0K8OSaMeLF/KNtpzp+gB43kmX+W9KFxhdrDhc2zfucmeT8a4splvbtIYnO9XtveZNZsF/MS5Fsw1LdkDDJeUsFzzT2TlA0kL4eCg5a77j6Rkj9VOyPvMl6x21NDx3JshgQ4cdnt49j80NTeXRBEvdMTeTeaYmsuH8ST186hMtGxPDVH8fj5+HCobxKfkjterMjqZNJlpubG7W1tQCsWbOGWbNmARAUFCRbuKvgrY3p5FfUExPoyeMXD+7Qc/8ysz9uLhq2nChh1eFCG0XYiszNYDKKyeMBHWznHKfyvCyjoTnJGuTkJSHWZpkzl9Xx783m46LhxSwbvhm/wdxpcF1aITlltR17clOS1UNGsizlglX5YNB3+Okmk4mlWzMBuGpULK4O2u64I5TTTtZZ0CKot1h6wNAIRYfVjqb7yjAnWfGT1I3DWgJ7NVeabLPiaNbR5VBTLEb8+s223nFbE2Weh1VyTDTa6KFig7y4eWICD87uz19m9afvGXP3Y4O8uHOK6Ej44qpjNOjtsBB1N9epV7wLLriAxYsX8+STT7Jz507mz58PwLFjx4iJcaLyiW4gt7yOt81du/4+b2CHW4rGBXtx+ySxyvlTvxymXmfHXyrL+lK9p3T8uZYkq/iIWCjV3rK2QXWheNPSmfi7M8v3JjdFtDJuJ6PRxDZzqeCExK4vfNuaPqE+TEwUZROfmdeTaxeTCaqdZySrtKaRx348xOVvbuXSN35jo7ljY4d4h4LGVdwMsZRKdsBzK4+yI6MUF43C1WOceF2sMzSNZAU70XwsMC/K2r71gqROaqwV5YIgRrK6iwn3iu2Br0WzKmuwNLxIvh60rtY55vn4hIpuj5ggL9X253NSN09IINzPndzyOj7d3oHXR6lFnUqyXn/9dVxcXPjmm2948803iY4Wdfu//vorc+bMsWqA0vn959c0GvRGxsQHMXdw5974/WlKIhF+HmSX1vHauuNWjvA8upJk+YQ2v8npxNyfLrN0jxqwoM12sD1OUG+xiK1R16HSjKOFVZTV6vBy0zI0JsB28QE3mOcGfbErm+KqhvY9qa5MjAJA8yK9DuzBr/exdGsmu0+VsTernBvf38mjPxxEZzC2/yAaDfiZ1ynr4Lysdzen8+YGcQPo6cuGnNXRyqmddrLOgmeyzBGSbzJtI3u7+LvnFyM62nUX0SNEUyOjHna83fXjlWaI8kOU5jlf9mDpKpjX8+ZltZenm5b7p4tR+tfXn6CqXqdyRM6tU0lWXFwcP//8M/v27ePWW29t+vhLL73Eq6++arXgWrJkyRLi4+Px8PBg7Nix7Ny587yP//rrrxkwYAAeHh4MGTKE5cuX2zQ+e9pzqpQf9+WhKPDIgqROryfk7e7CYxcPAuDtjekcLbDDonXl2WIhX0UL8Rd07hhNrdztPC/LoIfDP4r9QZfa99zOQFE6Vc5paXgxOj7I5mVlMwaG0zvUm9KaRm79cBe1je0ohbOM5HgFi5bYDmzz8WLWpRXholF4/vKhTYsxf7jtFLd+uJvqhpa/3uzSWn5IzeW1tcc5WVwtPthUMtj+O9hL1p/gKfNyEn+d3Z8r2zlP1Bk4bbkgQNRwsZUjWbaR0c3mY53JMpq1+wNo6OJ7BEvDiz7TIDC+a8fqiB4+L6u9rhwVQ+8Q8fr4zuYMtcNxap1+J2MwGPj222956qmneOqpp/j+++8xGGxbavbll1+yePFiHn30UVJSUhg2bBizZ8+mqKioxcdv3bqVa665hltvvZW9e/eycOFCFi5cyMGDVpy8qRKj0cTjP4m6+itHxna5LfKcwRHMSgpHbzTxz2UHbN8EI2Oj2EaPFCV3nRHX+bk/XXJ8FdQUiTfbnVxwt9vrZW5+0YHvjaVUcHwf25UKWrhoNbx342gCvVzZn1PB1f/bzs6MNspOnaSzoN5g5KmfRYJzw/heXDEqlscvGcy7i0bh6apl07FiZv53I0vWn2hKtkwmE+9sSmfqCxu4/4tUXlx9jGvf2U5RZX3z19vOMqFv9+Tw/MqjADwwoy9/Mtf4dwdu+iqUOvMyJcFOuN6XZSSr8JBsfmELp34T2+4yH+tMfWeL6pGGCkj5uPPHMehg7ydi3x4NL87UgzsMdoSLVsODs/sDoiKhpLqd1R7SOTqVZJ04cYKBAweyaNEivvvuO7777juuv/56Bg0axMmTJ60dY5P//ve/3H777dx8880kJSXx1ltv4eXlxfvvv9/i41955RXmzJnDX//6VwYOHMiTTz7JiBEjeP11G7QitbPX159gf04FPu4u/GW2de6oPn7JINxdNOzKLOO3E6etcsxWdaVU0MKSZOXtFbXw9pLyodgOv9Y+teTOyPK9yd4pmoS0wWA0sSPDPB/LDkkWiPVD3rtpNN5uWvbnVHDl29t47MdDrbeudZLOgt/vzeVoYRUBXq7cP725pG1GUjhf3DGOcD938ivqeX7lURYu+Y2tJ0q48YNd/Hv5EfRGE0Nj/IkO8KSwsoE7P9mDvgNJltFoYskGMWfp7ql9eGBGv06PsDsin3rzNfCPAzcnLH8M6g3u5uYXxUfUjqZ70Tc2l2HGjVM1FJvQaGD83WL/t5c7P5p19Fdxk9I7DPrPtVp47RI5THRKrMyFyo7PMe1J5g6OYGiMP7WNBl5fd0LtcJxWp5Ks++67jz59+pCdnU1KSgopKSlkZWWRkJDAfffdZ+0YAWhsbGTPnj3MmDGj6WMajYYZM2awbVvLd8u3bdt21uMBZs+e3erjnUFVvY63N57kv6vFmlh/mzuAMN+218Rqj0h/T64dK9pbv7L2mO1Gs0wmyLTc8etkqSCIMgOfCFEnnrfXKqG1qSJXjGQBjLjJPud0RuGDwN0PGqug4ECbDz+cV0lVvR5fDxcGRdlvsdoRcYGse3BKU1v3pVszueezlJYbwDSNZDlukqUzGHnVPK/yrgv7EOB19nzBYbEBbPzrVF68YhgRfh6cKKrm2nd3sOlYMW4uGp5aOJgf7p7IJ7eNxc/DhZSscrYVmUsj29HGfeOxYtKLa/B1d+GuKU440tMGn3rzz0CIk35titK8xqCcl2VdhQfA0ACegea1Aruh4deKuWbVhbD5v507hr0bXpzJ3QdCB4p9OS/rvBRF4f/mDADg0x2nyDptxxvZ3YhLZ560ceNGtm/fTlBQUNPHgoODefbZZ5k4caLVgjtTSUkJBoOB8PCzJ5yHh4eTlpbW4nMKCgpafHxBQUGr52loaKChoXlo1NKSXqfTodOpMwFQp9PRaIB7X/uCyNKdfKAXzUXumdKbq0ZGWTWuWyfE8emOLDGadbyIsQlBbT+po8oyca3Kw6RxRR8xHLoQvzZmDJq0HzFkbsUYPabTx7Fcw7aupWbPh2hNRoxxEzD49+pS7N2dNmYMmpNrMGRsQRcgRltbu767MsTCwCNiAzAa9O0Z/LKaIE8tTywYwNj4AP767QF+PVhAcdV23rouGX/P5jcBmoo8tIDBKxyjg33fLdf1m93ZZJfWEeLjxjWjolu83lrg4qHhjIn3585P93Ior4qJfYJ5ZP4Aeod6o9frifF341/zB/DXbw/yQwZMAowVeRja+Lrf2SwqGa4YGY27xqTa30xb0Ol0+DaIJMsQlOhwPwPtpYkYijZzM4bcFIxDr1U7nCbt/RvsqDSndqIFjFEjMeg7vtyBrVnn+mpQZjyBy9c3YNr2Ovqh13SswUf5KVxOrkMBdEOvVeX1Uxs5HE3RIQxZuzD2mWW14zr7z29LxvTyZ2KfYH47eZoXVqbx4hVDVI3Hka5xe2PoVJLl7u5OVdW5Q8XV1dW4uTl3p7VnnnmGxx9//JyPr1q1Ci8v9cpD/Aynebny//BwaaRYGwHhQ0isP8by5cesfq6xwRo2F2r493c7uXNgBzqRtVPs6c2MAEo949myekOXjtW7yochQHHKz+yo6HrZ5OrVq1v/pMnE9MMf4APsVYaS042aqNhC39pAkoDCXcvYVSIaH7R2fZcf1wAaPOsKVWtOowB/7K/w7lENu0+VM/+ldVyfaCDOR3x+TPo+IoGDp0rIdMDvvcEEL69OAxQmhdSxfs3KNp9zaywUh0K4ZyFpuwo583aV1gRB7lpO1PmBO9QXnWT1eb7uvBrYetIFBRMxdSdZvtx2peNqGWsuFzyY3+CQPwPtEV1mYhRQkbaZzSbH+xrO+zfYgY3I/JFY4FiNL0cd+Gejy9fXZGK872DCqg5S+Nk97Em4u91PHZD3Df0xUeQ7mG3bDgP2X6+tV6kbw4HTB1axrS7Z6sd31p/f1oz3gt9w4af9eQwgm2hvtSNyjGtsWSu4LZ1Ksi666CLuuOMO3nvvPcaMEaMHO3bs4M477+Tiiy/uzCHbFBISglarpbDw7AVzCwsLiYhouXwnIiKiQ48HePjhh1m8eHHT/ysrK4mNjWXWrFn4+fl14SvoPJ1Ox+rVqynvdwURxz7lNe/30V+9wWZlS4NO1zLj5S2kVWgYPuFCogI8rXp87c8rIQsChs5l3rR5XTqWkhsBSz8jXHeKeXPniHrrTrBc45kzZ+Lq2koJQ/4+XFOLMLl4MvTKhxnq5gB/bRyYkh0EH31NpC6TmTNmsHrNmlav78svbwFquWLaaCb3DbF/sGeYU1DFrR+nUFjZwMuHXLlyZDTXjI4lvPBlqIBB46aT1L9rP7fWptPpeOnLNZQ2KAR6ufLEoul4uHZszbyWlAdn8fYvotGDp76CeXNmg6bl4z78/SEgl9mDIrjhsmFdPrej0el0mA79BYBBUy4lqVcXSp3VVJIIb79BoK6gS38zra1df4MdmMubjwGQOOVq+vSZrm4wLbDq9S3sBe9OIbp8J+GjXoCwgW0/x6DD5fW/AhA088/MG6jS39CCWHjvA0Ibs6368+/sP7/nc8S0n18OFrCjLoJ3rxjRrufUNRooqm7A201LiI91uvE60jW2VLm1pVNJ1quvvspNN93EhAkTcHERh9Dr9Vx88cW88sornTlkm9zc3Bg5ciRr165l4cKFABiNRtauXcs999zT4nPGjx/P2rVreeCBB5o+tnr1asaPH9/qedzd3XF3P/cHwtXVVfVvavCl/4Gl+1EKD+D68z1wwzKbtIlNjPBnQp9gtp48zbepBSyeaeVWxdliTpw2YRLarl7T2BHg4olSV4ZrRSaE9u/S4c77fT72CwBK35m4egd06Tw9QtwY0Lqj1BTjWnUKaPn6VtTqyDDXe4/oFaz679ng2CCW3zeJx386zI/78vh8Vw6f78phv18OfoBLQAw44Ivo9iLxt+DykTH4ellnnuY1Y+N5Y/1x9AYNLhhwbSxv8eZOSXUDP+4XpXS3T+6t+vfQJhqrcW0UCzq7RA5xyJ+BdgnrB1o3FF0NrtV5EORY6zk5wmtth9WWQmk6AC5xYxz6Z8Mq1zcmGZIuQTn8A65bnoer2tFt8MRKMZfLKwSXpIvBRaVrFDVEvGdoqMS1Msvq69055c9vG/46ZwArDxey8XgJe7IrGde79eZUJpOJl9Yc5431J9AbTSgK/GVmP+6emmi1JkiOcI3be/4OpfBGo5H//Oc/zJ8/n9zcXBYuXMjXX3/NN998w9GjR/n+++/x97fdpPXFixfzzjvv8OGHH3LkyBHuuusuampquPnmmwFYtGgRDz/8cNPj77//flasWMGLL75IWloajz32GLt37241KXN4Lh5wxQfg4im681naoNrA1eZGAF/vzm6921pnVOabX4wUiBvb9eNpXZsXGLTlosQmExxeJvYHLbTdeboTF3eIFSPdmswtrT5sX045APHBXgR6O0a5cbCPO69ek8xnt41l3pAIFIx4NYh5Y47Y+KKoqoFDZeIF7KrR1luTytNNy+LZSRQTAEBhTnqLj/t0exaNeiPDYgMYERdotfM7EqVElGabvEPBW93R1i7RukKI+WZUkewwaBWWdZeC+oCXDeYxO6IpDwMKHPmxfWtVWrryJl8HLir+nde6NrdytywlI51XfIg3V48RryuLv0zlrY0n+Xp3Nl/szCK7tLlsrrJex/99u59X1x5HbzTh7qLBZIIXVh3jrk9SyC2vU+tLUE2Hkqx///vf/P3vf8fHx4fo6GiWL1/OsmXLWLBgAYmJtu+2dNVVV/HCCy/wyCOPMHz4cFJTU1mxYkVTc4usrCzy85vbck6YMIHPPvuM//3vfwwbNoxvvvmGZcuWMXjwYJvHajMhfWHq38X+qn9CVeH5H99JsweFE+jlSn5FPRuOtrwOWadkmf8YRwzp/PpYvxdrTtaybJhkFRwQyaGLh1gvRGqfBLGOmJK5qdWHpGaXA6LznaOZkBjCG9eN5P8mheCiGDGaFA5WOt5CxN/vzcOIQnKsP4lhvlY99tWjY6lyCwPgwxW/kfe7F8oDORV8sFUsWHnLxPhu1bL9LMVi7S9TSNdGyx2Cpbyr6JC6cXQXObvFNmaUunHYU9hA0W0Q4KtFUJ7d8uOMRtj6Ghw3z6MZcaN94jufPlPF9sQ6deNwIvdN70u4nzt5FfU8+2saf/1mP3/77gCTnlvP7Jc2sej9nUx4Zh1f7c5Bo8Azlw0h7ck5/PvSwbhoFFYcKmDqCxv49y+HKavpOWv0dSjJ+uijj3jjjTdYuXIly5Yt46effuLTTz/FaLR+c4TW3HPPPZw6dYqGhgZ27NjB2LHNoyEbNmxg6dKlZz3+iiuu4OjRozQ0NHDw4EHmzXOsuRSdMu5PYr2H+nJYdpdY3M/K3F20XD4yBoBPtp+y3oGzd4ptXOslmx1mOdbJdeIPui3s/1JsE2eINrBS+5jXQVMyN4Op5e+NJcka7oBJlsUdw8W8xNP4sfibQzTo7dj+sA0NegOf7RRvcK4YGW3142s0ClFxoiV1TUk201/cyGVv/Mblb27l1qW7uOLtrZTX6kiK9GPeEMdeqLkrFPO6UqbQdsw/cXThSWIrR7KsI3u72MaMVjcOe5v3PIQPgZpi+PwaqP/dPJXqIvj0cnFDGBOMugWCHWBx8kTznLmMjXJR7nYK8/VgzeILeeayIVzYL5QL+4UyJj4IjQJHC6vYdKyY6gY9fcN8eP+m0VwzJg5FUbhubC++/9NExvUOolFv5J3NGUx+fj2rDrXe5bs76dCcrKysrLOSlBkzZqAoCnl5ecTExFg9OKkVWhe4ZAm8OxNOroUf7oGFb4rFAq3ourG9eGdzBhuOFZN1upa4YCt0V8zZJbaxnW+3fo7eF4pRsao8yNws/m9NtaVnrO1xg3WP3d1FJYO7H0p9OQF1med82mQysc8JkixNjRgxPq0EcaywmpfXHG9aQ0Rtn+/IIq+iHj9XExfZKMnxCekFJyE5oJYPTxtIySo/6/NT+ofy2jXJuGodo4mCLSjmkayuzvt0CGHmJKvQ/t3duh2DHrLNr2vWvHnoDNy84ZrP4Z1pYp2wL6+D674RpeIn1sD3d4oEzMUT5j7rGKNYABHDwCsEaksgZ2fX1uvsQXw9XLlmTFzTupIAxVUNHMgtp6iygehATyb2CUGjObuaYUiMP5/fPo6Nx4p59tc00gqquOfzvXxy61jG2GKZIAfSoVdEvV6Ph8fZE6pdXV0domd9jxMxBK78CBQt7P8Cti+x+iniQ7yZ3C8Uk0ksRtdlunrI3y/2rVlW4eIOgy4V+/u/st5xLba/CY3V4pr3k6WCHaJ1gfhJAIRUnfuGLqOkhtM1jbhpNQyMVKd7Z7uYFyIOiuwFwNsbT5JW0L7uQrZU26jn9fUnAJgdY8TTresdBVvkJ5K3S3orfHnHON6+YSRvXDeCpy8dwmvXJPPuolH4enSvyd6/p5SIBvfdYiTLkmSdPi7v5HdV4QHQ1YC7f/N17UkCYuG6r8HNFzI2wbszRHL1yR9EghWWBHdsgJE32aRRV6doNGeUDK5VNxYnF+rrzrQB4Vw9Jo5JfUPPSbAsFEVhSv8wfrlvErOSwmnUG7ntw128vfEkJdUNLT6nO+hQkmUymbjpppu47LLLmv7V19dz5513nvUxyU76zRLD9QDrn4byLKufYtE48abyy93Z1Ou6WCJVsB+MOvAOhYBeVojuDEOvEtvDP0CjFVcmr6+AHW+L/cl/dZwXCWdiLhkMrTp3/se29NMAJMcFWKXluM1UidKGsMhezB0cgdEEL67q3Bp1pTWNVNVb58bUWxvTKaluJDbQk/FhVmxQ83t+ogxRqcxjbO9gZg+KYN6QSK4dG8eCYVG4dOMRLADqK1AqcwEwhTrGCGaX+MeAux8Y9SLRkjrvlOiWS9xYq1eTOI2o4XD1p2LEqmA/7PtcfHz0bXD7OghzwN+ZxBlie1ImWfak1Si8ek0yI3sFUlmv55lf0xj/zFru/iyFAzkVaodndR36i3DjjTcSFhaGv79/07/rr7+eqKiosz4m2dGoW6DXRNDVwvK/ii54VjR1QBjRAZ6U1+r4eX9+2084H0upYMxo6ycrseMgIA4aq+CoFReC/O1VaKiA0AEwYIH1jtuTmMs3g6uPgeHsu+bbTooka3yf1lvCOgTzSBa+kfxlVn80Cqw+XNg0n6wtOoORD7dmMu+VzYx4cjUjn1rDf1akUdmFZCslq4wl5lGsB2f2xaZ5jl+U2Fbl2fAkDsxcKljnGmi9hj1qUpQzml/IeVldkmVJssapG4fael8I96fCnP/AwAVw9Wcw/0Vwte46m1bT2zySlb9PTAmQ7MbDVcunt43lP38YwrDYAHQGE7/sz+eyN3/ji53WHyxQU4fmZH3wwQe2ikPqLEWBi16CNyfCsRVwfJVVS9q0GoXrxsXx3IqjfLz9VFMzjE4xJ1nFAUMJMprQtjKs3CkaDQy9GjY9B5tegKSFolStKyrzYJu5DHPav3ruXcquCumHyTMIbV0ppoKDEC+a1ZhMJranixe38edZd8MhmEey8I0gMcyHS5Nj+DYlh+dWpPHpbWPP21HvaEEVd3+Wwomi6qaPNeqNvLnhJB/8lsHsQRHcMzWRvuHt7wpY3aDngS9SMRhNXDI8inlDIljeSnMvq/A1z/WqzBM3cnraiG6RKHWt8oim2zSoD0sSy14UHoQhl6sdjXMymSDL3PSip83HaolvBIy7U/xzdL7hENxXjORm74T+c9SOqEfxcNVy1eg4rhodx6G8Cl5de5yVhwr523cHyCuvY/GsbjD3lQ6OZEkOKrQ/jLtL7K9+FIzW7Xx25ahY3LQa9mWXs9+8plFHNegNVJ0Q7dvv26zlmv9tt34d7vg/gWcQFB+B3e91/Xjr/g36OvHiOWB+14/XUykKJvO6JErurqYPnyiqpqS6AXcXDcPjAlQKrp2akiyRbDwwoy9uWg1bT57m852tZzcl1Q3csnQXJ4qqCfJ24/GLB7H7nzN4d9Eo+oX7UK8z8kNqHvNf28K7m9MxtXMk+tW1x8kqrSU6wJMnF9phSQpLkqWvh7oy25/P0RSKUtdKj27U4CnC/HNTcEDdOJxZaTrUFIHWDaJGqB2N1FGW0cesdqzzJdnMoCh/3rp+JH+Z2Q+AV9ed4P0tGRzMrTjr5qQzkklWdzFpMXgEiAQj9TOrHjrEx515Q8QCrB9v63gDjMySGu54/Ud8GwoxmBT2G3uzM7OUi1/bQmZJjfUC9QyE6f8S++v/DTWnO3+sihxI/VTsz3qq5925tzJTtGhtrOTubvqYZT7WqPhA3F0ceD4WnDWSBRAb5MVDc8Sdtid/PkxGCz/HjXojd32yh9zyOhJCvFmz+EJunBBPiI87M5LCWfnAZH68ZyJT+ofSqDfy1C9H+N+mlhf7PdPJ4mre3yLWpXrq0sH42aPhhKuH6MYFYJ6b1KOYF5st9+6tciBWFDFMbC3NiKSOO2leZylqhPgdkZxLrwliaxmNlFSjKAr3Tu/LYnOi9cTPh7notS3MemkjP6Q672uOTLK6C89A0ZgBRBMMK3eMumF8PAA/7sujvPb8xzYaTZwoqqKiVseP+/K46LUtuBWJF/Jqv0S+vm8WvUO9yauo50+fpnS9ocaZRtwougDWV8C21zt/nMM/ACYxitWTFpi0EVO0uIZnJVmW+ViOXipo0Iu71dA8ogPcMjGB8b2DqdMZuOOj3ZT+boHFd7eksyuzDF93F95ZNIogb7ezPq8oCkNjAvjgptFN7eBfXHWMI/mtdy00Gk088dNh9EYT0waEMbV/mJW+yHawzMuq7OLcTGejb2wa7SnzSlA5GCsKHwSKRvxsW24iSB1z+AexHdAN1t/siSwjWbkpoKs7/2Mlu7h3WiI3TYgHwNfDBaMJ/vxlKr90tSeASmSS1Z2MuV28CazKg0PfW/XQI+ICSIr0o0Fv5OvdOa0+bsXBfOa8sokZ/93EsCdWcd/ne6lu0DMrUKwz5N97FElRfnx++ziCvd04nF/J4z+d23Wu0zRauPBvYn/XuyLZ6oxDy8TW0hpe6hJTVDImFJSKbKgqoEFvYPPxEgAmJIaoHF0baorFQsqKFrybY9VoFP571TAi/Dw4XlTNje/vpKJONLLIr6jj9XWiKcVjFw8iMaz1BawVReHOC3szY2A4jQYjty7dxV+/3sefPt3DwiW/8fTyI2SX1lLbqOeBL1PZeKwYV63Cvy6yc7vopiTLee8qdkrhQTA0YvIMpNbNjkmtrbl5iTkpIEezOqO6GE79JvaTLlE3FqlzAhPAJ1x0Pc5NUTsaCfF6+NjFgzj21Fz2PTKLK0fFYDTBg1/vc8pW7zLJ6k5c3EXLVBDrZlmx06CiKCwaL9quf7LjFEbjucf+cGsmd36SwrHCatzMrc4UBe6blsgfos3deyKGAhDu58ErVyejKPD5zmzWHim0Wqz0nye6ATZUwu73O/78ihyxQCEKDLzYenH1ZO6+zfNZcnax5XgJ1Q16wv3cGR4ToGpobbJ0FvQJF0n8GSL9PfnktjEEebtxILeCBa9tYcXBfP7v2wPUNhoY1SuQy0ZEt3kKRVF49g9DCPV1J6+inq/35LD8QAGp2eX8b1M6k55bT9IjK/lxXx4uGoXnLx9GQoi3Lb7a1jUlWT2sw6C5VNAUOaL7lQ1Hir/HFOxTNw5nlPaTuPkSlQyB8WpHI3WGojQ3LJHzshyKm4sGjUbhmcuGMizGnzqdgf9tzlQ7rA6TSVZ3M/JmcPEQbUmtXGd88fAofD1cOHW6ls0nSs763I700zz5s+jAdfPEeHb9cwYHHptF6iOzWDyrPxrL5GrLizpwQd8Q7pgk5jj8c9lBqhv01glUo4GJD4j9bW+AvoN3PywlIHHjmhZhlbquzDtR7GTvZPkBUZ40d3Bkq4sXOozfzcf6vcQwXz65dSwxgZ5kldZy5ycpbDpWjEaBxy8ZdN7Og2cK8XFnxf2TeOmqYfxlZj/+OX8gL1wxjAlntLcP9HLl/ZtGszC57cTN6npqG3fzHW5T1HB147CFiCFiK0eyOs5S7SBHsZybJcnK3KJuHFKLtBqlqdPgZzuzqXCytdNlktXdeAc3L8zblTlJLfByc+GKkbEAvLzmGDqDEYADORXc9WkKeqOJi4dF8chFSfh7uuLr4Yq/p6tYg6LSXGJoeVE3e2BGP3oFe5FfUc/zK9KsF+yQy8EnQsw3SN/Qsece/E5sZamgVZWakyxj9k5WHxaJy5zBLScuDqVpjazWY02K8uOXeydxyfAoogM8mTMogvduGs2gqI6tqRTs486lyTHcO70vt03qzeUjY/js9nGkPTmHfY/MYsffZzC5X2hXvprO8+2hI1l5liSrG3aPi7CMZMkkq0PyUiFzs9hPWqhmJFJXWRYlztgM1UXqxiK1aHLfEEb1CqRBb2R1jnOlLc4VrdQ+4+8GFEj7GQoOWvXQt05KwNfdhb1Z5Tz7axpf787mqv9to7SmkaEx/vznD0PPvXOfby5FCUw4ZyFPTzctz1wqEq9PdmSRXmyldp1a1+Y7jJY7ju1ReAhyd4PGRb54WlmZdx8ATHl7qa2vJ8THjdHxQSpH1Q6/a9/eGn8vV165Opnf/jaNt24YadWmFB6uWvy9XHFzUfFPdk8sF6yvbFqI2BSZrHIwNhBp7jBYltn5+as9TX0lfH2TKBUceDEEdaNmKD1RSCJEjwSTAQ5+q3Y0UgsURWnqOphfp7Q4XcVRySSrOwrt3zwKs/E/Vj10dIAnz10u7n6+tyWDv36zn9pGAxMTg/n0trF4urXQittyl/SMUsEzTUgMYcbAMAxGEy+tOW69YC1J1tFf2t1tUbP3Q7EzYL5YrFCymmr3CEweAWgNDQxUspg1KMK6C1LbSjtGsnoEP3OJYk9KsvJTARP4x4JPN2p6YeEVBH7muZJWviHnlIqPiSVQjq4QZaKWxbctjEb44W4oywD/OLj4VfVilaxn6NViu+8LdeOQWjUhMYQvbhvNPUkGx59icAaZZHVXFz4EKHDkR6u/eM4dEsntk8Tdu17BXjwwoy/v3zQa39bW67HU+0e0nGQB/MVcc/vTvjwO57XewrpD4saJZgX1FZCxqc2Haw31aA58Jf4z6hbrxCA1UzRUhwwHYITmODeM66VuPO3VxpysHsMyP7GhEhqq1I3FXiwdx6K7YamgRWQPLxk0mURS9b+psGQ0LLsLPr8K3pkK/x0I70wTfwNMJlj5sHhN1bjC5e+JpVMk5zf4D6J6JT+1aeRacjwjewU6Xe8hmWR1V2EDbTaaBfD3eQPZ+Y/pbHhwCg/M6Hf+xWSbRrKGtfqQgZF+LBgmypFeX2+l0SyNFgYuEPuH225pH1O2DaWxGoL6QPxk68QgnWVjbTwA8wNzGBjpp24w7dXOcsFuz90X3M3fs56yVpa5syDRI9WNw5YsN796YvOLmtPwyWUiqcpLEW+04yZA5HAxB1HRio+/NxOWzocdb4nnXfoWxI5RNXTJiryDIXGm2F/3pBixlCQrkElWd2bD0SxFUQjz9Wi7c1pjDZSYk6bzjGQB3D1VzNlZdaiQ4iorrYdgmVd1aJlowNEao57EouVif9TNokOhZFUFtfBlgUhUhitWLAu1NVku2KynrZVlGcnqjk0vLHrqSFZFDnwwB06uA62b6Ei7OA1u+RX+uBH+cgTu3SPmEpdnmdfEUmDOs6KxktS9TH5QjFAe+Qk2PKN2NFI3Id9Jdmc2Hs1ql8JDgEmU7bUxx2lAhB/DYwPQG018m9L6gscdEn+B6GjYWH3ebovKwW/waSjE5BUMI2+yzrmls/yUpSHV2AcjCm5VWc7Rycmgg1rzcgU9fSQLzmjj3gNGsqoKzV1RFeiO7dstLDe/itM6vtyFszLo4aOFUHJMzDX842aY+Tj4/K5zZ1AC3LISxv1JJFf3pcC4u1QJWbKxmFGw4BWxv+k52P+1uvFI3YJMsrq7M0ez8vba//yWzoJtjGJZXDNGtIj/clc2JmsspqwocOHfxP6O/zWPZukbxQstgEGPdsuLABjH3S3KoiSr2plZysEyDbUab/RBoksQObvUDao9qs2LZGtcwdMJOiHamm8PGskyt24ndED3/pvgHyPmFhn1UHRY7Wjs4+RaOH1cfN23rISwAa0/1jcc5jwjkqug3vaLUbK/5Otg4v1i/4e7IWe3dY5bmQdHfgZdvXWOJzkNmWR1d2EDYcgVYv/nxWA02Pf8liSrlc6Cv3fR0Ci83bRklNSwPf085X0dMWA+hA+Bxip4rjc8FQ5PhcJzCbDi77B0HkpZBg0uvhhH3mqdc0pNjEYT/1l5DICrRkXjFj9WfCJ7p4pRtdOZTS9kCWnPauPeNB+rG5cKgrgR1dPmZaV8JLbDroGAWHVjkRzL9Eeh/zwwNMAX10JdeeePVVsKX14PLw2CL6+DTy+HBistUyM5BfmuoSeY9aSYsJ6XArves++529H04kze7i5cPFy8kfvOWiWDigKzngAXD8AEevPdpIZK2L4EsndgcvViX8yN4OZtnXNKTT7Ymsn+nErcNCbundoHYkaLTzjDSJacj3U2mWR1T5ZF4nvCvKzqIji2Quwn36BuLJLj0WjhsncguK+oZNj0fOeOYzLBD/eIOV4mo6iGyNwsGq1YI9E6fVKs1/bJ5bDsT83zR7srgx4le4e4lk5EJlk9gW8EzHhU7K99HIqO2Oe8Bl3zudpZLghwabJYt+XXgwXU66w08tZnGjycAw+egPv3wf9lwrVfiY5CyTegv3MH+YGyW5S1HSus4j8r0gC4pJeREB93iDFf59yU5pJNRyXbt5+tpyRZJtMZ7du7cWdBC8tNsJ4wkrXvc1EaGT0KwpPUjkZyRO4+Yg4ewI63RULTUSkfiTU6Na5wyypRlurhD9k74Lvbu1ZVVJouul0e+h5OrIbUT8WSA9/fBXVlnT+uI8vYgMtH85l87Imz165zcDLJ6ilG3gLxk0QDiM+uEq1rba04DQyN4O4PgfHtftqoXoFEB3hS3aBnzZFC68WjdRUTmwPjRS1+v9lw/TdwyevNawBJVvX37w7QqDdyYd8QJoab/zCG9BM/E/o6KHTwBVAtI1k+MskCek6SVZoO9eWi61zYILWjsT3LTbDCg/YvKbcno6G5mmOEHMWSzqPvDHET1qiD1Y907LnVxbDiYbE//RGIGwsxI+G6b0DrDkeXw3d3iK7Hp092rGV8zWn48GLx2hQ6AC5ZAkOvEp/b9xm8eQFkbe9YvM7gwDcA/8/efYfHUZ1vA35mi3rvki333gu2MdUYFzAdAiG0kEICARIC+SUhhZJGSPKlAAkl1ARMDR1jMOCCce9dtmVZvfe6db4/zs4WaXe1K+3ubHnu6/I1o9XszPFoy7zznvMetCaNQyRNlsUgK1ZoNMB1/xEBRls58MEPg39M+yTEM/16U2g0Eq6wdRl8d28MDLCPUiV1ndhV3gqdRsLvrpzmeAloNOILBwj/LoPMZLlKGyGWPU3RXYlOyWIVzAJ0ceq2JRRyJgL6JMDUIyruRauj74vvv8QsYOZ1areGwt2K3wGQgGMfAvV+FIXZ8yJg6hYZ4sV3OR4vXghc+S+xfugt4M1vAo/PA/5QCPxhJPDYXODkZ573K8vi2q29Usznecv7wNybgKufAb7zmSjM0lEF/PdqMe1AtDD1im6XAKoyF6vcGP8wyIolSVnAdf8V6yUfe583KhDs47F87yqouGquuJjbUNKIlm5jIFtFIaKU4V86JQ8FaQmuv1S6DIZ9kKWMyWKmE4DIAOtsf8toLuNeE0NdBQExDkWZCyzc35NDJcvAV4+J9YW3AXFJ6raHwl/eFGDa5WL9q3/49hyLCdj5vFg/886BBZNmfk0MVZh3C1A0V2S2zH2iMFfLKeCVa4Etj7vf957/iIBPGwdc95LrtDjFC8RUBMWLRID30U8iqludV8fXAsYuyOmj0Jo8Qe3W+IVBVqwpnAXkzwBki2Pwb7DYM1n+B1kT81MxY0QazFY5cAUwKGTMFive3iOykF+bP3LgBkrxi3CvMNhp667KTJYgSY6AM5q7DMZS0QvFyDPEMlBlq8NN+VcieNbGAwtuU7s1FCnOvkcsD77pU3ZIOr4G6KwBknOB6Ve632jSSuDyx4HvbQB+UQP8cB9w9x4ReMlW4NNfAVv/5fqc5lJgrW06mqW/dhSrcRafIvar0QMnPgGOvOvb/zHc2boKWqdfHVFdBQEGWbFpyqViaUu/BoXV4ijfPsSJPG9YOBoA8Mr2isDMmUUh8+WJJjR1GZCVHIclk/MGbqB0F2wtA7qbQts4fzCTNZDSZTBagyyLyfHZFSuZLMCp6mcUBlmmXuCj+8T6nBsGTjpM5MmIecC4JeLG9JYnvG4qyRZottq2mX8roIsffP9anZj0Ons8cNljwAW/Eo9/cr/IvHY3i8+kt28T3XnHnufaBbG/3MnAufeK9Y9/NrwS9OGgtxU48SkAwDr9GpUb4z8GWbFoqi3IKv0CMHYH5xhNx0XKWp8sCh0MweVzipASr0NZUze2loagUAcFhCzLeP6rMgDA5bOLEKdz8zGTmOl4XYRr6VmzAei1dallJsshLconJG44KrrvxKeLcQ+xQslkNRwBDJ3qtiXQPv+NKMSUnAcs/ZXaraFIc86PxXLPf7zeFJxa8wY0tXuBuBTgjCHMuSlJwHk/ARZ+T/y87tfAn8cDfxghsusJ6cCVTw0+Z+M59wLZE0QJ+s8f9r8d4eToB6KAWt50Me9rhGGQFYvyZwAZo8WFhLdBlsNRs1csi+aI/v5DkBKvw5VzxQXdK9ujaBBnlNtwvBFfnmhCnFaDb5891vOGygdmuA60V4peaONFUEiCUomzI0rHZCldBYvmxNYE1KkFQHoxANnx+R3plHFY22xdr654AkjOUbdNFHnGni/GT5l7RUl3N6Qj72Jiw8fihyv+OfSKxZIkysevfMQ21EIWEyNDEpmu9BGD70OfAFxmG0O263mgfOvQ2hIODrwhljO/pm47hiiGvkHITpKAqZeJ9eOfBOcYSnaiaO6wdqN0GfzkcB0aOvuG2yoKMrPFij98JOZG++ZZozEq28vgciWT1XwiBC0bAufKghHWDzyo7N0FozSTZR+PFUNdBRX2cVlRUPxCloH37xbZAEB0sZq0Ut02UWSSJJEdAoAdTwN9Ha6/bzgK7Yc/AgBYFt/teSyWrzRaYPEPgNu/BP7vFPCjA8B9Jf7td8w5ovIgILoaRuL8WR01wOnNYn1G5HUVBBhkxa5xF4il8gIONHsma3hB1rSiNMwblQGzVcabu1gAI5z1Gi348Rv7caKhCxlJetx1wUTvT1CCrKZwDbI4HsutaJ8rSxmTFEtFLxTRNC6rZA2w97+ApAUu/pOtHDfREE25FMieCPS1A/+9CuhqEI931AKv3QjJ1I3GlGmwLvllYI+bnA1kjnatJOirlY8AmWNFyff37oq8aoOH3gYgA8VninMQgRhkxapRi8SXT1s50FYZ2H2bjUDdQbE+zCALAG46U7y5Vm+vgMUaYR8SMaKmrRfXPr0FH+yvgU4j4bdXzEB6kt77k7JtpVjDNsjiHFluKUFWNJZw72oEGkUmFqPOUrctarBX/dweeRdkzsxG4FNbBuvsHwGLvs9sNA2PRiPmuErIAKp3Af9cCKy+Hnh8PtBSCjltBHaN+QGg0andUoeENOBrz4tqg8c+DN7wkGCQZeDA62J91rXqtmUYGGTFqvhUMVEeIErbBlLjUdGHOCFdTI43TKtmFiIjSY/qtl5sOt4YgAZSIO063YLLn9iMQ9UdyEqOw8vfXYTLZhcN/sQcW6aruyE8uzLYM1kMslykKkFWHWAxq9uWQDv9pVjmzxB3kGNN4RwxKXFPsygUEal2PQe0lIoy2krRAqLhKl4IfPdzcYOwtxU4/rEo8DVyAczXvwGjPk3tFg40Yh6w4Ltifc9L6rbFH+VbxFyrugRg2lVqt2bIGGTFsjHniGWguww6j8cKwN3DBL0WX5sn5lr6z9bTw94fBc5XJ5tw47Pb0dRlxNTCNLx359k4c5yPF6fxqY4L9qaTwWvkUHVxjiy3UvJEFly2iAA5mpRtEsux56nbDrXo4sSFJBC8ruTBVrMX+OwhsX7BL8XdfKJAyZkA3LEV+NbHwLKHgetXA99ZJ0qnh6t5t4hlyceObo7hbott4vA5N0T0DS8GWbFMCbICncmyj8cK3JiGm84cDUkC1pc0oqQuysoLR6gtpU349os7YTBbccHkXPzvjsUozvJS6MKdHFuXwXAsfsExWe5ptNE7IXGsB1kAMDpIN99Cob0KePUGUTl34grHxSVRIOnigNFnAefcA0y5JPy7ouZPA0acAVjNwP5X1W7N4BpLgONrAUjAmXeq3ZphYZAVy0adCUgaoOVUYMsx1wSmsqCzMTnJuHiGyCg8s+lUwPZLQ3OsrgPf/89uGMxWLJ2Sh6duno+kuCH0RbcXvwjDMu4ck+VZWhQGWe3VoouZpBEXULFqzNliWf5V5IzLslqBL/8f8MRCoLNGfK5c8+yQpw8hijrzbhbLPf8V75dwpky5MOUSx43YCMUgK5YlpAMFM8W6cgd3uEy9YjJPIODVub5/npgY9L191aht7w3ovsl39R19+NYLO9FpMGPR2Cw8edM8xOuGeDETzhUGmcnyLBorDCrjsYrmis/GWDVivhgH0d0Ynu9Ld7Y/KSYcto2PwQ1vxPbfkKi/GdcAcami18iht9RujWeGLuCgrX2Lble3LQHAICvWTVgmliVrArO/ukMiJZ2c65hPJ0BmF2fgzHFZMFtlvL4zwBURySdGsxU/eGUPatv7MD43Gc/cfMbQAywgfCsMGntEqV6AmSx37HNlRdG0CsqNpjHnqtsOteniHVUGlcAznHU1AhseFevLHhLjY7K8TIJOFIviU4FzbUVgPv+NuCEejo68Cxi7RNE0ZUhLBGOQFesmXyKWJz8HzIbh7895PFYQ+ilfPltc3G0tbQ74vmlwf1hzFLvLW5GaoMNz31wweJn2wSiZrJZT4VWprsvWVVCfBMRz4PwA6aIQDdqjJMiSZY7Hchas8brBsP53gKFdVMs964fhPz6GSC1n/kDcIGuvBDb/Te3WuLfnv2I596aoeC8zyIp1RXOBlALA2BmYu5bKeKwgTeS5aFwWAGBvZRv6TJagHIPc23m6BS9uOQ0A+Nt1czAmJ3n4O00bAegSAasJaK8Y/v4CxXk8VhR80AdcerFYBnqOPbW0lokLD41ejFWNdaNt47JOh/m4rIajwJ7/iPWL/sgxWETe6BOBCx8U6xsfBT66T8wpFy6aTgCV28S42Nk3qN2agGCQFes0GmDyRWL9WAC6DFYHvuiFs3E5ychNjYfRbMW+yragHIPce3JDKQDg62cUY9m0Icw+745G4+ja0xJGBU04Hsu7DFuQ1R4lQZaSxRq5AIgLwM2DSDfyDEAbJzK64fS+7O/z3wKyFZh6WWwXKyHy1azrgCX3A5CAnc8Ca36idosc9tqyWBOWO4orRbiICbJaWlpw4403Ii0tDRkZGfjOd76Drq4ur89ZsmQJJEly+Xf77ZE/kC7glC6DJR8P766lodNRJS5IQZYkSVg0VmSztp9qCcoxaKCSuk58cawBkgTcvmR8YHeuTFjdUhbY/Q6HkslKCVAwGW3SR4llVz1g6lO3LYFQZsvis6ugoE8UJZ+B8B2XVbkTKPlI3PVe+mu1W0MUGSQJWPJz4Osvi5/3vCSGi6jNYgL22crLK5UQo0DEBFk33ngjDh8+jHXr1uHDDz/Epk2b8L3vfW/Q5912222ora21//vTn/4UgtZGmLHnAfpkUfq2dt/Q91O7H4AMpI0UE5YGiTLZ7bZTHJcVKk9vElmsi2cUYGwgugk6UzJZzaWB3e9w2LsLRsfdtIBLyhLdPAGgo1rdtgwXx2O5Z5+sPgzHZcky8PnDYn3ODeE9ESxROJp6KbDw+2L9/R8CfR3qtufEp2Jy++RcYNJF6rYlgCIiyDp69CjWrl2LZ599FosWLcI555yDxx9/HK+99hpqaryXEE5KSkJBQYH9X1oaB7EPoE8AJiwV68PpMlixVSxHnjH8Nnlxpm1c1p6KVhjMHJcVbDVtvXh/n3ifKWX0A8qeyQqjbkmcI8s7SYqeLoONJeLLXZcQ9M+uiBLO82WVfiEybNo44Pyfq90aosi07EEgc4yoErvzWXXbohS8mH09oB1mQa0wMoTZQ0Nv69atyMjIwBlnOL4Aly1bBo1Gg+3bt+Oqq67y+NxXXnkFL7/8MgoKCnDZZZfh17/+NZKSkjxubzAYYDA4qux1dIjo3mQywWQyBeB/4z/luME8vjThIuiOfgD52Ecwn/vTIe1DW7YZGgCW4sWwBrGtozLikZ0ch+ZuI/acbsYZozOHvc9QnONI9e9NpTBbZZw5NhPTCpKHdI68nV8pfTR0AOTmkzCHyfnXdtRAA8CclAs5TNrkjRqvX23aSGiajsPcfBpycfifI080J9dDC8BavAgWWQN4OIcx9xlRMBc6jR5SRzVMjSfFxVgQ+Xx+ZSt0nz0ECYBl/rdhTS7w+Dcjh5h7/YZYRJ5fKQ7SOT+B7oO7IO/4N8wLblcnwOmqh+7Ep5AAmGbeEBGfwb62ISKCrLq6OuTluXY/0+l0yMrKQl1dncfn3XDDDRg9ejSKiopw4MAB/OxnP0NJSQnefvttj8955JFH8PDDDw94/NNPP/UanIXCunXrgrZvvRm4CBpoGg5jwzsvoSc+16/nS7IZq8q3QgNgY7kZnQ0BmnfLgxHxGjR3a/Daum1oKArcXdZgnuNwJsvAl3US2k0SJqTJmJgmQ6cBeszAK7u1ACTMiW/CmjXD+7u6O7+JxiasACC3nsbHH30AWVK/QtjSulKkAth2uBzNFcF9LQdSKF+/s9tljAFwcvd6lNQM/0aHWhacegtFAI715eGED6/vWPqMOCdxDLK7T+DIB0/gdM7SkBxzsPNb1LoDC+oOwKxJwLqeGTAO8zMp1sTS61cNkXZ+NdZELNelIaGzBvte+y1qMkNfXXV003rMkS1oTRqHTTtOAPA+b2Y4nOOenh6ftlM1yPr5z3+ORx991Os2R48eHfL+ncdszZw5E4WFhbjwwgtRWlqK8ePdd3u6//77ce+999p/7ujoQHFxMVasWKFaV0OTyYR169Zh+fLl0OuDeJeh/RWgYguWjjTAumCVX0+VqvdAt88AOTET5159mxiMHESVKWU4sO4E+pILsWrVnGHvL2TnOEw9sb4U/zstxkR9Vg0UpSfgjvPHYdupFhitdZiSn4J7b1gMaYjlzL2eX9kK+dj90FgMuPjsWUDG6OH+d4ZHlqE7fAcAYNGyy4Hsieq2xwdqvH41X5UAG9ZjUl4Cxq/y7/MibMhW6P76QwDApJXfxcQR8z1uGoufEZrMk8AXv8FMbSmmrfpLUI/l0/mVZeie+zMAQDr7biw77/qgtimaxOLrN5Qi+fxq0kqAL/+E+aYdmLPqNyE/vvZ1UYQjbcH1WHWO5++ScDrHSi+3wagaZN1333249dZbvW4zbtw4FBQUoKGhweVxs9mMlpYWFBT4PmZi0aJFAICTJ096DLLi4+MRHx8/4HG9Xq/6HzXobZhyCVCxBdrjH0N71p3+Pbd6GwBAGnUW9HEDz1+gnTE2G8AJHKjuCOg5CYe/c6i9tbsK//hCBFhLp+ThQFU7atr78Ov3j9i3uXPpRMTFxQ37WB7Pb9ZYoPEY9O3lQO6EYR9nWHrbAGM3AECfNQaIoNdDSF+/mSIY1nRUQxNB58hF7X6grw2IS4Wu+AxAO/hXYkx9Rsy8BvjiN9CUfwWNoTWoBY0UXs/v6a+A+oOALhHaxT+ANlb+DgEUU69fFUTk+V10G7Dl79BU74KmZhcwenHojm3sAU6LwkPaqZf49J4Oh3Ps6/FVDbJyc3ORmzt4t7TFixejra0Nu3fvxvz54k7jF198AavVag+cfLFv3z4AQGEhK4a5NfVS4NNfAqc3i3LaStU3X5RvEcsQzVUyc0Q6NBJQ296HuvY+FKQnhOS40abPZMHvPhLB1J0XjMf/rZyCXqMF//7yFD46UIuJ+Sm4fHYRVkwPcgGIrHFA4zFb8YsLg3uswbRXiWVSNhCnbhfhsBYNhS+UqoJjzvYpwIo5mWOAonlikvmj7wMLvqtue7Y/KZazvy4qXBLR8KXkiYITe/4DbP4rMPrN0B371AbA3CemBcmfHrrjhkhEVBecOnUqLrroItx2223YsWMHvvrqK9x11124/vrrUVRUBACorq7GlClTsGPHDgBAaWkpfvvb32L37t04ffo03n//fdxyyy0477zzMGvWLDX/O+Ercwww/kIAMrDred+fZzE7KguGKMhKjtdhUn4qAGBfZWtIjhmNPthfg7YeE0ZkJOLe5aIMcmKcFj+8cCI++fF5eOKGecEPsIDwqjCoBFnpI9VtR7hLV4KsasBqVbctQ8X5sQY33VZY6vC7qjYDreXAsY/E+qI71G0LUbQ5+x4xzOPEp0DdwdAd9/jHYjn5IlG1NspERJAFiCqBU6ZMwYUXXohVq1bhnHPOwTPPPGP/vclkQklJiX0wWlxcHD777DOsWLECU6ZMwX333YdrrrkGH3zwgVr/hcig3Knc+1/A1Ovbc46+D/S1izv/BaELYOeOEoPt91a2heyY0ea/28oBADeeOQpajYofcErWNCyCLFtmRgkiyL3UQkDSAlYT0OW5AFHYspgdGfgx56rblnA2/UqxPL0ZaKtQrx1f/QOQrcC4C4C8Keq1gygaZY8Hpl0p1jf/LTTHtJiBkrViffLFoTlmiEVM/4isrCysXr3a4+/HjBkD2Wkuj+LiYmzcuDEUTYsuk1aKtG17BXDof8Dcm7xvL8vAlsfF+oLbQtrlZm5xBl7dUYG9FW0hO2Y02VfZhgNV7YjTavD1M1QOKLJt47CajqvbDoCZLF9pdUDaCPFZ0VYJpBWp3SL/1B8EjJ1AQgaQP0Pt1oSvjFEi01e2Cdj6L+DiP4a+De1VoisTAJz3k9AfnygWnPNj4PDbwOF3gAt+KQKvYFImIE7KAUafE9xjqSRiMlkUIhotsODbYv2L34siAN6UbxH99XUJwMLbgt48Z3NHZQAADla1w2yJ0O5KKlq9XWSxLplViOyU4Bcr8SrP1he7pQwwdKrbFgZZvlPOUSSOy6rcKZYjFwAafhV6dfY9YrnnJaCnJfTH//L/iYzpmHOBMdF5MUakusJZwITlImO85bHgH2/PS2I55xuAbviFtcIRv1looIXfE2NkOmuAtfd731ZJK8+5AUjOCX7bnIzPTUGiXotekwWnm32bs4CEXqMFaw6KLl7XLwiDbnEpuUBKAQAZqD8y6OZBZQ+ywuC8hLtILn5R5RRkkXfjl4qu4KYeYMczg28fSG2VwJ7/ivUlPw/tsYlizbm2KYz2rQY6aoN3nPZqkckCgHnfDN5xVMYgiwaKSwaufEoMgty/GnjzW0D1noHbndoAnFwnxmUsvivkzdRoJEwuEMUvjtX5NmcBCZ8crkOXwYzirEQsGBMmVboKZoplfQgH3brDIMt3yjlqi+Qg6wx12xEJJAk45x6xvv1p+xQHIbH5r8xiEYXK6LOA4jMBixHY9s/gHWffKyJjNvpsICf856IcKgZZ5N6oRcD5truGh98G/r0U2PhnRxUxqwX45JdifcF3g99314OphSLIOlrLIMsf/9sjAomr546ERs2CF84KbONi6g6p1waLWWRwAXYX9EWkZrK6GoHWMrHuZQJicjL1ClGBtrcF2PtyaI7ZVuGUxRqkVwURBYaSzdr5fHC6B1stjjGWUZzFAhhkkTdLfgZ8fxMw/WoAMrD+d8B/rwRKvwA+/DFQf0gMGlexC8fUwjQAwLFalcfxRJDa9l5sPtkEALhmXhgFEkomK5TlY/vrrBV317RxQPLgc/jFvEjNZFXvEsucyUBihqpNiRhaHXDW3WJ9yxOAxRT8Y35py2KNPU/MZUZEwTdxhSgGZOoGdvw78PsvXS9uzCWkA9MuD/z+wwiDLPKucDZw7QvAFf8EtPFA2Ubgv1c5Bixe+ICqk0JOKbAFWXUMsnz1zt5qyDKwYEwmRmWH0WS7+bYgq+GIuNOlBiUjkzaCxRB8ke6UyXKq7hr2lK6CxRyP5Zc5N4qbD+0VwKG3g3usvg4xLgRw9KogouCTJFFpEAC2PxX47sF7XhTLWdcD+sTA7jvM8CqCfDP3JuAHW4E5NwG6RKBoHnDj/4AF31G1WcqYrOq2XrT3hODOaoSTZRn/2y26CoZVFgsQXU51iWJwvVrzZbGyoH+U82TsAvraVG2KXyrFpPUseuEnfSKw6HaxvvXx4AbWxz4ELAYgZ1LIJrknIptpVzq6B+98LnD77WoASmwTEM+P7q6CAIMs8kf2eODKfwK/qgO+tx6YuEztFiE9UY8RGeJOCItfDG5/VTtKG7sRr9Ng1axCtZvjSqMF8qeJdbW6DHIiYv/EJYk5ToDI6TIoy0DtfrHO8Vj+O+Pb4mZI3UExQXGwHHxLLGd8TdxZJ6LQ0eqA8/5PrG/+m8gsB8L2pwGrGRhxBpA/PTD7DGMMsijiKcUv2GVwcEoWa+X0AqQl6FVujRvKpLCqBVnMZPkt0opftFUAhg5Aoxdjssg/SVliXhsA2PZkcI7R1Siq1wLAzK8F5xhE5N2s60UmubcF2BqASoPNpY75t87+4fD3FwEYZFHEU8ZlscKgdwazBe/vF5XzrpkfpkFE0RyxrHEzZUAoKNkYBlm+i7TiF/WHxTJ3ctROgBl0i+4Qy5I14sIp0I68C8gWoGiuapVriWKeVgdcYKsivfWJ4WWuZRlY83+iNPz4C4Gp0V3wQsEgiyLeFJZx98mWk81o7zUhNzUe50wI7cTRPlPGyFTtVqf4RfNJscwaF/pjR6r0CMtk1dumCFCypuS/3EnAhOUAZGDjnwK7b1kG9trKts9gFotIVVMvB0adJcbdvnT50DNaR94DSj8XBdRW/TlmugAzyKKIN6MoHQBwtLYTRrNV5daEr7WH6gAAF00vgDZc5sbqL3cqoE8GjJ1A0/HQHttsBNrKxXoUT44YcJHWXdAeZEX/eICguuAXYnngNaBmX8B2K1VsEWPmdInA7G8EbL9ENAQaDXDT/0TXQdkCfPIL4LOH/Ct6Y+gE1trmuTvnxzGVnWaQRRFvdHYS0hJ0MFqsOF7PcVnumC1WfHrEFmTNKFC5NV5odcCIeWJdKbMdKq1lYo6suFQgJT+0x45kkdpdkEHW8IyYB8y8Tqx/8gvA1BuQ3Wp2PCVWZl8PJGcHZJ9ENAxxScBVTwHLHhY/b/4bsO7Xvj9/46NAZ42oVnjOPcFoYdhikEURT5IkzBqZAQA4UNWubmPC1I7TLWjtMSEzSY9FY9Wb18wnSsW3ql2hPW7TCbHMHh8zXRkCIpIyWcZuxxgiZfJrGroLfy26/5R/Bfxthug62NMy5N0l99VCOr5W/HDmDwLUSCIaNkkSAdKlfxc/b3kc2P3S4M87vdnRxXDVX6J+Xqz+GGRRVJg1UnQZPFDVpm5DwpTSVXD5tHzotGH+trePywpxkNVsC7LYVdA/SiaruzFg2YygaTgGQBYT6qbkqd2ayJcxCvja82LZ0wSs/70Itl6/CdjyBNDd7Pu+Ouuw6NTfIUEGJq4Q476IKLyc8S1HMYyP7hXBlqnP/bZdDcBb3xY9RGbfAExcHrp2hgmd2g0gCgRHkMVMVn89RjM+OlALIMy7CipGniGWDUdEX+741NAct8lW9CKbQZZfEjOB+DRRFr21HMibonaLPON4rMCbeikw6SJREXDz34H6g8DRD8S/jY8C484HyjYBklZsmz8TSM4BJiwDEkRlWFTtgu7t25BqqIWcNgLSxQEupkFEgXPe/4kx0wffBD79FbDpz6JY1Ij5Yh69/OlAY4kIsLrqxVjrS/6idqtVwSCLosJMW3fBkvpO9JksSNBr1W1QGHlpSzmau40YlZWEcyfmqt2cwaUWAOmjgPYKoHo3MG5JaI6rVBbMmRCa40ULSRJ97esOiHFtERFksbJgQGl1Yj6rGdeIsZTlW8RkwkrApdjzH8d6QobYvqMGOL4WEmT0xOVAf/P70GeNDfl/gYh8JEnAVU8DY88HNjwCdFQDNXvFv53Piptupl7AahKT1V/3EhCXrHarVcEgi6JCUXoCclLi0NRlxJHaDswblal2k8JCR58JT20UY1DuWTYR+nDvKqgoXiiCrPKtIQyylDFZDLL8ljVWBFktp9RuiXf2ohcMsoJCksR7t3ghcNYPgSPviDvaY88XF1wlH4ugqv6QeK3ses7+VOus67HReg6WZYxW8T9ARD7RaIF5NwOzvg40lQAtZcCht4CjH4peDQAw7gJRMCM1AnrQBAmDLIoKkiRh5oh0rC9pxIHKNgZZNs9uOoX2XhMm5KXgijkj1G6O78acIz6whzP5oT96WoAe2/gRBln+y7RlHlrK1G2HN7LsyGQVMMgKOo1GZKqcKTdMrBbRvbBqlxjTN3oxLLkzYFyzJtStJKLh0MWJIkIFM4Fpl4su/p31otx7zqSYLyLFIIuixuziDKwvacTO8lbceja7m1S29ODpTSKzcN/ySeE7N5Y7Y84Vy6odottBsCsSKV0F00bEbLeGYVG6d7WGcZDVUQ30tQManfjyJ/VotCIAcw7CTCb12kNEgRGfGrpx1BEgQvoOEQ3urPE5AICtpc2wWv2YKC9K/f6jozCYrVg8LjsyCl44yx4PpBYCFmNo5stSgixmsYYma5xYhnMmq86WxcqZBOji1W0LERFFPQZZFDXmFGcgKU6Llm4jjtZ1qN0cVW0+0YS1h+ug1Uh46PLpkCItZS9JossgAJR9GfzjNbF8+7Ao3QXbKkRXsHDEohdERBRCDLIoasTpNPaJdr862aRya9Rjsljx0AdigP/NZ47G5IIITd0rXQZDMS6LRS+GJ60I0MaJ4gbtVWq3xj170QuWbyciouBjkEVR5ewJosvg5pN+TIIZZV7achonG7qQnRyHHy+P4LEnSiaraidg7AnusThH1vBotIBSFS5cx2Uxk0VERCHEIIuiijIP1I6yZhjMYdptKYi+OtmEv38msjI/vWgy0hP1KrdoGLLGiXFZVpOYfyNYrBZH6XHOkTV0WWFcYdDU6xh3x8qCREQUAgyyKKpMyk9BTko8+kxW7ClvU7s5IfX0xlLc9Nx2dBnMWDgmC9fOL1a7ScOjzLkDAJXbg3ec9krAYgC08aKcNA2NvYx7GM6V1XgMkK1AUjaQkq92a4iIKAYwyKKoIkkSzpmQDSC2xmVVtvTg0bXHIMvANxaOwn++sxCaSCrZ7slIW5AVzAqD9q6C40W3NxqacC7jrlQWzJ8e8/O2EBFRaDDIoqjjGJcVO0HWy9vKYZWBsydk45GrZyJBHyXBQvEisazcLiaTDQZ70Yvxwdl/rAjnCYmrd4tl4RxVm0FERLGDQRZFHSXIOlDVhvbe6J/gssdoxqs7KgAA3zoryiZhLpwluvH1NAevG5pSvp1FL4ZHKX/ffBKwWtVtS39Vu8Ry5AJ120FERDGDQRZFnaKMRIzLTYZVBradiv4qg+/urUFHnxmjspJwwZQ8tZsTWLp4oGiOWK/cEZxjNHOOrIDIGC3KuJv7xDi3cGHoAhps5dsZZBERUYgwyKKodI4tmxUL47Le2SvmJbpl8Whoo2EcVn/BLn7RXCqWzGQNj1YHZNm6XDYdV7ctzmr3iaIXaSOAtEK1W0NERDGCQRZFpVgZl9VjNGNvRRsAYMW0AnUbEyz2cVlByGQZu4GOarHOMVnDl2ubly2cgiylaMrIM9RtBxERxRQGWRSVzhyXDa1GwqnGbpxu6la7OUGz83QrzFYZIzMTMSo7Se3mBIdSYbDhCNDXEdh9K3MnJWUDSVmB3XcsyrEFWY0l6rbDmTIeawSDLCIiCh0GWRSV0hP19i6Db++pUrk1wbPFlqk7a3y2yi0JotR8Md4HMlC9K7D7VoIBJTig4cmZLJZKMRG1yTKLXhARkSoYZFHUumb+SADA//ZUw2oNUvlvlW0pFYU9zhqfo3JLgixYXQYbjohl3rTA7jdWKcVDmsIkk9VRDXTVAZIWKJytdmuIiCiGMMiiqLViWj5S43WobuvFjtMtajcn4Np7TDhU0w4AWBzNmSzAqfhFgIOseluQlc8gKyCUIKunGegOg8qeFdvEsmAmEBel3WmJiCgsMciiqJWg1+KSWaKa2P92R1+XwW1lzZBlYHxuMvLTEtRuTnApQVbVzsDOwdRwVCyZyQqMuGQgvVish0PxCyXIGrVY3XYQEVHMYZBFUe1rti6DHxyoQXOXQeXWBNZHB2oBAOdOzFW5JSGQNx3QJwOGDqDxWGD22dcBtFfY9j81MPskx/i2cAiyKpUg60x120FERDGHQRZFtfmjMzFrZDr6TFa8tOW02s0JmJZuI9YeqgMAXDNvpMqtCQGtDhgxT6xXBajLoBKspRYBiZmB2SeFT5DV1w7U2yYhZpBFREQhxiCLopokSbj9fDH/0Utby9FtMKvcosD43+4qGC1WzByRjpkj09VuTmgoxS/KtwZmf/aiF8xiBVSurcJgoDKOQ1W1U0xCnDkGSI3SOeSIiChsRUyQ9fvf/x5nnXUWkpKSkJGR4dNzZFnGAw88gMLCQiQmJmLZsmU4cSJMSgtTyKycXoCxOclo7zXhtZ2Vajdn2GRZxqs7RDe3bywcpXJrQmjcErE8uQ6wWoa/P/t4LAZZAaWMb1POr1oqtoslx2MREZEKIibIMhqNuPbaa3HHHXf4/Jw//elPeOyxx/DUU09h+/btSE5OxsqVK9HX1xfEllK40WokfPvsMQCAd/ZGdgEMo9mKX757CKeaupEcp8Xlc4rUblLojDoTSEgXleuqdg5/fyzfHhxKJqujWnTZU0uFLeOpZECJiIhCKGKCrIcffhg//vGPMXPmTJ+2l2UZf//73/GrX/0KV1xxBWbNmoX//Oc/qKmpwbvvvhvcxlLYWTWzEBoJOFTdgYrmHrWb4xdZlvH/Pi3B4kc+x4Lff4bV2ysgScD9q6YiJV6ndvNCR6sHJq4Q6yUfD39/LN8eHIkZYpwbADSo1GXQ1OeYhJiZLCIiUkHUXqGVlZWhrq4Oy5Ytsz+Wnp6ORYsWYevWrbj++uvdPs9gMMBgcFSh6+joAACYTCaYTKbgNtoD5bhqHT8apMVrsHBMJraVteKjA9X47jljXH4frufYapXxwAdH8fouRwYuOV6Lv147C0sn54Zdez0J1PmVxi+H7uCbkEvWwLzkV0PfUXcj9D1NkCHBnDEOiJDz6Em4vX61uVOg6ayBue4Q5MJ5IT++VLoROnMv5NTCgP19w+0cRxue3+Di+Q0unt/gC6dz7GsbojbIqqsTldfy8/NdHs/Pz7f/zp1HHnkEDz/88IDHP/30UyQlqTuZ5bp161Q9fqQbCQmAFq9/VYKijiNutwm3c/zuaQ3W12ogQcY1Y60YlyojO96MvtKdWFOqduv8N9zzqzNbcDG00DQdx8Z3XkB3fP7gT3Ijp/MIzgbQHZ+Hz9dtGFabwkm4vH6nd8ZhAoDynWtxqDYn5MefUfUyxgMoj5uE/R8HIOvpJFzOcbTi+Q0unt/g4vkNvnA4xz09vvWIUjXI+vnPf45HH33U6zZHjx7FlClTQtQi4P7778e9995r/7mjowPFxcVYsWIF0tLSQtYOZyaTCevWrcPy5cuh1+tVaUM0mN/Rh7f+vAmnuyTMPXspCtMdE/iG4zn+6GAd1m89AAD48zUzcUUEj78K6PntXA2c/hIXjDTBumDVkHah2VkFnASSxpyBVauGto9wEm6vX2lfK/DRWoxN7sMoFc6v7klxo2zkkm9hxJTAHD/cznG04fkNLp7f4OL5Db5wOsdKL7fBqBpk3Xfffbj11lu9bjNu3Lgh7bugQJTsra+vR2Fhof3x+vp6zJkzx+Pz4uPjER8fP+BxvV6v+h81HNoQyUZm6zF/dCZ2l7diw4lm3LJ4zIBtwuUc7zrdgl+8K+b4uf388fjagtEqtygwAnJ+xy8FTn8JbcUWaM+6c2j7aBJjhTT506EJg793oITL6xeFYuyspvFY6M9vyymgpRTQ6KCbeCEQ4OOHzTmOUjy/wcXzG1w8v8EXDufY1+OrGmTl5uYiNzc3KPseO3YsCgoK8Pnnn9uDqo6ODmzfvt2vCoUUXZZNzRdBVkmj2yArHHx0oBY/fmMfjGYrzp6QjZ+smKR2k8LL2PPE8vSXopS7Ruv/Pli+PbiUCoPdDUB3M5CcHbpjn/hMLIvPBBLU6X1AREQUMdUFKyoqsG/fPlRUVMBisWDfvn3Yt28furq67NtMmTIF77zzDgAxCe0999yD3/3ud3j//fdx8OBB3HLLLSgqKsKVV16p0v+C1LZksgjqt5Q2oc8UgLmWhqm+ow+dfSbIsoxTjV24+9W9uHP1HhjNViybmodnb1kAnTZi3qahUTgHiEsV5cHrDvr/fFl2BFn50wPaNLKJTwEybHO4NYZ4vqySNWI5cZn37YiIiIIoYgpfPPDAA3jppZfsP8+dOxcAsH79eixZsgQAUFJSgvZ2x7wsP/3pT9Hd3Y3vfe97aGtrwznnnIO1a9ciISEBFJumFKSiIC0BdR192F7WgvMnBSeTOpiGjj785sMj+PBALQAgM0mP1h5RrUYjAbedOw7/t3IyAyx3tDpg9FnAiU9ENqtojn/Pb6sAjF2ANg7IGlp3ZPJB3jRxruuPAGPOCc0x26uAUxvE+rQrQnNMIiIiNyLmCu7FF1+ELMsD/ikBFiDmE3Ie4yVJEn7zm9+grq4OfX19+OyzzzBpErtexTJJkuyB1YaSBlXa0NptxCWPb7YHWADQ2mOCXivhnAk5eP+uc3D/qqkMsLxRugyWbfL/uUoWK2eSmHuLgkPJEtYfCt0x978KQAZGn80AmoiIVBUxmSyiQLlgSi5e31WJDSWNePCy0B//9V2VaOw0YFRWEv514zwUpiegqrUXk/JTkRg3hPFFsWjsuWJZvhWwmEV2y1cNtvL9HI8VXPYg63BojifLwL7VYn3OjaE5JhERkQe8VU4x5+wJOdBpJJQ1deNUY9fgTwggi1XGy9vKAQB3XTABM0akIzslHrOLMxhg+SN/JpCQARg7gdp9/j3XXvRiWqBbRc7yRYVBNBwRBUqCrWKrqCwYl8KugkREpDoGWRRzUhP0WDxeVDv7+JDniamDYUNJA6pae5GeqMdlsyN33ivVaTSOcT5lG/17bu1+sWSQFVxZ4wBdAmDqAVpPB/94B94Qy2lXisIbREREKmKQRTHp0lli7rQP9teE9Lj/2SqyWF9fUMzM1XDZx2V96ftzelqAphKxXrww8G0iB60OyLVNJB/scVkWM3D0A7E+85rgHouIiMgHDLIoJq2cXgCdRsKxuk6cbAhNl8Ha9l5sOtEIALhx0aiQHDOqjbGNy6rYBpgNvj2nYqtY5k4BkrKC0y5yKJghlnVBDrLKNwM9TUBiFjDmvOAei4iIyAcMsigmZSTF4ZyJOQDE5L+h8N6+GsgysHBMFkZnJ4fkmFEtbyqQlAOYe4Hq3b49RwmyRp0ZvHaRQ74tyAp28YvDYn5ETLvcvyIoREREQcIgi2LWJTNtXQYP1ECW5aAeS5ZlvL2nCgBw1bwRQT1WzJAkR5VBX7sMlitB1uLgtIlc2YOsIUwa7SuLGTjyvlifflXwjkNEROQHBlkUs1ZML0CCXoOTDV3YXdEW1GMdrunA8fouxOk0WGUL7igAlC6DvsyXZexxVCJkkBUaShn3tgqgr937tkNV/hXQ2wIkZQOjQzTpMRER0SAYZFHMSk/U44rZIqv08vbKoB7rnb3VAIDlU/ORnsgJcANm7PliWbUDMPV637Z6F2A1A6lFQAbHxIVEUhaQXizWlaqOgXbyM7GcuIJdBYmIKGwwyKKYdvPi0QCAT4/Uo8MYnGPIsoy1tlLxl89h2faAyh4PpBYCFiNQucP7tuVO47EkKfhtI2HEfLGs2hWc/Zd+IZbjLwzO/omIiIaAQRbFtBkj0jF3VAZMFhlb6oNz4X20thPVbb1I0Gtw3sTcoBwjZkmSUyn3QboMKhkPZRwXhYYSZPlanMQfnXW28vASMP6CwO+fiIhoiBhkUcy79awxAIDPazSoaRuky9kQrDtSDwA4Z0Iu58YKBmVc1mkvxS+6m4GqnWJ94orgt4kcRp4hlsEIspQsVtEcIDkn8PsnIiIaIgZZFPMum1WE+aMyYLRKeOjDowGvNLjuqOgquGJafkD3SzZKZqp6N2DwMOfZyc8AyKLaXfrIkDWNABTOBiQt0FkLdAR48u+Tn4sluwoSEVGYYZBFMU+jkfDbK6ZBK8lYX9KET22Zp0CoaevFoeoOSBKwdGpewPZLTjLHiEIWVrOYmNidE5+KJbNYoReXDORNE+uBHJdltTgyWRMYZBERUXhhkEUEYGJeCi4oFBmsxz4/EbBs1gf7xZ37+aMykZMSH5B9khtjlHFZGwf+zmJ2jMeatDJ0bSKHkUEYl1W7T5Ruj0sFRi4I3H6JiIgCgEEWkc3SIiuS4rQ4XNOBDccbh72/PpMFz24uAwBcewa7qAXVuCVieexDoH+AfPpLoK8NSMgARpwR4oYRAMd5D2SQddKWxRp3PqDltAhERBReGGQR2STrgettwdC/1p8c9v7e3F2Fxk4DitITcNVcBllBNWUVEJcCtJwCKrY6Hrdagc8eEuszruE8SmpRMk1VuwBTX2D2WaqMx1oamP0REREFEIMsIiffPns04rQa7Dzdijd2DX2CYpPFiqc3lgIAvnfeOMTp+FYLqrhkYMbVYn3vy47H978qupXFpwFLfq5K0whA7mQgpQAw9wKV24e/v752x7xoHI9FRERhiFd+RE7y0xLwgwvGAwB++c5BbDvVPKT9vLTlNKpae5GTEofrF44KZBPJkzk3ieXhdwBDJ9BwFPjsQfHYef8HpLDwiGokydGl89T64e+vbBMgW4Cs8aLwCRERUZhhkEXUzw+XTsSlswphssi4/eXdqGzp8ev5DR19+PtnJwAA/7dyMhL0nBsrJIoXAtkTAVMP8NwK4LmVQHcjkDcdWHS72q0jZbLg0gAEWUrpdmaxiIgoTDHIIupHo5Hwl2tnY/bIdLT1mHDHK7vRZ7L4/Pw/rDmKLoMZc4ozcO384iC2lFxIErDsIUAbBzQcAQztwKjFwK0fAro4tVtHSiardj/Q0zL0/VitwPG1Yn3CsmE3i4iIKBgYZBG5kaDX4l83zUdmkh6Hqjvwg1f2oKXbOOjzShu78O6+GkgS8JsrpkOjkULQWrKbeilwXwlw6d+ACx8Abn4XSMpSu1UEAKkFtvmyZODUhqHvp2qHmNg4Ps0RuBEREYUZBllEHozISMTj35gHvVbCF8casPLvm/DRgVqvc2g9+6Uo2X7hlHzMGpkRopaSi6Qs4IxvA+feB+gT1G4NORtn6zKoTA49FIffFcvJFwM6zj1HREThiUEWkRfnTMzBOz84GxPyUtDYacCdq/fg+me2oanLMGDbpi4D3t5TBUBUFCSifqZdLpaH3xlal0GrFTj6vm1fVwSuXURERAHGIItoEDNGpOPDu8/Bjy6ciAS9BtvLWnDLczvQ3mty2e6Fr8pgMFsxe2Q6FozJVKm1RGGseBGQPxMw97mW2vfGanWsV+8COqqBuFRgPIteEBFR+GKQReSDBL0WP14+CR/98FzkpMTjSG0HLn9iMx56/zC2n2rG5hNNeGrjKQDA988fD0niWCyiASQJWHibWN/1HGAdpKBMTwvw5FnAY/OA/a8B798tHp98EbuCEhFRWGOQReSH8bkp+O93FiIjSY/y5h68uOU0vv7MNtz6wg5YrDKunjcCF88oULuZROFr5rVAQjrQelp0G/REloEPfgQ0HgVaSoF3vg80HgNSC4Hzfxay5hIREQ0FgywiP00tTMMX9y3BY9+Yi+vOGIk4nQZmq4zZI9Pxh6tmMotF5E1cErDw+2L9wx8DzaXut9v3ihh/pdEBs28AIAFFc4HbvgByJoasuUREREOhU7sBRJEoKzkOl88uwuWzi/CTFZOxoaQRK6cXcOJhIl+c/1Pg9JdAxVbg9ZuBb68FEtIcv+9qANb+Qqxf8Evg3HuBlb8HEjIADe8NEhFR+OO3FdEw5aUl4LoFxUhP0qvdFKLIoNUD174IJOcBDYeB1V8HjD2O33/2kJhMunA2cPaPxGNJWQywiIgoYvAbi4iIQi+1ALjxDTGpcMUW4PmVwO4XgS//n+gqCACX/BXQMDtMRESRh90FiYhIHUVzgRvfAl6+Gqg7IApdKObeDIw8Q722ERERDQODLCIiUs+oRcDdu8W8Wcc+AlLygDHnAAtuU7tlREREQ8Ygi4iI1JVaAJz3E/GPiIgoCnBMFhERERERUQAxyCIiIiIiIgogBllEREREREQBxCCLiIiIiIgogBhkERERERERBRCDLCIiIiIiogCKmCDr97//Pc466ywkJSUhIyPDp+fceuutkCTJ5d9FF10U3IYSEREREVFMi5h5soxGI6699losXrwYzz33nM/Pu+iii/DCCy/Yf46Pjw9G84iIiIiIiABEUJD18MMPAwBefPFFv54XHx+PgoKCILSIiIiIiIhooIgJsoZqw4YNyMvLQ2ZmJpYuXYrf/e53yM7O9ri9wWCAwWCw/9zR0QEAMJlMMJlMQW+vO8px1Tp+LOA5Di6e3+Di+Q0+nuPg4vkNLp7f4OL5Db5wOse+tkGSZVkOclsC6sUXX8Q999yDtra2Qbd97bXXkJSUhLFjx6K0tBS/+MUvkJKSgq1bt0Kr1bp9zkMPPWTPmjlbvXo1kpKShtt8IiIiIiKKUD09PbjhhhvQ3t6OtLQ0j9upGmT9/Oc/x6OPPup1m6NHj2LKlCn2n/0Jsvo7deoUxo8fj88++wwXXnih223cZbKKi4vR1NTk9UQGk8lkwrp167B8+XLo9XpV2hDteI6Di+c3uHh+g4/nOLh4foOL5ze4eH6DL5zOcUdHB3JycgYNslTtLnjffffh1ltv9brNuHHjAna8cePGIScnBydPnvQYZMXHx7stjqHX61X/o4ZDG6Idz3Fw8fwGF89v8PEcBxfPb3Dx/AYXz2/whcM59vX4qgZZubm5yM3NDdnxqqqq0NzcjMLCQp+foyT6lLFZajCZTOjp6UFHR4fqL6xoxXMcXDy/wcXzG3w8x8HF8xtcPL/BxfMbfOF0jpWYYLDOgBFT+KKiogItLS2oqKiAxWLBvn37AAATJkxASkoKAGDKlCl45JFHcNVVV6GrqwsPP/wwrrnmGhQUFKC0tBQ//elPMWHCBKxcudLn43Z2dgIAiouLA/5/IiIiIiKiyNPZ2Yn09HSPv4+YIOuBBx7ASy+9ZP957ty5AID169djyZIlAICSkhK0t7cDALRaLQ4cOICXXnoJbW1tKCoqwooVK/Db3/7Wr7myioqKUFlZidTUVEiSFLj/kB+UcWGVlZWqjQuLdjzHwcXzG1w8v8HHcxxcPL/BxfMbXDy/wRdO51iWZXR2dqKoqMjrdhFXXTAWdXR0ID09fdABdjR0PMfBxfMbXDy/wcdzHFw8v8HF8xtcPL/BF4nnWKN2A4iIiIiIiKIJgywiIiIiIqIAYpAVAeLj4/Hggw/6NZaM/MNzHFw8v8HF8xt8PMfBxfMbXDy/wcXzG3yReI45JouIiIiIiCiAmMkiIiIiIiIKIAZZREREREREAcQgi4iIiIiIKIAYZBEREREREQUQg6ww8c9//hNjxoxBQkICFi1ahB07dnjd/s0338SUKVOQkJCAmTNnYs2aNSFqaeR55JFHsGDBAqSmpiIvLw9XXnklSkpKvD7nxRdfhCRJLv8SEhJC1OLI8tBDDw04V1OmTPH6HL5+fTdmzJgB51eSJNx5551ut+drd3CbNm3CZZddhqKiIkiShHfffdfl97Is44EHHkBhYSESExOxbNkynDhxYtD9+vs5Hq28nV+TyYSf/exnmDlzJpKTk1FUVIRbbrkFNTU1Xvc5lM+ZaDXY6/fWW28dcK4uuuiiQffL16/DYOfY3WeyJEn485//7HGffA0LvlyT9fX14c4770R2djZSUlJwzTXXoL6+3ut+h/q5HUwMssLA66+/jnvvvRcPPvgg9uzZg9mzZ2PlypVoaGhwu/2WLVvwjW98A9/5znewd+9eXHnllbjyyitx6NChELc8MmzcuBF33nkntm3bhnXr1sFkMmHFihXo7u72+ry0tDTU1tba/5WXl4eoxZFn+vTpLudq8+bNHrfl69c/O3fudDm369atAwBce+21Hp/D16533d3dmD17Nv75z3+6/f2f/vQnPPbYY3jqqaewfft2JCcnY+XKlejr6/O4T38/x6OZt/Pb09ODPXv24Ne//jX27NmDt99+GyUlJbj88ssH3a8/nzPRbLDXLwBcdNFFLufq1Vdf9bpPvn5dDXaOnc9tbW0tnn/+eUiShGuuucbrfvka9u2a7Mc//jE++OADvPnmm9i4cSNqampw9dVXe93vUD63g04m1S1cuFC+88477T9bLBa5qKhIfuSRR9xuf91118mXXHKJy2OLFi2Sv//97we1ndGioaFBBiBv3LjR4zYvvPCCnJ6eHrpGRbAHH3xQnj17ts/b8/U7PD/60Y/k8ePHy1ar1e3v+dr1DwD5nXfesf9stVrlgoIC+c9//rP9sba2Njk+Pl5+9dVXPe7H38/xWNH//LqzY8cOGYBcXl7ucRt/P2dihbvz+81vflO+4oor/NoPX7+e+fIavuKKK+SlS5d63YavYff6X5O1tbXJer1efvPNN+3bHD16VAYgb9261e0+hvq5HWzMZKnMaDRi9+7dWLZsmf0xjUaDZcuWYevWrW6fs3XrVpftAWDlypUetydX7e3tAICsrCyv23V1dWH06NEoLi7GFVdcgcOHD4eieRHpxIkTKCoqwrhx43DjjTeioqLC47Z8/Q6d0WjEyy+/jG9/+9uQJMnjdnztDl1ZWRnq6upcXqPp6elYtGiRx9foUD7HyaG9vR2SJCEjI8Prdv58zsS6DRs2IC8vD5MnT8Ydd9yB5uZmj9vy9Ts89fX1+Oijj/Cd73xn0G35Gh6o/zXZ7t27YTKZXF6PU6ZMwahRozy+HofyuR0KDLJU1tTUBIvFgvz8fJfH8/PzUVdX5/Y5dXV1fm1PDlarFffccw/OPvtszJgxw+N2kydPxvPPP4/33nsPL7/8MqxWK8466yxUVVWFsLWRYdGiRXjxxRexdu1aPPnkkygrK8O5556Lzs5Ot9vz9Tt07777Ltra2nDrrbd63Iav3eFRXof+vEaH8jlOQl9fH372s5/hG9/4BtLS0jxu5+/nTCy76KKL8J///Aeff/45Hn30UWzcuBEXX3wxLBaL2+35+h2el156CampqYN2Z+NreCB312R1dXWIi4sbcNNlsOtiZRtfnxMKOtWOTKSCO++8E4cOHRq0H/TixYuxePFi+89nnXUWpk6diqeffhq//e1vg93MiHLxxRfb12fNmoVFixZh9OjReOONN3y6s0e+e+6553DxxRejqKjI4zZ87VKkMJlMuO666yDLMp588kmv2/JzxnfXX3+9fX3mzJmYNWsWxo8fjw0bNuDCCy9UsWXR6fnnn8eNN944aIEhvoYH8vWaLFIxk6WynJwcaLXaAVVT6uvrUVBQ4PY5BQUFfm1Pwl133YUPP/wQ69evx8iRI/16rl6vx9y5c3Hy5MkgtS56ZGRkYNKkSR7PFV+/Q1NeXo7PPvsM3/3ud/16Hl+7/lFeh/68RofyOR7rlACrvLwc69at85rFcmewzxlyGDduHHJycjyeK75+h+7LL79ESUmJ35/LAF/Dnq7JCgoKYDQa0dbW5rL9YNfFyja+PicUGGSpLC4uDvPnz8fnn39uf8xqteLzzz93uRvtbPHixS7bA8C6des8bh/rZFnGXXfdhXfeeQdffPEFxo4d6/c+LBYLDh48iMLCwiC0MLp0dXWhtLTU47ni63doXnjhBeTl5eGSSy7x63l87fpn7NixKCgocHmNdnR0YPv27R5fo0P5HI9lSoB14sQJfPbZZ8jOzvZ7H4N9zpBDVVUVmpubPZ4rvn6H7rnnnsP8+fMxe/Zsv58bq6/hwa7J5s+fD71e7/J6LCkpQUVFhcfX41A+t0NCtZIbZPfaa6/J8fHx8osvvigfOXJE/t73vidnZGTIdXV1sizL8s033yz//Oc/t2//1VdfyTqdTv7LX/4iHz16VH7wwQdlvV4vHzx4UK3/Qli744475PT0dHnDhg1ybW2t/V9PT499m/7n+OGHH5Y/+eQTubS0VN69e7d8/fXXywkJCfLhw4fV+C+Etfvuu0/esGGDXFZWJn/11VfysmXL5JycHLmhoUGWZb5+A8FiscijRo2Sf/aznw34HV+7/uvs7JT37t0r7927VwYg//Wvf5X37t1rr273xz/+Uc7IyJDfe+89+cCBA/IVV1whjx07Vu7t7bXvY+nSpfLjjz9u/3mwz/FY4u38Go1G+fLLL5dHjhwp79u3z+Uz2WAw2PfR//wO9jkTS7yd387OTvknP/mJvHXrVrmsrEz+7LPP5Hnz5skTJ06U+/r67Pvg69e7wT4jZFmW29vb5aSkJPnJJ590uw++ht3z5Zrs9ttvl0eNGiV/8cUX8q5du+TFixfLixcvdtnP5MmT5bffftv+sy+f26HGICtMPP744/KoUaPkuLg4eeHChfK2bdvsvzv//PPlb37zmy7bv/HGG/KkSZPkuLg4efr06fJHH30U4hZHDgBu/73wwgv2bfqf43vuucf+98jPz5dXrVol79mzJ/SNjwBf//rX5cLCQjkuLk4eMWKE/PWvf10+efKk/fd8/Q7fJ598IgOQS0pKBvyOr13/rV+/3u1ngnIerVar/Otf/1rOz8+X4+Pj5QsvvHDAuR89erT84IMPujzm7XM8lng7v2VlZR4/k9evX2/fR//zO9jnTCzxdn57enrkFStWyLm5ubJer5dHjx4t33bbbQOCJb5+vRvsM0KWZfnpp5+WExMT5ba2Nrf74GvYPV+uyXp7e+Uf/OAHcmZmppyUlCRfddVVcm1t7YD9OD/Hl8/tUJNkWZaDkyMjIiIiIiKKPRyTRUREREREFEAMsoiIiIiIiAKIQRYREREREVEAMcgiIiIiIiIKIAZZREREREREAcQgi4iIiIiIKIAYZBEREREREQUQgywiIiIAt956K6688kq1m0FERFFAp3YDiIiIgk2SJK+/f/DBB/GPf/wDsiyHqEVERBTNGGQREVHUq62tta+//vrreOCBB1BSUmJ/LCUlBSkpKWo0jYiIohC7CxIRUdQrKCiw/0tPT4ckSS6PpaSkDOguuGTJEtx999245557kJmZifz8fPz73/9Gd3c3vvWtbyE1NRUTJkzAxx9/7HKsQ4cO4eKLL0ZKSgry8/Nx8803o6mpKcT/YyIiUhODLCIiIg9eeukl5OTkYMeOHbj77rtxxx134Nprr8VZZ52FPXv2YMWKFbj55pvR09MDAGhra8PSpUsxd+5c7Nq1C2vXrkV9fT2uu+46lf8nREQUSgyyiIiIPJg9ezZ+9atfYeLEibj//vuRkJCAnJwc3HbbbZg4cSIeeOABNDc348CBAwCAJ554AnPnzsUf/vAHTJkyBXPnzsXzzz+P9evX4/jx4yr/b4iIKFQ4JouIiMiDWbNm2de1Wi2ys7Mxc+ZM+2P5+fkAgIaGBgDA/v37sX79erfju0pLSzFp0qQgt5iIiMIBgywiIiIP9Hq9y8+SJLk8plQttFqtAICuri5cdtllePTRRwfsq7CwMIgtJSKicMIgi4iIKEDmzZuH//3vfxgzZgx0On7FEhHFKo7JIiIiCpA777wTLS0t+MY3voGdO3eitLQUn3zyCb71rW/BYrGo3TwiIgoRBllEREQBUlRUhK+++goWiwUrVqzAzJkzcc899yAjIwMaDb9yiYhihSRzensiIiIiIqKA4W01IiIiIiKiAGKQRUREREREFEAMsoiIiIiIiAKIQRYREREREVEAMcgiIiIiIiIKIAZZREREREREAcQgi4iIiIiIKIAYZBEREREREQUQgywiIiIiIqIAYpBFREREREQUQAyyiIiIiIiIAohBFhERERERUQAxyCIiIiIiIgogBllEREREREQBxCCLiIiIiIgogBhkERERERERBRCDLCIiIiIiogBikEVERERERBRADLKIiIiIiIgCiEEWERERERFRADHIIiIiIiIiCiAGWURERERERAHEIIuIiIiIiCiAGGQREREREREFEIMsIiIiIiKiAGKQRUREREREFEAMsoiIiIiIiAKIQRYREREREVEA6dRuQLizWq2oqalBamoqJElSuzlERERERKQSWZbR2dmJoqIiaDSe81UMsgZRU1OD4uJitZtBRERERERhorKyEiNHjvT4ewZZg0hNTQUgTmRaWpoqbTCZTPj000+xYsUK6PV6VdoQ7XiOg4vnN7h4foOP5zi4eH6Di+c3uHh+gy+cznFHRweKi4vtMYInDLIGoXQRTEtLUzXISkpKQlpamuovrGjFcxxcPL/BxfMbfDzHwcXzG1w8v8HF8xt84XiOBxtGxMIXREREREREAcQgi4iIiIiIKIAYZBEREREREQUQx2QREREREREAUaLcbDbDYrGo3RQ7k8kEnU6Hvr6+oLdLq9VCp9MNe+omBllERERERASj0Yja2lr09PSo3RQXsiyjoKAAlZWVIZm3NikpCYWFhYiLixvyPhhkERERERHFOKvVirKyMmi1WhQVFSEuLi4kAY0vrFYrurq6kJKS4nUC4OGSZRlGoxGNjY0oKyvDxIkTh3w8BllERERERDHOaDTCarWiuLgYSUlJajfHhdVqhdFoREJCQlCDLABITEyEXq9HeXm5/ZhDwcIXREREREQEAEEPYiJBIM4BzyIREREREVEAMcgiIiIiIiIKIAZZREREREREAcQgi4jIk7JNQHOp2q0gIiKiIaqtrcUNN9yASZMmQaPR4J577gnJcRlkERG501oOvHQZ8PpNareEiIiIhshgMCA3Nxe/+tWvMHv27JAdlyXciYjcaa8Sy5YyddtBRESkAlmW0WuyqHLsRL3W5zm6nnnmGTz00EOoqqpyqQp4xRVXIDs7G88//zz+8Y9/AACef/75oLTXHQZZRETuGDrE0twLmHoBfaK67SEiIgqhXpMF0x74RJVjH/nNSiTF+RamXHvttbj77ruxfv16XHjhhQCAlpYWrF27FmvWrAlmM71id0EiIncMnY713lax7GoEPv8N8OG9gFWdu3tERETkkJmZiYsvvhirV6+2P/bWW28hJycHF1xwgWrtYiaLiMidvnbHek8LULULeOf7gKlHPDbnRmDkfHXaRkREFGSJei2O/Galasf2x4033ojbbrsN//rXvxAfH49XXnkF119/vaoTKzPIIiJyR+kuCAC9LcD+Vx0BFgB01gBgkEVERNFJkiSfu+yp7bLLLoMsy/joo4+wYMECfPnll/jb3/6mapsi48wREYVa/+6CXfW2HyQAMtBZp0ariIiIqJ+EhARcffXVeOWVV3Dy5ElMnjwZ8+bNU7VNDLKIiNzpc8pk9bSI8VgAUDATqDvAIIuIiCiM3Hjjjbj00ktx+PBh3HST6/Qr+/btAwB0dXWhsbER+/btQ1xcHKZNmxa09jDIIiJyxyWT1QJ0N4j1wlkiyOpikEVERBQuli5diqysLJSUlOCGG25w+d3cuXPt67t378bq1asxevRonD59OmjtYZBFROSO85istgrA3CfWC2aJJTNZREREYUOj0aCmpsbt72RZDnFrWMKdiMg950xWY4lY6pOBrHFivbN+4HOIiIiIwCCLiMg95zFZjcfEMiUXSC0Q6521oW8TERERRQQGWURE7hic5slSJiNOyQdSbEFWTxNgMYW+XURERBT2GGQREbnj3F1QkZwLJGUDGttw1q6G0LaJiIiIIgKDLCKi/mTZtbugIiUP0GhERgtg8QsiIiJyi0EWEVF/pl5Atgx8PDlPLJVxWSzjTkRERG4wyCIi6s/gJosFiMIXgGNcFotfEBERkRsMsoiI+lPGYyWkA7pEx+P9M1ks405ERERuMMgiIupPGY8Vnw4kZjoeT+kfZDGTRURERAMxyCIi6k/pLhifCiRlOR5PtnUXtI/JYiaLiIgonL399ttYvnw5cnNzkZaWhsWLF+OTTz4J+nEZZBER9acEWQlp7jNZHJNFREQUETZt2oTly5djzZo12L17Ny644AJcdtll2Lt3b1CPqwvq3omIIlGfUyZLbxuTpUsE4lLEOsdkERERhYVnnnkGDz30EKqqqqDROPJHV1xxBbKzs/H888+7bP+HP/wB7733Hj744APMnTs3aO1ikEVE1J9S+CI+DYi3BVYpeYAk2dZt82T1NAFWC6DRhr6NREREwSTLgKlHnWPrkxzfuYO49tprcffdd2P9+vW48MILAQAtLS1Yu3Yt1qxZM2B7q9WKzs5OZGVlDfhdIDHIIiLqz3lMltJdUOkqCDjGaclWoLcVSM4JbfuIiIiCzdQD/KFInWP/ogaIS/Zp08zMTFx88cVYvXq1Pch66623kJOTgwsuuGDA9n/5y1/Q1dWF6667LqBN7i+qx2Q9+eSTmDVrFtLS0uwD3T7++GO1m0VE4c5ewj0NSMoW60r2CgC0ekfw1d0U2rYRERGRixtvvBH/+9//YDAYAACvvPIKrr/+epfugwCwevVqPPzww3jjjTeQl5fnblcBE9WZrJEjR+KPf/wjJk6cCFmW8dJLL+GKK67A3r17MX36dLWbR0Thqq9dLOPTgGmXA2UbgQXfdd0mOVdksbobAUwJeROJiIiCSp8kMkpqHdsPl112GWRZxkcffYQFCxbgyy+/xN/+9jeXbV577TV897vfxZtvvolly5YFsrVuRXWQddlll7n8/Pvf/x5PPvkktm3bxiCLiDxzHpOVMQq48c2B2yTlADguxmURERFFG0nyucue2hISEnD11VfjlVdewcmTJzF58mTMmzfP/vtXX30V3/72t/Haa6/hkksuCUmbojrIcmaxWPDmm2+iu7sbixcvVrs5RBTOnEu4e6KMw2J3QSIiItXdeOONuPTSS3H48GHcdNNN9sdXr16Nb37zm/jHP/6BRYsWoa6uDgCQmJiI9PT0oLUn6oOsgwcPYvHixejr60NKSgreeecdTJs2zeP2BoPB3p8TADo6xMWWyWSCyWQKenvdUY6r1vFjAc9xcEXa+dX2tkMDwKxNhOyhzZrELGgBWDrqYFX5/xVp5zcS8RwHF89vcPH8Ble0nF+TyQRZlmG1WmG1WtVujgtZlu1LT21bsmQJsrKyUFJSguuvv96+3TPPPAOz2Yw777wTd955p337W265BS+88ILbfVmtVsiyDJPJBK3WtYKwr39nSVZaHaWMRiMqKirQ3t6Ot956C88++yw2btzoMdB66KGH8PDDDw94fPXq1UhK8q9/KBFFpqVHf47UvhpsnnA/mlOnut1mcu3bmFL3LspyluJA8a2hbSAREVGA6XQ6FBQUoLi4GHFxcWo3R1VGoxGVlZWoq6uD2Wx2+V1PTw9uuOEGtLe3Iy3Nc4+XqA+y+lu2bBnGjx+Pp59+2u3v3WWyiouL0dTU5PVEBpPJZMK6deuwfPly6PV6VdoQ7XiOgyvSzq/uHzMgddXB9O3PgcLZbrfR7HwW2k9/DuuUy2C5xv2dsFCJtPMbiXiOg4vnN7h4foMrWs5vX18fKisrMWbMGCQkJKjdHBeyLKOzsxOpqamQfJw/azj6+vpw+vRpFBcXDzgXHR0dyMnJGTTIivrugv1ZrVaXIKq/+Ph4xMfHD3hcr9er/sYJhzZEO57j4IqI8yvL9jFZ+pQswFN700RJd01vCzRh8n+KiPMb4XiOg4vnN7h4foMr0s+vxWKBJEnQaDQDSp+rTen6p7Qv2DQaDSRJcvs39fVvHNVB1v3334+LL74Yo0aNQmdnJ1avXo0NGzbgk08+UbtpRBSuuptsM9xLQNoIz9vZC180hqRZREREFDmiOshqaGjALbfcgtraWqSnp2PWrFn45JNPsHz5crWbRkThqrVMLNNGALqBWW275FyxZHVBIiIi6ieqg6znnntO7SYQUaRpsQVZWWO9b5dky2T1tgAWM6CN6o9TIiIi8kN4dbgkIlKbksnKHON9u6QsALbBt70twWwRERFRyMRYTTy3AnEOGGQRETnzNZOl0QJJ2WKd47KIiCjCKQUdenp6VG6J+pRzMJxCJuzfQkTkzJ7JGiTIAkTxi54m7+Oy2iqBbU8Ci743eHaMiIhIJVqtFhkZGWhoaAAAJCUlhaRcui+sViuMRiP6+vqCWl1QlmX09PSgoaEBGRkZAyYi9geDLCIiZ75msgBR/KLxmPdM1s5/A9v+KTJfK34bmDYSEREFQUFBAQDYA61wIcsyent7kZiYGJLALyMjw34uhopBFhGRwtAFdNu+WHzJZCndBXuaPW/TWj74NkRERGFAkiQUFhYiLy8PJpNJ7ebYmUwmbNq0Ceedd17Q5yLT6/XDymApGGQRESlaT4tlYiaQmDH49vYy7l4yWR3VYtnXPpyWERERhYxWqw1IoBEoWq0WZrMZCQkJETPhMwtfEBEp/BmPBThNSOxlTFY7gywiIqJYwyCLiEjhz3gswCnI8pDJspiBrjqx3tc2rKYRERFR5GCQRUSk8DeTpUxI3ONhnqzOWkC2inUlk7XpL8Cnvx56G4mIiCjscUwWEZGirUIsfS21PljhC2U8FgD0dQBmA/DF7wDIwJl3AGlFQ20pERERhTFmsoiIFMrYqpQ837YfLMhqr3KsGzps+7fNIt9RO6QmUphpLAGeOhcoWat2S4iIKIwwyCIiUvTauv0lZvm2vRJk9bYAVuvA3ztnsmSrI1MGiK6EFPmOvAfUHQA+/SUgy2q3hoiIwgSDLCIihTK2KsnXIMu2nWx1X9iivdr155ZTjnUGWdGht1Usm08C5VvUbQsREYUNBllERABgNgLGLrHua5Cl1QPx6WLdXfGLDm9BVp3/baTwowRZALDnJfXaQUREYYVBFhER4OgqKGkcgZMvlIDM3bgsBlnRzznIOvKe689ERBSzGGQREQGOICkxE9D48dHorfiF0l1QlyCW7C4YfZyDKnMfcPwT9dpCRERhg0EWERHgNB4r27/necpkmQ1Ad4NYz50slspkxwAzWdFCCbIyRomlc0VJIiKKWQyyiIgA/ysLKjxlsjpqxFKXAGSNE+uGdsfvmcmKDr1tYpk7RSy7G1VrChERhQ8GWUREgP+VBRUegyxbV8G0IiAhY+DzeltEtosilyw7Mlk5k8Syq1699hARUdhgkEVEBDiNyfI3yFK6C/arLthwVCyzxgEJae6fyy6Dkc3YDVhNYl3pEtrVoF57iIgobDDIIiICHBmJQGWyavaKZdE8IMFDtUIGWZFNec1o44DMMWKdQRYREYFBFhGRMNzugr39Mln2IGvuwCArtUgsOS4rsilBVmImkFIg1hlkERERGGQREQmBLHxh7AYaj4n1EfMGjsnKny6WzGRFtr42sUzMBFJyxbqhHTD1qdYkIiIKDwyyiIgAR5AUiO6CtfsB2SoyVqkFrpksbTyQPV6sM5MVUfTmTkinNgBWi3jAOZOVkCG6DQKO0v1ERBSzdGo3gIgoLAx5niylu2AbcGwNULkNiE8VjxXNFUvnICspG0gtFOvMZEWUORXPQ3dwN1AwC7j0744gKyEDkCQgJR9orxRdBpV5s4iIKCYxyCIiAobeXTAhA4AEQAb+9x3A1ANobB+tIwYLspjJiiTJRtscWHUHgJevAs74jvg5MdO2Qa4tyGIZdyKiWMfugkREVotjUll/uwtqdUBihlg39dj2ZxZLt5msLNGFEHDMpUURQWfpcfzQ1w6UbxHrSpCVki+WLH5BRBTzGGQREfW2AZDFunLB7A/nLoZpI2wrkijfDgzMZGVPEOutpwGz0f/jkSr0SpCVMVosa/aIpT3IyhNLBllERDGP3QWJiJSugvHpgFbv//OTsoHmk2L98seA+sNAfJojK6ZLEEURLEbxWFoREJcKGDuBllNA3pTA/D8oeGQr9JZesV68CGgrF39PwJHJtAdZ7C5IRBTrmMkiIrIXvRhCFgtwZLISMoCx5wNn/wg441uO30uSI5uVlC1+zp0sflZKvVN4M3RBUrKdxQtdf9e/uyCrCxIRxTwGWURESvl1f4teKJJzxHLKpZ4zYfFpYqkEZLm27FVjydCOSaFlaAcAyLoEoHC26+8GZLIYZBERxTp2FySi2GbsFmOjAP+LXigW3S4moF3yc8/bKBfi9iCLmayI0ieCLMSnATmTXH9nry7I7oJERCQwyCKi2NVWCfxzEWDqFj/7O0eWIn86cM2/vW+z4LuAPgkYv1T8zExWRJGUICshXQTMqYWOEvwDCl80hrx9REQUXthdkIhiV8VWR4Cl0TkCoGCYcwNw64eObJmSyWo+AVjMwTsuBYYtyJKVbp/K3w8YOCbL1A0YukLYOCIiCjcMsogodikVAefeBPyiBph9feiOnV4sMlsWo6O7IoUvQ6dYKgVMlEwkJFGVEgDiUwB9sljnHGhERDEtqoOsRx55BAsWLEBqairy8vJw5ZVXoqSEXXOIyKbphFjmTAJ08aE9tkbjGNvDcVlhTzIo3QX7ZbIS0sXfUlE4SyzLvwpd44iIKOxEdZC1ceNG3Hnnndi2bRvWrVsHk8mEFStWoLu7W+2mEVE4UDJZ2RPVOb59XBaDrLBn7y5oy1oV2IKptCLX7cZfKJYnPw9Rw4iIKBxFdeGLtWvXuvz84osvIi8vD7t378Z5552nUquIKCzIMtBcKtazJ6jTBuW4LWXqHD/WlH0JbP0nsOpPQMYo/57rXPgCAEbMBy57DMif4brd+KXA+t8BZZvEWDttVH/NEhGRBzH16d/eLr4ks7I8l2k2GAwwGAz2nzs6OgAAJpMJJpMpuA30QDmuWsePBTzHwRWW57ejFnpTN2RJC3PqCECFtmni0qAFYO1tg2UYxw/L8xuGtFv/Bc3xj2EZuQDWxT/067lSbxsAwKJPgVU5z7NuEEvn8547HbrETEi9rTBXbIc8st/ExeQWX8PBxfMbXDy/wRdO59jXNkiyLMtBbktYsFqtuPzyy9HW1obNmzd73O6hhx7Cww8/PODx1atXIykpKZhNJKIQyuk8grNP/hFd8fn4fNqfVWnDyJavML/8aTSkTsfWCT9TpQ2x5PxjDyCj9zRO5l6EwyNv8Ou5C079A0Xtu7G/+FaczvFehfKMsicwom0HjhVciZLCq4fTZCIiCjM9PT244YYb0N7ejrS0NI/bxUwm684778ShQ4e8BlgAcP/99+Pee++1/9zR0YHi4mKsWLHC64kMJpPJhHXr1mH58uXQ6/WqtCHa8RwHVzieX83ueuAkkDRyJlatWqVKG6TjGqD8aeSkxg+rDeF4fsORruTHAIBx+SkY7ef51vz3aaAdmDJnEabN8v5caV8L8NEOTNJWYbxKr61Iw9dwcPH8BhfPb/CF0zlWerkNJiaCrLvuugsffvghNm3ahJEjR3rdNj4+HvHxA6uM6fV61f+o4dCGaMdzHFxhdX7bTgMANLmToVGrTclifiWNsSsgbQir8xtuTL1ATzMAQNPT5Pf5lg3iS1WbnAXdYM8de444TsMR9V5bEYqv4eDi+Q0unt/gC4dz7Ovxo7q6oCzLuOuuu/DOO+/giy++wNixY9VuEhGFC3tlwfHqtSE+VSyVOZgoeDpqHOvdTf4/X/kbxfvQoyG1QCxNPYCR1WyJiGJRVGey7rzzTqxevRrvvfceUlNTUVdXBwBIT09HYmKiyq0jIlU12+bIUquyIOAIsvp863pAw9Be6VjvavD/+bZ5smSluqA3cSmALhEw9wLdjUBcsv/HIyKiiBbVmawnn3wS7e3tWLJkCQoLC+3/Xn/9dbWbFltKvwCOrVG7FeGruRT49NdAW4XaLYkdreXiHwDkqDRHFuDIipi6AatFvXbEgvZqx3pPk3/nW5YdJdx9yWRJEpCcK9a7Gn0/DhERRY2ozmTFSOHE8NbXDqz+urigufeIoxsNOWx5HNj9ArDlMeDHR4D0EWq3KLrJMrDm/wDZAow5d+BksqGkZLIA0R0tMUO1pkS99irHumwFelqAlFzfnmvsgiRbxbovmSxA7Lu9AugeQtaMiIgiXlRnsigMlG8BLEZxQVuzT+3WhKfGEsf6fy4HTH3qtSUWHP0AOPEJoNEDl/xV3bbo4gGtrdAOx2UFV0eV68/dfmSYbFksi6QDdAm+PUfJZPlzHCIiihoMsijwyr4E/j4LOPEZULbJ8XjtfvXaFNacMq7NJ4Gqneo1JRZsf0osz/4hkDtJ3bYALH4RKu39gyw/Mky2IMukTRJdAX3B7oJERDGNQRYF3oHXgbZy4LOHgFMbHY8zyHKvt9X1Z1OPOu2IFV31YjlhmbrtUDDIcmtLaRMe//wErNYAdftWxmRpbL3k/Ql+bEGWWevHhPQpeWLJ7oJERDGJQRYFXkuZWNYfBBoOOx5nkOVeT4tYxtvGejDICi6lgIGvY2uCzR5kscKgswfeO4z/t+44Np8cQrn1/mQZ6LAFWXnTxHII3QVN/gRZ7C4YOyq2Af++EKjarXZLiCiMMMiiwGspdf05Y7RYdlQB3c2hb084k2VHJkspeGFkkBU0zlXiwibIslWrY5BlJ8syqlrF++BAVdvwd9jXBhi7xHrRHLH0q7ug+NsMKchid8Hod/BNoHoXcOgttVtCRGGEQRYFlrEb6Kx1fWzyxUCWbcLXOmazXBi7AatJrKfZgixmsoLH3CcKsQBhFGSxu2B/Hb1m9JlENb9D1QEIPpWugolZQMYosT6E7oLMZJFbyo2bocy/RkRRi0EWBZbSVTAxEyheJNYnrQQKZ4v12v2A1apO20KtrRIwdHnfptfWVVAbByRli3VTb3DbFcuUiyFJIyaMDQcMsgao63BU2DxY3T78HSpFL9JHAslDGCvVJSay9yvI4pis2KFMJs6AmoicMMiiwFK6CmaNA65fDXzzQ2D8UkeQtekvwO9ygb0vq9fGUOioAR6bC7zyNe/bKV0FE7OAONsFHIOs4OltE8uEdN+rxAVbgtJdkEGWwjnIqm7rRWu3cXg7VCb6Th/pFPz4eEF8+itgyxMAgI7EYt+PqQRzva2AxeT78yjyKDdvGGQRkRMGWRRYLafEMms8kJwDjD1X/DxinlgauwCrGTj+iTrtC5X6w6IboPMcWO4oRS8SMwG9EmR1B7dtsSzcxmMBzGS5UdfueqPhUM0ws1lK0Z386f6NlSrbBLz6DcBigHXSKpTlXOj7MRMzAUkr1rsDULyDwheDLCJyg0EWBVazUybL2ZhzgYseBWZdL37urAttu0Kto0YsDZ2i2IInSiYrKcspyGImK2jCOcjqY+ELRV27weXnYXcZrNkjlkVzncZKNXh+b8oysPWfwH+uBAztwKjFsFz5tOhm6iuNRtxoUo5F0UspWtPTDFgt6raFVHWgqh3v7K0afEOKCQyyKLCUMVnZ410flyTgzNuBM74tfu6K8iBLKf5hNQFmg+ft7N0FMwF9olhn4YvgCcsgi9UF+1O6C6YmiDmtDlW3o8dohuzthoUnxm6g8ZhYL5rn6C5oMbo/58Zu4H/fBT75BSBbxI2hm99xvD/9kexn10SKTMrnimx19E6gmHTvmwfx49f3o7RxkPHYFBMYZFFgtXjIZClSC8Sys857hifSOVdY9NYNTCl8kZjhyGTFWAn3v607jr9+Oki3ykDpaxPLsAqy2F2wv3pbkLVksghS1h6qw7QHPsED7x329jT36g6Ki9+UAiCtUARLcbZz7i6j/t5dohS3Rgdc/CfgqqeGFmABjkwWy7hHL4vZMT0AwIA6hllloKpN9ESpdxpXSrGLQRYFjnP59sGCLIvRkcWJRh3OQZaXDIVSiCFGC1+09Rjxj89P4LEvTqIhFF9K9kxWRvCP5SsGWQPUtYvXwrKpeUiK08Jqux/z6ZEhZMBr9opl0VzHY/m2CYk3/911W6sVOPGpWL9+NbDo+8MrkMIKg9Gv/+c7/9Yxq9sMWGwfVp19ZpVbQ+GAQRYFjnP59qQs99vo4sXvgegel9VZ41j3dvHstvBF7GSyqlodAeXJhhB0rwjr7oIMshTKXeCJeal4+wdn4Zmb59seN6Ctx89Kg+6CrBW/AyAB+1cDpesdjzefFFkJXSIwYdkw/gc2nCsr+vX1Gy/IIicxq8Ppo4lBFgEMsiiQ2ivFMmO09+1SC8Wy/6TF0aTD1+6CzoUvlDFZsZPJqmp1BJQnQ9GHnZmssGcwW9BsK9lekJ6AKQVpWDG9ACMzxfujpM7P81RtK3qhVDgFgOKFwMLvifU1P3F0XVaqEBbMBDTaof4XHPypZEiRaUCQxb91rOowObLenX2ctoEYZFEgKReJiRnet3MelxWNzAagx+lupi9BVowWvnDOZJ2oD0WQ1SaWYZXJYpDlrKFDFIqJ02mQmaS3Pz45X5ynkno/zlN3E9B8QqwXznH93dJfiXFXzScdN4hq94llUb9th4rdBaNf/+6CXfxbxypmsqg/BlkUOMpFYlyK9+2UTFa0VhjsHzz6VPgiE9Ani/UYCrKq29hd0N5d0NjJ8s9wVBYsSEuA5DQeanKBCLKO+ZrJ6moUJdgBIHcKkJLr+vuENJGxAoCqnWKpZLKUydOHi90Fox8zWWTT4ZS8YiaLAAZZFEhKhaXBgqyUfLGM1kzWgCDLW+ELJZMVq90FnYKskHYXDKcgK9WxbmTZX6XoRUFagsvjSpB13Ncg641bgPqDooz61553v83IBWJZtUsUvbAHWXP8bbZ77C4Y/RhkkU2H0bm7IDNZxCCLAslgu0CM9zGTFbVBVo3rz54yWbLcr7tg7JVwr3YKsho7DWjvDfLdv3AMsnTxgMbWLY5dBu1FL/LT3QdZJfWdg8+XZbUAldvE+s1vA/nT3W9nD7J2Aq1l4oaINh7InTzk9rtQgqyeJhHEUfRRJhFXeiIwyIpZzpmsDmayCAyyKJB8zWRF+5isjn4FPTxdOBs6AavtbpdL4QvfgyylXGykUgpfaGw3AIPeZTAcgyxJ4rgsJ45MVrzL4+NyUqDTSOjsM6Om3X25f6PZFsj0toq5sQDRVdCTkWeIZe1+oHKHWC+YAWj1np/jDyXIspod4wEpuiifKdnjxZJZy5jFTNYwdTUCH9wD1OxTuyUBwyCLAke5QBw0kxWlQVb9YeCrf4g74s48XTgrWSxdgm2CVFsmy2oCLIPfBdt8ogkzHvwEb+ysHEaj1dPRZ0KH7YtoTnEGAOBkQxCDDFkOzyALEOODAAZZACpaROA9IsN1AuA4nQbjc8VnS0ndwC6460saMP3BtXhle7mjjHZChveAKXMskJQj5u3b+k/xWKDGYwGALs5RyZIFEaKTPciaIJbdjY5qlRRTXDNZDLL8duh/wO4XgC2Pq92SgGGQRYFjz2Slet9OCbK66qLry2jt/cC6B4Cdz4mfk22VxTwGWU5FLwBHd0HAp3FZO063oNdkwQcHagbdNhwpXQUzk/SYNTIDQJAzWaYeR+Yw3IIseybLy/i9GFFqG5s3LnfgzZpJXopfbCxphMkiY9PxRkd1z+Qc7weTJEeXwfqDgKQBZnxt6I13h8UvopvynlWCLHMvYOxWrz2kGtfqguwu6LeeZrGMoqw/gywKHOWLJS7Z+3ZK4QuL0ZHNiQb1h8VStlWIU8Z1DJbJSrRN3KyNExd5gE9dBnsMImA4XNMx+BiVMKQEWSMyEzEhT1xQBzXI6m0TS41u8NdoqCkVBvtiO8gyWaz2TNb4vIFB1tgc8XdzHssHiwloOIaaRvEFXd3W68hkJQ0SZAGOLoOAmKR4zNlDa7wnLOMe3ZRMVlqh40YZ/9Yxp8tghtHK7oLDotywiKIeHQyyKHB8LXyhi3cEFtEyIXFPi+vcWACQM0ksPWUnlAtBJZMlSX6Vce8xiWCupdtoL3sdSZTxWCMzkjC1UGQo9lW2wRqscWbOXQWdSoOHBY7JAgBUtvTAZJGRqNeisF91QQDISxXjtBo7xVxaeP+HwCMjgX8twrdqHgYA1LT1+Z7JAoBpV4qs8+K7gDN/EIj/hiulDd1N3rejyKR8rsSnOWUt+beONU1dBpefmckaAuW9FEXfgwyyKHCMyjxZg3QXBKKvwmBjiVg6F/3ImyqWnj4wDr0tlgUzHI/5UcZdyWQBwKHqyMuAKHNkjchMxKyRGUiJ16G1x4TDNUH6v4TreCyAQZbNqUaRDR+bkwyNZmAgnKsEWV0GoK0C2PMSYBY3GKaajwIQNx2MHbaueUnZgx80ZwLwk+PAyt8HJ/hWug1zTFZ0sn+uZABpI8R60wnVmkPqaLDd+FEmUO8zWWGysKKoX/qYySLyzNdMFiC6VgBAy6ngtSeUmmxBVvEi4MqngJWPAPm24MndB0bLKeD4WrG+4LuOx5Ugy4cy7j1Gx8S1h2vavWwZnuxBVkYi9FoNFo8XF8SbTgRp7AqDrLCnjMdy11UQcAqyOg3A6a/EgzmiW26G1I0kiICrp81288aWRarv6MPt/92NraXN7g8czMwmuwtGN+fPlVFnivXTX6rXHlJFY6cYkKV0aQbYZdBvzGQReeFrCXfA8WV08vPgtSeUGo+LZe5kYM43gMU/8H7hvONZADIwYRmQM9HxuNKn35fugk5BVkRmspzGZAHAeRPFBfGm47EYZLG6IODIZI3LcT9mLjdFBFkNnQbIpzeLBydfBJNevNcKJRFEGduVTJZ4Tb2+sxJrD9fhxS39Kn+GArsLRjfnz5Wx54n1si+jq6gTDarR1l2wIC0eSXFaAEBHsOd9jDYGpyArSt4/DLIocPzJZE26SCzLNgKmyBtPNICSyVLGYQGegyxjD7D3v2J90e2uv1PKuPvSXdDouEsWiZmspi5x50/JTpw3SYxn2F3eii5DEO4AhnWQxeqCgO+ZLKPZCqsSZI05F51xopjOCEkEMlalkp8twNlf2QYAwZ/s2h12F4xesux4zyakiZ4MGj3QURU9vTTIJ8o40dzUeKQm6AAwk+U35Ttatvh0DRQJGGRRYFjMonQt4NuYrPwZov+6qQdQLpYimXMmS6FcOJt7Xee9aj4hvpgTs4DxF7ruZ4iZrNr2PjT3G3gb7lp7RJCVlRQHABidnYxRWUkwW2XP3bqGI6yDLCWTxSAL8JzJStBrkZqgQyGaoW07DUhaoHgRmrQiQC+UxLQIGtv0CI3WFMiyjH22IKujV4WLHnYXjF7GLsek1wnp4iZZ8ULxc9km9dpFIWcPslLikZYgxmWx8bcS7gAA97xJREFU+IWfnKvrRkmvDgZZFBgmp3lBfCmPLUnAxOVi/cQnwWlTqBh7gPYKsZ7jFGQ5d5t0/sDosFVUzCgGNP3egvbCF/4FWQCCVzAiCPpMFnv7s1Li7I+fa+syuKMsCEGWUjI/LIMsjslq6TaitUdclIzL9fwZkpsaj0UaUeQChbOBhDRUWsV4vulJIpCON4gg6/a3y7GjrAXN3SKg71DjoofdBaOXcuNGGycmlQeAMeeKJcdlxZRSW1fnUVmJ9kwWJyT2g3NWGHAMPwFE8GWOrJvICgZZFBhKV0GNTpRo94XSZfD42sjtf1uxHTj+sVhPygaSnaqZ6Zy+eJ0vnjttkwenFg3cnz/VBW3dBUfaxjRVtg4emIULJYul00hIjdfZHx+dLTJ5SlfCgOqoFkt3511tDLJwypbFGpGRiKQ4ncftclOcgqwx5wAASg0icJ6R2gUJViRb2gAA1cZk/ObDI/bnqjJGQukuaOpxfE6Seg69Dbx8DdAdgBs5yp33+DRH8RT7uKxNkfu9FiRGsxX/212FuvYoGCLgxGqVccI2x+Ok/FSkMpPlP+esMOA6Z9Y/ZgHPX6ROu4aJQRYFhnPRC18rdY09T9wBbKsAWk8HrWlBU7ULeH4F8Na3xc/O47EU7i6elUyWUmHRmT/zZNkyQUUZIshSZbzJELXYMguZyXGQnF4v6Yniyyko/5f2KttBRgZ+38PFIAunmhzl273JTY3HHE0pAKA5ez72VrTiaI8IskZrW5CGHuggvqxbkOaS4e00mIM3D5snccmAznbzpDtIRV3Id9ufAk5+BpQGoOiSuy7II88AIIm/Nf/eLj4+VIv73tyPRz4+qnZTAqqipQe9Jit0kozRTpksjsnyQ1+/ceXKd2HzSdELpWYvYI2888kgiwLDXvTCh/FYirhkIGu8WI+UQcKddaJyFCC+qJ0VzBq4vbuLZ18yWYOUcLdaZfTaJiMuShfZskgMspTxWIr0RPFzW08QM1kMssJSebMIspRspie5KXqMlcSNiu983IWr/rUFNbbugummemRLIqjqlBNhhN7lubIMdBlD/EUtSUCKMkktL7pV12PLYPW/qBsKd0GWLt4xPxuLnbgos91IKW+OnF4XvjhWJz63C5IAnVZjz2R19Jnw/v4a1LRFRxGHoOrrN9xB+S7srLc9IAM9LSFtUiAwyKLAsE9E7ENlQWeZY8Sy9bToIrf1X+Gd1Xr9ZuClS4HSL4DyLeKxs+4GLnoUOP9nA7f3O5PlYUxW9W5gyxOAVQRWfWaLvSdKoZLJ6onAICu5f5AVpEyW2eiY+Dq9OLD7DgTlIi2Gg6yKFnEhMliQNU7figTJBCN0ONAlzls1xEWttrMGBVpxDluQiiWTcwc8X9Uug7zoVp9yoRaIIEspZtJ/0usUUe0SXfUgh/oOMa6mKcKKNA2mxBZkFSaJL+U0WybrtR2V+OGre/HwB4dVa1vYazoJ1O73nMlyfg/1RN641qgPsjZt2oTLLrsMRUVFkCQJ7777rtpNik7+lG935hxk7X8N+OR+4PPfBLJlgdNSBlTtEOv7XgWqdor12TcAZ97uOh5L4a5qXKctyEp1E2QpRUOcx2SdWAc8fzHw6S9FcAfXohcFaSKT1RZBQVarhyArIylIQVZnDQAZ0MY7ChGEE+dg3Gr1vm2UqmhWBo577y44WhaZ4NPWfFihwbkTc/DMDy6FDAmSxYB5SeLC1xCXhW+fPRaAeF1l215rqlQYTGYmKyxYrUBfm1gPRJDVYeuVkD7C9fEUBtXuNHSIsViNnQbIUTReraRefL8X2YIspbtgne3/e7Q2dm+eeWUxAy9eAjy3EmjtN4ehPchyvIekCCweFPVBVnd3N2bPno1//vOfajclutnHZPlQWdCZc5BVd8CxrqZ1DwBPneuoRqc4+oFj/dBbItuUkAHkTvG8L+Xi2blSjvLFnOZD4YuK7cBrNwAW250/27npMYggKylOG7zAZJjMFs/BQostIMxMdu3O5ZzJCuiXsH081gjfxwyGkr2brez6WokSVquMB987hLf3VHncprxFZG8Hy2QVmisBAKdk8f45Y3QWphXnQrJlD+bpywEA8Wm5OHdiDn57xXQ8/o25SE9ydOEJOXYXDA99bY7B9QEJsjwU02HZfrfqO0XQYTBbgzMXokqO2YKoIttHV1qi6/dadVsvTF6+D2NWw2Ggq05Mc1O92/V3bjNZkff5GfVB1sUXX4zf/e53uOqqq9RuSnRzLnzhD+cgq8E2GFbp1qUGWQZ2vSgCvuP9Sssffd9pO9sH5qjFA8uwO7NdPMu2/sZ9PV2OO6nuMln2ebJsJfH3rwYsTuOTbAFaj0l8QYkgyzaOKYyCrFe2l2PGQ59g2yn3FbxaukXQOHBMlvhyMlkcY84Coj2Mx2MBogqlxlZRLwq7DB6qacdLW8vxp7Ulbn/f3muyZ2KLs7wHWdl9IogqlcX7Z+6oDPEL29/2zEQRhI0YUQxJknDz4jE4d2Kufe4adboLMsgKC843zgIxJ52967eHIIuZLBd17Y5ugk1dRpHJiMBxNs76TBactmXhC/tlshQWq8xxWe5UbHOs1x1y/Z09yHJcDzKTRbFrKIUvgH5Blq3UcmedfexRyPU0AQbbHU5lzBUgLtKrdgKQgEkXOx4fvdjr7qp7xYftur2l6DNZcMeTHwIAZH2S+/ma+meymkUVNXu2zBaAdtszWTpkKNkfL8UitpxswgtflYWsi8aW0mb0maweg6zWbnGh27+7YFKcFnqtyDQFtPtju7jwDsvxWIDIrkVx8Qvlb9nSbXT7Gqy0ZbFyUuKQEu+5fDsApHSJbiWnrOLCdnZxhviFLchKahJf1rpU1/FYyt1lVeau4Zis8OB8QR/I7oL9gyz+vQcwWaxo7nYEWW31lcBTZwN/nQac2gC0VQL/u23gzc0wd6K+C1YZyEzSI82WwEqN1w/YLtqKfQSE8zVWfb9xa266C0biXIPev81ikMFggMHg+CDo6BB3u0wmE0wmdTIFynHVOr4vNL0d0AKw6JJg9aedKYWi/pfzXUXZAlN7rWPwcAgo59bSUGKvRyZXbIXZ9rjm8HvQArCOXAjr/O9AZ5sbyzxiIWQv/9/yTg1GAKisq8fNz22HtrkSiAP6EvKgMw+82JM08dABsBq7YTGZoGsuhQTAUrwY2sZjsHZUw2IyobNXvEYT9Rok60VQ0t7r+TX607f2o6qtD1PzkzF/dKbf58dfXbZsQXOXweW9oyybu0S3kbQE7YA2pyXo0dxtRHNnL3KTA/MRpWmtEK/PlEL/Xp8hpItLhdTbCnNPq9fXlDvh/hnRavt7Gy1WdPT0DZgH61SDeP8XZyYO+n/Qt4obD6fkQozLSUaSTvy/NamF0AIARBBnSch0+Vunxonftnb3Dek8DeccS4lZ4n3dVQ9LmP6N1BaK17DU2WC/6LH2tg/7b6HrqIYEwJSUDzjtS0rMFn/vzrqw+Xur/RlR295nL9aUjXZM+PgbQJeoKiy/eSsQlwKpvRLW1tOwjF2qShuH4kiNyI5OzE2GJPXCZDIhcWCMhVONnVg8NiO0jQtnsgxdxVbYO+/biqfJkCBBhrWvQ1wDddbbt5G76gFteHzP+doGBln9PPLII3j44YcHPP7pp58iKcl7N5ZgW7dunarH92ZG1UGMB1BaVY+ja9b49dyVunQkmF3vKn619i20J40NYAt9c3TzB5hrW5eajuOz91+HUZeKOeVrMBrAcXMRjh/twPkJxdDAgvV7ayDv9/z/NduqKU2QqrHndBMu1Yg7qfXGROx3c54K2o9gEYC2hhps+fAdXGor976nJQkLAHTVnsT6NWtwoEUCoIWhuxM7Nm8AoEO30YL3P1wDXb/8tFUGatq0ACT895NtqB/pfzbr5RMadJqA70+1QuPDkKbKOnG8IydPY80aR3l+5TVcXi9+f+LQPqyp2uvyXJ1V/O6T9ZtxKj0wmbdFpftQAOBAeQsq/Hx9hsoSI5AOYMeXn6MxbWh3wMP1M2JLvXi9AsA7H32KzH7zla+rFr/X9LZijZe/j9bSh0tthWNK5UJMkTrt2+e3J2CR7QsaAHadakVdi2NfrY0aABrsPnAEea1Dr/Y1lHOc01mKswF015/GF2H6+gsXwXwNFzdvxjzbendzzbD+FlqLAZfaun5/uu0QzNpS++9yO07jLABddaewPsz+3mp9RpR3Asol5490byO16xR69Vkw6NKQ0Xva3pWzp+E0Pg+zc+bNpirx2SXZsqTr1q1DdTeg/F9HJcuo6JawcddhZDYdVK2d4SbJ0IDlbqpv9ukzkGhqRX3FSez46CNc0lFjD1Qay48C4y4Ki++5nh7fMpMMsvq5//77ce+999p/7ujoQHFxMVasWIG0tDRV2mQymbBu3TosX74cer2bWyRhQPvhJ0AjMH7qbIw9e5V/z218wlG1z+acWeMhTwrdDN/KOZ5RmAhUOB5fPjkV8uRV0L76AtACTDjjAoyffTlw8SWAJOFiyXuP2/+2twCn3sD52gN4XfotdlknAwDk7HFYtWrgeZLKkoFTf0dmSjxWLpwM7AfkhHTMWXkj8PQ/kSp3YtWqVTDtrwVKDqIoPxtXXzYfv9y9DrIMnLXkQuSkuF7BNncbYd22AQDQmZCHVavm+3VujGYrfrRVzAk2c/F5GD3ImBkAeKpsK9DZicSMXKxaNX/Aa/j3hzYCMGDlkrMxvcj1ffVi1XbUV7Zj6ux5WDEtMNlM3TOPiPafczFmjLsgIPsMNG3Tv4DKCiycPRXyVP/eQ+H+GVH1ZRlw6gQAYN6Z52JqoWu34q/ePQxUVGPxjAlYdeEEzzuq3Q8cANqkdHQgBZecORWrFipdQFfB3PkdSHX7AXMf5k25DHB6fx7+9Di21J9GQfFYrFrlpViNB8M6x43jgZN/RIrU6/Z9T6F5DWt2VNg/31N0luH9LZpPAgcAOS4FKy67xvV3DWOA0j8hVRM+f2+1PyPWHWkADu0DAMy2TSauv/RP0I1cBPnVawCrGVLLKSSjJ2zOmS/2rDkGVFZg5sQxgHwKy5cvR4fBir8c3IiUeB1uOm88/vBxCbTp+Vi1au6g+4sV0oHXgSOArNFDsjqyQvG544Ca3cjPTMKqZedBt88xDCIvWXyeh8P3nNLLbTAMsvqJj49HfHz8gMf1er3qf9RwaINHtkIN2sR0aP1tY9bYAUGWrqcBUOH/qm07bWtAAmDug656BzDjCvtYKF1msa1dvrXtcMI83G28C39OeAFn4Djmak4CANp1uRjr7v+XIC4+JVMv9O1igL+UNR76THEhKRk6oJeNMFjE3fqUeD0S4uOQGq9DR58Z3SagsN9+2/ocA273VrRDo9XhZEMXRmQmDjr+BQA6nLrPtvRYMCF/8P97j61oRXuv2eU1q9frodPp0GobP5afkTTgNZ2ZLN5/XUZr4F7vtipguqwxqryufGIbo6czdw+5jb1mwATYK+mFix6To7KWu79rVZvoTjg2N9X737xNjMcyZozHgvxMXDp7hOv2WcXinxsZAXpdDelzOF0U6ZD62qCXZEAXN8gTYldQv+cMjh4TkqFjeMfpEdlmKW3EwP2kizFaUk8z9BoA2vB5P6p1HdFkG5cpwYqJku3zeMQcIHsU8IPt4m/z6BhIxi7oYQH0CY4nyzJw7CMgf7q4XggjbbYpIXLSEoB2cX4LkvR47tYFyEqKQ4vtu66ytS98r9/UUC2u+aSJy4ESR+ZSk1EM1OyGxtgFTZ9rURSNbSLxcLgW9vX4UV/4oqurC/v27cO+ffsAAGVlZdi3bx8qKiq8P5H8M9QS7oCj+AUAJNnmMFKpwqDUYuvyMfVysVQGZioDnPuX6nWjsqUHx+rEXY5ugxkfWM/CxjOeAABoIS42GzVu5tQCgDilumAvoLQlezyQkOao3NhZh16jo4Q7AHuFwfbegcUvmjodj3UazPjjx0ex8u+bsPyvG7HdQ2EKZ86FAupt834MpttWnleZdNhZp8EMky1IzEwaeLEZ8AmJ+9odY/76z2cTToZZ+MJgAVY9sQXL/rYRzWE22afz3FTuqmAqg8IHK9+OZnGTIm/sDLx5+1nIThl4Q8wTVasLJmYCknivRuKEmlHDufCFuQ8wD+N94m0qjqQsx987AgfrB4Py3TFO14wkyQCTpAcybQGTRiOmQ1EqrPZ/j1TtBF6/EXj3jtA12EfKd1z/SrkXTM7D7OIMjMkW10QVLT1RNTfYsJV/JZYzv+b6uFIB2NDlKN+utZ1blnAPP7t27cLcuXMxd65I0957772YO3cuHnjgAZVbFmWU6oL+lnAHXIOs8bauXLaxSCElW4EW2/ihuTeJZe1+0VdcuQPq7gvVeReyjOue3orLn/gK7b0mdBvFxWVX/kJg4gr7dvWyh+IT9hLuPY7KglnjxTK1QCw7ahzVBW2ZKGWuLHcV+Rq7XAOjf38psgG17X34xr+34auT3i8CnIMd34Msi609A4MsZSLipDgtEvTaAb8PeJCllG9PzBraTYBQGWaQdbhVQn2HAY2dBo+l0tXiPDdV/9eo0WxFbbvIto4arCuqUoJbqd7mB0d1QRWCLI3GUcadFefU09uvXHjfMMq4K3NkuftO0Ggdk567GXcSi+pt45OXZokbe5WaYkDr1JNCkpymOuj3naTModl0PNjN9Ftzly3ISnaf2RiRkQiNBPSaLGjsDK+bX6pprxY3zCQNMP5Cx811AEiz3Qg1dDreO7limIVk7IbWGlnnMOqDrCVLlkCW5QH/XnzxRbWbFl2Mtnmd4ocRZGn0wOizxboKmaxEUwski8HRDn0yIFsccznEpYiMkhdNXUbUtvfBaLaivqPPPuFicrwOWPpr+3ZVZk9Blq2Eu7HbEfBlK0GWbV6tzlrHPFm2IMVbYOLug704KxFLJufCKgMfHaz1+n9yvvPf4MOXhMXqmOOq22iBwexajl+58+cuiwU4/i8BK+HuPBFxOBtmkLWnyVGR5PVdldhT0epl69Byfg219cu2rj1cB6sMpMTrkJs6SGbKZBtsrLxP/JBmm7vGOasWUp4uIGmgtgpg94uBn8qj/5xMwynj7i2TBThNSNzv7nv9EWDLE8Dnv3WdJyjKKTfoFiaL7/YTsps5C5WL7f7vkWbbd2FPs2N6kzChlKXvPx2JIk6nQVGG+LxSJlyPeae/FMvCOUBiBpDh1MU73TnIst2QyhoPaMV3Q5w5sqY4ifogi0LEVn4TcX7OkwUARXOBonnA/G8CGaPEYx3eL/yDIaXPFthljRV32LLHiZ/LbB8Ig2SxAKCipdu+3tFrsnebS4nXAYWzcGjmz/Cq+QLst45xv4PETDEezDm4UzJZyvE7a9HTL5PlLTBRgiz7fEIA7r94Kq6aKz7MSuq8f2g53/n3JZOlZO8U/dukBFnZKd6DrIBlsrptH9QpBYHZX7DE2wL4IUyS2tFrwpE2EWQtGCMC+L9/diJgTRuuTqcup86vh5ZuIx5+X1T6+845YyFJg5SuVC6whpCRVDWTBQApSpDFTNagPv4Z8MGPRKAVSAMyWcEMsmxFe/pnsv57FfDpL4Ev/wK8fjMQI13IGmyZrEkQcxYeNI0Y2H1Oyf71D0xbHBVq7ec9DMiy7Ogu6CHIAhzdoJVu0S9+VYb/bisPfgPDVdkmsRx7rlg6z1+pdBc0dTuyxakF9ptU8aYATCIeQgyyKDDskxEPIZOlTwS+tx645P+5ZGtCLdlgC7KybdXNlODmtO0DQWmbF84TDnb2me3d5pLjbXP0zLoN95tvQ2uv1e3zoU8EFt0u1mXbXVwl2LN3F6xFz4AxWbYgy0sma8W0fCyflo/rFxTj4hkFmFIgLuqP13V67SvufOfflyBLCQAVrf26DA6WyVL+L4ELsmx3RZNzvG+nNiVLOoRM1rqjDbDIEibmJeMnK0TXiqowumvq2l3Q8Xr43UdH0NxtxOT8VNx5gZeqgophZbJ8G5P1109LsPJvm9x2dR0WTlDru6pdYnl8bWD329Mvu2sYRpCldGlP85Ahd/f3NnYDXbbvGW2cCLgbj4mfrRZgz3+ATX+JysCrzvbdkWc4DQA4YhmBTkO/rLLyGd1/TJYyPhkIqyDLeXxx/zFZzkZlOcZlNXcZ8NAHR/Drdw+hui28snIhIctOQdZ5YukcZKU5ZTiV4Dolz/7aiDczyKJYZBzGmCxnabZAprcFMPk2/idQUuxBli24UoKtukO2tg2eyXIOsjr6HGOylCp+SmDRP/Bwce69YvwQIJaJtq6FqU6ZLNt+lSBLyf64u4BstBVBKMpIwL9vOQN/vGYWJEnCuNxk6LUSOg1mrx/2zhfIyt1Ib7r6fXG2dru2Sfm/e7rzF/hMlu2uaJKHYiPhQukuOIRxImsOidfuJTMLnTI2oe0W195jwrde2OH2Dq1L4QtbJkuWZXx8ULT7t1fOQFz/Cd7csQdZ/s9ZmJYo3oOdBjOsVvcXsQazBf/+sgwl9Z3YXtbidpsh83SXnlx11TuyfWWbBnYPq9oNvPWdofV2UDJZyg2zUHQXdA6ylBs+2nhg9FlivexLoKUMeG4F8P7dwBe/BWpc5w6MdH0mC9p7TdDBjIR2ETAdl4sHdmW3d6l1eo9YLUDracfPSnYjDLR0OcYXJ8YNHF+sGJkpbgpVt/aiqtXxel5/LAZvuLSeBtorRZGT4jPFY87dBZNzxJANwDEuPcUpk8Ugi0Ktu//doFCzmEWlJsBxoThUCRmiuxzguOMXIrmdR8RKwSyxVIIt2+SmvnUXdA6yzPa/TXK/bn2tPSbP2aOEdGDJ/ba2zHA8rmSyOp0zWbbCF4kiYHF39135IstNSXB5XK/VYHyuCIq9dRn0t/BF/9dj/4CyeZDuFQEfk2Ur+2r/Ag9XQxyTZbXK2F3RBgBYOjkXqcrYoxB3i3tlRznWlzTiqQ2lA37nksmyvZ7qOwzoNVmgkYA5Tl1ZvVIuuIcSZNkyWbIMdBndf2buLm+1jyf0ZfyhXzyN0SEXUv0hxw/mPuD0ZjGWymj7bF1zH3DoLWDz3/zbsanPEaQrVe2GGmSZDY6/o6dMlj3Icuou2OOUVR9j6yp1+kvgvbuA6l2O7RqODq1dYWqrrYrtJH0jJIsRPUhAtZyNpgFBlnIjwqnqbXsVYHH6DgmjIGuw7zLFCNuYrJq2XtS0xXiQpYzHGnGGo+eT0kUwLlUUjVG+C23VZJGSb//+jmOQRaH0/OYyzHjoE3xxTMUKRs5jSIZbvU2SHHcZQzkuq60CaX1VkCUtMGGZeEzpLqjwqbugY0xWc5fB3pVACbIybR/GRrPVfjHn1sLbgK+9AFz2mOMxJcjrcJPJ8qG7oLuiApMLxIfZMS9BlnN2rNtoGZCp6m+wIGuvLSAY46Fcd8x2FxxikFXW3I1ugwV6jYyJecn2TJbRbEWft9dYAMmyjDd3iQIjte29MJod3WHNFqv9pgDguBFQ1iTeK8VZSe6zWLtfAl6/yTWjbRx6d8EEvdZ+HE9dBr884eimFPBKYO7u0vvJapWjvgy0S5AFABseAf7fZODfF4gslpLlOfo+YPXQ7dodJYslaR0XdUOtLqhkpzR6R0+D/pQxWc5/byV4SMp2BFknPgXKN4s7+5Ntk/AqXQijwLG6Dvxwtfib3ThWvOer9aMhQ4Omrn43BZPcZHtb+t20CaPugvbxxYMEWUrhi+q2XpdeI1+VNoXsMzpsKNmpojmOx3Jtk8MrRS+U70JlkuK0Qkd3QY7JolDaUtoEWQZ2nlaxkpiSyk/OA3S+z1vjkQrjsjQnPgEAyMULxRwngKO7oMLTHUsnzpmsunbHxWGyLRhKjtNCrxWD+1u9ZWokCZhxtevEi87VBQ3uuwv2D0yMZqv9ON6CLG+ZrP7dzgbLZnUbXb80Wp3myursM2F3uXitnj/JfRlu5wIFnrp1+cXeXTA6g6yDVeJu/MhkQKfVICVOB6V+RGeIugzuKGuxB01WGS53a/u3QclQnrbdkFDmkRngy78ARz8AKrc7HhtGd0HAeVyW+/Py5QnHxV3ggywlszG0IEuWZVz/72248K8bB1TsjCZS/UGxMnKhWFbvFpmMxmPAazc4NuysdX1tDEapLJiYKSqaAUPPZCkBW1IW4KlYi5LJcv4eUz6LknNFwSd9kqMXyPSrHDf4oijIuvf1/eg0mLFobBa+Plp8tjUminHGDZ39vkvc3YhwLnoBOKbkCAMtg1QWVIywdResbXftLthnsmKbD3NVRhV7zxKn7+OcicD1rwLXvih+jneq4pw/E8ibJj4HAExoDPA4zSBjkBXhqtvEh5Sq8y+0inmXAjYTu71bXOi6C0pKkDVxpePBpCzRdU+R5j2T1WUwu9yZq7EFWQl6DXRa8VaTJMk+cXCrm4l6vUrJF3c7rSYU9Yk0uqO7oC3I6he4KeVldRrJvo2zKb4EWf0Ct0GDrAGZLMfzvyptgcUqY1xOMkZ5yGQpAaMsByhIiJjugkMrfHHAFmQVJ4uAVKOR7GMAQ9Vl8PVdlS4/l7e4jk101tYrusoqQdnYHDdBltXiuGOtjPcEnLoL+p/JAhzjstydl+YuAw5VO+6SNva/AByuYVYX3FfZhh1lLTjV2I0Kp7Gf0caeyTrrLsckpMqdbqULuTLtx5F3fd+xc2A0jEqeAJwCtizP2yjZsvZqRyEL5+6CujigeJFj+zPvcPw/oyTIkmUZJxvE+/fRa2ZB1yTm7+tKmwhAjFFyoXxGOxe+UMq3Z4vnhGd3Qe83l/NT46HVSDBZZOyrbAMAJNqmX4m5LoM9TtlcZ1NWAXlTxbrzfYtz7xU3MsYtAQAYtMMc9x9iDLIiXHWr+LJt6lIxyGqxBVmZAQqylO4XwxmU7I++Dki22cetzkGWJLlmswbJZPW/8KmzTbCqXPAqMofaHU4XB0y9HABwheE9AM7VBW1jsvrtUwm+c1LiodEMvOM62VZhsLSxy6WLl7P+F6SDFb8YUPjCqbvgJlt3rPMnew544nVa+xfQsLsMyrJTd8FwL3zhdOHnR5ewg9VtAIBRKY7nKBmbUGSyzBYr1tjmWitMF+P+XMYm2rJGqbb3gejGaPUeZHXVA1Zb25U5+ABHJmuI3ZK9VRjc3G9S7uB1F2zyr5ubzUcHHBmRAd2sooTWYnB0Jxq1GLj8ceDc+4DvbxJjOADxObzi92L94FvA298H9vx38J07B0bKzbNAZLI8Ub4vzL2OYztnsgBg3PliWbwIGDHfEWS1Vbi+7iOUwWyF0SJe6zmp8faxZpqC6QCA0/1vFiif0c7zZCndBcecI5Zh1F1QmYjY03QkCp1Wg4I08dl4qFq85i6dJW7aqtoLSQ2egixndQcd69OuEMtzfgzzJX/HxskPB69tQcAgK4J19pnsXbnCI5M1LjD7G+4XoL/KNkGymtAVn++4W6ZQxmVp9IN2N3OeIwsAam1ZxuR+QVaGLxUGPVl8JwBghXUzctE2oIR7e69rQQ1v47EAoCg9AakJOpitMk41dbndRgl0imwX0HWDZLJ6+hUVUDJ2suwIspZMdt9VUGEvftE7zItJY7e4yAEip7sgZNfsjRdmi9WefVEyWQAcxS8CNa7Ni84+M/pM4kJq+TQxDqXSKcjqtAXpBekJTl1ljThtC7LGuAuynLsEuWSyhj4mC4DXyotKpcMzx4kL54AXvlBef7IF6PXvwspqle2BLODIUAfCC1+V4YonNqNZzRt1Nml9lZAgi6x9Sh4w+3rgwgdEN/TL/iG6Dl34ADBxubgp0dMEHHgN+OjewScudg6Mhvsd49z10BNdvGNcVrst09vd7wJz4feBpb8Crn5G/Jyc7QjAmo4PrW1hRPn80UhAssZkD5hSRs0E4DqGGYDj/27qcQSZSndBpdx3T1PIKw974uuYLMBR/MJs6wJ/zkTxeVDTHmNl3H0Jss77qVhe9bQohAEAWj3kOTehNz7Me6T0E5Iga8yYMfjNb36DioqKUBwuZtS0OT5o3GWyrFYZeytaYbL4f9fULy2nxTJQ3QVDHWTZuh+0J44e+DulwmBqAaDx/nZRyrcrgY8yB0hynPtMltcxWZ6MPAPyyIWIgxk36dYNqFposcoumaTBgixJkuwTJdZ4KOOuZCIm5osgYLDugl22ebLy08Qxlf9nTY+oKJeg12DRWC93gBHA4hdKtxNdwvCLsgSbPlEMygd87jJY2tiNXpMFyXFa5DnFHaHMZCmvt0S9FuNsAVNF88DugmmJeqTbqmC2dBvt75dx7oKsjirHujIHn8XsqDI25DFZ4v3SvwpndVsvPj0igqzbzxfv+aYuQ2DGBCp0caJ6KuB3l8G9lW327seA4w56IDy3uQz7q9qxI9Al64cgvdd2jVAwc+AvC2YAd2wWgZcuXlyAzfsmAEm8LrqbBj7HmUsmy5Y1HmrhCyVI9pbJApy6DNpez/0zWXFJwHn/5+j+CDh1GSwZWtvCiPN7X2o6AchWICEDI0aMASC6Fbu8x+JSRHl7wJbxdSrfPmK+433fGR7ZLHfVBaX9rwIf/WRA0K+My1IsGCNeO209JvUrRIeSPcjyctNzyf3AfSXivR7hQhJk3XPPPXj77bcxbtw4LF++HK+99hoMBvXvmkW66jbHhUxTl3HABcFDHxzGVf/agpeDPbN4a4C7C4Y6yLJ90Zq0bu6OK194GW4CsH6UcSjTCtNcHlcmIlbYy637OybLxrxQTFb8Xe0aJHWLv22CXot4W+W0Fqf9KsF3bornPuODzUulfFFOzBN9oQfrLqh8YRRnii9E5YL2aJvIYiwel40Evec5RQBHkDXsbrDdTuOxPA1QDxeS5BiQP8gFo8Uq441dlXhqo7gzPK0oDc69Qb2NPQo05RgpCTr7OLtyN90F0xJ09r/rkdoOGC1WxGk19spbLtqdgizljrbZ6SbAEIOsvFSRje2f+f/v1nJYZeDsCdk4a7z48jdZZLfVOodFyWw4l/X2wYcHXC8qA5V16ugz2Qfih6pIijdJBlsQ0r/okDtTVgGXP+YIWAab8sN+cRfITJafQZbzmCxPcsVk4tEwLkv5TklL0DvK0udNQ2FGIvRaCUaz1bVnhCS5dqttrxQBtDZOnEulwm6YFL9QCl84dxfUrv8tsPPfQNlGl22LMhxTqKTG61CUkWjvceDpBmfUsZiB3jax7i2TpdE4xuZHuJAFWfv27cOOHTswdepU3H333SgsLMRdd92FPXv2hKIJUanaKZNlscou3c8OVbfbJwU9WB3EYMXU5+gjHamZLNvgZ7PGzcXelEuApb8GVv5+0N0oXaSmF/UPsvp1F0weRiYLQNe4S7DVMg3JkgEpH/5AfHDBMbbleH0X2ntMePC9Q3h7r/gy8pTJApwnMh54kdVnstjHak3yMZOldBdU7twpQd/RNvFxM1hXQedjHakZeKe5tr0XL35V5luFNRUnIu7oM+Gnb+3HuiN+XFBnjBLLNu9Z/w0lDfjpWwfwju3vO2uE62su1Z7JCn6Q1WW7OE9N0GFUlngNVrb02LutOt/NVrK4yuDvUdlJ0LoZK+i2u6BSvh3SkKuYKmPGap2yQn0mC17bKc73NxePQZxOY29nwLthK8Vz/Jiewmi24oP94jN2hu3v3DTEGzT9Hat1ZExDVSSlo8+EV7aXuw0UE0xtYsWH6TLsUm2Ba+cg7zOl7HpKPhBv+44ZauELX8ZkAUC6bZLV/t0FvRXhiaZMlu07JT1RDzQqQdZU6LQa+0240wO6DNoC0J4mR1fBzDGi25h9GpPwyGS1dBmRgh5MPf4U0HwSktUMSclSl2912XZEhuPGkHJjaYRTafeY0NcG+7yj3rraRpGQjsmaN28eHnvsMdTU1ODBBx/Es88+iwULFmDOnDl4/vnno37+j0DrX5lHGQxttcp44L1D9rHzAyr4BFJbOQBZ9I8P1IVsyIMscaFh0rq5O67VA+f9xHVOBw+UC7IJ+a4TMvcPsjKVIhVDGZMFoMcs4z7T7eiQkyBV7wJ2PA1AZDMAEZi8urMCL20tx6lG8QU2LtdzVzmla5m7TJZy4aWRHPsYbEyW0l1QmeW+o8+Mth4TTtmu55Z4KXqhmDlCvAaUynkKWZZxx8t78NAHR/Dhfh8uVH25cxwk/950Cm/sqsJf1/kxtkLJmLZ5zz4rRSNGZSXhGwuL8c3FrpnWNPuYrOBnJ5QMSGq8zv437zKY7TcRlPFPaQmO7oKOudI8vC473GSynMu3DzErme9mXOHnRxvQ1mPCyMxEXDhVXLArNyUCHmQNYXqKL47Vo6nLiNzUeFw9V2RGApXJOlrrCDJClcn689oS/PKdQ7jmyS2oanUtfJBgsnXD8yfI8jU7qPw+JV+9TJYvN32UTFYUTEjsuMGic8pkiQpySjf18gHFL5zmylKKoChjo5ViImFQYVCWZTR3G3GZdisK9/wV2k2PIt7s9Hqq6BdkOXUXVNYdkxT7OMZs01+A/902+PjDcKVkkxMyAK3O66bRIqRBlslkwhtvvIHLL78c9913H8444ww8++yzuOaaa/CLX/wCN954YyibE/H6p5iVC4LPjtZjj+0iBgjywErnO02B6o6ljFsIdSZLmzDIht4pXdvG9xtjkuJhTNZQuyL1GMyoQQ6e1lwnHjjxKQBHN8Ujte3YdVpcBFw+uwj/unEeLptd5HF/3roLKgOXUxP0KLR9IdR39Hkdq6J0F3S+c/fx4TpYZQljspMw2tOFtZNZIzMAiIys87G2nWqxZ0FqfXld9x8DESJdBjP+s1UESv0vJL3KtAVLrd6DLGUOtpXT8/HI1bPsGRpFSDNZBiWTpUeCXmuvoqUMane8hhzdBZWL+7E5Hrr9uXQXtGWylPLtcUPrKgg4MlnOc9gpmf7zJ+Xas2pKkDVgHp/hGkKQ9dpOkQX52vyRKLC1P1BjskIdZPUYzfbs6+nmHlz71FY0OAW8jiDLj65CKbZtB+suaM9k5TrGZBk6PF6wyrKMJzeU4rUdFQNvAPucyXIKspyL8Hj7PFK63bdX+VVlNBx1uHQXPCIezJsGAPbvAaUAjp3zXFlK5WJlbHQYZbJ6jBYYzFbkwnad0noaiUomFgCqdgFmx/t0hFN3QaXrYJE9yPLhu8xqBTb+CTj4BlB/eNjtV4UvRS+iTEiCrD179vx/9t47zo3qXB9/ZtTranuv7r3bYFOMwRRTQ0J6geQmvxQgN5DchHzTc1O46SSkJ6QQCCGBhBBjMMUYN4x7L+v1entfrXqf3x/nnJnRqGulLbaez2c/0kojaTSaOee87/O8zxslEVywYAGOHTuGHTt24J577sGXv/xlvPzyy3j22WcnYncuGigpZrbIf3o/WaDcRhfWvXYfwrks4JaDDYK5kgoCk8ZkheLVZKWJcEQQZXHNCtYop+6CIIM7AJzQLCQP9BwCBEFkso73OMSGv/esa8KmRdXQqBJf6tYkQdYYq6cxqFFh0YHjSK3KcBK5Elt0Ww1qkVF5ej9ZWF01Kz1GaUa5CQaNCu5AOMr18Ffbz4n37enILVlt0wQP6n/d2yEeT6cvlL4UK00mq58mVCqt8RMDUk3WRDBZtCaLnuesLovZuMvlgspebQtrixAXUXJBJZOV/XXKAsA+h09cOJ+ggcZ8mcw3Ue3WuJHhIrHH7sX2MyRR8M6V9aKLWbLrLxOclPXHm4iA/PkjvXD5Q6grNqClzITeMR9+vV1qNptzuWD3fqCbliS45XJBmbw2gcnM+SE3Ht5yCl945ig++qd90T0IM2ayOsWET5jX4dnjSdwlWV1mJCglFqYp2PhTpglKEmjKZDWVJpALsmtk+Jxk387WF/H6aE0SWKKjWEXGJc7RDZ08yAp5gd7D4r/y2lOWgKzJRC7oGQLCdDySJ6GmEwpBVn6watUqnD17Fr/4xS/Q3d2N73//+5g7d27UNs3NzXj3u6e/k8hEgskA2WA16PRj2OUXm9t98poZUPMcQhEh9xlZhlybXgBSkOV3ZNVPJmMkkwumiRF3ABGBkHnlZp3oMAgAZoXxhSQXzLImiwYxfbpmUhDsswOj7SKT1TXqxagnCJ2ax4KaBItYGaSarMRyQateA42KRwXN8MuZACVYTZZJKxkhHKU241fPTi/IUqt4sf6ESQZP9jqw7fSguE1aTGC87vJ5RiQi4Hc7zkc9lnZhc5pMVj89/omCrIlkspx+qSYLIBJGQKpRlIwvNOJ+ASQJdMviOAxryB/tvicyWTK5YJaooI6XgVBElDMyNkduWCMxWfmSC6bXaP0/R3oREYDVzSVoLjOhlBrY5KIvYjgi4HTfxDJZT+4lC+33rmnAl2+dLz425g0CARc0EXqd5ILJGjwN/O564I+3koQdcwQ0VQAaPaChybAEJjP9MoOfl08O4LtbZPK9tJksWmPp6odjgFzTfWEzPvO3I4mZeK1ZchlNlWh0DRAJWap6tEkCSzQ18HT/jKXiMWukio8YuWDtCnLbvT9WLsgc6di4PolgbRQqNHQudA/CGBiM3qhjl3jXqFWLLoSMyWKywbSCLFbXBxSCrGmECQmy2trasGXLFtx1113QaDRxtzGZTHjssccmYncuCgTDEfTTwGlpvQ0AMOjy41+HehCKCFhcV4S5VVZRXpI395q8MFlssSNkX5icCRiTFc/4Ik2wRU+JUQu1ihfrnIB4TBYzvsguG81eZzGbROkFeg/BZtSKvawAYEmdDVp16ks8GZMVJfcAUFVE5Q1JpHpuWpNl0qnx8/euwJWUvTKqBKxuSr/YdVGtDYAUZP2DMrTsO6XHZLEaiIkLsobdAfSO+cBxkiNj2tefrYnc2i8klQqxa7+qKAGTJTbdnbiaLDMNsljt3l7aZNMpq8tY1VwMFc/hzmW1+OE7l8Q3vVCyPH6FXHAcTJZOrRLZoL4xHwacPgw6/eA4YE6VVEtZMUVqss7TLP9lLWRRUkZdzJy+UHrGL0nQPuwW+5sBgNOf34C8dcCJgx12qHkO71hRh/WzyzG70gx3IEyCL1ozJWhMsp5xacBMjXSUgcZLXyYNrQMuIt0CAF4tFdwzxiSBHbiyF5lYHxoOScFPKibLWAKoyfn62mtbyfsKUjIsLjgufTXHzp8Ar34T2PNo8u0mCWz+qICdPGCRkiqsHrN92B0tx2RNpwdOSklcJhdkQS1jEicRTLlSwpPfkYMAm6edPKmiboMK84vFdUXgOInBZxLCtOYHeWAlD7imA/qPA71HJk1ZMpmYkCDrmmuuwfBwbObBbrejpSVHDWwvMfSN+SAIZME5p4o6Tjn9+McBciG+fTmRKTA6OuGAPh5EwkDfEXI/HcvddKHWiRPThEgGfawma/xBVhnNNLOsPhAnyJIxR9mYvbDmviVGLVCzjDzYcwhAtORpRZoBTdKaLF8oaptqa2xNixKMaTPpVGgoNeJPH16Nxz+8EvctDKe0bpdjcR0zv7BDEAS8fJIsom5cUEX3N40glQ3qE1iT5ZL1R2ONdtM2n7HVA+AIa5Mgwy4Ignj8Ky2JmKyJs3CX3AXJOXLzIhJIvHF2EN12r3gOWfQarJ1RhqNfux4/fNdSqBNJWJVZ2hi54Pj6nVWJ5hdenKTues1lJhhltZN5q8myypisNIrXGWPJasmseg3UNDAdGadkUF6PBeSfyTpBj/WyBhsqLHpwHIePXknm/8d2nkeEOS5aqjKr72Wsl9z44tyrwNkXpf9ZkGUql/odppBusuPL+riJDbZ9dmmjVA5pHCdKBi2jpI5mhAZZfWM+eAIh/G7H+di6TTHIsiMp6LiPobPJt5sksPGnjLOTB1hADGL6oOI5+IIRfPIvB/C3fTRwsFRSl1WBBMkqrWR4wRbnqXqiTQCYZNfGS79dsYcybzOuJbcdu6PUOD9/33K8/tlrMKOcJN/Y+qxvLI2SjqggaxoxWQEP8PubgMdukoLmVAzwRYQJCbLa29sRDsdOKH6/H93dk+8SMx3B6OWaIr2Ydd3bPoLjPQ5oVJxYj1WXqXtNJmjfQSY2vQ2oW53b957Iuiw/65OVvfGFGGRZSAbLKqs9MSuCLPZcRADcgfgLLU8ghHufOIAXjsZmvEfcZOIqNmkl18OegwCiJU8rGjILsuItskQmi9b4VNtiLbBj9p0GGex7cxyHNc0lqMlQ5cWCrOM9Dpzud6J92AOtihdNPNKywJ8EuaBbFmRKFr1pXn9qncR2JKjLGvMG4ae2+kz+poQ1yW+aazCmykJ/78ZSEy5rKYEgAH/f1yVjQ8nzRoURTAyYcxirm2FBVmD8NVmArC5rzB9XKgjk0V3QVAFwPCCEJZY1Cdh1xgJDnudEydF4zS+OUQkvYx7zfa4wR8QKmcT1tqU10Kp49Dv8cAwQKaGQaX8cubsgS1q9/n/R23TtpdvK2kekcKpjx3cJVYo4fCGSiGIsir4oPYc0GmQ1B0gg5NWScblvzId/HOjGN58/gR+/rAiS0pn/BAHoP0ruj5xPvN0kgjHptjCVarLfCiRBPIOeey8c68MXnzkKX5DOh3WrpDcpbib27YDM3n140k1BWBBugVRTZvbTQH/29UTW7LMDQ5IVv1EmoQdI7acq3ZKO6Rpk9R0B/GOEUT63jTxWYLJyg+eeew7PPfccAODFF18U/3/uuefw7LPP4pvf/CaampryuQsXLVhmvLbYIC4IGFt1+YwysgCHvLAyA4ezdHH0aXK74A5ArU26acaYqCBLEGTGF9nXerDFWDpMlk7NQ0uz+PHqoABgy7E+PH+kF//3YmyvFCYXLDFpgOql5MHew1HmFwCwvDEHTJZCLij1GfLCEwjheE/07xOJCGLgmHIxnQJNpSaUmXXwhyK47wkSRF42o1QMXFLKBQVhUvpkuWRBZlZ9UMS6rPa4T7NaEZtRk5AZnFAmS1GTBQDvWkX6Az3+5gUM0sW11RBfKh4DtoBgVtYxNVnjDLJEh0Gv2IdtniLIqshXTZZKTQItIC3JILOal7tH5qou642z5Nq4di7Zn/wHWWTcKjNJc4VOrRLl04FRejzMGQZZLCgL+ch84XcCnTSomncbuRWZLHmQlZzJYnLBumKDKDHtGvVI9VippIIMLMgSCFOjMhNWvc/hw7kBcm7HyMWY+UWy+W+sU3p+tH3Sg454EJsRh+kxkwe5AH723uX44qa5sOjUCEUEsTVFVJDFpIKAdMwjwYkpJUgCFmSZBVfsk0UNQB2VPV7YFfs8hYrnxKRPSsngNAqy+sZ8+MnLZ0ngyIxnAKk1RyHIyg3uuOMO3HHHHeA4Dh/60IfE/++44w68+93vxtatW/GDH/wgn7tw0UKafA3iwp7hhgVStogVVuacyQr5gRMkgMbCd6B3zJtQQubyh+Iu4JOC1WXlO8gKeklWGUBwXDVZdAFBf4vomqzohTDHcTL3t/jHhfW3Oj/kjjEvYIN7sVFLarJk5hermkpgM2pwxcwyMeOdCoxhcPlDCIWjjUbkznCAVJPVO+bDN58/gZsf2YGXjksF556gxMwpGbxMwfMcPnv9bADAWboYuW5ehbgoG/MGksstA26y8AImVi7okwVZ4vWXQZCVwmGQXftVCUwvAOn8c/lDSe32cwGHoiYLAG5aWA2LXo1Bpx+BUARVVr0YcKZ+Q8osyIMsQZBZuI9TLihzGDwZx1kQkNgWpy8kMpM5Q5oNiX3BsHity39rVpc1Hiar3+HD8R4HOA6i+Ui+TVJY0FKqmK/YOMXkghkzWRqD1FzY1Q907CFjenET0HQFeZzJ7mRMSrpywVKTFnWimYtXYrLSlTyx5sIUehv5fn0OnyhBjPktWZLRa0/8vn1Hpfshb+o+YZMANn+YAlRRID/+IE3nP3bVDMym9ZBsnI8KskpkJSVao2R8M8nmF+w3M4TjBFmWSqBhLbmv6JelhGR+kQGT5ewFwhPTPDwb/Hp7G3708hn8adcFoOdA7AaT0LdyspDXICsSiSASiaChoQEDAwPi/5FIBH6/H6dPn8Ytt9ySz124aNEjkwsyJgsgEvCN86WBTGSycl2TdXYroYAtNfDVrMHNj+zAzY+8ERNMRSICbn7kDWz4/jZ4E0jj4mKimCyaDRM4HmE+vvQqHQwlYbLiBRupjAnktuWsboRBYrK0hEGsXECe6D2EUrMOu76wAb+7e2Xa+y5nGJTZbMkZjnyHGhmT9fJJ4gD3/BFpscikgjwH6DXjH17eubIeyxts4v/XzqsUg6xgWBDt7ONiuJXcGorHvTDPBG7mrqhTZ3f9pXAY7HckdxYEpPNPECT3v3xBWZMFAHqNCg9unI3mMhMe3DgbLz1wVfr1eIx9ZI6lkRAQDuScyTo/5Ma5QXKdLVAwWVa9RgxmWgfiLKLGA1b8n8BwgYH9znoNL7LNgDTGKI0ZMsG20+TaXVxnEw0I/KEIAqH8ubmyRFSpOTr5w65njrkDZspkARJD4uoH2t8g95uukBIW4nayZAuzV08gF2T7W2LWiU22s2KyVt4D34ybxH+tZSTI7h/zoZPWYsX8lunMf33Hov+fgpJBpoTQB2gNlYLJYmAGQeK1VrVIMo8oUdTtiw6Dk2t+MeL2g0ME2rA79klLNdB4Obl/IUWQlW6vrCj2SpgSvcISoZWOq52jnmgmi6HAZOUW58+fR1nZpRO5TgT6RK2+ASUmLZhJ17J6m9jjBUCUXCkbk4WEOPcKuZ1/Ozrtfoy4Axh2B/D0vmjXmwGnHxeGPRh2B8R+NGlhwoIsGsDoLONqpswkUSzglQcu8WRzopwrAcPHmCyANOSVQ2SyGFNVRtgeJi8zatXQqdM3mNCoeJio5bwySGaZyCIjY7LIudU54hUlkq+fGRSLdiXTCzW4HDSn5nkO/3vHIpi0KqybSaSCBo1KlFsmtXFnmd6qRblrlJ0G4skF+50+BMNpLmBTMFmSfXvipIBeoxJdGPPNUDBXOmUy4e51zXjts+tx37WzopjdlHDTDHVxk/RYwJ0TC3dAOoffah9FRCByMHmiimFWBcmun+mP30cpa8jNL5Kgb0xSK8ivpdIc1GS9dooEstfMKY9iIPN5rrCarFJTfCZL7aHugpn0yGJg7JezHzjPgqwrpYQFQzwmayx+kMXG2TKTFvXF5JzrGs2CydKacP7aX+FLwXvwCncZMPsGAEQN0DniFT8rinHW28htMuMLZjrFkEBeHA/BcARbjvWO2zwlGYSDj+Pv4U+jheuB1ksTJwomi2GmGGTRa02tA1rWA+CAekW9Nzvuk2x+MewOwAIPOESvqwReTQLw2pXEit/RBdgTuwFWxWmQHoOgT2prwcxWprDDoNiIfnRI6nUmxyUUZI1Pz5MEjzzyCD72sY9Br9fjkUceSbrt/fffn6/duGjRK3OdUvEcSkw6DLn8uGFBdBaQ9WNw+UNw+EJRGdFxgUldyudE1Zs8trMdd69tEp3D5I0GT/SMYUWadUJRvbLyCfb+2gwsg+NAkguSBUNKJos5DMoWNSd7HTjT78Sti2skbTqAY4q6pyh3QYA6MUFq9pgFigwauAPhmCCLsWbsvKm06sFx0fL/MW8QhzpHsaKxRLJvH2c9lhzza6zY9dC1MFAmhOM4FBk1GHT6YfcEEsvQxCBrcc72JR3I5YKlJi20ah6BUAR9Yz7Ul6QRIKRispyp5YIAYR+HXAHCRqbvnJ8x2Pe16nP0m3tkWW+1nkg+A66cWLgD0fVNAHD32qa4CYHZlWbsbhuWJEy5AgsIUsgFE8lCpZqs7BbIgVAEO1rJMb5mTgVUPAeTljT+dvpCMXK+XEE5RjKw5ux6thDPVC4ISIv34bNA7yFyv+mKWPe/eMYXniGyiNVEH2fRotusRX0JOec6RzyAMUMmC8Sk5/HwRrxpeRseq6gBcDpq3owIJGEkSrzTYrLo+FbSAoy0Sc5taeA/R3rx308dwjtX1uH/3rEk7ddlgsjhpzCD68EN/D6oPDRASPDbzlQyWQBw56/JNVI5P3pjufnFJGLYFYCVi1Prbq4kDpY6M1C9hMjlOnZT59hYsKRJ0oCXsa1qA0kant8+ZeuyguGI6A9QMnaCPGhrJPJGxt5fQu6CeQuyfvSjH+F973sf9Ho9fvSjHyXcjuO4QpCVBVgjQ+b2tnF+JV491Y/bl9ZGbcca4I24A+ge9eYuyGL6b3NlVL1Xt92LrSf6cRO1ce6QNRo83jPFmaxxQGnhnqwmS/68XJ734N8O40SvA75gWHSPA4Dj3dHHbUQuFwSAIjp4J8mWpYLVoEHPmC8myBpW1JppVDzKzboYQ4BtpwdJkCVK5dJn0tKB8rwtpkHWWDLzCzmTNYFwy9g8nudQazPg/JAb3XZvekEWY7LGuojNNx99LPvGYl3a4sGq12DIFUiLnfCHwrgw7IGa59BQYkxsr66AIAgxfbLGDbGXShlpzBrykV5ZOWKy5DJLi04tmnQoMasyMZMVCkcQFoSMGGPpQ9OTCyqdBRmY3C5bueDBjlG4/CGUmrRYRPv1WPQaMcjKF0QmSxHEFRs1AASxkauQjVyQLd6P/xMQIkRqyuSApnJJgio3vjAUS0G8szeq12M4IogJplKTDnVyJqskQyYL0phdbNJS+/pYn4phlz9OkGWP/4a+MYnpnncr6ZeVgVzwND2n24dyY4j16+3n0Dvmw+dvnCvKgiPOfqgALFadB8fm2URyQXqtnR9yIxSOkPHHUBzfIp+xIJMcZI24A2iBrK1EkCRGBXMVxJRN41oSZF3YBSx+Z9z3Yb950r6ZLMgqqpMaXE9RJqtr1CsqW2o8J4lernY5SWQ4ewi7x2ooLwHkTS54/vx5lJaWivcT/bW1teVrFy5a+IJh0b662koybN+5cxH2PHRt3Oak1bK+MDmDi2amzJWilphJuJ7YKzEqciZrSgZZtEeWMI4gKxIRxCwUkx1FuQvGYXVE4wsa1EQigqhj/u0bZLIsphK91kGXaG3rDYTFBqKiXJBlyMbBZMVj1gRBEIMsuYmGnAm4ntb/vUZrPNwK+/Z8wWYg+5NQLigIQD+tWZjgIMtF2TwWdNRk0nASIDImXkMctOLo7gfSZLIkh8HUC+d3/moPrv/Rdmz4wet432/fTG8/Qep4QnRCtWQiCUyESBjwUrtnU5lUSxdwyyzcxxdkWfQa8fx875qGhPs9my78zvZHM1mCIOBtP9+FDd9/PbuGwBnKBZVjeiLjiyf3duBrzx1PaXTCxpkl9TbwVGfOztV8yQW9gbDoOqqsySo2amGFG1qBBo0JJGVJwRbvzC67+UrpOVld1if+1YU9bXRxznEJzS9GPQExCCo2alBPa7I6Rz0IMzlrqh5ZUe9HjmuJkTDbSskkoGAm2Xsnmv/6Sc8tWOukXokZyAUZ0zA4TodKgKxHvvvCKaJieWyvKJfmaSL2Mv4k2VCtl9oyKFBTpIdRq0IwLODCSIrATwyyJk8u6A2E4Q2GYeXo+qaoDgIbq+TnL3MYZHNRHBSnw2Qx1qqoTkoeTFEmS77mmy/QuuiaZVK7GWOJ1KvuEsCl800vIrDJ16hViYt1AAlrYCSnsSwWBPEgCDImq0JcPF47j0x0cqMG+YB5us+Zfl3KhDNZ8Qf/dDDqCYiZGxaMsKDFqFWJCxk5ROMLuqgZdPnFonMmT1rdXIIysxbhiIBTfWQ/WUZUK6ujkpiPzqxtfOPZuDv9IQTo7yVfFFQXSXKtB6j737FuB7rtXnGCHa99e8r9pQFoQht3+wUiBVVppZq1CYJLUaNUm6n5Ba+SJtI4gbPYiDgVkyX2ykq+cO62e3G40y7+f6BjNO36TXb+chxgzKDRdEJ4RgBW42AoIUwWkFO5IADcsKAKtTYDPnxFc8JtZleSz5af1wBhn492j6Hb7sWF4SyYAFZzlKJwvU/RiJiB/e7nh9yimVAwHMHXnjuOP+xqx/6O0aTvy85DZuYAZBaQZwPGumlVvNhPjaHYqEUlbVYbUJmy+32tMgWHtQ5YK1PHyOqydvbx+PfhntjXKX4LFsAWGzVQq3jRAc4TCONYazsAYChiTnv3RsU6WlbbGhtkRS2yU81/jLUqmyUZxGQgF+ymhhu56APXO+YDi+v3tI3gf/5+GAj5ofLbAQDFoMlVc0XC2liO40TJoDKpEYMpwGSJTpk8HZMMNvFcinLHZKYdSVjGYiqXHb1YgixZqcMsjjJwVYuAmuXkfjZM9TTGhARZb3/72/Hwww/HPP5///d/uOuuuyZiFy4q9FCpYFWRPi1zASbd8uTKZcw7SrLsAGCuELXl62YSrfSQyy8uvi7IshqBcER080qJCQ+y0p8wlWDZwGKjBhrK5rEgStkji0Fkjqh7X9do7GKtpdyMBTXkODDzC/lkLf72bKEQ9GTtuMT2Vx5kjdCFhkmrgkErLaBZZr2p1Ii5VVasnUEmvd9sbxPNTXImS00AG33/hBILJhWsmAeo8rsvSkh1aeSYsaL59kwW5GxhqDC/CIUjojS1Ms5CTY5U5ioMu8+RxcocytykdG2UQV5/Fi+ZkDFYdtpQTHpKyZksJhfMgVPkD965BDs+f03SQNVm1IrMtLxWhNVKAVm6thbVA+CIFCxJ8X5vgpqseVVW1JcY4PKH8MIxUtd1qtcpSoxP9yU36mDjtbyW0aJPLyDPFsOyeizlnFVi0qKBI0k7rybLWo05N5HA6o5fAPcfJMEHA01CBaGGA6ZoS/4EDYnZIpolzXRqlWg0owuSsfjoSOzy6S9vXsAHfvdm8rYbiM9CR8k/U1m4M6mYrV4yiHEPSvNZCrBzwOUPwRMY37qAXQPs8t97fjR+o+0UDOXMcoX5RSKIQdbkuQuKrRV09DfTF0FgMmD592QBsGco4W/DaqtHk0nf2e9dVC9LwE0tuaAvGIYnEJIlngTUccwptgmYeS1w9ReAG789Wbs4KZiQIGv79u3YtGlTzOM33XQTtm/fPhG7cFEhUYYzERir4MpVkMWkgnoboNaJQd+cKovYxPP8oBuCIIgXHCvuVNYXJUQqTXq2aHsd+NsHiQsVIA184zC+GHJG1y0BwMJaK66cVYa71zbFfY1V0Sy2K85iraXMhMV15Djsv0Cy08rJGgAp2GYDewJHulQoUgR9gGyhoZD3sIzjWhpUf3L9TABErvTYjnYAwB3LarLaj3Qh9cpKMDFNUj0WIHMXpAvXGOesdMDYSYX5BXPE06p4lMWRHMkRr+4vHliQtWFeRXqujTKw91ayE1lDXo8FSMmPgCtnFu4M6SSoGJslr8vqc0iL4a5M+p8x6MxS/Y+815ECfbJkmhw8z+FdK4lE+K9vkYXWoU6JvYoXZP30lbN42893wukLimNNbRwmK181WYl6ZAHkWp5NM94OQ112H6A1Add/E1j6XtLWQg6asBgUigBwomwRgCQXPP4s8MMFwLfrgJ+ugL+PyA7lDH59sREcIqjnyPx30B4d7PtDRDb3xtkhvH4mOsgYVdTRyoN7xnhHyQVTJRnli26DTZIXJjDLid7PSFRNLZu/skW3nVyXTF476glAiCeFTRVkVcYxv4gHFmRNorvgMAuytPT619sgzLoeIV4LofEKaUO9VdrfBHJOxm56g+HEbW6YSY61hiozOCKNTdLoeCLhC4Zxzfe34daf7hDHnzI4oOeCEMARdplXAdc8BDRfNcl7O7GYkCDL5XJBq41tjKrRaOBwTG7X7umIXpm1bzoQmaxM+lQlg8z0IhwRxKCvxmZAcxmZeNqGXBj1BMVJ+3raIDntuizRwjbHTNbunwEn/kX+ANLrC4Cgz14uqDS9AEjm888fWYNPXTMz7muUNVBs4SNf97WUm3FZCxmgd7YOQRCEmMlahG18xbDx5IJiXxvFYv4dK+rw6HuX439uIM1i180sxZJ6G+mzE47gylllMS6XuQZzJLPLmKyOYY9UIzNJzoKA3MKdXHezZIuHtNsoxGGyAqEIvvwvou1/+4ralMwRWzgnawQuCIJYo3J5S6lMhpnewot915zUYwGSBIg5iIlMVm7lgumC2biflQdZY+NksgAp+JfXariHgK79AAhjyaRc8eps37GiHjwH7D0/grZBFw522MXn4gVZf32rEwc77NjZOiyTC0q1bdY8B1mJemQBZCybzZNxy6mvjXl+3KheCgA4GyHvHc1k0SCr9xCx2g44geFW2Nqej9nf5jITajAME+dHQFDhtcFo9cPO1iHx+PUqGssmY7JYIm1YXh/F5j+/A4jEkdjL5WOAxGalUZfVN+aLUpUPulI0wU0Bdj4x1UU4IsAzEkcKm8D0gqGZ9mvrTHVNTQF3QZGZ1dBjpy9CZNXH8J/Fv4ZQvyZ6Y8ZmJZAMmnVqaFRkLE+ozGC92YylQFEtsOJD5P//fBYI57cPYjo4N+hC75gP5wbd2E3nkxVFZK3n0lbEJj4uIUxIkLVo0SI89dRTMY//9a9/xfz58+O8ooBkEJ0F02SymPGCe5yyABGi6UUFhlx+BMMCeA6otOjQQin/84NuUSpYXaTHsgaSaTvek2bQlC+5INt3Jg/JBZOl6JGVDpTNiJlccMMcaSKaUW7CisZiaNU8Bpx+nBt0xfbIYhinw2CRIVZaJvaJUSyM9BoVbl5cLQY6HMfhXhpMalQcvn7bgpz0yEq+v9E1Wa+dHsBV33sN333hFNlg6Ay5LZ+b1/2IB7m7IAA0lpqg5kkGvTdZLxQ54jBZv3mjDa0DLpSZtfj8jam/V1WRVKyfCJ0jXnTbvdCoOKxsKhZlmAlr3RRgsqicOQsyuSDL/oo1WfI+WRPXWHq26DAYXy6YtpmJEpU0yJIzWX/7IPDbDUDnXgy6/IgIgJrn4jKWVUV6rKdjxV/f6sQhWU3dqT5HTDDPruXjPWNiC4B4ckFWT5hrDCdI2AAkYTKHI0HDqC5LJisZapbi1SuexKeDnwKgDLJkQV3jOuCqzwEAzHZi1iBPZt23YRY+t5wc1zahBif7PaIhEQD854jE3iivc3Y9sfeTB85L620AFEYmbP4TIiTwU0IMsuqjv4czeVsAAFHW8QAw4BhfXRZjc1vKTTBSibQ3bpCVnMliCZ5U8ubJNL744652XPV/r2H/BRL0lLCaLPZ7cXGW1GIAHD/I4jhODL4Tml8wMyDGWF77VXJ/4Diw/7FMv0bO0RFHCn9FOW20rcnCyOYiQn6r0ym+/OUv484778S5c+ewYcMGAMArr7yCJ598Ek8//fRE7MJFhUSuU4lgpIs9T66ML2RMFhuwq6x6qFU8WiiTdW7IjRZ64TWUGLGMTiQHO+1w+0MJa5VEiEEWzeTlyo2GSQzYZCS3cM+y5yjLOJdl0F/GkkAueMPCKlTb9DDrNGIQs7KxGLvODWNn63BsjyyGcToMxnMXTNQ8NB6um1eBb9y+AHXFBjHQzieYXJDJ2v5KHS3Fxs1+uig22PK+L0o4ZXVKALG9byw14tygG60DLtQk6uslB5uYKZMlCAJ+v4NM0v/v5nniuZEMTOqWrJB81zlyPSytt8GoVYuTffpBFmOyciUXpNlpMciiAZV/cpisOVUkyDrRKwUu/bJFqXLBmjYYk9VHmSxHL3BhJwCgd/+/8Q0XWaxWWvUJGcv3rWnAq6cG8OfdF+Cli32eI+YVfQ6fqHRgTmgAabUgCIBOzUclT5jcM29yQZHtjz1vrVpAz5FFeZ86D0EWgDe8jbCjHYBCNl+1EODVhNF655+AgRPA9u+h1EmSNXJ5Y0OpEQ11LuAE0KGqRygg4ESvA8sbihEIRbD1hDzIij4vlMkxNndrVTzmVVujtgFAJOAqHRD2k0Qjmw8BYm6kZLJEl8T4jZXl6FawbON1GJQbqRQbtfAEvAiOkfnVIRilXlIpmCylGVRCsLHBN0Z6L+Wx5jYcEfC9F09jdXMxNsytxJ92t6NjxIOut8h3svH0uyWbZ0qSM1kACb4HnP7ETJayAbaxhCQEXvwicPLfwOqPZvCtcg+lI6Rew2OBwQ4A6EUFmiZ+l6YMJoTJuvXWW/HPf/4Tra2t+OQnP4kHH3wQXV1dePnll3HHHXdMxC5cVGBZspp05YI0u5Q7JksKspgsgi0cW8rJoogwWeTCayo1YWaFGfUlBgRCEbxxNo0MlOj2J8TP5GUDQZAKclmQJVq4Zy8XFJuGpjAikENyfiO/SeeIFJD+7x2L8IWbJKaCGYrsbB2K6rcShTzKBZU1WfHAcRw+eHkTNsydmKwVs3Af8wTh8ofw2mnyu4rFw5PAejCw60xuYy/KztJtbMuYLEcPEPLj3KAbw+4A9BoeNy9Kr96NfWb7sDuh1fjedjJ5X05lqaJc0JueXFAZUI4bLDstygVlTFaOLNwzwfxqK1Q8h0GnH/00mTJu4wuALO4BUlcR8gNnXxKfaj/4Kl44RhbsV9BrPx42zK3AwlqrGEA1lRrFBMcpmWRQvnA7SpMQtcWGKLY53zVZoy4PVAjHlQtyo+3QcUF4BB36ucTfdzyQ1wJHyeZtDcCn9gIf30nOuUryu5QEemGFW6wlFjFIgi+PdQYA4EinHaPuAB7beT7KmbFHwWSJMm+axJhXbYVBo8Kq5mJRATGk7HvGFu5KNYdnmPT2gsyCXnSsTM1kKdnX8ToMyo1UGFMXpjXPeyLzpA1TuMrFm4PiwlAMsE5UjOHJE147NYBfvn4O//P3oxjzBnFukKhzmJuiFdTYSx4EK5GG+2NSJisSls4BeduAulXkdvhcyu+RDK0DLvxpd7vokJwNOuj6RasmIUVTqQnlEaIauhDJzzU9XTBhFu4333wzdu7cCbfbjaGhIbz66qu4+uqrJ+rjLyokalKZCLlnsiS5IBuwWZDFarLOD7nFfgkNpUZwHIeN88ggu/VEf+rP0OhJXw0gd5JBv5NkBgFpMspBM+J+R3qW2nJIcsEgwhFBnKjktsoMLMja3TYsFimXGBXZO9agMEsmK94Ex4p7YxYaUwA2WTDwysl+0f5+1B0gwXSATn7aiVuQA4RxUsoFAbn5hQv9Dl9qBsRURoMJkrXeR4OhpfU2cSJLhUqrDha9GhEBaKOLA0EQ8Itt57CDJjpYkDCTyuIylQvmvCZLaXwRZeGeW+OLdGDQqjCL/nbH6EKdNYMGgH6nL/22FHJYa8mCKRIiC/czL4pPLcFZmDUR/OMTl+M7dyY2buE4Dp+5TmpPsKTeJjJvZxIEWQy1CjbVki6LkA3CIXy29UN4XvtFlCrHLYCwRwDOCrVwh3PbxBwgPQiZ6ykQxwCqdAYxKAAIQ0DH0rlcR2xQOEgMMTRVJHj44+4LWPOdV/AdKlNe1mADAPTKrm/iukbmXhs1OSgz67DnoWvxh3tWJ+x7ltBhkI3x5kpATRN7LNhK0eAakIIiJu3LJMgSBAHPHe4RSwHkNdm1xQYx+cfRIOvNyDxifMD2NwlY4tEXjCTvP8erpGAjz+YXR7rsAEhJwL8OxbKEJiGNICtNJgtIYOPuG4PY1oLV6gFAKa33dnRJCagMMeDw4d2/3oOv/Os4/vJmdqZZgCQXvO+amVjZWIwPrW2CzU/WWGcDWTqGXiSY0D5Z+/fvx+OPP47HH38cBw8enMiPvijwu53tePS1VjHbMRWYrG5FkFVfYoSa5+ANhsUGtSzwum4+kQu8eqo/vaxJKhvbTCG3lY0nF8wSTD6UUZBFa6BCEQEXht0IhgWoeC6ute+i2iJY9Go4fSGxqDSWyRpvTVasHn7EnbkMcqJgk/XJev6IlL21e4MQQn5AoJP0BLIeAHHuCobJuS2vU2LmFwcujOKmn7yBTT95I7GTFEAcUMS6rHaRcVrVlP6ExXGcrKaInOdHu8fw8JZT+NI/SS2QWHdHz6eUro0KsJqsnMkFY5gsykT67FLbiAkOnBfV0jYK1LRHzmQJQrQRRjrwBcO46ZEdOBGhiZGut4C21wAAEU4NI+fHemsfVjSWpDQ32TC3AkuoccKqphLRhl9ufjHqjv0tlcmcvDJZjm5Uh7owj+9EjSoO8zBAApQzkTq48/DxF0Y8UYGV2x9KbkBDpZwL+PZogyFBEIOssmZiqHN+yI1AKIKZFWZ8eF0zvnsneXzQ5ReDbxbkqnkuyoWziLb8KKFy7DFvMDpgT1SXrJQKAjK5YBwm6/g/gcc2ic8xuSAz3MgkyNp3YRT3P3kQ7/jlbtg9AfQ7fAhFBKh5DhUWvZj8U3vJXNsplGOg5loSuFYkryO16NSi8VPK83CCzC8Y8wuQmlglDGF6ncmDHyWY9Husi8gb421Cg++ReMktxtZpLdEGEsYSKdgcid23VAiGI/jUEwfEmvI/7GpP2cg8ES6MkGBzTUsp/v6JtXjP6gYY3OQ8PeUtRiibRNRFggkJsgYGBrBhwwasWrUK999/P+6//36sWLEC1157LQYH4/RTyDEeffRRNDU1Qa/XY82aNdi7d2/ePzMf+PUb5/G9F8kgb9apoxoRJwOzcM+du2Ask1VrI8GBRsWjoYQsguyeIKqL9Fg/pxwAsLqpBEUGDUY9QdGSPCnYAJIrSYB8QA64iFRwnMYXgiBkxWQZNCqo6QKKZVlrbKSuTQkVz2HjfJIFZIvfGHdBVgDtH8sqKJVqsqQFyHASR7DJBqtJ8oci2EaDeYBkVh0umbw0Bz2VMoG8qN4ka8g8g8q4Tvc7MeIOYMwbFFsfJITMYfAtGmStzCDIAiCyMMwWmSUEesZ8EARBDLKYJDSea2MyiExWzizcFTVZzMJdnrGe4MCZLUaPdTvgD0sNe1nyIV77hWR4/cwgTvY6sMtNF8a7HyUsnaUGveXrAABXaM+k9V4cx+FXH1iJb96+AO9aVS8yWYnkggyJmCx5f6d+hy/rRVcUZHbeleE41t6UyTot1CNOPDhusGuHXQsRgbAliSBQyeB87gLK5QkmZy9x++NUmDVvKbQqHjwHfOGmudj6mavwlVvnY1aFGVoVD0GQFA4syC02xfYIAwh7zGLp0XQaErMgiyXWAID1aIpnfLHv98CFneDPvgBAkgsurSfzayY1WawEYNDpx9f/fUI896tteqh4Thw/9D5yvQ4Ixei/6bfApw+lHIt5nhNlxymTPBNgfiEIAo7KZKadI+S7LqyVSgu0IRZkJWGyzFVElSOE48v5Q34sCRyCDoH4TJbS9EIOxmYNtyb9LvHwxJsdeKt9FBadGmadGm2Dbuxozfx4BsMR9NDAvbGUjs2CAJWTnKcdQtm46/6mMyYkyLrvvvvgdDpx/PhxjIyMYGRkBMeOHYPD4cD999+f+g3GgaeeegoPPPAAvvrVr+LAgQNYsmQJbrjhBgwMDKR+8RTD25bW4K4Vdbh5UTX+946FaTu4MQt3d7Z9sgSB1A0wyJgscZCVsWqsLgsAvnLLfDHIU6t4bJhL2KxXTqUhGcz1QKpskOjslVm4ZxdkOf0hMXhlzSrTAcdxYmBzgmbI62yJF4+fvX4ODBpJSlOsND7QmaVsWhoOU0qUmLTQqXmEI4JYNyTWZE1BuaBJKwWpwbCAVU3F4vFxOuxkI14zaY2IDRoVVDIWYka5GcrLddgVwOFOOzZ8fxu2HJMWn+GIgIeeOYpjHhsAwNV3Dp0jXvAcsJzKkdLFLAWTxdjJQCgChzck1vgxcxPGZCVtjCkDCzhy7i6oZLJYYofjAdXEno+L6mwACJNlp2sgs06NuTSgydRh8EX6Wx+PNJEHWBZ69g1oNRAmZHH4ZNrvV1Wkxwcub4JGxWNeFVkAtg64YpgUOeT27UAsk7X5aC/WfPsV/G5HYolTuojIxqMSf1fsBgPku54VsmeyxjxBfP/F0zgsc1lkeO0UOXduXCjVBCVTdfSbSGuKhaoLaCqTBQa0HgslLSgpsuCv/99leO7eK/Dxq2eIczHPc2KTcCbrV9ZjKcHznMhmRffKspFb1isyHCQGCHGZLFqTxZKHcrAEhXsQYQHopYkWJm3MhMmS28w/e7Abf9zdDkAK2slcIcAUJJ85KBShymYgEr80EE9RERdMehivH1eO0O/wiyyPHA9snC1arqsD9FgnM77geYnNiicZfPNXuOvEvfiY6nlxPI6CaHqR2yCL1cZ/8pqZuGslOZf+sKs94/fpHvUiHBGgU/Nin1S4BsCFfAiDR59QmjHbfzFhQoKsLVu24Oc//znmzZOKIOfPn49HH30UL7zwQl4/+4c//CE++tGP4p577sH8+fPxy1/+EkajEb///e/z+rn5wBdunIPv3bUEj75vOe5Yln4/EeN4Ldyfej/ww/kkoxIOioyQT1cqLt5YBhUA5tKJ/spZZVETGwAspNKbHnsaF12uO7srgyxH97iZrAGarbTo1eJxThesNw1jsuLVYzHU2Ay4d4PUcytu4MPcm5TfMw1oVDzWUPOD7WcGEYlIPbmmolyQ4zhUU/b0hgWVeOye1SimAYKT9d6bYFkZADj98S3NDVpVzO875PLjpRN9aBty4/kjUi3FwY5RPLm3A8+epw6UPYTVmFdtzbj2SekwOCKjCs4OOMV+OezYyQ1F0oFLdBfMQTArCBLbrKzJYue0xoSYaDXPmFtlgZrnMOwOoN1JPruqSC8uLDNxGAyGI3j5JEkwbY6swbH69wEL3w4sez9w5YM4xJE5stl7FEi3p5oMdcUGWHRqBMIRkb1kbCVbGALRjYiB2MbVbB//czTzhI0SvhGplsXsUQRZ4RAwQgr3iVww8992zBPEB37/Jn72Wiu+vTk6OJUbLV07r1KsQ0qWcDwSJgzRTK4bGkG2HZUKopwEYcsbisX5TA6WcGTBt+QsmPgaEeuy5OYXSibr6buBHy2QTFKKZEyW1gTo6PbKJBtLXLgGMBYgSRyNisOCGjJPD7n8aTOWLOhgjNN/qFS7liYIi01aWOGGWiDjh11lS9k0XQ52HqZksphlfRpuitmC1WO1lJmgo3WwKp7D5S1leOTdy/C/t84GH1JYuCdCMvMLel6t4k9PGJMlCAIOdJD3XdNSgg9d3gSAtEJJV8XA0CEz7eL8DrJefOP7AIARvhRBqDNm+y8mTIiFeyQSgUYTO8BoNBpE4jXayxECgQD279+Phx56SHyM53lcd9112L17d9zX+P1++P3SQMeaJQeDQQSD+ekhkgrsc7P9fB1PBlC3P5TVe6jbtoELuBDqOQqhuAkaCBA4HvsHBIQiAiotOlSa1eJ7f+iyelj1KrxtaQ1CoejJzKQhk+iYx59yX3h9MVQAws4BRHJw7HlHP+T5tPDgWagEcv4FeTIxZnp8uqkWucKiy/i1LHvMsq81Rcnf40OX1eM1Ws9WYlDFbKsyloIHEHL0QcjieF0xowTbzwxi2+kB3La4UqybM2u4cZ/74z2H4+En71yMjhEvblpQCZ4XUGTQoGfMB8cYCcoFjRGhCb5mx+giyaSN/X1uWVSFv+3rRolJg7MDbgyMedBHJYMDDp+4/bFuOwDglEAXUb2HARAWK9HxS3R8m0pIINo+7IbL68eQU5rsjtPPsRk0ECJhBCNhmLWsKWbq6zMSEdBNe3AZ1Dn4bb12aCJkvAhqi4BgEByvI5MUzeYLGsOE/6YqEKnZyT4njo7QIMuqQ6WVLIyPd9vxxJ52bJhbntIkZue5YZH980OLP1k/hm/dvkB8fo+3DvcKHAxBO4KjXYAl86bec6steKt9FEc6RzCzzCCyD0vqirDvgh0AUGnWRP1eejW51r3BMDw+P07QWpTjPWNwenz4wrPHcbR7DHcuq8V7V9fFMulJ4BnuAkt38KNt0efJWCc0kRDCnAZ9KEZtMLPzKBIRcM8f3sKRLrK/rQOuqNe/2TYClz+EUpMW8yqMMGlV8ATCsLt9qLHG/w67BvRYLZhg49wI9h4Ta7RUvUfBAwiXzEo6H1XRbH73qBvBYFC85mwGTcLvxpIcPaNuBIM2AACvtZD5zzOKSMAP9blXwQU9wPBZAEDIVBU1zqstVeD8YwiNdkKwtZAHBQFqzzA4AIKrH/1Bcv7WFxtg05PZMBgWMOT0pPWbDtKk4ofXNeL5IyRBBADVVi2CwSCKdDwqODsAwC6YUGyxIBwOIZxmpYKF7tOoy5f0PODNlVABiNi7EM7TeHC4kwQhS+uLUGnVYXfbCGZXmKHmIrhubhngFoCtZNsgb0g6x/HWWvJbjnbGnDsqRw94APP5Cxhxxn5v3jVIvquuKOa7crZmqAFEhs5mdBwuDHsw4g5Ao+Iwu9wInZpHkUGNMW8IvaNucZ2WDtoGSaK6vtiA0InnoT75b/E5p74a8ACne8dw4/zyqNcFwxE4vMGoNgmpkI91RLZIdx8mJMjasGEDPv3pT+PJJ59ETQ3RDnd3d+Mzn/kMrr322rx97tDQEMLhMCoro11tKisrcerUqbiv+c53voOvf/3rMY+/9NJLMBonPjMux9atW7N63bAPANRwegPYvHlzRq/lI0HcGiAZ0QM7tsKjLcN6AH6VBU++sg+ACtUabwwjWQlg17bjMe93ZpgDoEJH71DKfZnbM4o5AC6cPICjrsz2Ox4Wdu3DDNn/F/ZvRQuACHhs3bYD4LiMj/HeQfJ9VAFnxsfW7+IB8KI0K9h3Bps3n076mvdXk0T+i1tiGeCVjhBqAZx463Wcb8+cfRI8AKDGm+eG8LfnXwaghlEl4OWXtmT8XomQ7TmcCByALVTmHvaS43nk8CGsAeAORPBKhr/JeHF8lJwPYZ875nyYC+DLi4Cnz/M4Cx57Dp3ABRcA8GjvGxG339pGvsfRCFko1Qj9qFI5UONpw+bNyQuclcdXEACDSgVvmMOfnt2Co73kvQFg697jAHjoII0LXW4AUKN/1JXyfH61h0ProAoaXsDQ6f3Y3J7q6CSHydeH6wAEeT02v/QKAKDI0471sm08QQEvT/BvCgBFEXLcTtmpRHVsEMMXBgCo8OKJAbx4YgDrKiN4Z0vypOHT9Le1aAQ4gxz2nu7C5s2Sq9fpPhUGUIxqjGDXlqdhN81I/GYJYPSRz9i86yj0vYdx/Cz5vyw8Ag3PQcsD+3e8CrmnRlgAeKgQAYfH/7kFZwZUADgEwwL+9/GX8J82svj98SuteGbPWTy4WLFqFgQs7voTfBobzlTdHvVUS+sRMBPnsfbD2C77/Upcp3ElADtfDAE83KFIRmPEoBc40KGGihMQFgjb+PfnNsNIVzb/bCfffYbRhy1bXgBC5Hu98voOtCfo2rH9uArXRxqxVnUCx7b+BR2lVwEANpx6FRYA+/oE9CU5B91D5DP3HD6NWsdJ/Os0+d893IvNm+MzL2o32eZb/z6GsbbDqDQAM/t7sQBAT+txnHL/GRuZuybFjqPtGDsn7cflfg0qABzZsQWdJ0nwowm5sYkmLhzdZ9FFiWGb4MIrL22BUa2CJ8Thmc0vozqN5c0pejyHLpzB7VUCfjykggAOw51nsXnzGZwd41DOkYB3ULBBG/ZmNC96x8j779p3CHxXYnO0mtF+rAIweuE4duRpPHjtJNkXbrQTFQIAqFDJjYnfx+zrxbUAgiojNm+R3EHjnb9zeocxF0Dn6cM47I3e3/U9Z1EEoIxzIDjaGXO85vS+hbkALgw6cUTxnNXbg2sAhPpO4oUMjsNbdN1SZ4zgFTq/qyPk2njxte04k4Gw53V6ToQd/Wjd+xLk9ibDEfJGO460YpY/us70H+d57OjjcP/CMJozFBLleh2RDTye9BwdJyTI+tnPfobbbrsNTU1NqK8n2dnOzk4sXLgQjz/++ETsQtp46KGH8MADD4j/OxwO1NfX4/rrr4fVmn0vpfEgGAxi69at2LhxY1xGMBVG3AF84+A2BCMcbrjxpqhakZRw9AIkkY7l85pIge1pQFfaAI++CsAgNq2Zh01rG9N6u5K2Efz+zD6oDGZs2rQu6bb83k5g63NoqjCjftOm9Pc5AVTPPgsMAoLGBC7oRpOGSJM4gw0br78+q2Pcuf080HoWC5prsWlTYrvleNg8dghnxki9gFbN4xPvuBZ6TXra9Xjgt2wD9r+FBY2VmLc+8+MlCAIeO78dfQ4/3MWzAJxHpc2ETZuuyHqfGMZ7DqeDF52HcWasH7XVlcAgYLJVYFMOzptMEDnSC5w6ipqKEmzatCruNudebcXO/jYUVzegvcMOwAUvNNi06QYAwJ9/uxeAHe+/ehF699WiOtyNF+6ywrzg+oSfm+z4/rH7TRzsHEPt3OUw+HqAQSIf8htKAYyivqIYmzatBkCkb9878gZ8ggo33XR9wrrPk71ObN67B4CAr9yyAO9eNf4mslzXXuAkoLZWSb/bcCtw+iviNsai0gn/TQGgrH0Eb/5uH4ICOR6rFszE5TNK8Jdz+8RtBHMZNm1amfA9IhEB3/r+dgB+fHLDHDz84hkMBtTicQ6FI3jgzVfQqy5BNTeCdYuaIMzN/Lv6Dnbj9WeOw6MvxaZNq/C3gf3A8DCuXbMYn6gkxgyspYAcf6bnSae+BRFBagXxyoABQAC1Nj267T4MBdXiuSpiuBWaX5LAeObbHpJqUACM/OoXAG0PZ4uMYtNNNxEDgKI6cMdcwFkQm2s34A5yGY0R+y6MAofeQo3NiEAogn6nH7OXrxPNSn7yk50A3HjfNUuxaVEVfn1hNwZ7nFi8fBWunl0e836hcARf2PcqTgiNWIsTWFzJY+H1mwBnHzQHeyGAw/I77k1agzPyZgde6TkFXXEVbHPqcWj3fvAc8Nk712J+dfw1xOWeAD7w+3043e/C79pM2HL/OlhODAE9T6G21IjqhVXAiejXrLv5PVESMtW/XwCOHMOSlgosWie7fo7SY68JoMtFzt+NK+di0xVN+GnrTrQOujFv2RqsnVGa8nj/qn03MObENWtXYv3sclgaO/DckV58+q5lKDVpcbrPiY5TewAAA4IN85qqsWnT4pTvy7DdfwxHRnrQMGMONl3dknA7rqscaH8UJWpfXsYDQRDwzaOvAwjgXRsvx6JaK244PYS1M0rE1hz8rkfIeFU+E5s2bUo6BvP7eoG+Z9FQbkatYn/Vpz8j3m8SOnHTTZ+NGnf5La8DfUDDnKWoU87rQQ9w6kvQht3YtP4yqVlxCrz57xMAurBhcRM23Ujkr7+5sAfDPQ7MX7oK18yJvTYS4fknDgG9A7h6+XzMHtABfYCgNYELuFE09ypgD+BSWWLWe794dDcicGLU0oJPbUruPCl+3QlYR6QLpnJLhQkJsurr63HgwAG8/PLLIoM0b948XHfddXn93LKyMqhUKvT3R5ss9Pf3o6oqvgxDp9NBp4tlATQazaT/qNnuQ5FJKr0LChz0mbxHwC7eVfvtgIYeG0slDraR51Y1l6a9X8VmIl9y+kKpX2MhNUa8dxR8Lo69l8jIuMoFQNde8D0HyP+Na8V9yfQYD9JC5WqbMePfxmaUzrOVjcWwGNN3J4wLC2FsVb5hqLI8XlfPrsBT+zrx3BFSUFxm0eX0vM/ndVRKz62Qn2RxOa1pwq9ZH03wW/SJv2eFlUhTRz0h8fxx+kIIg4dOzeM0rZ+6dWktqj1rgaNPo3jsBKC5OeXnxzu+9SUmHOwcw4ArCLtXku+eoZ9TbtGLrym3ksk9EIogBB5GTfwp4pdvnEcwLGDj/Eq8//KmtE14ksJvBwBwpjLpOxhtUZtwOsukjMPrZlXivg0z8MirpH6otsSENS3l+PC6Zjh8Qfx9fxf6Hf6k+3agYxQDTj/MOjU+uLYZP3z5LNyBMAbcIdQVGzHgJgXkfUIpgFaoXX1AFt91cT1ZaJ3qdUKlUmOMOgaWWQxY0pB4Ib1uZjkOdo7hHwei2RZmxnD32mZ8a/NJeAJhRDgeOrUsIUR/OwDQnHgGWP956X+vVCPK+ezQ7PkJ8Or/Ajf/QHRCFYrqgE7AFcpsjLB7yQVXbtFBq+bR7/Sj0+7HimYNxjxBUc62fl4VNBoNTDraiymMqM/oG/Ph4S2nsLTeBm8wglYtWeCrBo6TsbT7TbL/VYugsSZfgNaVkAC2Y8SLb1Jlwgcua0x67CuKNHjio5fh5kd2oM/hw74OBy63NMMMINh9FLomIhHE3FuIw6HeBo2lPLo+0UYSHSpXnzT+B6SFIOceQidVOC2uL4ZGo0GFVY/WQTdGveG0jjmr6awqImPrh6+cgQ9fKbGt5UVGNHNk7hiADXXFmc2LbE50BeOXmIgoIe0POGcvNCoVMZfIIYZdfvG8X1hfDINWjU1LZHXwQR+w95dkHy77ZNS+xj1/zYTLjVnLhAJRrsezhQsICDzM8vpuas6lMpXGzuuaIsBaBzi6oHFcAIqS9yJjONhJzouVTdLajTWi9wSFtH8zXzCME71ELthcbgF/liRnuFt+DNgaYTLNA/a8gfZhDwROFdXjkblvvnl+NOMxfaqsx9PBhPXJ4jiSobrvvvtw33335T3AAgCtVosVK1bglVdeER+LRCJ45ZVXcPnll+f986cKdGpeZK8ytnGX2567h0VnQZe6BKOeILRqHgtqUhR9yqAssE4KlpXJVS8M5rJUrciszbs167fMpkcWg9yCnzUcHhdMdPIfR4NGZrfPivlLMyhanmywuoaAl5mZ5Ebe6/QF8eOXz+DcoCvltqyg3pzE0pxp0PscPrHhM0BcvnrHfHD6QlDzHLF9r1lOnqQJgWxQTZuW9475RHdBIH47AKNWJRokJGpIbPcE8PIJwsB+5rrZuQmwAGCIykmYUxpAzFwqFwG8GqhYAKz7dG4+Kwt86uoWLCmJQKPisLKxGGoVj6/cOh+fXE8Wmb3UFj8RXjxOFp/r55TDpFOLtv7MPIg1hnbo6EIpy6L+mdRG3OkPoWvUG2UhngyMyWBzxKomGUvCc7hzea0oMYw5N+QOsIefjDLt0PsUTr5v/JDcnntNdMrTlRIlhJ0aM6QLZsRQZtahuYwcTxZYddJ6wTKzVnStY9el0vjit2+04dmD3fjqc0TiHqogNu7oOwpEIkD7DvJ/05Up94ldb6f7nWgdcKHUpMUD189J+bpSs05k1/ZfGMUTPeUICirovH3A2Zfpmy8FPvRv4F1/jjWAsdDrRm58IftduJAXfj9Z2DLTiyq6r21pjG2CIIjGHInaetgMatym2gUA2BVZIL5/upDcBVOsD8yVxGk0EsyLjfs52ry91maIb2h16C+Ae4AEOIvekfoNmYmXV2Hi5YomAObz7bHmF+w1iViqUhrk9seWZ8SDyx/C6T4SZC1vlK5xduzT7ZEIAN9/8TRWO17CTwy/wYp6EzDaLu1TwxrUlFhg0qrEfqAMvmBYLJM41eeMcq2UY8wTRG+qVidTHHljsh555JG0t82njfsDDzyAD33oQ1i5ciVWr16NH//4x3C73bjnnnvy9plTDRzHwahVwekLZW7jLg9wPMNik9euIBHRLqkrispOpAIze/AGwwiGI9DE6QslgjmM5SzIohnVKlmQxamAWYllWKnQJ/bIyjwYscoc2dKRaqQEC7JcA8m3S4LrF1ThzuW1eIZms0umYI+sRGA9WoI+qpXOUT+l9/7mTRztHsOhTjv+cM/qpNuy5IEpSZDF3BrZ4pphwOkXrYtbyk3kuqqlQVZ39kFWlRhkeTHiCsQ8Ly885jgORQYthlx+2D1Bscm4HP8+0otAOIK5VRbMr8mhhLrtdXLbKJOn8irg428AkdCE2/ErwfMc7pkdwTUbr0ORSTou7Ph6AmE4/aGo65pBEATRup05rs6utOBUnxOn+1zYMLcS3XZy3vqNNYADklV3htCoeMyuMuNYtwPHe8akXmgpjA2WNxZDq+IRoNbv71hRh2PdDniDYaxpLkGpWQebUYsRdwCjnkB0Ykme2Bk9Txos168GQn4Yw2RBN2psRrHnPJE4AcQSPUTGT1NFE1Q8h3CE9G2q16U37jAmuMyiQ3MpsVs/T4OsbrGHo/RbsevS5Y9ONr56KnrMLG1cCIzqCGtkvyALslJLp6tlgUWJSYtffWCFuIBNhRWNxXhqXycOXBiFRs1htdCEpdw54AL9/PIkwZrYkFhyKlUm3Mq5MdiKSsSxcnVTCZ450I0drUMpA0GHNyQ2Wk/U1kPXdxAz+F54BB02h9fg2gyDLOa4m9LCXaUhgZazlyQjmLNujsASajPiyGoBAHt+QW7X3pfeuJQoYaywoJ/PXcCIO4D6EtnclcxdEAAa1wLnXwe2fw9YeGdKp8ODHaOICOS6kF/DmQRZrQMuPH+kB7/dcR47dE+jThgC2l+SAnzqpshxHGZWWnC4046zAy6xpYjS0n1P2whuXiwl19z+EH6x7Rx+v/M8guEIdn5+AyqySGRPBeQtyPrRj36U1nYcx+U1yHrXu96FwcFBfOUrX0FfXx+WLl2KLVu2xJhhXOwwadU0yMqQyZIP0p5hIEwyDud9ZPBZ1pDgwk8Ai8za2ukLJe/BJFq4D5PM6Hgy5pGIlPGSM1lNV5ABMEu3moEsGhEzsD5ZFp0ai+JYAWcMkcnKvsG3iufwg7uWYGVjCf60ux23yAa+qQ5mkRzx06xsFo2IBUHAttODePS1VvhDEXzuhjk4Sp3W9renboqdHpNFznklqzzo9IsLxNl0MkLVYpKxdfWRxRNbSGUAZindPuyBOw6TrXTEsxk1JMjyxrfy/cd+svh/x4rx12GJCPqADur42rI++jmOm/QAi4HjEJPZNmrVKDJoMOYNom/MFzfIOtPvQvuwB1o1j/VzyIJwTpUFOCwF26ythWCtJUHWOOypF1QX4Vi3Awc6RuENkt/clsRCHAD0GhWWN9qwp41kzhfV2rCyqRhvnB0SF0DFRg0JspRdg5VswuG/kiCLLiL9ggbO0sUkyGIYaSPBMwDe1oBKSwQ9Yz70jvlQX5peJTzr8VRu1qG5jAVZ5PpnzKDcrt5Me0Z6ZMnGtkEX2obcUPMcrppdjldPDeCaBbVAzzyg9xDQ+jJ19OOAxtQKmBKTFjcsqMSoJ4gf3LUkesGcAoxZONxlhwDgLczBUv6ctEHFvPgvBFIyWQBQhjHYZHVhV8wqo583BocvGPfcZRiiLJZFp05cO3zoLwCAFyKr4IYBVUWxSZpkYJI1hy+N+dhaQ4OsHqBmWUafkwrnaPuDGeVx5pBwSLJMX/C29N5Q3o5GvpZx0SDL1gDYO9DM9eG1kRGg3kb6x5kqZEFWAiZr7X3kehs9D7z0JeC2nybdlddOkfXBupnRiV1rmkHW7nPDeO9v90AQAB4RVHN0/44/S2511qiAcFaFGYc77TjT78SmReQc7VGwU7vbhqKCrG9tPokn3pTqQs8OuApBlhLnz4+/iWGucO+99+Lee++d7N2YVBhZQ+JMe2UpmSyahewNk4G6JsNMlVrFw6RVwR0Iw+ENphdkhQOkn5V+HFlz7yhA7dpRPo8sXIVI2lLBSERA+7AbzWUmUR4ViQgYcGYvF2Td0a+ZWwF1MkYvXeRALgiQxMd71zTgvWsaxr9PEwiWnY34s2eyHn2tFd9/SXJB+uDv94r35b3gEoFdX8mCrER9YwZdflHGwRrdQmsk5+vAccJmZRVkkXOzdcAZ93nlNWhjk20cueC5QRcOddqh4jncvjT9Xn0p0fkmYTXMVcmz9VMU1UV6jHmD6B3zSQGyDC9RqeAVM8vEc4P9xiyIZ71kNCX1QBeyZrIAYGGtFU/tA7adJgsqNc/BkuScZFg7owx72kagUXGYWWHGt+5YhB2tQ3jXKmJYRWy+3bENjt10nqhYQM7Vsy+RxSQNsgYEG8lud8peI0QkeVFRPWps/STISqeHIoUoF7To0EwXxOcH3RAEIT6TRQNkl2weZCzWmpYS/O5DK+Hwhshiv2oRCbJ20IRx1aLEbIIMHMfhVx9IbICSDC1lJtiMGlGO+RY/Bx8FdY1TaaV+S/HAxgZXP/CjhcCqj0i/C0U5Z8dsGftcV2xES5kJbUNu7D43jBsWJG4ZMExZw0RSQQR9wLFnAAB/D18NIPP1AQvyUjJZAPm+3fujmbscgUlOmaQ3Cj47ACppNaapQGEBUiRI1jI6CxAJS0xW9RI4XS5YQiPY/sZr2FDUA+6PtwK1KwFPCiZLawLu+Dnw2CbgwJ+ANR8HKhfE3VQQBLxyikgUN8yNJhrSPfaHOu0QBHKu3r/aDNWrNHF3hjos2hqjEuKzKBt4pGsMj7xyFmuaS0QmizHnO1uHo5RNp/ui5ypWvzUdMWE1WQDpW3X69OmY3kkF5B9scvFkHGQNRd+nGuLuEBmobRn0S2GwpFuXpTVKC+XxSgbZ99DbAI0eaFhLBr55t6X18sd2tWPDD17HE3tJdiUSEdA56kEoIoDjSOF1prh6djme+eRafOttCzN+bVyYqLwy4ASC01vHnA1EOVSQar+zYLL+dYhM2O9cWRfjwBaPBVIiHbmg1aCOagzLMOj041Qfa+4tSygw5nXwZMxr0gELspjUR0kIKxdN7Jq2x5ls918gE/6qpuKszvmEOE+lgi3rJ7zZcC7AJIN9CeoHTlO26vIWaVG2pN4GgEhvxrxBMSiwlDeRDZx9pPl7FljdTD7nLM3IF5u0adXOXTuvAiqew+rmEmjVPBpKjXjvmgaxppedGzFBFhtf598G8BriHjjSJmbq+1EMTTl1i9NZSW2RHEW1qKKJqp6xzIOscrMW9cVG8By5TgedfonJkgVZxjg1Wa+cJEHWhrmVRC5L2RRRVs4YxdUfTXu/sgXPc1guU4fsi8gSDmWzAVWSQNlYJjUoHusEdv0shskq58awoCY6CcDYrDfOJldAsLqZhH2Nzr0K+McwrCrHnsg8qHkuox5IQPpsCtmYJnnGkYxIBFEuGC/I8tAaKX1R8t9DDq0RUNPz0D0I/Pwy4DfXSPturoKqaS0A4Ir+v8Dxn6+SJETXXtH4ImmA37gWmE0dP0/HtndhaBty48KwBxoVJ/7uDOnKBVmN1E2LqnBHi2xModJfFEc7TbOk06unBvDDrWfw0DNH0Uuv8fVzysFzROK7/Btb8evthLVl1y6bg/sKQVZyeDwefOQjH4HRaMSCBQvQ0UEWqvfddx+++93vTsQuXPIwMSYrU7lgFJM1Itb7dAbIhWMzZi7jYYYPaUkC5JLB8YBJ6Bjb84FngPsPio58qXCihzAMx3sciEQE3PSTN3D197YBIOYQSWvLEoDjyIRqSSLRyAj6IpLtBMbNZk1HsGaafDA7JssfCotyvf++bjae+thluHN5LW5dQjLE6dQzpiMX5DgurqFI96hXnNznylkzceGUnXyszKyDWta2ob44+rgo94Vd0zELaQBdI+TYMpOBnKFtG7lVSgWnCeTmIvHAGhDLzSfKzDo0UCnZwY5RHKeMVlVtPQlUIERLvzLA7EozKmRBcHGa4/SCmiJs+fSV+Nl7lsd9nr1PjPEFG29sDUD9GnK/bRuCYyRp0S/YYFywiRhHXPsVoGap9FpTOaAxoMaW/BjGg9z4QqvmRWle25BbYrJk57tZMQ86fEG81U4WzdfNU9T1yGXll98LLP9g2vs1HqyQmRGoLOU4F6EyqvIUNtc8T+oX7/4PAA7wDMHfS3zfBS25Xsu4MZFZYLiCmi7tOJt8zhiitX1liZiss4TJOGG9AgJ4VFr1mbWLgcz4Ih1jrHg1aDmALxhGJx3nZlTESdSxtUi6LBYDq8vqOUjqEfuOAOeoKZulCsYbvoIwp8JG1X4UDe6LfX0qFpXVlre+nHCTV2lC4bKW0pg5Kt0gi8maq4sM8SXNsvYNAGKSlW1DbrTS5M+cKgs+e8MclJq0cPpD+OmrrbQVA/mMFTThMOCIb4wxHTAhQdZDDz2Ew4cPY9u2bdDrJfr4uuuuw1NPPTURu3DJw6RVQ4UwvL4MGQ653CDgIoXAANp9ZPAZH5M1iUGWWpe014kSzJVt0OnHkNsvZqYByalp0sFxOanLmq5gNSfaCF2kaTKrBzg/5EYoIsCiU6O6SI9Ssw4/fOdSfOoa4t6ULMgKhCLY0zaMPjoZmPXJM5xy9oiZprxxdhDBsIBiowZ1sjoSFNGMbZY1OjzPRclZa20GmLRSTUUMk5VELsgkbVH7N174xsjCAwBars7d+04gqqgtv7Kgm4FJcKyK82J5gw0A8Mdd7Rh2B2DRqbGssURaQGYZWHNcdKa6OINxelalJaETIXs8xgGNMSbGMilQbtsG3whZAA+jGNbiMuDu5wkjJA8YaBKhJkWgqoQgCFJNFg0opbosKciSn6smBZO1r30EoYiAplIjGksVC+ralUTpsObjwMZvprVPuYCcyXrP6ga8EaH9F+vSkCAaikmdsY1IvdUDxwAA4TJyvMthR5nit71sRilUPIf2YQ/ah9xIhKRMliAAZ0mD2I5S4sCYqbMgEC1ZS+bUSTZm42Jug6wLwx5EBFJ7Vh7vu+YiyGLoo03MLNVA+RwEl0tsqaCSHT+dNTVrNmsjue3cK7ZGUIJJYzfMjTUKyZTJqrHp4yeBFEFWrc2AFY3FmFlhFqXpr58h65OqIj0+uX4mdn5hAziOKEGO94xBEAC9hse86vhGGdMJExJk/fOf/8TPfvYzXHHFFVGShQULFuDcuXNJXllArmDUqvCc9ku4/vU7SeFmuohnj6rWo9tHLsh0M6RySA5C6di45yrIot/DVJZ8uwRgVtuDTr+YVSkza/HUxy7DI+/ObdHtuMC+3yXIZFl0aqh5DgaOZr0ylAsyHfjsKkvUOMWktonqGcc8Qbz717vx7l/vwclekoRIJhcEJIdBAFhIWyCw+r7FdbZoaVcOFhNKxzO2KOW42AW4LRFbAckWO5Ni/pQYaiXSGEt1VjVnUwFVRZItfzww1t6qcJljxkGv0dqpq2aXE1a8iJqKdO4Bnv8MMHQ24326MssgKxmKRbmg0viCSqhMpVKQdX47giOkCMutLY8+p6OCLPJd2aJcWRSfCO5AGL4gqbNl19NMKu966/yI6KoYbXwRfS0f6iTsodzKWoRKTazSb3o4532YkmF5ow1XzS7He9c04PKWUnwv9C58Vfc5YNV/pf8m9PiqOBKoeGxEdljF26FTmFZY9RrR3fbxPRcAAKf6HDGlBawmSxmkAQD6j5EkkMYIZ/VlAKLHnHTBVC6hiJC63YzIZGVvEBMPzM6+pcIcX2LL1iKJjCgSgW3fezj2Oaqo0W/8InpRhmHBgv41D8lem4bBmK0BKJtDHKDbXot5usfuFVnbeEEWG5tSlXGwJEi6TBbPc/j7xy/H1s9chWVUIs2uTXaO6DUq1FCTFMao1tgk4xTGbE1HTMjIMTg4iIqK2B/V7XbnrsdKAUlRovZhAX8BNk97TG+GpIgT3AimCngCZHKzGbJnsjKSC443aPDZyW0Ke9NEYBPMoNMvFmFWFxmwpqVU0vBPBVzCTBbHcbAZNTCCBllpygVD4QjCEUF0eVMaXLCAyReMIETtrRmcviDe9evdONBhh0mrQnWRHnOrLFhGGYpEkLNHSiZ0SZ3iHM1B7UG1rC6lxKQVF6U2gyZG0lNEF9Ij8eSClMmqzyWTZScLO9gak283hcEWA4mZLLJwscQwWdGLp2vY4ocFWa98A9j3e+DNX2a8T/Lee6l6ZKULSS4oOzcEQRqfjWXE6U1nBXx2mLtIrZ3fqJBly13yRCaLHMN0mawhmpQwaFTiNXoZrXnbfIxk2C16dZRjnmh8QeWChzvtAICldPE3FaBTq/CnD6/Gt9+2CDPKTXDDgD87lsEXSb1ce3pfJ/64qz3GPGbERNj4Ct4R51XAh68ghhp/fasT3958Ejf++A18+Z/RfZeGkjFZZ7aQ2+arcfOyZty4oAr3rGtKub9KGDRSn76U6wO5XPD0FmAkN2ZrUj1WgiSd2LcqUyaLbh83yKKSUH0RPmn9GTb4f4COyo3S8+kEWYDEZp2NlQz+fFsrQhEBl7WUxLK2SI/J8gXD0QESS/xVL5E2UgRZAJmbOY6LaflRLXOfZCz0G61kLCEW8+RcK8gFU2DlypX4z3/+I/7PAqvf/va3l1RT4MlECS/LDqbLCkUisiJPm/hwyEgW8jwXu2hIB1JNVhpMFmNmxstkMVtvXXrWwEoMy+SCrAFxRS4L/3MFMcjKvlfWdIbNqIUBdAGYRjPivjEfln5jKz7914M43Ud14pXKIEvK/CrNL1442odTfU6UmbX4xyfXYvdD12LLf1+V1AoZiGayFijs+xfX2aI3ZnJBnx0IJJbzJIM8q1wsC7LiLZiqrczEIXqx6w+FRaamrjiHTNYYtZyzTS83SzlS1WQxabTyvJhbbYFeQ6ZhjpOagYuBNXNEzaL3XYVFL9b2laSwb08XtngBeMAltvaAqYwwQLRpr9ZP5o+gSdEKwlwpJbxsNMiiNVkj7iB8wdS1w5KzoBRAMukbY7hqFX3eRJddfwiCIOBIlx0AsER5zU0RlFt0sOjUiAhExgYAzx/pwaunYhOlvmAYDz1zFF997jg61dEJiz49MR0pgz3u51w9qxwzyk1w+UP49fY2AMA/D0WzFEndBc+8RG5nX4/6EiN++YEVWNGYIdMDsjZk10hK8wsWmIT9wJPvAh5ZBjz5HsDekfx1KcAaEcc1vQBkcsEMvx/b3h8n0DVLro46sw1jMKMPxUAJNYtJN8iaeR25VdRl9di9eOotMs5++trZcV/KgiyXPxSTTGRg45tBoyLbsyCLGYjxGqmGOA7mVyuDLGleYkHWwQ5irlRXbBDZ7X6HD5EMmpRPJeQ1yDp2jOiBv/Od7+CLX/wiPvGJTyAYDOInP/kJrr/+ejz22GP41re+lc9dKICiWOWR/lF2HU8En11sPowy6cL060ngU2TQgM+wsBWQFhrp1WQlaOKXKdjiVJt5wb4nEBIn7UA4grPUCntK9m24hOWCAHEYNHCsJiu1XHBv+whc/hCeP9KLN9vIOaa04NaqeNE4QimhOUd78tyyuAZzq9KvzStLwmQtrlcwWfoiQEv3KcsanSrZuVpq0ooL03gtFOpKyMK0a9QT9Xiv3QdBIBNswuL3bMAWRbbEk/NUB1sMjHmDMeeILxiGP0TGD6VcUKPisbjWBoAs9MXgmwXWDKxXToZ4N7VeX9Ocg2bnSGB8wcYatUGS6F71WWDW9WgtuRqPhm7DaPmq6DfiOKm3EZ1brHo1dDxZSPXYU0sG5T2yGMw6tShJAmJrB82ymqzOES9GPUFoVTzmVmeXfMs3OI5DCzUOaB1w4Z8Hu3HvEwfxsT/tj2YTQYLOEF2IPtEuJUH8ggbdHFnEF2OMMI8K8DwnslkMDQpJMOuTFWPaE/ID3dSogS3yxwF2jaQsJ1DrgFUfJcmZigUABOD0ZuCZ/29cn98+TNYKLWUJ5g/POJksEXTtxGuiArYSed1j4zr62jQDOla35+qTEssAfvn6OQTDhMW6fEb8/Vb2MI3Ctu8Cf30f+oaJvLbapidkCQuyGteS/lx3/pq4NyfAghppbtNr+Kgm3SzIYi64tTYDysw6cByRj8ZTVkwH5DXIWrx4MdasWYMTJ05g586dCIVCWLx4MV566SVUVFRg9+7dWLFiRT53oQAKK5cFk8W201kBq6wbt4ZcpNmYXgAyuaA3hP/37FG885e7EUyQOclZTZYYZGVu680yeAzHqdMgo7KnFC5huSCAaLlgGkxWx7DEDDlpMfzsyuhAnOO4mIJ5BlYo3lSaGbPDFio8RyQTbLKpLtKjwhJnkhLNL7KTDDKWACBMVrlZT/cj9hpm2f9RTxAu2fdl9Vh1xYbcyrzFIGv6MlkWnVo0E7n7sbdw2892iLIjtmDhOMTtVbWButrdsVRWj6aUTnrSTIwp8KG1TTj1zRtx1ezyrF6vhGh8IV/wsLFZXu9auxx439P4ff238L3Qu1FujXN93PZT4B2/B1quAUDlvnRITUcyKHcWlENu+KFksuTX8SHKYs2rsUKnTtBcdwpgDh2Pvv7v43joGWKUEIoI2NEanUhjQScA/PmsdF0Pw4KTTnK9axCWpPMK3LmsDisbi8VjpkyCsnmw3KIYM0YvEMZVa07KYqQLKchKIwl78/eB/z4KfHIX8PGdxF23YxfQsSfrz2eGP0tP/RB4/oHYoFQMsrKsyWJouoLcWqqi2lawusdhdwBY+j7Ccs3ZlN5n6CxSQk5mSrGHJhA/vC5xnzUN7WEKxGERd/0MOPU8Qud3AKDSXkGQgixrDXHfXHhn0t2rKzaIY2B1UfQ80qwIauuKjdCoeHGunK7mF3kNsl5//XUsWLAADz74INauXYtAIIDvf//7OHHiBB5//HEsWrQonx9fgAxFnExmlO6ELXfRMUoTl1PNgqzsJChMLjjs9uPJvR3Y2z4i1sPEwJgjuWCAvr8ucyZrWOGkxezc4y6GJxsmWtNxiQZZxUYtjMz4QmOCIAgJpQ8A0DESzdaUmXVxJXRSBjxaxtQ+RF7flCjrmQBMclNm1kHFc6IRxWJlPRaDWJeVJZMl076XmrS4dl4FWspNuG1JrNGERa8Rr205m9U5kgdnQQCwU7lgDhZokwWO40Q2a+/5ERzpGsM7frELhzrtYm2JWauOy/x/9MoWbL7/SnxobZP0YMs1xDb8mv9H/k9XfRBnv/Sa3AUQbAE45g0izOQ7Yj1WbIZc6f4XBVsDsPDtUaYSxVrynt3pMFnMiEHx3lfIatFqEzFZgTAOddgBAEsTXXNTBPdeMwst5SYMOP3wBsPQ0nYhr5+OHuPlQZZLMKBLIMdhRLDiWL8PowKd+xK0BTBoVfj7J9bibx8nJRwOb0h0+AuEIuLCO4bJGiHyQpS05KTHHTPGSqtXlhxVC4El7yb3WQPpLDDmDaIMY6g+/mtg3++khsEMWbsLKrZfeQ+5LYuW75VGMVmXA589DSx6R/qfY6HSQ2cvCYRGL8BBA0d5DVQ8xK3LCgXE9ZOu5036PnqyjmQyYUs10gHPc5hHJYNKYxRlkMWuXWYqNDBNzS/yGmRdeeWV+P3vf4/e3l789Kc/RXt7O9avX4/Zs2fj4YcfRl9fX+o3KSAnMAtZBFnyyVM2QNh5GwDJ6jlTMCbrWLcDbJ4eciWggnPOZGUeZDH7dgaW3S8wWVMPJWYtDJTJCqr0uO6Hr+P2R3cmrPFgdQ4Mc6rinx9GrVTLwRCJCKK0RDlBpMKyhmLMKDfhbctI8MQkTzH1WAzjtHGPqskyarGwtgivPrgeNy2KPzmyQKprRFrsduXDWVAQZEzW9DW+AKQFjFWvxvxqK0Y9QTzwt0Mik6WUCjKoeFIQHsUOqtTADd+SFo2ekbgyr4kGC74FQcY0eBIHWQPJgqw4KKabpSMXTMRkLam3icFUrS36XGXXcTgiYG/7sLj9VEZDqRGb778Sn1g/AzcuqML37iL9u7afHYyyOR9wRs9TrREyZowIFpzsdaJXIL8Pl2IMYUFOIBwRZa6sf6BJq4qSeJEPoA7RrH5onBCZrHTKCZRY998AOGLE0X8i45cLgoAxbxAL+HbpQWVQOl4Ld4AwbvPfBnzwOeBt0aY2jC3OWh7HVEeOXmD3o8BPFuNaP7HXZwnuhC+NF2TJEjylIwcAUCMldh6Zyol0M00w8wulxX9dsSGqnyNjVCstrEZ4eppfTIjxhclkwj333IPXX38dZ86cwV133YVHH30UDQ0NuO222yZiFy55RAdZ6coFZbbnsgFlCKQIM1u5IBvE2SQJRGfhopCrGiOmT85CLpgoAJySTJaZBlljXcS45BJDqUkrygX7vTzODbpxvMeBP+xqj7s9Y7LuXE4WJGtnxLf4ZzIjuXyuz+GDPxSBmudiZEmpUGTQ4JUH1+OhTcRl7d2r67Gk3haXWQIAWKnbXJYOg2VmHUxaFXguvf41dXRxGsVk5aNHlmcECNKxiTnqTVN85IpmXDOnHH/92OX4/d2kBql9yC1K67IxCRIlRmE/EPQk33YCoFHxotxHlAzGkwtSsH5a8WSp8VCsI0FDrz111joRS6ZR8fj41S1YUGPFupnRC2HmLgiQJB+QJLExhaDXqPD5G+filx9YgRsWVEGv4dHviO7XqJxDzwpkTBuGFWPeIHoEci6lCrJMWjXYWpcF0id6SS3O/BprLBsrZ7JygHT7NcVF6QzJYS+OjXkqeINhhCICFnAyp8JEQVamFu7yIKuonjC4LVcD5mjn7ZJEvejShYXOIc4e0gICwJwICYRF452xbqDvGOCKTsbGDXBl68U693GoESI97dhxSZPFYnj36nqsnVGK96yOloerVbxYB6iW9XaslJlfTEdMXPMHipkzZ+KLX/wivvSlL8FisUS5DhaQAiE/0L2f3I9EgOP/jG4WHA+0J5YhIhVBpi09iZILSpPVgEDkFdnKBS1xnNfkAVcUmKuOz55e0BBMkAEdF5MVf7CbkkxW+VxiXe4dBQaOp97+IkOpSQ0DR36vIb8kk3r01daY39EXlNzy/t+meXj5gavx/10Vf6HAHAblvVtYPVZ9iRFq1fiG0tuX1uJfn1qXmCWS2xVnARXP4dcfXIlH37s8rtmFEiKTNRqHycqpsyBlscyVSQumpwOumVuBx+5Zjfk1VpRbdOA5ICJI50kiJisptCaS9QbGz+bnCKzptxhkye3bFWAL5XTnimL6VdPplcXmjPI4Jiz3bpiF/9x/ZUwikOc5kc0CSC1lQqvuKQq9RiVa1W8/Iy2SB+nxYN/nNd0GdOhm49kwqf3pofLBVJJjnudi2qwcpwGp0h0OQN6CrHh9+tJC6Uxym0mrGgp2vi5SXZAelAdZ4RBpng6MTy6YxOSHSXITrTtSQs5kjbQDAKo4suYz69WkL+GPFgC/XAd8fyZw4M/iS+MGuDLlk07wYyHXThJ1LFhnUvY0MbfKiic+ehlWNcUGqUwRUlWkF1uLMCarIBdMA9u3b8fdd9+NqqoqfO5zn8Odd96JnTt3TuQuTFvwkQDUP5wN/GYD0Qgf+wfw9IeAv7yDBB89B4FDT0RLSo49A3ynFjjxHAxhWZCV7mTtlgVZJmmA6A2TgTabHlkAUBSHsk7IZOlkg3ogQd0Ww4nngG/XAgf/EvtcgDFZWdRk0clL3k+I5xL0C5lsqHVSQe25zDN50x1lOikQH5QFWU5/CD99Nbqha9eoF4JAJDAlJi1mVpgTBktSfx2JyTo/nJ3pRVYYp1wQIH2TEskDlYgXZLGarJzKBS8C04t4UPEcSmjtShu1hLZmw2RxnJQxz9L8ItcQGxK7mVyQMVnRi85IREjYhDnhe9MhNZ2aLMlSPLNxWN4o/PaltdOyV+fV1MjkjbOSwoP1Enr/ZY344OWNeO/tN+OPi/6I7RHSw6iHyQWd3WSd0Lk3YVKSycrGqMMfM3uSu8OJGKZywdIZ4/xWBHF7sWUCM+3J5sw+yFrIy4IshyzI8tkB0DVWurbqDAYFk5UALAmWKsjqHPHg9kd34oWjCqaNMUvOHmCUMHIV3CiMWhVpdD5wHOJ3AIC2bdJuxQ2yoteLK/nTqLEZok0vcgQWZMmVIWKj94LxRXz09PTg29/+NmbPno3169ejtbUVjzzyCHp6evCb3/wGl112Wb534aJAhNdKmaILu4Dz28j9ngPAtu8Aj90M/PMTQPsO6UXntwMhH9C2DbqQLEBJd7JmTUIVTFZXgLjXFGfZeyUjJkujlzK5vviNFKUd20u7nW+LfY4FWRkYX5zsdeDcoEs0vpBbujLDgimJGRvI7blXJ3c/JgFlOolp6veS4Y1l0f9zpDeqhqFjhCx+G0pNKRda5jjugqKzYIb1WFlBlAtmH2RlAtYHq8tO2CtvICxeozmVC7IgaxqbXiQCk7G1UZv/VL3TEoLJjLI0v8g1xCArBZPl9IfEnF9MHU8C2LSSXFBIUYPGrsVMj6u8PvOOZZll4acKmEEOC+ABicmqsRnwjdsX4pbFNVFSSonJ6gJO/hv43UbingcAb/0O+Ne9QIQcG6uMyRIEASd6KZOlaDeBUEDqc5cjJssmnl9ZMlnM+MGVec3/mCcICzyoh+y1cuMLFnDobaRuMhNoTYCK/h5J6k9LZA6eya6Bp/d14nCnHX/ecyH6CRZk9R0V1z2VnF26ThgTx8B+P0jXaZR9vmLcWc2fQrVVB7RTgiSHMu/ljSRwlRtAsVY5/dO0IXFeg6ybbroJjY2N+OlPf4q3ve1tOHnyJHbs2IF77rkHJtP0ouinAiL1NCDt2E2yUAzb/0+qazj3ivQ4s2p1D0CbaZDVtR84RaWcTVeSQUFjBEpnYpAmFNKdOJWINykmZLIAqYGwPwWTxbJy8epWMrRwH3D48Laf78Rdv9wtZlDmyqQSlVOxRxYDC7Iu7Eosn0yFsW7gpS8Re95phBINmZg9gg6DLnJ/w9wK6NQ8Bpx+0VYbkEwvGtNgZsQmpjK54HnqLJip6UVWYExWwBk7SeYBUq8scv6w2o8Skzbr6z4umLPgRcZkAVIvtHMD45ALAlOQyVLIueS1uzKweh69hk/bIp1ZuHuD4ZRyMTftRyaX/6UDeQ+gCbl28wAm2e0d84rtT4boHFohC6zkPcQGeGZ80QO0v0EePP4MYaJe+B/g4J+BbmJsIAZZ3iC67V6MeYNQ8xxmKdpbwN5B7Ns1RolBGidYEJ89k0VrnLJgshy+EOZxijnPKZNoZ2vfDhBWmr0uDblgMCyIbUXi4WCnHYDUWkMEY5ZG28WHymFHkY4mEtn8UUKZR9l6KW4jaBpYuqxEhnml6igs279OrPLVBuIQmiPctLAKL3z6SnzuhrniY1XWQk1WQmg0Gvz9739HV1cXHn74YcyZMyefH3fRQ2igQdaZLcDQGXKfDWwcnWjaXpde4LWTW9cA1EEpQBFSZUQjYeA/DwAQgCXvAepWAAYb8Kk3gQ+/JE5+2Rpf6DU8NKpo5iB5kEWDm3id0uVgAYWyl1A4RBg9IKFcMKzoJr71ZD98wQhG3AHsayeNQOdWSQ0rp2Q9FkPZbFL8GvYDBx8n3d8zNcHY/wdg10/J3zSCTU2DLOhER6w6mwErm0iGbNc5SfogBllpyP3i9clqF+WCE7BQ05pI9hSQ2J88gjFZdk8QTl8QR2hPocV1RYUeWWmCsQis7i8ruSAAGKksKcuGxLmGTclkseJ5BZPFFmqZBOUaXgpOk0kGwxFBbBCfaZDFTDjes3r6sqflFh10ah4RgTgxCoIQ1wikQjZPeQ2sVqebsBwAmRf/9iEgQsc1ujZgckGHLyS2LJlVaYkNlnNs3w5IQbycyRpxB7Dxh6/jO5tPpn4DM2OyspMLLmTOgmzdEY/JyrQei6FsFrmtSty+yKBVwUDbLiQyv4hEBBymQVaP3RfdpiSOEYWai6BOR4MxFmRVLiC3zl4gzK5V+rtHBVlk3HnBtwjbwktgQADY/TPy3FUPAsW5c4XlOGLxrlVLoQmzch92B6Lk+tMFeQ2ynnvuOdx+++1QqaZuo7/pBIExWWxhUjqL2H82XQm8m9Yh9R6SJmN26xqAOiAFKFzARUw0EuHU8+R9dEXAxm9Ij9saAFOpOHkWZ2l8wXFcjGRwyOWHIAhoHXDFNibW08EulVyQsVWOHlH2QB6X1aPFCbKO94xhyTdfweYO6XLYekIaoAN0f+RBVvlUdBZk4DiJzdr8WeDxtwNHnsrsPVh2eiCNSW0KQSeQ89or6MTea2UWnegauFPWwLNzJH1LcrM2OsgKRwR0DE8gkwUANcvI7ektef8os04tXt/ddi+OdJGJOadObL4xYJCeXxdxkMUQTyadFsQ2FlOFyZIFWZ4RybyEGQ5QZBNkASDOZUjekNgTkBZbpjgNnpPhD/esxgMbZ+Prty3M6HVTCRzHieNWx4gHY96gOE/JLe3l52DEXAUBHLhIEOjaJ71Z/1HpPk3MypksVo8V3/Qit/btQJwgHmTcPjvgwq+2t2Hv+RTXAZMLekeInDFdeEbQfPznuEtFE9UzryW3crOh8QZZ7/gD8F+vSgFOAqSqyzo/7IbDJ81FUdeKuQJAbMBbp7aTOyzIKptFSjGEiFjrW2RMbOHe6tbj86rPIVRH+qihdCaw9v6k3yMXsOo1YmJE2XJlOmDC3QULGAfMldGDWcMaspi++3lgzk2EwRAiUl0Wkwu6BsD7FRKjZBM2y07NuSnGXhSQBr9sjS8AKas7m8oPRj1BvHi8D9f98HV8W5mtypTJioQA14D0OAu+eA2gjt3n3eeG4Q9FsLWHQ9eoFy5/CLtaY81BZlaYJcebqcxkAcCSd4EMtHSwvbAj2daxYAHt4Klc7lX+QX9rD3SiNLDMrMPaGWRS3NM2IrKWF0bSZ7KMsiamAHCocxSBcARaNU+KgCcCi+4it0f/NiE9k8S6rBGvxGTV5qhxa/tO4KcriaRFrQcqp++CNxHKFYYMqXrUJIQoF5wa7oL1VEr6/OFe9BzbRh4smx1jfJFtkMVaDPTYvXD4gnFlQl56HXIcoFNntoxZVFeE+6+dFZUtn46opxn+zhGvyGJZ9eqo5tPyNiPFZgN8GsqKRoIAF+f708Ss3Mqb1WMtUNZjATl3FgSkAMPpC4kMzYVhqfbsq88dj1GeRMFQTOZ6IDM2a88vsKLtUczjadJgHm0v5LNLa4ts7dsZTKVEGZQC8rqseGAsFkOUZFClibtuq1XRNSBTOBmKpXoqKhm0KlwlAYjfeRRm/NeGBVB/4Glg0/eBDzybUX+s8YDN0YUgq4D8o2GtdJ8xWwzNV5NbJhlkF1PQnbhrOcXXnjuOux/bS4qCWe2TPnZB5QuGRZmGLUvjC0DK6i5vKBYb0D13mGSMmF2sCLYfqWpR5H1k5HVZKUwvmLFFRODw6LY2vH56EIFwBCaFDKXcohMzKlO6JgsAmq8CHuoC3vlH8n/v4cxez84Bz9D4e5RNJOg54IUOwTCZiMvMOiyqLYJFp8aYN4gTPQ50271ij6zGktRMlJnVZPlDEAQBD79wGgBw25KaiTNAmXcrCUiGzhCmOc9gBheHu+xoHSDX0OL6HAVZb/4CcA+QbOj7npZshy8iKJmsi8X44pbFNVjVVAynP4RtW58jDzZcFrPdeJmsHrsX7/jFLlz9vddi6nNYssOkVU9Ld8BcgDFZnaOehD3DbAaNOL+WmnXwaGWBcNmc2DUETcxKTFZIvPblSg4RQ9SxNYdBVpFBIyoP7fQcapctrk/2OvD3/Z3xXkrAcVIZRSZBlpskZneEF+D5GV8DFryN1JoBko07uwazqcnKAKwh8XCCHp2HFEGWvGk8gCjJYARk7qqkNu7iOkpvkwyHaG0sM4kacPhF042gi6wV7bDgPWsaSI386o9OqPqAmUu1F4KsAvKOxsul+/Vrop9rWU9u27aRGhx5UBImF+uoQAMN2YTtC4bxh13t2HZ6EP850is17tXFDqqsHkvFc2JTymzAsrozys0opRr8HdSOttehGDAyZbKA6LqsFPbtI7KB7NlDPfjBVrKAfs/qBpFx02t4GLVqceGZU4e1fEFnliRmA6eSS0SVkB/rwdO53a98gjJZXkFabJSZtVCreKxpIRPj1/59HB/47ZsIhCKYW2VJ67eU12S9fHIAe9tHoFPzePD62Xn4EgmgtxJ2GQCOPJ33j2Ps36+2tyEiANVF+tw14GZM87VfJQmBixBlMUzWxWF8oVXz+MX7V6DWZsBsP+nFF1Eu1iEFWZl+b8YM7zo3jDP9LviCEbF9AAOT7WZaj3UxgZlfdI54RGdB5fXJ85x4HpaatPBqZEFW5QJg0/eAlR8Gln2APCYyWczCPSDWxsXIqsNBoOstcr9maa6+FlQ8JwZ5rCaJMVlMsiiX88eFJYsgi657XossQ3fDbSRYY9JDlqQWjS+ylAumiRKxLi15kMWSvgnNLwD0GskcVSYog6wiKciiDoNzq6zQqDj0OXxiTXPQSWouDdZy0WV3otFM657bRwpBVgH5RvNVREdb1CAVUTI00ABs+CzgHkRULwSKCwKhkYPO2NoUAHhib4fEYsQLsrzkoifZpuwziLcsrkFzmQnXza8Us29MY9w/5kdELgdItyYrEZPFgsYEzoKMyVJzAsIRQbTFvXlxtdgwr5T2vPnG7Qvx5VvmizU+Ux5F9UQWEAlmVl8lP9bTSTJIzwEPZEEWPb/uXtsMjYrD/gujaBtyo9ZmwO/vXgU+DSaK9clyB0J45BWSvf3IFc2oLprgYHvxu8jt8Wfy/lHvWFGPUpMWgRBhruW2uuOGm5olmMpz955TDBcrkwWQAPI3712AxRyRiz3ZG9srJ2u5IJViH+2WkoRRNSIg7oNA5vVYFxOYbLNz1Cv2yFKec4BkflFm1sKrlTEwlQuA6sXALT+S1hKKmqy2QTcCoQh4TpJxiuh6iyQwjWVAZWIjh2ygNL9gMrF3riTytkOd9uQW/2ZFcJQOaILODb10zloUTeAZozVBTNaIOxjznC8Yxkkq4dxE+x52KIMPFhwCOKsnv01JOE6QZYsOskw6NdY0kwDytdNkjOZ9JPCuqspdP6xMwZisjgKTVUDeYWsAPrIV+NBzsW4+xhKppxRzH5RBUOsxwpPgoKdX6rcjp2D3XxiF00EvxiRMli1L0wuG96xuwGufXY/mMlNM7UIgHMGIPIMjWrhnEmRJ369viEojEzBZw24yQb29OYKv3DwXX7t1Ph67ZxWWNRRjdTMNsijbtrC2CB+5onnq9shSguOAatKMMiPJ4HRlsiib6aVBllbNi4zrFbPK8Npn1+Pdq+qxuqkEf/7I6rTrqdhibswr1Si8/7LcuSqlDcb6OHslOXCeYNCqcM+6JvH/nJpeMAnqxRxkKcY1S7buglOMyWKYL7RBy4UwKBThKzs8YuabIWu5YJxrUhlkMSbLoLl0mSypZlJisuIFWTPLybzXXGaCVy4XlNdBMudSJhekvxmra6206kkjWzlYH8aW9QCf26Wk3PzCEwhhgMohNy2qhkbFYcgViGqUHgNWk5QJk0UVLx5BHmTJgjXPiFTvXpu6rmo8KBWDrFj1SfuwG8GwgCKDBpe1kN+zMybIogGR3oYLPJH1FYVoYisek2WX5Jfr55AxedvpASAcgp62/2monzw3zuaCXLCACUXNUqCkOfZxuRY5TpDF6YugtZCLsq9XcsxpV0yOA4N0ARQ3yGKmF7nrlaOU1QCK7t66dJks2aBLMzOdIx5851/USSkBk8UcfCoNAj5wWQPuXteMa+aQQfq2pTWYU2nBndO0aSUAoGoxuc06yJpGTBYzvqBywTKTNopxrSs24rtvX4y/ffxytJSn35jaRGuyuka9CEcE6DU8qpWZ3YmA1iRJVcaS1CXkCB+4rEmsTVxab8vNmwZ90vllmiaMcBYoktXDAOOQC4pM1tSwcBfRsRsAcN64COEI8Ld90efjeGuy4r0Xg4fVZOku3SCLyfeG3QFxDo8XZH311gV44r/WYP2sMkWQJXO4M0S3CWAyeVbXWhsvGXXuNXI745rxfI24kHqxBUQWy2bUoMKqxzwqGVTWJUVBKfNLBzTIckEvXavi+/QCx/5Byi4qFyW1YM8FkjFZo/Sxcosuis2MAqtxLW5CT8QGADAF6LouKshixhfStXtD6QDKMYo320bgHhsUH5/VOHlBFjO+GHYHIGtzNy1QCLIuNrDMcJwgC/oiWEpIEGYflgYf1u9nCV1EeZx0Mo8TZDFpHXO/yQXiTQxRlqRMLphuM2JAtCQ9P+SGCeS9QpoEQRatyTLHWQtUFxnw4meuwt3r4gS10wWZMlmCEH2sB04Cr3wD2Pw/Yj+NKQuFXLAszrmVDZhckClUmkpNk1dwzwqOJ6BfVpFRg1+8fwU+f+NcsUZr3GDtAXhNXHOdiwXyehhgHEwWC6r9jswsqfMBeWsMOp5YZxKZ+jMHuqJc3xxZBlmlJm1MH8VETJZRe+nKBYsMGjEYOtBB5uyKOONdkVGDtTPLwPMc3FrK8BhLo+p2YLCRW8qOFymUKrXKulXvKNBDGhejJR9BFmOygmI9ViOty2HJHmWQ1Tfmw6eeOIBdrUMy44sBpA3RmVbGZLFj5OwFDj9J7i99b2ZfJguUxLGxZxjzSoluVpc36PQT0zKGWTeQ1j6X34vOkA0AYPAPkPk7SJPqBptMLthFJreBk6h7+iY8Y/gWwuEgth0iKha7YMKCuvxKJJPBoteI/fMGp1lP4kKQdbGBDS7xJF76IlRVE0bGNzYgTlwsU/S+NQ2YW2WBQaDBSpwgS9J+5y6LH5/JkgVMaRtfKOSC9k4E+0/DSIMseyg2MPSHwmJXdUvuyLmpheql5Lb/OGnMnAoBF2kFwOAeAN74AbD3V8DWr+ZlF3OGgOQuCMQ/t7KBsvZjQhoQJ0IciUc+cdXscnxi/YzcBZXyeqyL3BmOJZCMWlWs3Cpd6IsgtmKYTDbL3gH8aCHw9N3kf1qnMmPOIhQbNeh3+PHGWSnznS2TxfNcTK1jopqsS9n4ApDYrCFXACqeExOlieA01CF8/XeAt/82+tpjckGRyVIEWYzJ8owAv7sB+M0GMkeUzQGKcq/ykMsF2fqkibIZiYKsZw924z9HevGxP+9HT5gmb1xZ1GTFkwue3w507wd4tdRKI49gTFa8ZsTykg2bUSOaUXTJzS/M5aS1z+K7cCFI1nFa/2h00KmzAtZaABxpSu0eAs5sASdEUC/04Gb+Tfxz5xEAgIu3Tnr9I5tzB33Ta84oBFkXG5gWOQGTVVFBaOQiwYnNR0kRJ2OyWspMuG/DLJg5EuC4QAbW9iE3dp8jdU1MGx0vY5Yt5EzWnEoyIMRlspLJBUMBqWs9QAKDX6zFVdvuQjW1Lh30x072TCqo5jkYLtb5uqSF1KOFvMBwa+rt2XHm1ZK2my3y9jwKnPhXXnYzJwgq5ILm3DCuSllSY1nq3lp5A2OyJkAumBeI9VgXr1SQgZ1/WbNYAMCrZEzDJNZlbXkIcPYAp18gWW9qAqCx1eL2pWSh/fR+yXAo2yALAGpsJImn1/BR78Xg9rMg69JlsgDJYRAA/vvaWZiRhgQ6suqjUrN6BiYX9NkBQYiRtop1csf+AXTukfpjzb4h211PClEu6A6KdTiMyWKB5LHuMQTDUjKwlyZmXf4QvrWdXifO9GuyBKrecEMvBZnl88gtSwzN3EgCmDyjSNanTAm7eF0RKXydrF9aPHR5DfAL9DoZosl3nZWMK2qdlJgf65Da/wD4hOZ58HS8CemKx/2dxgtmflFgsgqYXLAgi8rloJPJcfRF4OjCpphz4h/7u+APhdFDLVobS024aWEVrBw5i/95kiy2P/LHt/Ce3+zBhWE3Bp3kuYocNuOVB1nXzCX7H12TRb6D3z2KMU8CuZqcxWLmH34HNGEPFvDtAIBeb2wUxfpQlJi0F29SneelvhnuweTbAjJ3SSvQfCW5v+l7Unf3bQ/nfh9zBRogOkEWH7lisgwaFeReJ82TyWRNoFwwL7gEnAUZ2NiWtbMgw2SbX7S+DJx6ntwP+chvyJzWLNV4xwpS2/HyiX6xgex4gqy71zbj8pZSfOjyJgCxi01PgCTULuWaLACYXUmCqtVNJfjkNTOzfyMWxIcDQNALkzZ6vBPlgqwOa+n7gTt/C1z9+ew/MwlsJjmTReWClLVrLjXBqlfDH4pgX7vE7MoTs/uH6RrAPUDa2aSCIETJBcWkSOV84MMvAhu/AVx+L3Djd8b71dKC2AzaG4pxUWRMFruuGuhx+d//nMCWY9HMnSAIcPpDGBBokDRA66vlMm02n3TtBzr2kPu8GvO4dtyhIf/z5vxa1qcDZn4x6J1eC7VCkHWxgWUlGMplfXz0RWLGqohzY9+FUexsHUJEAExaFcrMWvCIwAgSdO3s8MPtD+EctTQ/2++SMVm5kwu2lJugUXGYV20VGx7GY7K8zlF88dmj8d+E1WNxqpgmeTM5EnB2uGNPd7HGbJxuiVMe6TZ0BiRZpt4K3PZT4NNHSPNBpkXPRIIx0aCZNzvtB5erIIvjOLEuC5CyapMCUS443YOsS4HJokHWeI2CmPnFZP3mSplw72GqHOAAcwXmV1th0qrgD0XQPuxGJCJkXZMFADcurMKTH7sMs6mywZHA+OJSZ7I+ckULvnnHQvz6gyvG53irNRPlAgB4R8FxXNQ5W2czEKl5+xvkgVUfARbfRXox5gGS8UVQkgtS9QDPc1g3k4wdH/njW3juMJGtssRsQ4kRQ6DzXSSUHvsb8oMTyDnF6czRbT0aLgPWfRq44VvxDcfyAFZrFwhH4A9FB4liTRY9Ru9Z0wCLXo1zg258/PH9aB2Q6qndgTAiAtADGiT1HyO38iBr7iZy+8rXidrFVAGs+igA4CaOmNtUV0++8VdBLljA1ABjshjKFEEW1V6XqsjA9b0XiaywkRXys8a9AI4NRaJsebvtXrGzfC7lghUWPV5+4Go88V9rxF4cfQ45k0UmWjO82Nk6GL8/BmOyNEapBokWjJdxJGjocnNiwTQDs0jNpZHHlATLVKYTZDG5oM5C5ATF1KpcHqgl61EymaCZfq+aBOa5Mr4AAKMsaz6pNVmK3ibTDpeAfTuDxGSNMxhoXEtuX/3f9K7hXCISAQZOkPtMPsya0JorAJUGPM9hDk2Qneh1whUIgXlgjCfAZAFarLtgoRkxQAwqPnBZo1jDlDU4LtbGXca+1hYbSE2S30EStcxMKU9gxhfddi96qAywoUQac79x+0Jc3lIKTyCMB/92CEMuv7hmWNlUjBDUcKpYXVYakkHZukdryE/gmAlMWrXIJCoTDMo2OtfMqcCOz28QHfi67dLaib22EzT53nOQ3MqDrJUfJqoVdgxargaufECqhQegMU9+QuyylhI8/uGV+PCccOqNpxAKQdbFBpMyyJI1LNYXiYtts+AGh4jY1I5Rsaxxb0BQodMZiSou7Rr1SEFWDuWCAAnyik1a0Ra7d8wrBlPHaZsrNRdBwOuKrz0WgywDcOuPgY/vBJa+L2oTl2DA8Z7oui65XPCiRkZMFt1GLjWVv0ckFC3PnEqg7lg8zfxX5jDIYoW/Bo0KlTk+/zMCY7I8w6LEZVrhEqrJumFBFdbNLMX71oyzp9pV/wMUNwGOLuDFL+Zk39JGwCkZ4TDr6s695JbJkAHMpdbaJ3sdoqxbp+ahH0cvK+Zyl7gm69IOsnIKse6Pml8YyHj3gOF5GJ+8E9j3O/J889WkniePYAFEt90LQSAW3vL62nKLDo//1xrUlxgQDAs40ePAEO0VtqqJjP0Oga5p0pnzaIDhFbQwGyahNYcCPM/Boo9fl6WUC7L7zJyEmWWc6nOgnwaefSqaHGHtWFhADZB5fdVHpP+brybJkw1fku3Q5F9npWYd1jSXoGiaLdUKQdbFBiWTZamWMhL6InGhzAkR3LlAWkSzLAirx/FwRgActp6QskDHuh0I0fRkqSk/i8xKKxngfMGIOLH+4a0BhARyqlrgweEue+wLmVxQYyAMTNVCaTFK4RH0ONodPeDmw5J+SiKjIIvKDfTW6Mc1RklSMtHZ9HRBpSHvWb8Ed69tworG3BXsMhenxlLj5Nm3A2QxxALgCXIYzCkuoZqsGpsBf/mvy3Dd/MrUGyeDzgzc8QsAHHDw8cz6/4wX7FpX6YBSWvfTvZ/cymzAWf+iU72OcdVjyZGKyZpsx7OLCmKvLDsAwmS9R/UK7heeAM6/Dhx5ijyfh75YShQrmLlr51bGjLkqnhMbLe89PwJBADQqDovryNhoj9A6slT9NQExWeWS27dPMliQO+aNVt+wa0HJXhbLHBn3XxjFjT9+A/c+QZirIS2V+7FkibJ1xppPAGoDKbdoWU8eW/kRiIZXrNdmARmjEGRdbFDWZOlt0mJGX0SCEBUJkL60oUbsLC42ZqUL7ICKBF27zg2Jb3WEBjclJi206vycOnqNSgx4esd8cPlDeO5Ir+h0aOa84n5EgTEr8obDtuggywU9TiiYrJECkxULuVxQDo7L7H0mGqGAmJG8askcfO22BVBna5sdByxrPqlSQYbpLBm8hIKsnKJxLXEKBYChsxP3uXTRDYNNsutmdZsyJmt+NRkvTvY6x1WPJYfosuYNIiLrwSXVZE1+hv2igUIuuARn8Q31H8hjFimYzkdfLCWUQdZ18yribtdcRtYtbJ1SadWLtvajYcpIJZmrfMEwAqGIqODxCFMoyErAZIlBlmI/Gfs36gniBFUodVNTszFDXfSbK4MsSyVw93+AD/5TmltUauCzZ0lyZ/7t4/06lywKQdbFBp2ZMA4MhmKgfg3JUFRSqQeVBRTzHjx2zyp8cv0M3LKYTpZ08oxoyITJOr4DpIgSyG09VjwwyWDfmA+n+xzwhyLwcGRha4UHR7riDJpyJotByWRBL9LnDIzJKi0EWRLYAkpnjX1uKgdZrIcQx8dKHXMAxmRNqukFw3R2GLyE5II5BwuymIX2RIBd6/oioEixWLNKQdacKjJe9Dl8ou12roKsiAC4AlJG310wvsg9RCaLjKNXe16EhgvjpO1q4P4DxF1v/RelGt187opWJdr3W3RqrGyK3wi3uZyMxYfpmqC6iNivFxk0cICVQMRnsgKhCK79weu4+ZE3INDknGcqMVl6KcEgh90TbXzBwAJTuyeAYSqdZHAZo9dCcZvA160Amq+KfsxcTgyvVFPjmExHFIKsixFyyaDBBtz+KPC5VslpUJaxWlxnw//cOFfSzVMmi9PHNiJmKJ+gIKtnzIuz/WTwC2pIxsrCeXCsewxhWVYTgFSbIgswBUWTRLegjzbUADAsGl9c5ININkyWUi6Y6ftMNJiLlN5GbOtzDFbYv7p58nuGTFuHQUEoMFnjwaQEWXZyq7cBVkWQJWM4zDq1aCf95nlSSDveBateoxJVE/L2HR5qYGQqMFm5g1iTZQcAtOjIPKCZcwNJXt7wLWB9fizb44EFDVfNKU+onGGtNNh6gJUb1BUb4BToWiDBXNU75kW33YuzAy7Y7SSwnIpyQYdPSi4EQhExwWAzRCeG5UwWqzVnUBuLpTYQQPwgq4C84KIOsr71rW9h7dq1MBqNsNlsk707Ewe5+QVbcBplF5hiMI0CzehoTdJFqOY5MasE5Na+PR6YHOtMnxOtA2R/BMqqlKr9cAfCaBt0Rb8oDpPl4sxwCNL/7jhM1kihJisW8j5Zid4n3rkz2WBMliE/QdCDG+dgx+evwYa546yvyQWma0NivxMI0yyrscBkZYzJZLIMtqRMFgDMo5LBN9tIwiMXC9Z4dVmiXLBQk5U7KJisCo787jNbZkzK7lTQgCmRVBCQmCwGlqCtLzbCgeRB1pAsEBkaJkkBj6AXmx5PNuIxWewa4LjYBudyJmtIwWRZ9Rpp7ACkNWABecdFHWQFAgHcdddd+MQnPjHZuzKxUDJZSiRbcNMFtsEsva6x1Ig6WWf5XDsLKrGIFq4e7R7DWRpkqQ1kwT/HRgo3YySDYpAl7afdE0SPIC3k3IIeTl9ILJoGpJqsglxQBn+CmqxM32eiwRq1GuNLS8YLnueiroNJBQuyRtsndTcyBmOxtGZAO0WO5XSCGGSdn7jPZAkVfRFhH3lZ4CSv1QEwVyYZBHLQHwzRdVkMBQv3PEBRkwXXALlVmmlNEL5yyzx87oY5uG1J4h5N1VY9dDKWKy6TlUAuKJfUjdrtAEgidmbF5Fu4A7Lz3icPssh6xarXRPfygpQoHvUEYpgsi14d3eOrwGRNGC7qIOvrX/86PvOZz2DRokWTvSsTC2Z+oTaQPkdKKAdTOWiQpTMVib1dZpSbUWOTGKF812QtrCUDwIleB870k/3RW0iWbWNkJ17Wfhbe9reiXyTvk0Ux5g2iWxZkCdQUo99BBld/KAwnlZ1c/EyWjdymJReU1WDEvM8UCLLCIWDwTGyvrjwzWVMKpTS7PHxucvcjU7B6LNrDroAMwRZKI20T16tOHA9sRBUhl2FbqqI2vXxG9O8qt93OFmwekjNZ7oLxRe4hV7hEIoCbBVmTw9yvaCzBp66ZmbTJMs9zUUZE1UVknVJfYoQTyd0F5UzWiJ0k6NzC1AmyrGJyQUoKK3tkySHKBd1BDNEyiE2LyPW5vLE4mskqBFkThos6yLpkwTJPiSjhZHJBmvXhdBZxsJlRYRZ7MAD5lws2l5pg1qnhC0bQS7u4m62EnZjh2o+ZfA/WXPh19IviyAVHPYGoIMtqIQML6wzPsj1qnht/s9CpjpwbX9hzsltZYetXgEdXAWdfin6c1WQZ8sNkTSkU08W2zy4xeNMBhXqs8cHWQIxdgm6Jacg3xJoseu2zekCNMWaxdllLKbb895X4wk1z8aHLG/HOVYqC+yyglAuGwhHiCAfStLWAHEEuF/SOkn6IwKQxWemiWWZEVFUkMVmp+mTJmayefpL8CauNUybhytYkciZLDLLiMMTRxhdkbfOZ62bj8Feux00LqwpB1iShMEIp4Pf74fdLF5/DQRacwWAQwWAw0cvyCva56X4+byiFCoCgL0Iozmt4rQUqAGHPCCKK53mvgzynMWHdjBIc6LBjdaMtqr9UiVGV92Mxr9qCt9oJM1Fi0kBjjB4UHJw5ah94v5Pst0onfqdhpw89AsmsCmoDyqwGtA770DPqRjBoxasnSa+Z5jIjQiEyoUzWb5x3qIzQABD8DoQCfrJQSwC1zwEOQEhthKA8PzTk3Il4RhHO4Fhleg4ng6rvKHgA4c59iDRvkPbNNUTOAX1RzHl90YHTQG2tBefoRqj/FIKVSwFM/fOXc/RBDSBiLM3o/JkKyOU5nD14qK114MY6EBo8C0Gf/4SCyjNCrjetBZFgECpLNXgAgqVKHDflmFFqwIy1DeL/6R6vRMfXQuuuRt1+BINBOGWLTg0vTPlzfqog1fnLacxQAxC8owjZu8l8YSxFKAIgMnWPcUOJlPQto2uTaotWZLIivjFxrDnZ68Sf9nTg/g0zMOCU1WcHXIAa0BqtWZ9PuR4fTFpm+BIQ33PYRZLJVr065nPMWsL4EZaXML1WHQ+jBgiFQuCs9eKCP6g2AdPwupkaYzAy2odpF2R94QtfwMMPP5x0m5MnT2Lu3LlZvf93vvMdfP3rX495/KWXXoLROLk1BFu3bk1ru1LnCK4A0BcwYO/mzTHPtwz0YhGA3rYT2K94fkX7adQBOHGuEzPKz+A7qwDHmTcxMMgBINKME/t3Y+D4+L5LKpj8PBjRWswHcKq9Bwtkz9vdPmyW7fvizlNoBnD2Qg9O08d39nHwUSbLDw1CzmEAPF7fewjq7oP41REVAA7zDQ7x2KZ7jKcb+EgAtwLgIOClf/8DIXXi4t4bnUPQAdi+9xCcR4ejnmsa7MISAH0XzuCtOOdWKuTi+K7vb0cRgM4Tb+KwS9qHJR1H0ATgTOcgzmSxb9MNayNFKEc3jrz2LDpLCUM01c/f2X27MQ9A57AHh6bpbzTZx/jyiAUVAI5s+yc6S4dTbj9erOk4iyoAR852oGNkM+YO+DAHwFBAh115+A2Vx3d0gMwFB46dwmbHCdj9AKAGDwEvv7gFk9kXfDoi0flr8XZjA4CgYwD7Xv031gJwRgx4bYpfp84BsjbhIGD/jtdwiAf8YcBJjS+cQz3YRr/DX1p57B3k4RroQL+XA1tjGEECrkBYiFpXZINcjQ9nRsj36uwbEvdpdw95zD06GLOfEQHgoIJAGwhzELBr28tgaktt0IGb6LYvbd+LkOpoTvZzMjDZYzAAeDyetLabdkHWgw8+iLvvvjvpNi0tLUmfT4aHHnoIDzzwgPi/w+FAfX09rr/+elitceRTE4BgMIitW7di48aN0GjSKCQWbkLo/HKUVczHpjhUP3fYDnQ/gZoSMyo3bSIP2juAgBsqpwUYBeYtXY25SzeJrylvH8XjraQO6q5bboAhz1r44OFebPs7GQRWz63H3NJZQI/0vFULXLVJ2j/Vv18AhoBZ8xZjxlryePu2NrzYTtzXdCX1WNbQgn072lFS24yGJTXo3L0HGhWHL77nWli0XGbHeBpCOH4vuJAP11+1RjJOiAP1EcLkXnndzTFuYtwxD9D1J1TZDNgkO/6pkPE5nATqVmIj3FCkQq38HPj734BhYPbiyzBzZfr7Nl3Bb34FOHgCS+rNmLtu47Q4f/mtO4FeoG7OUtRsmF6/US7P4fGAf+FV4MBxLKm3YtH6/B9D1R9/BjiARauuxMK5m8CdigD/eA4l86/Cpo25+/xEx/fMK614o68N5bUN2LRpPtoG3cCBnTDpNbj55hty9vkXO1Kev55h4NRD0IbdWDO7AjgHmKtnZDTOTwaqOux48txeVFj1uPWWq8XH/3W8DwgDej4kfoen/7gfGByGoawOujEfMEzUMiaOBFn19Y1YneX3zfX4UN4+it+cfgu8zoRNm64AAJx+uRW40Ib5MxuxadO8mNd848hrGKWSwlKzDrfcvD7q+bD1HAAB11/z9nHv32RgqozBgKRyS4VpF2SVl5ejvDx/en6dTgedLtbYQaPRTPqPmtE+zNmY+DkTkdDx/jHwGg0Q8AB/uJHU4lhJUbPaaANknzWnuggaFYcKix5WU35rsgBgaYMkg5ldZYWqOJqZVIe90cciTAZJld4MFX3c6Q/jhNCE52Z8DbddtwE1bSSzNegK4m8HSMR208JqVNpMIvU7FX7nvEFfBLh80ITcUb9tFII+IEz03BpzSex24rnjIOdOhhj38RUE0eCCd/ZE74OfSFpV5lLxHLioQfveqeznxWM65c9fWt+jMpdP299o0o8xNT1RjbVPzDGkNZpqEx0PFt4BlL0BVfkcqNS5/3zl8S02kfn4jdZhXP7w69gwl8z/Jq16ap/rUxQJz9+iKmJI4xmGqnM3AIC3VGU1zk8kVjWX4ZPrZ2BxXVHU95rf3AC0ApzfIT7OzC667X4xGAEAE2WySkpLx31O5Wp8KKG1705/CH3OILafHcSolxl16eJ+RolJK36vMnOcba7/GgCmSZq+mPQxmO5DOph2QVYm6OjowMjICDo6OhAOh3Ho0CEAwMyZM2E2Tw0HmUmB0vji+DOSk9AIdStT2HeXmnV49pPrxN4N+UZLmQkmrQruQJgYcMy4BbjtpzjT3oXZRx6GJuKNfkGAuQtKxhd2WijdXX8bUD0DlUO9AICuUQ/azpDmxe9ZnZjRueigLwJc/cnNL+R2t9ocWLj3HweGzgCzb0l/P5Mh6AFCVEvv6Il+Ls8W7lMOosNg6+TuRybwUHlbwV0we0x0ryw2T7B5g+OA6sUT89mQjC86R8iY/48D3QAAo266LxWnIMrmAB27gPNvkP+nuOkFQBwG/+fG2PKQqxa1AK2ANuJDOBiASqMVg6yuUQ981DxldqUZplEyp5SXTJ1xSeqTFcI3nj+BrSf6oabav0StEYj5BVnblObA2bOA8eOidhf8yle+gmXLluGrX/0qXC4Xli1bhmXLlmHfvn2TvWuTC6WF+1u/i90mjrPcwtoiNJROTF0az3N4aNM8vGNFHS5rKSXWwcs/iFApyd5rw4ogK46Fu91DBlRmbcp6aBzuGoPTH0KpSYs1zZfIghxIL0BijYi1FnLM030PQQBe+jJw8C/Rj//9I8DTdwMDJ7La5Rh4ZDUofke0Pa/oLngJWLgDQOlMcjs8gXbe40UhyBo/mIW6s29iPi9ZS4cJgLKhcThCzvWCfXseUD6H3Ir27VWJt53iWDNP6gt14MwFhCMCRqi1ea/Dh1G6PljVVCLWZJXYps7cwQKpQDiCfe1kbgvRc99mjB9AyR8vM+e31U4B6eGiDrL+8Ic/QBCEmL/169dP9q5NLlhG0jcG9BwEeg7EbhOvEe0E4/2XNeL7dy2BRiWdphojCf70gjLIit+MGJDsTisVTZSvnlMe09DvokY6QZa4oEpQfyh/D/nCfuAEsOsRYPNngQhxNkIkIrIsXK6a5irtyuVsltgn6xIJnG2NMjvvCVpwjxeFIGv80FIVRiC9wutxIeQHQnRsZcm5CYYyyGIwFuzbc49yBSM0ST2ycgGtVgs/T5Qtbxw7hxF3ADRGgSCQP44DVjYVi3JBXj/56x4Gk1YlmlbIpY1AfAt3ACiW9c8qNRWCrKmAizrIKiAB2EI5HAD2/ILcr1sdvY12asopdQYyCCYOsmLlgkV04FH297pmztSXQuQUaTFZSXpkyd9DCBPbWwaWVQ96gKGz5L5nSLT+5Vz9We60Ah6Fm5qji9wGZDLCS0UuqNaKBibcREnHxgtR0lkIsrIGSyQF3fn/LHGs4BKPCXkG630EEGkXg6nAZOUejMlimAZywaSg5+yhsxcwJOuLxVBi1OLGBdUo09IgRpvYdXeiwXFcQllgvGbEAFAs6/FVkAtODRSCrEsRWjPA0Qnq1H/I7dX/E73wmQJMVjxojWS/DIIv+gm24IjLZJHBRqvmUUoHIZ4Drpp1iTVETSfIGjhFbpkkSQmNAeA1se/DmswCQN8Rcuvolj2fo8apSiZrjH4GY7F49ZRNEOQFTDLIaimnMoI+KTC/VALhfEBLx7hICAgF8vtZrB5Lb40vH54ANJaa8JN3L8XTH78ca2dIzeULTFYecBExWQCgNtkAAEHPGM4PxSYlSs1aGLQqWHl6HU2hIAtAVA38/GqrWJOVSAooD77KCkHWlEAhyLoUwXGSZDDgAsAB9auj2awpGmTpTSQzZYQPoVBYeoIxWXQB4vAFxZqsYpM08LC6rBWNxSLDdclADLLsibdppwXPjWvjP89x8YM1lyyI6j1Mbse64z8/HngTyAXFeqwSXFKNcyqIjS9/Zssk70gaYL8RpwJ0k1Pfc1FAI1sI5pvNmuR6LIbbl9ZiVVMJFtRIbFqhJisPsFRFX5vTnMlS0fPWCg/2XxiNeb7UpCO6QZb8mWLrHqtBSiRcOasMj7xnGb508zw0lcUPBotlNVkFueDUQCHIulQh19eXzyGTaN1K8r/GBPBTcwJjQZaKE+DxyhYYipqsZw90IxQRMLPCjCqrJDepLSZywvWXmlQQSM1kRSLAhV3kftOVmb2PnMliQZasXorLGZOVQC7oucRMLxiW3w1wPPjWl1DkaZ/svUkOeT3WJLEiFwXUWsLYAvmryxrrIoY1LOkySfVYSiyslQIAk67AZOUcHCe2hoBKO/3HUxZkcW4c6IgTZJm11DSLFmtNYSZrdqUFmxZV47+uTNwHNirIKjBZUwKFme5ShTwzWbeK3NavIbeM5ZqC0BkkKZjPLXOWE90FDRAEAY/vuQAAeP+aBnAyZuOBjbPxyfUzcM+6ponY3amFVEHW4EnCNmiMQM2yzN5HKRcUhGi5YK6YLLZQt9ImyUq54KUmQyubCSwkjSVn9/1rkncmBQqmF7kDY7OCeQqy3votcPxZ4LVvkf8nmclimFlhhlZNli0FJitPYHVZ5srprwqgBk4WeHGsm8xXtTapbrvMrAMCLFnLRZUbTAXIg6w5ValZtuIouWCByZoKKARZlyrkgRQLshrXAev+G9j4jcnYo7TAqdTwCGTwEIOsUIDUJwCAxoC950dwdsAFg0aFO1fURb1+XrUV/3Pj3EtTz58qyGrfSW7r1wCqJFLKVHJB3xhg74gKsnLHZFHGqmoRuWVs2dAZcmupzs3nTCdc+VkI4FAztn9q98wqBFm5A6vLCuRJLsjOIzauTpHEm0bFY04lWWwWgqw8gdVlTXOpIADR+MICD4JhwlYtrbeJT5eZtbK2JeYpF1QyuSDPkQRDKhSML6YeCkHWpQq5/KOe1mLxPLDx68Cid0zKLqULH0eCLL+HDo7ybK7GhD/ubgcA3LGsZsKaJ08LpAyyqDSo6Yrk7yNvAcCgDKL6jkTbq7v6s+vl5HNILBUgLdTFIKubvO+518j/zVdl/hnTHRVzIdQSqS83cHySdyYJLrVm0fkEc1HNF5M1rHCrnCJMFgCsm0nML5rLLiGDm4nEzI0kOJl1w2TvyfghygWl62RZg028XypnsqaYVBCQmKymUhP0mtRJhRqbAUatCrU2w6WZSJ6CKPwKlyrYQllnJV3epxF8nAEQHAh4XcDwObKABwBOhdODPrxwjNiJf/DypsnbyakIsQl1nCBLEIALlMlKFWTFlQsOkdvKRUD/UaD3CKnroODCAWjCGS4IIxHgdxtJYHXfASL9YAv16sXkNuAin9O1l/w/45rMPuNigbUG6Aa4iWpQmw0KTFbuwOSC+ajJEgRA2RJgitRkAUTyfefyWsxKI7NfQBaomAt8vn3K1mVnBL3EZDHMqrTApFXBHQhHywV1U+98Yj3iZlemZ8hh1qnxwqevTCsgK2BiUGCyLlWwSbN2xbQrQmcNBsOuIeC31wGP3USe0Bjxk1fPQhCAmxZWYV715PR1mbJgwRGzZZZjpI0sglU6oGZ5eu/DgqxIRKrJmnktue18E3D2Rr1MF4rzucnQewgYPEXeu/cQ3XcaZFlrASO1c379u0TWVDIDKG7K7DMuEgiWKnJnKjclLgRZuYM2j72ynL1SA2KGKSIXBEgrjtmVlqha2wJyjIshwALEucoiY7LKzFosqiOPz6wwS70hpyCTdeuSGlw7twIfubI57dc0lppEF+UCJh/Ta3VdQO7QcjVhsZa8e7L3JGMEaJCltrdHWXqHVHpsPtoHjgP++7rZk7R3UxjMKSrgBMLRHeRFR8DKBcS9LBlYgM6YKp9dqt1YdBe5Pb+dNLsGBxSTCUIfTNKfKx7OvSLbP2qmIV+oL30vuX/wcXI7Y0Nm738xwUyCrAKTdYmAFejng8kapj3XrLWAmi7WphCTVUABaYPa0dt4KWlQbtHhV+9fia2fuQrNZSZg/x/IE6zn4BRCU5kJv7t7FVY1FSTW0xWFIOtSRct64Asd0zLICqpokOXsiHpc5SWStRvmV6XlxHPJQW8DQLO/XoWdLQuyqpekfh8mJzz7EuB3SSyWrogEaSUtEC1xzZVAETEf0WUaZLW+Kt3vO0LqT0K0CbWxFLjywegF+yUcZBWYrEsMLOueDyaLNbaumAfMvUW6X0AB0w1ULliqJvMGxwElRi2KjBrMqrQA514FTm8mLRGu/sJk7mkBFykKQdaljGkqtwipSBbX4OqKepyjC/tCgJUAKrUk+1H2m2JBVs3S1O9Tt4pI84Ie4OS/JWdBczk5p2bfKG1rrSGBFgBdiAZZ514Dfr4W6Nqf+DN8DqnOCgD6jkr1WCotWWQabMD6h8hjvDp1LdnFjAKTdWlhIpiskhnArT8BPrbt0r62Cpi+oHJBG5ULlpq0UKvosjcSAbZ8kdxf/TGpP1gBBeQQhSCrgGmHkJpkcY0eGmRZ6wCtBTutNwOQikULiAMDlR14JJklBEGqeUqHyeI4YMl7yP3DT0rOgiZq+Ttb5kpVVCsGWaJc8OWvAQPHgaNPJ/6M89uJBJHt7+BpyRLeWColCFbcA1x+L7Dpe2LW8lLE9GCyCu6COYNYk5WHIIuZXpTOIGYAyXrmFVDAVAa1cDdR44uo3lFDp0lvSI0RuPp/JmPvCrgEUAiyCph2iNAsrsVLF92Na4HPnsYvrfcDKARZScFYBDmTNdZJ5IO8GqiYn977LH4nuT2/Heg5RO6bqBFFw1pAS9lEa63Yb0UftAP9x6SALllAwOqxFr6d7LMQBtp3RH8HgLBzN3wLWPnh9Pb7YgVjsvxOIuGcalDW0xUwPuSzGbGcySqggOkMmtAxhBzgEEG5RRZkdR8gtzXLpHrlAgrIMQpBVgHTDhG6wNBGaDGrqQzQmuDwEfOFQpCVBGyBKzMMEaWCFfMAdZpd4osbgcYrAAjA/j+Sx1jzSrUWmH09uV82K0ouyB/6i/QeyaRt/bTfU+NaoIratbdto9+hwITEQGdBiKcmBaylwVSCsp6ugPFBm4ZccKybNGrPBJEIMHqe3C9J39GsgAKmJOhYwyOC9y2y4BNXyxIHPbIgq4AC8oRCkFXA9INGYbVKB9IxL3HMKzIWgqyEYAGKnMnKxPRCDmaa4qcyQCYXBIBN3yf1HMs+IAZfxsAg+GMyiaDC4j0KrM7LUi31xOrYTb9DYZEeDz6Njdxhx9XRC2z9CjB6YdL2SQQ731S6KWmVPO2gSSEXPPca8KMFRJqbCZw9JBjm1YCtcVy7WEABkw6VRmSp/ndjFdaWuoFt3yXtR7oLQVYB+UchyCpg+kGnWKRRmZoYZBWYrMQwxqnJEoOspZm91/zbAbVB+p/JBdnnrLibMGO0Xsjs7wfnswNa2vTR2UdkZPHAmhubKyQmKxIiphcr7s5sPy8ReDVU8sIYwr2/Bnb+BHjzV5O3Uwzs95TX0xWQPVigGkjgLnjwzwAEoOutzN63/wS5LW4iUtwCCpjuYP0U3YPAjh8C274DvPj/iHQdAGpT9IUsoIBxoBBkFTDtwCsz4cYyCIJQkAumA7EmSxZkMWle1aLM3ktvBebdIv1vroi/na0BAnV5EkpnAe94jDwe8pEeW0oEvaSXF0ACt8Z1JHNf3Ax8ZCtpP1BADGKYrMFT5JZZ7E8m2D4xg44CxodkTFbID5x5idzP9Ldv3UpuG9dlv28FFDCVYCont+5BYLSd3D/4OOnjaCgW+zgWUEA+UEhVFTDtoNKbox8wlcHlDyEcIaxIIchKAqXxhd8pufaVz8n8/Za8W3IJNCUIsnQWhP7rdWx/ZQuuettHoNFqSc8unx1w9scWHbOFoUpL3KH0RcCDp4hMtJBdTwgpyKJM1tBZ+oR9MnYnGo4ecmutmdz9uFiQrCbr/HYpSZFJkCUIpPcdEO0QWkAB0xlMYeEektUBUwVFzbICs15AXlFgsgqYduB1iiDLWCZKBbVqHnqNahL2aprAoKjJGjpDbk0V2TkstVxDXMg0RmL5nAhFdXDpa6QJzVJNbuPVZbGFoalC2l5fVAiwUsAnygV7gXBIMjDw2idtn0SITFb15O7HxQLRXTCOXPDkv6X7AVf6vbSGzpJMv0oLNF897l0soIApAcZkeYZi55uaglSwgPyisGopYNpBY4huNvyP017MbS7UY6UFpbvgIA2ysmGxAIBXAR9+kSz25DVZqWCpIj1K4jkMsvqdTN6vgGgmy36B1LABU4TJoosbayHIygkSMVmRMHB6c/Rj7kFAm8TEon0HcOZFKaHRuI70xyqggIsBLMiydxDDC4AkEsKBQj1WAXlHIcgqYNpBHmQFBRU+9/wF/OoDRKpWCLJSQCkXHDpNbsvG0e3eXA6gPLPXJGOymLOgKcP3vMQRxWQxqSAwRZgsKhe0FOSCOUGiPlmvfpMEVYZi4uTo6iP/FycJsl78omR+AwCzrs/9/hZQwGSBJev6jpJbjRG4+YfErXbmxsnbrwIuCRTkggVMO2iNVvH+CCyICBxO9ToAFIKslGDugr4xIikbL5OVLSykd1Z8JovKBRMZaRQQF1FM1rAsyPr/27v36Kjqe+/jn0kymSTkShJykYT7xWrgAK0Y2j4iIkI9Aq31gi4qirRVtGLtc+Csp4qcrlVL5fSsVl3UtoL2sd54vC211aICtYqogFUUIyCCXMIlkAuEhCHze/7YmVsykxuzZzKT92utrNmzZ+89P775sTPf+f32dzfVhq/iGC2+a7IYyYoI30hWwHTBT16Q/vk/1vJ3VvhjfeKwVUzmRIjrszwe/znAi+uxkEi8X9Z5CwFllUj/Nkea+Tvrno6AjUiyEHdcGf6RrGPGSrg+q7Yu9CbJ6kRarqTWaUGnjkdmJKsnOrwmi+mCPdHkzJNJSrFGN7a/7H+h5bT/RsCx4p0uyEhWZLStLnjquPTST6zlytukiu/7C9GcPCL9v5uk345pn1DV75fOnJKSnNLUZdJl93V8bSUQb7xJlnf6NNeFIopIshB30vr5R7JqjJVwba9mJKtLklOk9FxrueGgdKy1OELUR7JaS3mHHMliumBPeJJSZbzTX756N/jFWE4ZbG7wV7tjJCsyvLexONNkXYf1zgPW6HThuVayJLVO45U1kvXFeish++ip4ON4Rzz7D5G+tUiqvDUarQeip+3fEc5BiCKSLMSdjMwc3/IxWQnXl0etaTMkWV3gvS5r3/uSaZFSs6L/7Z5vJKuD6YLhSsIjLE/FNaFfCCx+Ee2pg95RLFe25MrqeFt0jXckS7IqAr670lqe8nN/FU7vh8vqf/lHvD55Ifj3f3Sn9Zg/ws7WArHTdkYE9+pDFJFkIe64XGlyG6tMe03rdMHWW2QpmySrc94y7ns3Wo8FI6J/rxDvH7oT1e0/9DNdsMfM8EuDS/FntMbQO5LVVCf97t+kV+6KXqN8RS/4BjlinOnyTfvd8GsriTpngjT6cv823i8p9gaMah7bJR3a5n/uvYVDwXBbmwvETFqulBRQ443zEKKIJAtxx5GUpFNySZKOmeBvxhnJ6gLvSNae1iQr2lMFJSmztfBFy2nrepJAVBfsuRSXdP6V1nK/Qim33Fr2jmQd+NAa+Qi8ZstulG+PPIfDP5r1xXrr8YIfBX9ZkhlwTVagT1/0L3unCzKShUSVlOT/skkiyUJUkWQhLp1ypEmSTrv6B60nyeoCb5JVv896LBkb/TakuPztCCx+4fFYN42UqC7YU1+fb30AH36p//o770iWN7be+8VEA+Xb7eGtMHiidcptYZviNW1HgrPPsR4/ed4/euydLhjtwjdANAV+YUeShSgiyUJcanakS5LKy4Lv/0KS1QUZAYmpK1saOyc27cgMUfzi1HHJeKxlbxKG7in6mnRXlTTrQSmt9fpF70jWydb7o505JZ1pjk57KN9uj8DrsiQpb0jw87bXNF7wQyklXarZKe37wCr/7v2ipYCRLCSwwC8cOA8hikiyEJd2ZIxVg0nXuV+/KGg9SVYXBCZZFyzwj3ZEm/d9A0dVvJUF0/OkZH6XPZaWLSUlt5bslz/G3ptQS1JTvX3v7/H4l33l2/lwE1HeCoOS9YVE2//HbUeCS8dJ5822lrf+WarZZS2n9w8+JwCJJnAkK5PCF4gekizEpYofrtK26zdrfEWF8vv5byhIktUF3hEiZ4Z0YQxLNvtGWQKTLCoLRlS46YKSfVMGd74u/apc+miN9dw7XTCb6YIRFTiS1T/Eva3S8yRHwJ/4/GHSuLnW8sfPSgc/tJYZxUKi845kpedJzrTYtgV9CkkW4tKAnHRVjrSuMSjJ9Z80SbK6YPil0oDzpEv/K7YV/Fyt9ztrDhhR8SVZFL2ICN9IVq31GDSSZVOS9flr1n2xdvzdeu4rfEGSFVGpgUnW0PavJyX7L/hPSbeuiRs0yUrI3Cel1++1XqPoBRKd9+8c14UiykiyEPdKctJ9yyRZXZBdIt36jjVVMJbSWpOswA/7J7xJFuXbI6LtSNbJwJGsWnve03uD6xPVUssZ/xRQPuBEljNgumB+iJEsyT9lMH+YVWXN4ZDG/8Ba11hjlbYOLPsOJCJv0RdvtVUgSlI63wTo3UpzrJGs1OQkpTn53iBu+KYLBoxk1e6xHrlhZGTEYiTr+JfWY0O1dOKQVcgkKYXRyUjrbCRL8sc8MAn7xs1S7V7rGrnxc/m/hsR37kyrz4/+91i3BH1Mwn4i/fLLLzV//nwNGTJE6enpGjZsmJYuXarTp0/HummIsJJcayQrO90pR7Rvqouec4UYydrzjvV4ztej355E5E1kfddk2ZxkeVr8iXLDIX95/sxiayQFkRN0TdaQ0Nv4RrICbjbsypT+/TfSRf+bBAt9Q2qGdNF/WJVXgShK2JGszz77TB6PRw8//LCGDx+ubdu2acGCBTp58qRWrFgR6+YhgkpaR7Jy0hO2OycmbwLgvSarqV6q/shaHjQpNm1KNIEVHI2xP8mqP2DdYFqSmuv8Fewomxx5gdUFw41kff0m67YIY6+LTpsAAD4J+6l0+vTpmj59uu/50KFDVVVVpZUrV5JkJZgLhvRXboZTF42kIl1c8V2T1ZpkffWeNbUsb7CUc07MmpVQAqcLNtVKnjP+1+xIsrxTBb0ObLUeKd8eed6RrPT+VtW0UMovlK5fE702AQB8EjbJCqWurk79+3d8P5Dm5mY1N/tv0llfb30AdLvdcrvdtrYvHO/7xur9e7uCjBS9u3iykpMcPY4RMbZXqPg6UvopRZJpqtUZt1tJu99SsiRPWaVa+D10S9j+m5IppyS5G+U+vk+BZWFaGo/LE+E4O47uDPqj4tm/RUmSWjKLI/5e0dbbzhFJyWnW/5e8IQnx/6W3xTfREF97EV/79aYYd7UNDmOMsbktvcLOnTs1YcIErVixQgsWhK+qdu+992rZsmXt1j/xxBPKyMgIsQeAnsg9uUsXfb5Mjc58rT3/f/Stz3+h/JM7tLX8Zu3N/1+xbl5iMB7N/PBGOWS0acgdmrj7t76X9uVdqM2DI3uftHMPrNHIQy/5np9xpCrFnNYnpddoZxFV7CKprOYtjd/7R+3Ov1gfld8Y6+YAQJ/R2Nio6667TnV1dcrOzg67XdwlWUuWLNHy5cs73Gb79u0aPXq07/n+/ft10UUXafLkyfrTn/7U4b6hRrLKysp09OjRDgNpJ7fbrbVr1+rSSy+V00mJcjsQY3uFjG/NDjl/XynjytKZOz5Ryophcnjcct/6vpQX5kJ+hNRR/03572FyNNWpZco9Sn7zv3zrPcOmquXapyLajuTnb1bSpy+0W39m1u9lzv9+RN8r2nrdOeJMsxw7XpUZ9G0po+MZGvGg18U3wRBfexFf+/WmGNfX16ugoKDTJCvupgveddddmjdvXofbDB3qvwj4wIEDuvjiizVp0iT94Q9/6PT4LpdLLper3Xqn0xnzX2pvaEOiI8b2Copvv3xJkqP5hJyHP5Y8bimzWM7CEdb9fNBtIftvWo7UVKfk2i9bVzgkGSU11ysp0n3dW1kwp1yq2+tbnZJXJiXI/6tec45wOqUx8Z24htJr4pugiK+9iK/9ekOMu/r+cZdkFRYWqrCwa/db2b9/vy6++GJNmDBBq1evVhIlhIHew1tdUEY6vN1aLBxJghVpabmS9vor/eUMlOq+sqfwhfdGxOUXSh/7kywKXwAA+pqEzTr279+vyZMnq7y8XCtWrNCRI0dUXV2t6urqWDcNgCQ506TkVGv5yGfWY1Zp7NqTqLJbKzV6K/15y31HOsk6ddx/0+PyiW3awO8VANC3xN1IVletXbtWO3fu1M6dOzVw4MCg1+LsMjQgcaXlSCePSEeqrOfcHDXyRkyVPv+b5G60nvcfKu3eEPkky1u+vV+h1H+Yf31aruRMj+x7AQDQyyXsSNa8efNkjAn5A6CXcLVeMOpLsphWFnGjvhP8PL81ATpzSjrT3H77njpx2HrMLg1OlhnFAgD0QQmbZAGIA94bEp9s/YDOSFbkZZdKpeP9zwMrN3pvBB0J3mOl5UiZRf71JM4AgD6IJAtA7PiKX7TiA7k9RgeMZmUW+UcQIzll0Hs9litbSs+TklurtDKSBQDog0iyAMSOq839JRjJsseogBsBZ/T3J7eRTLKaA0ayHA4pq3U0iyQLANAHkWQBiJ12I1kkWbYYcK503vekYVOkvMEBSVZt5N7DO13Qmzhntv4uGZ0EAPRBCVtdEEAcCEyyMvKllPY3AkcEOBzSVav9z20dyWpNssbPtYprDJ8aufcAACBOkGQBiJ3A6YKMeESPHUlWYOELSRr/A+sHAIA+iOmCAGIncCSLqYLR4437poelVdOl43vO/pjNbaYLAgDQh5FkAYidtMCRLJKsqPEmWUe2S3s3SlseO/tjekfF0kiyAAAgyQIQO0HTBalCFzXJqcHPv1h/9sdsW/gCAIA+jCQLQOwwXTA2iiusx+yB1uOBrdKp42d3zOY212QBANCHkWQBiJ00Cl/ExPlXSjf+Tbp9s1QwSjIeafdbZ3fMtoUvAADow0iyAMQOI1mxkZQsDZokOdOkoZOtdWczZdDTIp1usJaZLggAAEkWgBiihHvs+ZKsdT0/hneqoEThCwAAxH2yAMRSep40+NvWdLXMoli3pm8a/C3JkSwd+0Kq2y/lnNP9Y3inCia7uKE0AAAiyQIQSw6HdMNL/mVEX1q2lFsuHd8t1e7pWZJF0QsAAIIwXRBAbDkcJFix5r0erqG6Z/v7il4wVRAAAIkkCwDgnap54lDP9m/mHlkAAAQiyQKAvu5skyxGsgAACEKSBQB9XVZrktXQ0ySrznrkmiwAACSRZAEAMluvyTrRw2uymluTLKYLAgAgiSQLAHDWI1lUFwQAIBBJFgD0dWc9kkXhCwAAApFkAUBf5y180VgjnTnd/f0ZyQIAIAhJFgD0dRn5UlLrvelPHun+/r7CF4xkAQAgkWQBAJKSpH4DrOWeTBlkuiAAAEFIsgAAZ1f8gvtkAQAQhCQLAHB2xS8YyQIAIAhJFgBAymydLvj5a9L9w6V3V3Z9XwpfAAAQhCQLACBltY5kff6qVfxi23Nd26+pTnKftJZJsgAAkESSBQCQ/GXcvY7v7tp+W/6v9Vg4WkrPi2ybAACIUyRZAAD/SJbXySNSc0PH+7SckTY9bC1feIvkcNjTNgAA4gxJFgDAX/hCktSaLB3/suN9PntZqttr3WdrzDV2tQwAgLhDkgUAkAacK+WPkEZMk0rHWes6S7I+WGU9fv0myZlua/MAAIgnJFkAACk1Q7r9A+m6Z6T+Q6x1xzq4LutMs7T3XWu54mr72wcAQBxJ6CRr5syZKi8vV1pamkpKSjR37lwdOHAg1s0CgN7L4ZDyWpOsjopfHPyX1NJsTRUsGBGdtgEAECcSOsm6+OKL9cwzz6iqqkrPPvusdu3ape9///uxbhYA9G5dGcnyjmKVXUjBCwAA2kiJdQPsdOedd/qWBw0apCVLlmj27Nlyu91yOp0xbBkA9GJdGcnyJlnlF9rfHgAA4kxCj2QFOnbsmP7yl79o0qRJJFgA0BHvSFbtV1KL21o+UuUvhGGM9BVJFgAA4ST0SJYkLV68WA8++KAaGxt14YUX6uWXX+5w++bmZjU3N/ue19fXS5LcbrfcbretbQ3H+76xev++gBjbi/jaK+LxTctXSkqaHGea5K7ZLWUUKOUPk6XUTJ35ycfS8S/kbKyRSXbpTMHXpD7we6UP24v42ov42ov42q83xbirbXAYY4zNbYmoJUuWaPny5R1us337do0ePVqSdPToUR07dkx79uzRsmXLlJOTo5dfflmOMNcQ3HvvvVq2bFm79U888YQyMjLO/h8AAHFgyvYlymo6oHeG/Yeandm6+LOfS5L+/rX/VuGJTzVu7yM62m+U3h75f2LcUgAAoqexsVHXXXed6urqlJ2dHXa7uEuyjhw5opqamg63GTp0qFJTU9ut37dvn8rKyvTOO++osrIy5L6hRrLKysp09OjRDgNpJ7fbrbVr1+rSSy9lqqNNiLG9iK+97Ihv8tPXKWnn39Uy/X6Z3HKlPGXdbPjMnDVyfPaSkrf+WS2VP5Fnyj0Reb/ejj5sL+JrL+JrL+Jrv94U4/r6ehUUFHSaZMXddMHCwkIVFhb2aF+PxyNJQUlUWy6XSy6Xq916p9MZ819qb2hDoiPG9iK+9opofFuvy0pu2C+lpvlWp9R+KR2tsl4rHavkPvb7pA/bi/jai/jai/jarzfEuKvvH3dJVldt2rRJ77//vr71rW8pLy9Pu3bt0t13361hw4aFHcUCALTKLbMe6/ZJqf3862t2SIe3W8sDvhb9dgEAEAcStrpgRkaGnnvuOV1yySUaNWqU5s+frzFjxmjDhg0hR6oAAAFyBlqPdV9J9QE3cf9ivdRcLyU5pfzhMWkaAAC9XcKOZFVUVOjNN9+MdTMAID7llFuPdfuk9P7+9Uc/tx4LRkgp7a99BQAACZxkAQDOgnckq+GglJ7X/nWmCgIAEFbCThcEAJyFfoVSsksyHv81WIEGnBv9NgEAECdIsgAA7SUlSTnnWMumxXrMKfO/XnRe9NsEAECcIMkCAIQWmFQlpUhlE/3PmS4IAEBYJFkAgNACk6zMYqlwlLWcmhn8GgAACEKSBQAILTcgkcoq9l+HVTzGmk4IAABCorogACA0b4VBScoukUbOkC77pTR0csyaBABAPCDJAgCEFjglMKtESk6RKhfGrj0AAMQJ5nsAAEILHMnKKo5dOwAAiDMkWQCA0LLP8S9nlcauHQAAxBmSLABAaM40KbPIWmYkCwCALuOaLABAeN+8Q9q1Tiq/MNYtAQAgbpBkAQDCq1xIsQsAALqJ6YIAAAAAEEEkWQAAAAAQQSRZAAAAABBBJFkAAAAAEEEkWQAAAAAQQSRZAAAAABBBJFkAAAAAEEEkWQAAAAAQQSRZAAAAABBBJFkAAAAAEEEkWQAAAAAQQSRZAAAAABBBJFkAAAAAEEEpsW5Ab2eMkSTV19fHrA1ut1uNjY2qr6+X0+mMWTsSGTG2F/G1F/G1HzG2F/G1F/G1F/G1X2+KsTcn8OYI4ZBkdaKhoUGSVFZWFuOWAAAAAOgNGhoalJOTE/Z1h+ksDevjPB6PDhw4oKysLDkcjpi0ob6+XmVlZfrqq6+UnZ0dkzYkOmJsL+JrL+JrP2JsL+JrL+JrL+Jrv94UY2OMGhoaVFpaqqSk8FdeMZLViaSkJA0cODDWzZAkZWdnx7xjJTpibC/iay/iaz9ibC/iay/iay/ia7/eEuOORrC8KHwBAAAAABFEkgUAAAAAEUSSFQdcLpeWLl0ql8sV66YkLGJsL+JrL+JrP2JsL+JrL+JrL+Jrv3iMMYUvAAAAACCCGMkCAAAAgAgiyQIAAACACCLJAgAAAIAIIskCAAAAgAgiyeolHnroIQ0ePFhpaWmaOHGi3nvvvQ63X7NmjUaPHq20tDRVVFTor3/9a5RaGn/uu+8+feMb31BWVpYGDBig2bNnq6qqqsN9Hn30UTkcjqCftLS0KLU4vtx7773tYjV69OgO96H/dt3gwYPbxdfhcGjhwoUht6fvdu4f//iHrrjiCpWWlsrhcOiFF14Iet0Yo3vuuUclJSVKT0/X1KlTtWPHjk6P293zeKLqKL5ut1uLFy9WRUWF+vXrp9LSUv3gBz/QgQMHOjxmT84ziaqz/jtv3rx2sZo+fXqnx6X/+nUW41DnZIfDofvvvz/sMenDlq58JmtqatLChQuVn5+vzMxMXXnllTp06FCHx+3pedtOJFm9wNNPP62f/vSnWrp0qbZs2aKxY8fqsssu0+HDh0Nu/84772jOnDmaP3++tm7dqtmzZ2v27Nnatm1blFseHzZs2KCFCxfq3Xff1dq1a+V2uzVt2jSdPHmyw/2ys7N18OBB38+ePXui1OL4c9555wXF6p///GfYbem/3fP+++8HxXbt2rWSpKuuuirsPvTdjp08eVJjx47VQw89FPL1X//61/rd736n3//+99q0aZP69eunyy67TE1NTWGP2d3zeCLrKL6NjY3asmWL7r77bm3ZskXPPfecqqqqNHPmzE6P253zTCLrrP9K0vTp04Ni9eSTT3Z4TPpvsM5iHBjbgwcPatWqVXI4HLryyis7PC59uGufye6880699NJLWrNmjTZs2KADBw7oe9/7XofH7cl523YGMXfBBReYhQsX+p63tLSY0tJSc99994Xc/uqrrzaXX3550LqJEyeaH/3oR7a2M1EcPnzYSDIbNmwIu83q1atNTk5O9BoVx5YuXWrGjh3b5e3pv2fnjjvuMMOGDTMejyfk6/Td7pFknn/+ed9zj8djiouLzf333+9bV1tba1wul3nyySfDHqe75/G+om18Q3nvvfeMJLNnz56w23T3PNNXhIrvDTfcYGbNmtWt49B/w+tKH541a5aZMmVKh9vQh0Nr+5mstrbWOJ1Os2bNGt8227dvN5LMxo0bQx6jp+dtuzGSFWOnT5/W5s2bNXXqVN+6pKQkTZ06VRs3bgy5z8aNG4O2l6TLLrss7PYIVldXJ0nq379/h9udOHFCgwYNUllZmWbNmqVPPvkkGs2LSzt27FBpaamGDh2q66+/Xnv37g27Lf23506fPq3HH39cN910kxwOR9jt6Ls9t3v3blVXVwf10ZycHE2cODFsH+3JeRx+dXV1cjgcys3N7XC77pxn+rr169drwIABGjVqlG655RbV1NSE3Zb+e3YOHTqkV155RfPnz+90W/pwe20/k23evFlutzuoP44ePVrl5eVh+2NPztvRQJIVY0ePHlVLS4uKioqC1hcVFam6ujrkPtXV1d3aHn4ej0eLFi3SN7/5TZ1//vlhtxs1apRWrVqlF198UY8//rg8Ho8mTZqkffv2RbG18WHixIl69NFH9eqrr2rlypXavXu3vv3tb6uhoSHk9vTfnnvhhRdUW1urefPmhd2Gvnt2vP2wO320J+dxWJqamrR48WLNmTNH2dnZYbfr7nmmL5s+fbr+/Oc/64033tDy5cu1YcMGzZgxQy0tLSG3p/+enccee0xZWVmdTmejD7cX6jNZdXW1UlNT233p0tnnYu82Xd0nGlJi9s5ADCxcuFDbtm3rdB50ZWWlKisrfc8nTZqkc889Vw8//LB+8Ytf2N3MuDJjxgzf8pgxYzRx4kQNGjRIzzzzTJe+2UPXPfLII5oxY4ZKS0vDbkPfRbxwu926+uqrZYzRypUrO9yW80zXXXvttb7liooKjRkzRsOGDdP69et1ySWXxLBliWnVqlW6/vrrOy0wRB9ur6ufyeIVI1kxVlBQoOTk5HZVUw4dOqTi4uKQ+xQXF3dre1huu+02vfzyy1q3bp0GDhzYrX2dTqfGjRunnTt32tS6xJGbm6uRI0eGjRX9t2f27Nmj119/XTfffHO39qPvdo+3H3anj/bkPN7XeROsPXv2aO3atR2OYoXS2XkGfkOHDlVBQUHYWNF/e+6tt95SVVVVt8/LEn043Gey4uJinT59WrW1tUHbd/a52LtNV/eJBpKsGEtNTdWECRP0xhtv+NZ5PB698cYbQd9GB6qsrAzaXpLWrl0bdvu+zhij2267Tc8//7zefPNNDRkypNvHaGlp0ccff6ySkhIbWphYTpw4oV27doWNFf23Z1avXq0BAwbo8ssv79Z+9N3uGTJkiIqLi4P6aH19vTZt2hS2j/bkPN6XeROsHTt26PXXX1d+fn63j9HZeQZ++/btU01NTdhY0X977pFHHtGECRM0duzYbu/bV/twZ5/JJkyYIKfTGdQfq6qqtHfv3rD9sSfn7aiIWckN+Dz11FPG5XKZRx991Hz66afmhz/8ocnNzTXV1dXGGGPmzp1rlixZ4tv+7bffNikpKWbFihVm+/btZunSpcbpdJqPP/44Vv+EXu2WW24xOTk5Zv369ebgwYO+n8bGRt82bWO8bNky89prr5ldu3aZzZs3m2uvvdakpaWZTz75JBb/hF7trrvuMuvXrze7d+82b7/9tpk6daopKCgwhw8fNsbQfyOhpaXFlJeXm8WLF7d7jb7bfQ0NDWbr1q1m69atRpL5zW9+Y7Zu3eqrbverX/3K5ObmmhdffNF89NFHZtasWWbIkCHm1KlTvmNMmTLFPPDAA77nnZ3H+5KO4nv69Gkzc+ZMM3DgQPPhhx8GnZObm5t9x2gb387OM31JR/FtaGgwP/vZz8zGjRvN7t27zeuvv27Gjx9vRowYYZqamnzHoP92rLNzhDHG1NXVmYyMDLNy5cqQx6APh9aVz2Q//vGPTXl5uXnzzTfNBx98YCorK01lZWXQcUaNGmWee+453/OunLejjSSrl3jggQdMeXm5SU1NNRdccIF59913fa9ddNFF5oYbbgja/plnnjEjR440qamp5rzzzjOvvPJKlFscPySF/Fm9erVvm7YxXrRoke/3UVRUZL7zne+YLVu2RL/xceCaa64xJSUlJjU11ZxzzjnmmmuuMTt37vS9Tv89e6+99pqRZKqqqtq9Rt/tvnXr1oU8J3jj6PF4zN13322KioqMy+Uyl1xySbvYDxo0yCxdujRoXUfn8b6ko/ju3r077Dl53bp1vmO0jW9n55m+pKP4NjY2mmnTppnCwkLjdDrNoEGDzIIFC9olS/TfjnV2jjDGmIcfftikp6eb2trakMegD4fWlc9kp06dMrfeeqvJy8szGRkZ5rvf/a45ePBgu+ME7tOV83a0OYwxxp4xMgAAAADoe7gmCwAAAAAiiCQLAAAAACKIJAsAAAAAIogkCwAAAAAiiCQLAAAAACKIJAsAAAAAIogkCwAAAAAiiCQLAABJ8+bN0+zZs2PdDABAAkiJdQMAALCbw+Ho8PWlS5fqt7/9rYwxUWoRACCRkWQBABLewYMHfctPP/207rnnHlVVVfnWZWZmKjMzMxZNAwAkIKYLAgASXnFxse8nJydHDocjaF1mZma76YKTJ0/W7bffrkWLFikvL09FRUX64x//qJMnT+rGG29UVlaWhg8frr/97W9B77Vt2zbNmDFDmZmZKioq0ty5c3X06NEo/4sBALFEkgUAQBiPPfaYCgoK9N577+n222/XLbfcoquuukqTJk3Sli1bNG3aNM2dO1eNjY2SpNraWk2ZMkXjxo3TBx98oFdffVWHDh3S1VdfHeN/CQAgmkiyAAAIY+zYsfr5z3+uESNG6D//8z+VlpamgoICLViwQCNGjNA999yjmpoaffTRR5KkBx98UOPGjdMvf/lLjR49WuPGjdOqVau0bt06ff755zH+1wAAooVrsgAACGPMmDG+5eTkZOXn56uiosK3rqioSJJ0+PBhSdK//vUvrVu3LuT1Xbt27dLIkSNtbjEAoDcgyQIAIAyn0xn03OFwBK3zVi30eDySpBMnTuiKK67Q8uXL2x2rpKTExpYCAHoTkiwAACJk/PjxevbZZzV48GClpPAnFgD6Kq7JAgAgQhYuXKhjx45pzpw5ev/997Vr1y699tpruvHGG9XS0hLr5gEAooQkCwCACCktLdXbb7+tlpYWTZs2TRUVFVq0aJFyc3OVlMSfXADoKxyG29sDAAAAQMTwtRoAAAAARBBJFgAAAABEEEkWAAAAAEQQSRYAAAAARBBJFgAAAABEEEkWAAAAAEQQSRYAAAAARBBJFgAAAABEEEkWAAAAAEQQSRYAAAAARBBJFgAAAABEEEkWAAAAAETQ/weVm7CidHr5CwAAAABJRU5ErkJggg==", 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", 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" ] @@ -146,6 +153,14 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39d4c111", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 10b16bcbf97be90cea7def090f1e6d5232e0af29 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 30 Dec 2024 23:37:51 -0700 Subject: [PATCH 03/50] benchmark --- benchmarks/stateful_paths.py | 220 ++++++++++++++++++++ diffrax/_brownian/path.py | 19 +- diffrax/_brownian/tree.py | 2 +- diffrax/_integrate.py | 3 +- examples/underdamped_langevin_example.ipynb | 82 ++++++-- 5 files changed, 302 insertions(+), 24 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index e69de29b..677f3e24 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -0,0 +1,220 @@ + +import math +from typing import cast, Union + +import equinox as eqx +import equinox.internal as eqxi +import jax +import jax.numpy as jnp +import jax.random as jr +import jax.tree_util as jtu +import lineax.internal as lxi +from jaxtyping import PRNGKeyArray, PyTree +from lineax.internal import complex_to_real_dtype +import diffrax + +class OldBrownianPath(diffrax.AbstractBrownianPath): + shape: PyTree[jax.ShapeDtypeStruct] = eqx.field(static=True) + levy_area: type[ + Union[diffrax.BrownianIncrement, diffrax.SpaceTimeLevyArea, diffrax.SpaceTimeTimeLevyArea] + ] = eqx.field(static=True) + key: PRNGKeyArray + precompute: bool = eqx.field(static=True) + + def __init__( + self, + shape, + key, + levy_area = diffrax.BrownianIncrement, + precompute = False, + ): + self.shape = ( + jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) + if diffrax._misc.is_tuple_of_ints(shape) + else shape + ) + self.key = key + self.levy_area = levy_area + self.precompute = precompute + + if any( + not jnp.issubdtype(x.dtype, jnp.inexact) + for x in jtu.tree_leaves(self.shape) + ): + raise ValueError("OldBrownianPath dtypes all have to be floating-point.") + + @property + def t0(self): + return -jnp.inf + + @property + def t1(self): + return jnp.inf + + def init( + self, + t0, + t1, + y0, + args, + max_steps, + ): + return None + + def __call__( + self, + t0, + brownian_state, + t1 = None, + left = True, + use_levy = False, + ): + return self.evaluate(t0, t1, left, use_levy), brownian_state + + @eqx.filter_jit + def evaluate( + self, + t0, + t1 = None, + left = True, + use_levy = False, + ): + del left + if t1 is None: + dtype = jnp.result_type(t0) + t1 = t0 + t0 = jnp.array(0, dtype) + else: + with jax.numpy_dtype_promotion("standard"): + dtype = jnp.result_type(t0, t1) + t0 = jnp.astype(t0, dtype) + t1 = jnp.astype(t1, dtype) + t0 = eqxi.nondifferentiable(t0, name="t0") + t1 = eqxi.nondifferentiable(t1, name="t1") + t1 = cast(diffrax._custom_types.RealScalarLike, t1) + t0_ = diffrax._misc.force_bitcast_convert_type(t0, jnp.int32) + t1_ = diffrax._misc.force_bitcast_convert_type(t1, jnp.int32) + key = jr.fold_in(self.key, t0_) + key = jr.fold_in(key, t1_) + key = diffrax._misc.split_by_tree(key, self.shape) + out = jtu.tree_map( + lambda key, shape: self._evaluate_leaf( + t0, t1, key, shape, self.levy_area, use_levy + ), + key, + self.shape, + ) + if use_levy: + out = diffrax._custom_types.levy_tree_transpose(self.shape, out) + assert isinstance(out, self.levy_area) + return out + + @staticmethod + def _evaluate_leaf( + t0, + t1, + key, + shape, + levy_area, + use_levy, + ): + w_std = jnp.sqrt(t1 - t0).astype(shape.dtype) + dt = jnp.asarray(t1 - t0, dtype=complex_to_real_dtype(shape.dtype)) + + if levy_area is diffrax.SpaceTimeTimeLevyArea: + key_w, key_hh, key_kk = jr.split(key, 3) + w = jr.normal(key_w, shape.shape, shape.dtype) * w_std + hh_std = w_std / math.sqrt(12) + hh = jr.normal(key_hh, shape.shape, shape.dtype) * hh_std + kk_std = w_std / math.sqrt(720) + kk = jr.normal(key_kk, shape.shape, shape.dtype) * kk_std + levy_val = diffrax.SpaceTimeTimeLevyArea(dt=dt, W=w, H=hh, K=kk) + + elif levy_area is diffrax.SpaceTimeLevyArea: + key_w, key_hh = jr.split(key, 2) + w = jr.normal(key_w, shape.shape, shape.dtype) * w_std + hh_std = w_std / math.sqrt(12) + hh = jr.normal(key_hh, shape.shape, shape.dtype) * hh_std + levy_val = diffrax.SpaceTimeLevyArea(dt=dt, W=w, H=hh) + elif levy_area is diffrax.BrownianIncrement: + w = jr.normal(key, shape.shape, shape.dtype) * w_std + levy_val = diffrax.BrownianIncrement(dt=dt, W=w) + else: + assert False + + if use_levy: + return levy_val + return w + + +# https://github.com/patrick-kidger/diffrax/issues/517 +key = jax.random.key(42) +t0 = 0 +t1 = 100 +y0 = 1.0 +ndt = 4000 +dt = (t1 - t0) / (ndt - 1) +drift = lambda t, y, args: -y +diffusion = lambda t, y, args: 0.2 + +brownian_motion = diffrax.VirtualBrownianTree(t0, t1, tol=1e-3, shape=(), key=key) +ubp = OldBrownianPath(shape=(), key=key) +new_ubp = diffrax.UnsafeBrownianPath(shape=(), key=key) +new_ubp_pre = diffrax.UnsafeBrownianPath(shape=(), key=key, precompute=True) +solver = diffrax.Euler() +terms = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, brownian_motion)) +terms_old = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, ubp)) +terms_new = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp)) +terms_new_precompute = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp_pre)) +saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, ndt)) + +@jax.jit +def diffrax_vbt(): + return diffrax.diffeqsolve(terms, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + +@jax.jit +def diffrax_old(): + return diffrax.diffeqsolve(terms_old, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + +@jax.jit +def diffrax_new(): + return diffrax.diffeqsolve(terms_new, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + +@jax.jit +def diffrax_new_pre(): + return diffrax.diffeqsolve(terms_new_precompute, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + +_ = diffrax_vbt().block_until_ready() +_ = diffrax_old().block_until_ready() +_ = diffrax_new().block_until_ready() +_ = diffrax_new_pre().block_until_ready() + +from timeit import Timer +num_runs = 10 + +timer = Timer(stmt="_ = diffrax_vbt().block_until_ready()", globals=globals()) +total_time = timer.timeit(number=num_runs) +print(f"VBT: {total_time / num_runs:.6f}") + +timer = Timer(stmt="_ = diffrax_old().block_until_ready()", globals=globals()) +total_time = timer.timeit(number=num_runs) +print(f"Old UBP: {total_time / num_runs:.6f}") + +timer = Timer(stmt="_ = diffrax_new().block_until_ready()", globals=globals()) +total_time = timer.timeit(number=num_runs) +print(f"New UBP: {total_time / num_runs:.6f}") + +timer = Timer(stmt="_ = diffrax_new_pre().block_until_ready()", globals=globals()) +total_time = timer.timeit(number=num_runs) +print(f"New UBP + Precompute: {total_time / num_runs:.6f}") + +""" +Results on Mac M1 CPU: +VBT: 0.282765 +Old UBP: 0.015823 +New UBP: 0.013105 +New UBP + Precompute: 0.002506 + +Results on A100 GPU: + +""" \ No newline at end of file diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index b027a426..97593f4b 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -115,20 +115,21 @@ def _generate_noise( self, key: PRNGKeyArray, shape: jax.ShapeDtypeStruct, + max_steps: int, ) -> Float[Array, "levy_dims shape"]: if self.levy_area is SpaceTimeTimeLevyArea: key_w, key_hh, key_kk = jr.split(key, 3) - w = jr.normal(key_w, shape.shape, shape.dtype) - hh = jr.normal(key_hh, shape.shape, shape.dtype) - kk = jr.normal(key_kk, shape.shape, shape.dtype) - noise = jnp.stack([w, hh, kk]) + w = jr.normal(key_w, (max_steps, *shape.shape), shape.dtype) + hh = jr.normal(key_hh, (max_steps, *shape.shape), shape.dtype) + kk = jr.normal(key_kk, (max_steps, *shape.shape), shape.dtype) + noise = jnp.stack([w, hh, kk], axis=1) elif self.levy_area is SpaceTimeLevyArea: key_w, key_hh = jr.split(key, 2) - w = jr.normal(key_w, shape.shape, shape.dtype) - hh = jr.normal(key_hh, shape.shape, shape.dtype) - noise = jnp.stack([w, hh]) + w = jr.normal(key_w, (max_steps, *shape.shape), shape.dtype) + hh = jr.normal(key_hh, (max_steps, *shape.shape), shape.dtype) + noise = jnp.stack([w, hh], axis=1) elif self.levy_area is BrownianIncrement: - noise = jr.normal(key, shape.shape, shape.dtype) + noise = jr.normal(key, (max_steps, *shape.shape), shape.dtype) else: assert False @@ -145,7 +146,7 @@ def init( if max_steps is not None and self.precompute: subkey = split_by_tree(self.key, self.shape) noise = jtu.tree_map( - lambda subkey, shape: self._generate_noise(subkey, shape), + lambda subkey, shape: self._generate_noise(subkey, shape, max_steps), subkey, self.shape, ) diff --git a/diffrax/_brownian/tree.py b/diffrax/_brownian/tree.py index 306956b0..fc550629 100644 --- a/diffrax/_brownian/tree.py +++ b/diffrax/_brownian/tree.py @@ -350,7 +350,7 @@ def evaluate( # now map [0,1] back onto [self.t0, self.t1] levy_out = self._denormalise_bm_inc(levy_out) assert isinstance(levy_out, self.levy_area) - return (levy_out if use_levy else levy_out.W, None) + return levy_out if use_levy else levy_out.W def _evaluate(self, r: RealScalarLike) -> PyTree: """Maps the _evaluate_leaf function at time r using self.key onto self.shape""" diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 099a7104..87a2b1eb 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -347,7 +347,6 @@ def body_fun_aux(state): # Actually do some differential equation solving! Make numerical steps, adapt # step sizes, all that jazz. # - (y, y_error, dense_info, solver_state, path_state, solver_result) = solver.step( terms, state.tprev, @@ -1170,7 +1169,7 @@ def _wrap(term): terms, is_leaf=lambda x: isinstance(x, AbstractTerm) and not isinstance(x, MultiTerm), ) - + # print("diff terms", terms) if isinstance(solver, AbstractImplicitSolver): def _get_tols(x): diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index f9237d11..624cea7c 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "9deba250066ddc39", "metadata": { "ExecuteTime": { @@ -46,7 +46,64 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, (None, Array([[[ 1.5437187 , 0.15094286],\n", + " [-0.1776888 , 0.7148498 ],\n", + " [ 1.124776 , 0.7197403 ]],\n", + "\n", + " [[-0.18969345, -0.72713757],\n", + " [ 0.57686734, 0.6250485 ],\n", + " [-0.54804486, -0.82060134]],\n", + "\n", + " [[ 0.2385169 , -0.273696 ],\n", + " [ 0.28720167, 1.115761 ],\n", + " [-0.23067027, -0.4854902 ]],\n", + "\n", + " ...,\n", + "\n", + " [[-0.2060602 , 0.5322451 ],\n", + " [ 1.3253211 , -0.8300134 ],\n", + " [-1.047963 , -1.1495486 ]],\n", + "\n", + " [[-0.5335223 , -0.10977904],\n", + " [ 2.0500367 , 1.009181 ],\n", + " [-0.21443863, 0.37549132]],\n", + "\n", + " [[ 1.4900465 , -0.94098794],\n", + " [ 0.28333724, 0.79191744],\n", + " [ 0.26032442, -0.7804612 ]]], dtype=float32), 0))\n", + "((20.0, Array([6.90372 , 0.675037], dtype=float32)), (None, (None, Array([[[ 1.5437187 , 0.15094286],\n", + " [-0.1776888 , 0.7148498 ],\n", + " [ 1.124776 , 0.7197403 ]],\n", + "\n", + " [[-0.18969345, -0.72713757],\n", + " [ 0.57686734, 0.6250485 ],\n", + " [-0.54804486, -0.82060134]],\n", + "\n", + " [[ 0.2385169 , -0.273696 ],\n", + " [ 0.28720167, 1.115761 ],\n", + " [-0.23067027, -0.4854902 ]],\n", + "\n", + " ...,\n", + "\n", + " [[-0.2060602 , 0.5322451 ],\n", + " [ 1.3253211 , -0.8300134 ],\n", + " [-1.047963 , -1.1495486 ]],\n", + "\n", + " [[-0.5335223 , -0.10977904],\n", + " [ 2.0500367 , 1.009181 ],\n", + " [-0.21443863, 0.37549132]],\n", + "\n", + " [[ 1.4900465 , -0.94098794],\n", + " [ 0.28333724, 0.79191744],\n", + " [ 0.26032442, -0.7804612 ]]], dtype=float32), 1)))\n" + ] + } + ], "source": [ "from warnings import simplefilter\n", "\n", @@ -71,28 +128,28 @@ "y0 = (x0, v0)\n", "\n", "# Brownian motion\n", - "# bm = diffrax.VirtualBrownianTree(\n", - "# t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", - "# )\n", - "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea)\n", + "bm = diffrax.VirtualBrownianTree(\n", + " t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", + ")\n", + "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, precompute=True)\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", "terms = diffrax.MultiTerm(drift_term, diffusion_term)\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", - "# solver = diffrax.Euler()\n", + "solver = diffrax.Euler()\n", "\n", "def _path_init(term):\n", " if isinstance(term, diffrax.ControlTerm) or isinstance(term, diffrax.UnderdampedLangevinDiffusionTerm):\n", - " return term.control.init(t0, t1, y0, None, 100)\n", + " return term.control.init(t0, t1, y0, None, 4096)\n", " elif isinstance(term, diffrax.MultiTerm):\n", " return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", " return None\n", "\n", "state = jax.tree.map(_path_init, terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", - "# print(state)\n", - "# print(terms.contr(t0, t1, state)[0])\n", + "print(state)\n", + "print(terms.contr(t0, t1, state))\n", "\n", "# @eqx.filter_jit\n", "# def f():\n", @@ -106,6 +163,7 @@ "\n", "# f()\n", "\n", + "\n", "sol = diffrax.diffeqsolve(\n", " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat\n", ")\n", @@ -114,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "62da2ddbaaf98f47", "metadata": { "ExecuteTime": { @@ -125,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 9921407f7bcde0758807adafdfcd8ae770f60a1f Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 30 Dec 2024 23:54:12 -0700 Subject: [PATCH 04/50] timing --- benchmarks/stateful_paths.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 677f3e24..ceb759d0 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -216,5 +216,12 @@ def diffrax_new_pre(): New UBP + Precompute: 0.002506 Results on A100 GPU: - +VBT: 3.881952 +Old UBP: 0.337173 +New UBP: 0.364158 +New UBP + Precompute: 0.325521 + +GPU being much slower isn't unsurprising and is a common trend for +small-medium sized SDEs with VFs that are relatively cheap to evaluate +(i.e. not neural networks). """ \ No newline at end of file From 7956b5b323b9894cbc83eca8b2b4973ed457f5b7 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 31 Dec 2024 14:17:48 -0700 Subject: [PATCH 05/50] more work --- benchmarks/stateful_paths.py | 63 +++++++++---- diffrax/_brownian/path.py | 25 +++--- diffrax/_global_interpolation.py | 4 +- diffrax/_integrate.py | 45 ++++++---- diffrax/_local_interpolation.py | 6 +- diffrax/_misc.py | 2 +- diffrax/_progress_meter.py | 4 +- diffrax/_solution.py | 2 +- diffrax/_solver/align.py | 3 +- diffrax/_solver/base.py | 25 +++--- diffrax/_solver/foster_langevin_srk.py | 13 ++- diffrax/_solver/implicit_euler.py | 5 +- diffrax/_solver/leapfrog_midpoint.py | 4 +- diffrax/_solver/milstein.py | 5 +- diffrax/_solver/quicsort.py | 3 +- diffrax/_solver/reversible_heun.py | 4 +- diffrax/_solver/runge_kutta.py | 2 +- diffrax/_solver/semi_implicit_euler.py | 4 +- diffrax/_solver/should.py | 3 +- diffrax/_solver/srk.py | 8 +- diffrax/_term.py | 66 +++++++++++--- examples/underdamped_langevin_example.ipynb | 99 ++++++--------------- test/test_integrate.py | 18 ++-- test/test_solver.py | 40 ++++++--- test/test_term.py | 30 +++++-- test/test_typing.py | 20 +++-- 26 files changed, 290 insertions(+), 213 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index ceb759d0..97b35853 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -1,7 +1,7 @@ - import math from typing import cast, Union +import diffrax import equinox as eqx import equinox.internal as eqxi import jax @@ -11,12 +11,16 @@ import lineax.internal as lxi from jaxtyping import PRNGKeyArray, PyTree from lineax.internal import complex_to_real_dtype -import diffrax + class OldBrownianPath(diffrax.AbstractBrownianPath): shape: PyTree[jax.ShapeDtypeStruct] = eqx.field(static=True) levy_area: type[ - Union[diffrax.BrownianIncrement, diffrax.SpaceTimeLevyArea, diffrax.SpaceTimeTimeLevyArea] + Union[ + diffrax.BrownianIncrement, + diffrax.SpaceTimeLevyArea, + diffrax.SpaceTimeTimeLevyArea, + ] ] = eqx.field(static=True) key: PRNGKeyArray precompute: bool = eqx.field(static=True) @@ -25,8 +29,8 @@ def __init__( self, shape, key, - levy_area = diffrax.BrownianIncrement, - precompute = False, + levy_area=diffrax.BrownianIncrement, + precompute=False, ): self.shape = ( jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) @@ -65,9 +69,9 @@ def __call__( self, t0, brownian_state, - t1 = None, - left = True, - use_levy = False, + t1=None, + left=True, + use_levy=False, ): return self.evaluate(t0, t1, left, use_levy), brownian_state @@ -75,9 +79,9 @@ def __call__( def evaluate( self, t0, - t1 = None, - left = True, - use_levy = False, + t1=None, + left=True, + use_levy=False, ): del left if t1 is None: @@ -162,27 +166,46 @@ def _evaluate_leaf( new_ubp = diffrax.UnsafeBrownianPath(shape=(), key=key) new_ubp_pre = diffrax.UnsafeBrownianPath(shape=(), key=key, precompute=True) solver = diffrax.Euler() -terms = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, brownian_motion)) -terms_old = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, ubp)) -terms_new = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp)) -terms_new_precompute = diffrax.MultiTerm(diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp_pre)) +terms = diffrax.MultiTerm( + diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, brownian_motion) +) +terms_old = diffrax.MultiTerm( + diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, ubp) +) +terms_new = diffrax.MultiTerm( + diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp) +) +terms_new_precompute = diffrax.MultiTerm( + diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp_pre) +) saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, ndt)) + @jax.jit def diffrax_vbt(): return diffrax.diffeqsolve(terms, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + @jax.jit def diffrax_old(): - return diffrax.diffeqsolve(terms_old, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + return diffrax.diffeqsolve( + terms_old, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + ).ys + @jax.jit def diffrax_new(): - return diffrax.diffeqsolve(terms_new, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + return diffrax.diffeqsolve( + terms_new, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + ).ys + @jax.jit def diffrax_new_pre(): - return diffrax.diffeqsolve(terms_new_precompute, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + return diffrax.diffeqsolve( + terms_new_precompute, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + ).ys + _ = diffrax_vbt().block_until_ready() _ = diffrax_old().block_until_ready() @@ -190,6 +213,8 @@ def diffrax_new_pre(): _ = diffrax_new_pre().block_until_ready() from timeit import Timer + + num_runs = 10 timer = Timer(stmt="_ = diffrax_vbt().block_until_ready()", globals=globals()) @@ -224,4 +249,4 @@ def diffrax_new_pre(): GPU being much slower isn't unsurprising and is a common trend for small-medium sized SDEs with VFs that are relatively cheap to evaluate (i.e. not neural networks). -""" \ No newline at end of file +""" diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 97593f4b..32586437 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -38,9 +38,9 @@ class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): """Brownian simulation that is only suitable for certain cases. - This is a very quick way to simulate Brownian motion (faster than VBT), but can only be - used if you are not using an adaptive scheme that rejects steps (pre-visible adaptive - methods are valid). + This is a very quick way to simulate Brownian motion (faster than VBT), but can + only beused if you are not using an adaptive scheme that rejects steps + (pre-visible adaptive methods are valid). If using the stateless `evaluate` method, stricter requirements are imposed, namely: @@ -117,6 +117,7 @@ def _generate_noise( shape: jax.ShapeDtypeStruct, max_steps: int, ) -> Float[Array, "levy_dims shape"]: + # TODO: merge into a single jr.normal call if self.levy_area is SpaceTimeTimeLevyArea: key_w, key_hh, key_kk = jr.split(key, 3) w = jr.normal(key_w, (max_steps, *shape.shape), shape.dtype) @@ -152,12 +153,12 @@ def init( ) counter = 0 key = None + return key, noise, counter else: noise = None counter = None key = self.key - - return key, noise, counter + return key, noise, counter def __call__( self, @@ -183,6 +184,7 @@ def __call__( key, noises, counter = brownian_state if self.precompute: # precomputed noise + assert noises is not None and counter is not None out = jtu.tree_map( lambda shape, noise: self._evaluate_leaf_precomputed( t0, t1, shape, self.levy_area, use_levy, noise @@ -197,7 +199,7 @@ def __call__( # brownian motion, the solver could just decrease the counter return out, (None, noises, counter + 1) else: - assert noises is None and counter is None + assert noises is None and counter is None and key is not None new_key, key = jr.split(key) key = split_by_tree(key, self.shape) out = jtu.tree_map( @@ -337,11 +339,12 @@ def _evaluate_leaf( - `key`: A random key. - `levy_area`: Whether to additionally generate Lévy area. This is required by some SDE solvers. -- `precompute`: Whether or not to precompute the brownian motion (if possible). Precomputing - requires additional memory at initialization time, but can result in faster integrations. - Some thought may be required before enabling this, as solvers which require multiple - brownian increments may result in index out of bounds causing silent errors as the size - of the precomputed brownian motion is derived from the maximum steps. +- `precompute`: Whether or not to precompute the brownian motion (if possible). + Precomputing requires additional memory at initialization time, but can result in + faster integrations. Some thought may be required before enabling this, as solvers + which require multiple brownian increments may result in index out of bounds + causing silent errors as the size of the precomputed brownian motion is derived + from the maximum steps. """ UnsafeBrownianPath = DirectBrownianPath diff --git a/diffrax/_global_interpolation.py b/diffrax/_global_interpolation.py index 3eebafbc..270c3986 100644 --- a/diffrax/_global_interpolation.py +++ b/diffrax/_global_interpolation.py @@ -19,10 +19,10 @@ from equinox.internal import ω from jaxtyping import Array, ArrayLike, PyTree, Real, Shaped -from ._custom_types import DenseInfos, IntScalarLike, RealScalarLike, Y, Args +from ._custom_types import Args, DenseInfos, IntScalarLike, RealScalarLike, Y from ._local_interpolation import AbstractLocalInterpolation from ._misc import fill_forward, left_broadcast_to -from ._path import AbstractPath, _Control +from ._path import _Control, AbstractPath ω = cast(Callable, ω) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 87a2b1eb..b3b65959 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -41,6 +41,7 @@ from ._global_interpolation import DenseInterpolation from ._heuristics import is_sde, is_unsafe_sde from ._misc import linear_rescale, static_select +from ._path import AbstractPath from ._progress_meter import ( AbstractProgressMeter, NoProgressMeter, @@ -1105,18 +1106,26 @@ def _promote(yi): ) terms = MultiTerm(*terms) - def _path_init(term): + def _path_init(term, end): if isinstance(term, _AbstractControlTerm) or isinstance( term, UnderdampedLangevinDiffusionTerm ): - return term.control.init(t0, t1, y0, args, max_steps) + if isinstance(term.control, AbstractPath): + return term.control.init(t0, end, y0, args, max_steps) + return None elif isinstance(term, MultiTerm): - return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, AbstractTerm)) + return jax.tree.map( + lambda x: _path_init(x, end), + term.terms, + is_leaf=lambda x: isinstance(x, AbstractTerm), + ) return None if path_state is None: path_state = jtu.tree_map( - _path_init, terms, is_leaf=lambda x: isinstance(x, AbstractTerm) + lambda x: _path_init(x, t1), + terms, + is_leaf=lambda x: isinstance(x, AbstractTerm), ) # Error checking for term compatibility @@ -1125,7 +1134,12 @@ def _path_init(term): args, terms, solver.term_structure, - jtu.tree_map(lambda x, y: x | {"control_state": y}, solver.term_compatible_contr_kwargs, path_state, is_leaf=lambda x: isinstance(x, dict)), + jtu.tree_map( + lambda x, y: x | {"control_state": y}, + solver.term_compatible_contr_kwargs, + path_state, + is_leaf=lambda x: isinstance(x, dict), + ), ) if is_sde(terms): @@ -1145,7 +1159,8 @@ def _path_init(term): if is_unsafe_sde(terms): if isinstance(stepsize_controller, PIDController): raise ValueError( - "`DirecBrownianPath` cannot be used with PIDController as it may reject steps." + "`DirecBrownianPath` cannot be used with PIDController as it " + "may reject steps." ) # Normalises time: if t0 > t1 then flip things around. @@ -1169,7 +1184,7 @@ def _wrap(term): terms, is_leaf=lambda x: isinstance(x, AbstractTerm) and not isinstance(x, MultiTerm), ) - # print("diff terms", terms) + if isinstance(solver, AbstractImplicitSolver): def _get_tols(x): @@ -1256,20 +1271,12 @@ def _subsaveat_direction_fn(x): tnext = t0 + dt0 tnext = jnp.minimum(tnext, t1) - # reinit for tnext - def _path_init(term): - if isinstance(term, _AbstractControlTerm) or isinstance( - term, UnderdampedLangevinDiffusionTerm - ): - return term.control.init(t0, tnext, y0, args, max_steps) - elif isinstance(term, MultiTerm): - return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, AbstractTerm)) - return None - if path_state is None: passed_path_state = False path_state = jtu.tree_map( - _path_init, terms, is_leaf=lambda x: isinstance(x, AbstractTerm) + lambda x: _path_init(x, tnext), + terms, + is_leaf=lambda x: isinstance(x, AbstractTerm), ) else: passed_path_state = True @@ -1316,7 +1323,7 @@ def _allocate_output(subsaveat: SubSaveAt) -> SaveState: made_jump = False if made_jump is None else made_jump result = RESULTS.successful if saveat.dense or event is not None: - _, _, dense_info_struct, _, _ = eqx.filter_eval_shape( + _, _, dense_info_struct, _, _, _ = eqx.filter_eval_shape( solver.step, terms, tprev, diff --git a/diffrax/_local_interpolation.py b/diffrax/_local_interpolation.py index 390f07eb..7e0e598b 100644 --- a/diffrax/_local_interpolation.py +++ b/diffrax/_local_interpolation.py @@ -15,9 +15,9 @@ from equinox.internal import ω from jaxtyping import Array, ArrayLike, PyTree, Shaped -from ._custom_types import RealScalarLike, Y, Args +from ._custom_types import Args, RealScalarLike, Y from ._misc import linear_rescale -from ._path import AbstractPath, _Control +from ._path import _Control, AbstractPath _PathState: TypeAlias = None @@ -26,7 +26,6 @@ class AbstractLocalInterpolation(AbstractPath[_Control, _PathState]): - def init( self, t0: RealScalarLike, @@ -46,6 +45,7 @@ def __call__( ) -> tuple[_Control, _PathState]: return self.evaluate(t0, t1, left), path_state + class LocalLinearInterpolation(AbstractLocalInterpolation): t0: RealScalarLike t1: RealScalarLike diff --git a/diffrax/_misc.py b/diffrax/_misc.py index ac61b813..7c6fa53b 100644 --- a/diffrax/_misc.py +++ b/diffrax/_misc.py @@ -148,7 +148,7 @@ def static_select(pred: BoolScalarLike, a: ArrayLike, b: ArrayLike) -> ArrayLike # This in turn allows us to perform some trace-time optimisations that XLA isn't # smart enough to do on its own. if isinstance(pred, (np.ndarray, np.generic)) and pred.shape == (): - pred = pred.item() + pred = cast(BoolScalarLike, pred.item()) if pred is True: return a elif pred is False: diff --git a/diffrax/_progress_meter.py b/diffrax/_progress_meter.py index 8a813be6..8d5f9c04 100644 --- a/diffrax/_progress_meter.py +++ b/diffrax/_progress_meter.py @@ -123,7 +123,9 @@ def _step_bar(bar: list[float], progress: FloatScalarLike) -> None: if eqx.is_array(progress): # May not be an array when called with `JAX_DISABLE_JIT=1` progress = cast(Union[Array, np.ndarray], progress) - progress = progress.item() + progress = cast(float, progress.item()) + else: + progress = cast(float, progress) progress = cast(float, progress) bar[0] = progress print(f"{100 * progress:.2f}%") diff --git a/diffrax/_solution.py b/diffrax/_solution.py index 8c3d06b1..65ab53c1 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -5,7 +5,7 @@ import optimistix as optx from jaxtyping import Array, Bool, PyTree, Real, Shaped -from ._custom_types import BoolScalarLike, RealScalarLike, Args, Y +from ._custom_types import Args, BoolScalarLike, RealScalarLike, Y from ._global_interpolation import DenseInterpolation from ._path import AbstractPath diff --git a/diffrax/_solver/align.py b/diffrax/_solver/align.py index 45422105..c6bc6105 100644 --- a/diffrax/_solver/align.py +++ b/diffrax/_solver/align.py @@ -14,7 +14,6 @@ UnderdampedLangevinTuple, UnderdampedLangevinX, ) -from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -44,7 +43,7 @@ def __init__(self, beta, a1, b1, aa, chh): _ErrorEstimate = UnderdampedLangevinTuple -class ALIGN(AbstractFosterLangevinSRK[_ALIGNCoeffs, _ErrorEstimate, _PathState]): +class ALIGN(AbstractFosterLangevinSRK[_ALIGNCoeffs, _ErrorEstimate]): r"""The Adaptive Langevin via Interpolated Gradients and Noise method designed by James Foster. This is a second order solver for the Underdamped Langevin Diffusion, and accepts terms of the form diff --git a/diffrax/_solver/base.py b/diffrax/_solver/base.py index dc5767ce..992fe72b 100644 --- a/diffrax/_solver/base.py +++ b/diffrax/_solver/base.py @@ -34,7 +34,12 @@ _SolverState = TypeVar("_SolverState") -_PathState = TypeVar("_PathState") +# Should pathstate be a TypeVar? Originally I had it as one, but it doesn't seem +# to matter since no solver actually provides a specific type for the typevar +# (thus it was totally general for all solvers, which was like, why is it a type +# var then?) In Term it makes sense because control/ode terms are specific +# parameterizations of the type var +_PathState = PyTree def vector_tree_dot(a, b): @@ -72,7 +77,7 @@ def _term_compatible_contr_kwargs(term_structure): return jtu.tree_map(_term_compatible_contr_kwargs, term_structure) -class AbstractSolver(eqx.Module, Generic[_SolverState, _PathState], **_set_metaclass): +class AbstractSolver(eqx.Module, Generic[_SolverState], **_set_metaclass): """Abstract base class for all differential equation solvers. Subclasses should have a class-level attribute `terms`, specifying the PyTree @@ -214,7 +219,7 @@ def func( """ -class AbstractImplicitSolver(AbstractSolver[_SolverState, _PathState]): +class AbstractImplicitSolver(AbstractSolver[_SolverState]): """Indicates that this is an implicit differential equation solver, and as such that it should take a root finder as an argument. """ @@ -223,25 +228,25 @@ class AbstractImplicitSolver(AbstractSolver[_SolverState, _PathState]): root_find_max_steps: AbstractVar[int] -class AbstractItoSolver(AbstractSolver[_SolverState, _PathState]): +class AbstractItoSolver(AbstractSolver[_SolverState]): """Indicates that when used as an SDE solver that this solver will converge to the Itô solution. """ -class AbstractStratonovichSolver(AbstractSolver[_SolverState, _PathState]): +class AbstractStratonovichSolver(AbstractSolver[_SolverState]): """Indicates that when used as an SDE solver that this solver will converge to the Stratonovich solution. """ -class AbstractAdaptiveSolver(AbstractSolver[_SolverState, _PathState]): +class AbstractAdaptiveSolver(AbstractSolver[_SolverState]): """Indicates that this solver provides error estimates, and that as such it may be used with an adaptive step size controller. """ -class AbstractWrappedSolver(AbstractSolver[_SolverState, _PathState]): +class AbstractWrappedSolver(AbstractSolver[_SolverState]): """Wraps another solver "transparently", in the sense that all `isinstance` checks will be forwarded on to the wrapped solver, e.g. when testing whether the solver is implicit/adaptive/SDE-compatible/etc. @@ -254,8 +259,8 @@ class if that is not desired behaviour.) class HalfSolver( - AbstractAdaptiveSolver[_SolverState, _PathState], - AbstractWrappedSolver[_SolverState, _PathState], + AbstractAdaptiveSolver[_SolverState], + AbstractWrappedSolver[_SolverState], ): """Wraps another solver, trading cost in order to provide error estimates. (That is, it means the solver can be used with an adaptive step size controller, @@ -276,7 +281,7 @@ class HalfSolver( [`diffrax.Euler`][]. Such solvers are most common when solving SDEs. """ - solver: AbstractSolver[_SolverState, _PathState] + solver: AbstractSolver[_SolverState] @property def term_structure(self): diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index 19c43ba5..759198c0 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -100,8 +100,8 @@ class SolverState(eqx.Module, Generic[_Coeffs]): class AbstractFosterLangevinSRK( - AbstractStratonovichSolver[SolverState, _PathState], - Generic[_Coeffs, _ErrorEstimate, _PathState], + AbstractStratonovichSolver[SolverState], + Generic[_Coeffs, _ErrorEstimate], ): r"""Abstract class for Stochastic Runge Kutta methods specifically designed for Underdamped Langevin Diffusion of the form @@ -453,7 +453,14 @@ def check_shapes_dtypes(arg, *args): rho=st.rho, prev_f=f_fsal, ) - return y1, error, dense_info, st, (drift_path, diffusion_path), RESULTS.successful + return ( + y1, + error, + dense_info, + st, + (drift_path, diffusion_path), + RESULTS.successful, + ) def func( self, diff --git a/diffrax/_solver/implicit_euler.py b/diffrax/_solver/implicit_euler.py index c2f434d1..feaa4f3d 100644 --- a/diffrax/_solver/implicit_euler.py +++ b/diffrax/_solver/implicit_euler.py @@ -1,9 +1,10 @@ from collections.abc import Callable -from typing import ClassVar, TypeVar +from typing import ClassVar from typing_extensions import TypeAlias import optimistix as optx from equinox.internal import ω +from jaxtyping import PyTree from .._custom_types import Args, BoolScalarLike, DenseInfo, RealScalarLike, VF, Y from .._heuristics import is_sde @@ -15,7 +16,7 @@ _SolverState: TypeAlias = None -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree def _implicit_relation(z1, nonlinear_solve_args): diff --git a/diffrax/_solver/leapfrog_midpoint.py b/diffrax/_solver/leapfrog_midpoint.py index e43ca2b8..a1fc6ebc 100644 --- a/diffrax/_solver/leapfrog_midpoint.py +++ b/diffrax/_solver/leapfrog_midpoint.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import ClassVar, TypeVar +from typing import ClassVar from typing_extensions import TypeAlias from equinox.internal import ω @@ -14,7 +14,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = tuple[RealScalarLike, PyTree] -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree # TODO: support arbitrary linear multistep methods diff --git a/diffrax/_solver/milstein.py b/diffrax/_solver/milstein.py index 0d4872ce..945300a2 100644 --- a/diffrax/_solver/milstein.py +++ b/diffrax/_solver/milstein.py @@ -1,11 +1,12 @@ from collections.abc import Callable -from typing import Any, ClassVar, TypeVar +from typing import Any, ClassVar from typing_extensions import TypeAlias import jax import jax.numpy as jnp import jax.tree_util as jtu from equinox.internal import ω +from jaxtyping import PyTree from .._custom_types import Args, BoolScalarLike, DenseInfo, RealScalarLike, VF, Y from .._local_interpolation import LocalLinearInterpolation @@ -16,7 +17,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = None -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree # # The best online reference I've found for commutative-noise Milstein is diff --git a/diffrax/_solver/quicsort.py b/diffrax/_solver/quicsort.py index a05955e7..4f21bd6f 100644 --- a/diffrax/_solver/quicsort.py +++ b/diffrax/_solver/quicsort.py @@ -14,7 +14,6 @@ ) from .._local_interpolation import LocalLinearInterpolation from .._term import UnderdampedLangevinLeaf, UnderdampedLangevinX -from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -45,7 +44,7 @@ def __init__(self, beta_lr1, a_lr1, b_lr1, a_third, a_div_h): self.dtype = jnp.result_type(*all_leaves) -class QUICSORT(AbstractFosterLangevinSRK[_QUICSORTCoeffs, None, _PathState]): +class QUICSORT(AbstractFosterLangevinSRK[_QUICSORTCoeffs, None]): r"""The QUadrature Inspired and Contractive Shifted ODE with Runge-Kutta Three method by James Foster and Daire O'Kane. This is a third order solver for the Underdamped Langevin Diffusion, and accepts terms of the form diff --git a/diffrax/_solver/reversible_heun.py b/diffrax/_solver/reversible_heun.py index 4393b867..adeb5eb8 100644 --- a/diffrax/_solver/reversible_heun.py +++ b/diffrax/_solver/reversible_heun.py @@ -1,6 +1,6 @@ from collections.abc import Callable from typing import ClassVar -from typing_extensions import TypeAlias, TypeVar +from typing_extensions import TypeAlias import jax.lax as lax from equinox.internal import ω @@ -14,7 +14,7 @@ _SolverState: TypeAlias = tuple[PyTree, PyTree] -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree class ReversibleHeun(AbstractAdaptiveSolver, AbstractStratonovichSolver): diff --git a/diffrax/_solver/runge_kutta.py b/diffrax/_solver/runge_kutta.py index 9bd7340a..e7491693 100644 --- a/diffrax/_solver/runge_kutta.py +++ b/diffrax/_solver/runge_kutta.py @@ -347,7 +347,7 @@ def _assert_same_structure(x, y): return eqx.tree_equal(x, y) is True -class AbstractRungeKutta(AbstractAdaptiveSolver[_SolverState, _PathState]): +class AbstractRungeKutta(AbstractAdaptiveSolver[_SolverState]): """Abstract base class for all Runge--Kutta solvers. (Other than fully-implicit Runge--Kutta methods, which have a different computational structure.) diff --git a/diffrax/_solver/semi_implicit_euler.py b/diffrax/_solver/semi_implicit_euler.py index f5067c4d..376cd409 100644 --- a/diffrax/_solver/semi_implicit_euler.py +++ b/diffrax/_solver/semi_implicit_euler.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import ClassVar, TypeVar +from typing import ClassVar from typing_extensions import TypeAlias from equinox.internal import ω @@ -14,7 +14,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = None -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree Ya: TypeAlias = PyTree[Float[ArrayLike, "?*y"], " Y"] Yb: TypeAlias = PyTree[Float[ArrayLike, "?*y"], " Y"] diff --git a/diffrax/_solver/should.py b/diffrax/_solver/should.py index d4819c67..caab54d3 100644 --- a/diffrax/_solver/should.py +++ b/diffrax/_solver/should.py @@ -10,7 +10,6 @@ ) from .._local_interpolation import LocalLinearInterpolation from .._term import UnderdampedLangevinLeaf, UnderdampedLangevinX -from .base import _PathState from .foster_langevin_srk import ( AbstractCoeffs, AbstractFosterLangevinSRK, @@ -57,7 +56,7 @@ def __init__(self, beta_half, a_half, b_half, beta1, a1, b1, aa, chh, ckk): self.dtype = jnp.result_type(*all_leaves) -class ShOULD(AbstractFosterLangevinSRK[_ShOULDCoeffs, None, _PathState]): +class ShOULD(AbstractFosterLangevinSRK[_ShOULDCoeffs, None]): r"""The Shifted-ODE Runge-Kutta Three method designed by James Foster. This is a third order solver for the Underdamped Langevin Diffusion, the terms of the form diff --git a/diffrax/_solver/srk.py b/diffrax/_solver/srk.py index e39630fc..7877e319 100644 --- a/diffrax/_solver/srk.py +++ b/diffrax/_solver/srk.py @@ -39,7 +39,7 @@ _ErrorEstimate: TypeAlias = Optional[Y] _SolverState: TypeAlias = None -_PathState = TypeVar("_PathState") +_PathState: TypeAlias = PyTree _CarryType: TypeAlias = tuple[PyTree[Array], PyTree[Array], PyTree[Array]] @@ -200,7 +200,7 @@ def __post_init__(self): """ -class AbstractSRK(AbstractSolver[_SolverState, _PathState]): +class AbstractSRK(AbstractSolver[_SolverState]): r"""A general Stochastic Runge-Kutta method. This accepts `terms` of the form @@ -279,8 +279,8 @@ def minimal_levy_area(self) -> type[AbstractBrownianIncrement]: def term_structure(self): return MultiTerm[ tuple[ - AbstractTerm[Any, RealScalarLike], - AbstractTerm[Any, self.minimal_levy_area], + AbstractTerm[Any, RealScalarLike, None], + AbstractTerm[Any, self.minimal_levy_area, _PathState], ] ] diff --git a/diffrax/_term.py b/diffrax/_term.py index 56e202b1..2d0d44b1 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -247,8 +247,8 @@ def _mul(v): [`diffrax.diffeqsolve`][]. """ - -class _CallableToPath(AbstractPath[_Control, _ControlState]): +# question over stateful custom functions comes up here too +class _CallableToPath(AbstractPath[_Control, None]): fn: Callable @property @@ -259,6 +259,25 @@ def t0(self): def t1(self): return jnp.inf + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + max_steps: Optional[int], + ) -> None: + return None + + def __call__( + self, + t0: RealScalarLike, + path_state: None, + t1: Optional[RealScalarLike] = None, + left: bool = True, + ) -> tuple[_Control, None]: + return self.evaluate(t0, t1, left), path_state + def evaluate( self, t0: RealScalarLike, t1: Optional[RealScalarLike] = None, left: bool = True ) -> _Control: @@ -290,6 +309,10 @@ class _AbstractControlTerm(AbstractTerm[_VF, _Control, _ControlState]): vector_field: Callable[[RealScalarLike, Y, Args], _VF] control: Union[ AbstractPath[_Control, _ControlState], + # can we allow stateful functions? This would have no way to "init" and thus + # the user would have to provide a custom init path state which sounds + # not ideal, probably just be easier to have them make an abstract path? + # Callable[[RealScalarLike, PyTree, RealScalarLike], tuple[_Control, PyTree]], Callable[[RealScalarLike, RealScalarLike], _Control], ] = eqx.field(converter=_callable_to_path) # pyright: ignore @@ -303,8 +326,12 @@ def contr( control_state: _ControlState, **kwargs, ) -> tuple[_Control, _ControlState]: - return self.control(t0, control_state, t1, **kwargs) # pyright: ignore + if isinstance(self.control, AbstractPath): + return self.control(t0, control_state, t1, **kwargs) + return self.control(t0, t1, **kwargs), control_state + # TODO: support stateful conversion here + # more broadly, add derivative function to path for __call__? def to_ode(self) -> ODETerm: r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ may be thought of as an ODE as @@ -332,8 +359,8 @@ def to_ode(self) -> ODETerm: - `control`: The control. Should either be - 1. a [`diffrax.AbstractPath`][], in which case its `.__call__(t0, path_state, t1)` method - will be used to give the increment of the control over a time interval + 1. a [`diffrax.AbstractPath`][], in which case its `.__call__(t0, path_state, t1)` + method will be used to give the increment of the control over a time interval `[t0, t1]`, or 2. a callable `(t0, t1) -> increment`, which returns the increment directly. """ @@ -560,7 +587,6 @@ def _sum(*x): _Terms = TypeVar("_Terms", bound=tuple[AbstractTerm, ...]) -_MultiControlState = TypeVar("_MultiControlState", bound=tuple) class MultiTerm(AbstractTerm, Generic[_Terms]): @@ -598,10 +624,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _MultiControlState, + control_state: PyTree, **kwargs, - ) -> tuple[tuple[PyTree[ArrayLike], ...], _MultiControlState]: - # print(self.terms, control_state) + ) -> tuple[tuple[PyTree[ArrayLike], ...], tuple[PyTree]]: contrs = [ term.contr(t0, t1, state, **kwargs) for term, state in zip(self.terms, control_state) @@ -680,8 +705,11 @@ def is_vf_expensive( return self.term.is_vf_expensive(_t0, _t1, y, args) -class AdjointTerm(AbstractTerm[_VF, _Control, _ControlState]): - term: AbstractTerm[_VF, _Control, _ControlState] +_AdjoingControlState: TypeAlias = Union[None, PyTree] + + +class AdjointTerm(AbstractTerm[_VF, _Control, _AdjoingControlState]): + term: AbstractTerm[_VF, _Control, _AdjoingControlState] def is_vf_expensive( self, @@ -725,7 +753,16 @@ def vf( # The value of `control` is never actually used -- just its shape, dtype, and # PyTree structure. (This is because `self.vf_prod` is linear in `control`.) - control = self.contr(t, t) + contr_state_struct = None + if isinstance(self.term, _AbstractControlTerm) or isinstance( + self.term, UnderdampedLangevinDiffusionTerm + ): + if isinstance(self.term.control, AbstractPath): + # contr_state_struct = eqx.filter_eval_shape( + # self.term.control.init, t, t, y, args, None + # ) + contr_state_struct = self.term.control.init(t, t, y, args, None) + control, _ = self.contr(t, t, contr_state_struct) y_size = sum(np.size(yi) for yi in jtu.tree_leaves(y)) control_size = sum(np.size(ci) for ci in jtu.tree_leaves(control)) @@ -763,9 +800,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _ControlState, + control_state: _AdjoingControlState, **kwargs, - ) -> tuple[_Control, _ControlState]: + ) -> tuple[_Control, _AdjoingControlState]: return self.term.contr(t0, t1, control_state, **kwargs) def prod( @@ -943,6 +980,7 @@ def contr( control_state: _ControlState, **kwargs, ) -> tuple[Union[UnderdampedLangevinX, AbstractBrownianIncrement], _ControlState]: + # same stateless function as above return self.control(t0, control_state, t1, **kwargs) def prod( diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index 624cea7c..1cce9734 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "9deba250066ddc39", "metadata": { "ExecuteTime": { @@ -46,79 +46,22 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(None, (None, Array([[[ 1.5437187 , 0.15094286],\n", - " [-0.1776888 , 0.7148498 ],\n", - " [ 1.124776 , 0.7197403 ]],\n", - "\n", - " [[-0.18969345, -0.72713757],\n", - " [ 0.57686734, 0.6250485 ],\n", - " [-0.54804486, -0.82060134]],\n", - "\n", - " [[ 0.2385169 , -0.273696 ],\n", - " [ 0.28720167, 1.115761 ],\n", - " [-0.23067027, -0.4854902 ]],\n", - "\n", - " ...,\n", - "\n", - " [[-0.2060602 , 0.5322451 ],\n", - " [ 1.3253211 , -0.8300134 ],\n", - " [-1.047963 , -1.1495486 ]],\n", - "\n", - " [[-0.5335223 , -0.10977904],\n", - " [ 2.0500367 , 1.009181 ],\n", - " [-0.21443863, 0.37549132]],\n", - "\n", - " [[ 1.4900465 , -0.94098794],\n", - " [ 0.28333724, 0.79191744],\n", - " [ 0.26032442, -0.7804612 ]]], dtype=float32), 0))\n", - "((20.0, Array([6.90372 , 0.675037], dtype=float32)), (None, (None, Array([[[ 1.5437187 , 0.15094286],\n", - " [-0.1776888 , 0.7148498 ],\n", - " [ 1.124776 , 0.7197403 ]],\n", - "\n", - " [[-0.18969345, -0.72713757],\n", - " [ 0.57686734, 0.6250485 ],\n", - " [-0.54804486, -0.82060134]],\n", - "\n", - " [[ 0.2385169 , -0.273696 ],\n", - " [ 0.28720167, 1.115761 ],\n", - " [-0.23067027, -0.4854902 ]],\n", - "\n", - " ...,\n", - "\n", - " [[-0.2060602 , 0.5322451 ],\n", - " [ 1.3253211 , -0.8300134 ],\n", - " [-1.047963 , -1.1495486 ]],\n", - "\n", - " [[-0.5335223 , -0.10977904],\n", - " [ 2.0500367 , 1.009181 ],\n", - " [-0.21443863, 0.37549132]],\n", - "\n", - " [[ 1.4900465 , -0.94098794],\n", - " [ 0.28333724, 0.79191744],\n", - " [ 0.26032442, -0.7804612 ]]], dtype=float32), 1)))\n" - ] - } - ], + "outputs": [], "source": [ "from warnings import simplefilter\n", "\n", "\n", "simplefilter(action=\"ignore\", category=FutureWarning)\n", "import diffrax\n", + "import jax\n", "import jax.numpy as jnp\n", "import jax.random as jr\n", "import matplotlib.pyplot as plt\n", - "import jax\n", - "import equinox as eqx\n", + "\n", "\n", "t0, t1 = 0.0, 20.0\n", "dt0 = 0.05\n", - "saveat = diffrax.SaveAt(steps=True)\n", + "saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, 100))\n", "\n", "# Parameters\n", "gamma = jnp.array([2, 0.5], dtype=jnp.float32)\n", @@ -131,25 +74,37 @@ "bm = diffrax.VirtualBrownianTree(\n", " t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", ")\n", - "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, precompute=True)\n", + "# bm = diffrax.UnsafeBrownianPath(\n", + "# shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, precompute=True\n", + "# )\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", - "terms = diffrax.MultiTerm(drift_term, diffusion_term)\n", + "terms = drift_term #diffrax.MultiTerm(drift_term, diffusion_term)\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", "solver = diffrax.Euler()\n", "\n", + "\n", "def _path_init(term):\n", - " if isinstance(term, diffrax.ControlTerm) or isinstance(term, diffrax.UnderdampedLangevinDiffusionTerm):\n", + " if isinstance(term, diffrax.ControlTerm) or isinstance(\n", + " term, diffrax.UnderdampedLangevinDiffusionTerm\n", + " ):\n", " return term.control.init(t0, t1, y0, None, 4096)\n", " elif isinstance(term, diffrax.MultiTerm):\n", - " return jax.tree.map(_path_init, term.terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", + " return jax.tree.map(\n", + " _path_init,\n", + " term.terms,\n", + " is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm),\n", + " )\n", " return None\n", "\n", - "state = jax.tree.map(_path_init, terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm))\n", - "print(state)\n", - "print(terms.contr(t0, t1, state))\n", + "\n", + "state = jax.tree.map(\n", + " _path_init, terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm)\n", + ")\n", + "# print(state)\n", + "# print(terms.contr(t0, t1, state))\n", "\n", "# @eqx.filter_jit\n", "# def f():\n", @@ -165,14 +120,14 @@ "\n", "\n", "sol = diffrax.diffeqsolve(\n", - " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat\n", + " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat, adjoint=diffrax.BacksolveAdjoint()\n", ")\n", "xs, vs = sol.ys" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "62da2ddbaaf98f47", "metadata": { "ExecuteTime": { @@ -183,7 +138,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/test/test_integrate.py b/test/test_integrate.py index 555d6ade..388b1a51 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -611,7 +611,7 @@ def __mul__(self, other): class TestSolver(diffrax.Euler): term_structure = diffrax.AbstractTerm[ - tuple[Float[Array, "n 3"]], tuple[TestControl] + tuple[Float[Array, "n 3"]], tuple[TestControl], None ] solver = TestSolver() @@ -643,20 +643,24 @@ class TestSolver(diffrax.AbstractSolver): "a": diffrax.ODETerm, "b": diffrax.ODETerm[Any], "c": diffrax.ODETerm[Float[Array, " 3"]], - "d": diffrax.AbstractTerm[Float[Array, " 4"], Any], + "d": diffrax.AbstractTerm[Float[Array, " 4"], Any, Any], "e": diffrax.MultiTerm[ - tuple[diffrax.ODETerm, diffrax.AbstractTerm[Any, Float[Array, " 5"]]] + tuple[ + diffrax.ODETerm, diffrax.AbstractTerm[Any, Float[Array, " 5"], Any] + ] ], "f": diffrax.MultiTerm[ - tuple[diffrax.ODETerm, diffrax.AbstractTerm[Any, Float[Array, " 5"]]] + tuple[ + diffrax.ODETerm, diffrax.AbstractTerm[Any, Float[Array, " 5"], Any] + ] ], } interpolation_cls = diffrax.LocalLinearInterpolation - def init(self, terms, t0, t1, y0, args): + def init(self, terms, t0, t1, y0, args, path_state): return None - def step(self, terms, t0, t1, y0, args, solver_state, made_jump): + def step(self, terms, t0, t1, y0, args, solver_state, made_jump, path_state): def _step(_term, _y): control = _term.contr(t0, t1) return _y + _term.vf_prod(t0, _y, args, control) @@ -664,7 +668,7 @@ def _step(_term, _y): _is_term = lambda x: isinstance(x, diffrax.AbstractTerm) y1 = jtu.tree_map(_step, terms, y0, is_leaf=_is_term) dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, diffrax.RESULTS.successful + return y1, None, dense_info, None, None, diffrax.RESULTS.successful def func(self, terms, t0, y0, args): assert False diff --git a/test/test_solver.py b/test/test_solver.py index aa618712..d49d3a4c 100644 --- a/test/test_solver.py +++ b/test/test_solver.py @@ -90,13 +90,27 @@ def test_multiple_tableau_single_step(vf_expensive): solver_state1 = None solver_state2 = None else: - solver_state1 = solver1.init(terms, t0, t1, y0, None) - solver_state2 = solver2.init(terms, t0, t1, y0, None) + solver_state1 = solver1.init(terms, t0, t1, y0, None, None) + solver_state2 = solver2.init(terms, t0, t1, y0, None, None) out1 = solver1.step( - terms, t0, t1, y0, None, solver_state=solver_state1, made_jump=False + terms, + t0, + t1, + y0, + None, + solver_state=solver_state1, + made_jump=False, + path_state=None, ) out2 = solver2.step( - terms, t0, t1, y0, None, solver_state=solver_state2, made_jump=False + terms, + t0, + t1, + y0, + None, + solver_state=solver_state2, + made_jump=False, + path_state=None, ) out2[2]["k"] = out2[2]["k"][0] + out2[2]["k"][1] assert tree_allclose(out1, out2) @@ -194,8 +208,8 @@ class Term(diffrax.AbstractTerm): def vf(self, t, y, args): return {"f": -self.coeff * y["y"]} - def contr(self, t0, t1, **kwargs): - return {"t": t1 - t0} + def contr(self, t0, t1, control_state, **kwargs): + return {"t": t1 - t0}, control_state def prod(self, vf, control): return {"y": vf["f"] * control["t"]} @@ -288,13 +302,13 @@ class ReferenceSil3(diffrax.AbstractImplicitSolver): def order(self, terms): return 2 - def init(self, terms, t0, t1, y0, args): + def init(self, terms, t0, t1, y0, args, path_state): return None def func(self, terms, t0, y0, args): assert False - def step(self, terms, t0, t1, y0, args, solver_state, made_jump): + def step(self, terms, t0, t1, y0, args, solver_state, made_jump, path_state): del solver_state, made_jump explicit, implicit = terms.terms dt = t1 - t0 @@ -369,7 +383,7 @@ def _fourth_stage(yc, _): dense_info = dict(y0=y0, y1=y1, k=ks) state = (False, (f3 / dt, g3 / dt)) result = jtu.tree_map(jnp.asarray, diffrax.RESULTS.successful) - return y1, y_error, dense_info, state, result + return y1, y_error, dense_info, state, path_state, result reference_solver = ReferenceSil3(root_finder=optx.Newton(rtol=1e-8, atol=1e-8)) solver = diffrax.Sil3(root_finder=diffrax.VeryChord(rtol=1e-8, atol=1e-8)) @@ -396,10 +410,12 @@ def f2(t, y, args): y0 = jr.normal(ykey, (2,), dtype=dtype) args = None - state = solver.init(terms, t0, t1, y0, args) - out = solver.step(terms, t0, t1, y0, args, solver_state=state, made_jump=False) + state = solver.init(terms, t0, t1, y0, args, None) + out = solver.step( + terms, t0, t1, y0, args, solver_state=state, made_jump=False, path_state=None + ) reference_out = reference_solver.step( - terms, t0, t1, y0, args, solver_state=None, made_jump=False + terms, t0, t1, y0, args, solver_state=None, made_jump=False, path_state=None ) assert tree_allclose(out, reference_out) diff --git a/test/test_term.py b/test/test_term.py index 5260db2c..0e3db380 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -15,7 +15,7 @@ def vector_field(t, y, args) -> Array: return -y term = diffrax.ODETerm(vector_field) - dt = term.contr(0, 1) + dt, state = term.contr(0, 1, None) vf = term.vf(0, 1, None) vf_prod = term.vf_prod(0, 1, None, dt) assert tree_allclose(vf_prod, term.prod(vf, dt)) @@ -30,10 +30,16 @@ def test_control_term(getkey): vector_field = lambda t, y, args: jr.normal(args, (3, 2)) derivkey = getkey() - class Control(diffrax.AbstractPath[Shaped[Array, "2"]]): + class Control(diffrax.AbstractPath[Shaped[Array, "2"], None]): t0 = 0 t1 = 1 + def init(self, t0, t1, y0, args, max_steps): + return None + + def __call__(self, t0, path_state: None, t1=None, left=True): + return self.evaluate(t0, t1, left), path_state + def evaluate(self, t0, t1=None, left=True): return jr.normal(getkey(), (2,)) @@ -43,7 +49,7 @@ def derivative(self, t, left=True): control = Control() term = diffrax.ControlTerm(vector_field, control) args = getkey() - dx = term.contr(0, 1) + dx, state = term.contr(0, 1, None) y = jnp.array([1.0, 2.0, 3.0]) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dx) @@ -57,11 +63,11 @@ def derivative(self, t, left=True): # `# type: ignore` is used for contrapositive static type checking as per: # https://github.com/microsoft/pyright/discussions/2411#discussioncomment-2028001 - _: diffrax.ControlTerm[PyTree[Array], Array] = term - __: diffrax.ControlTerm[PyTree[Array], diffrax.BrownianIncrement] = term # type: ignore + _: diffrax.ControlTerm[PyTree[Array], Array, None] = term + __: diffrax.ControlTerm[PyTree[Array], diffrax.BrownianIncrement, None] = term # type: ignore term = term.to_ode() - dt = term.contr(0, 1) + dt, state = term.contr(0, 1, None) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dt) assert vf.shape == (3,) @@ -77,6 +83,12 @@ class Control(diffrax.AbstractPath): t0 = 0 t1 = 1 + def init(self, t0, t1, y0, args, max_steps): + return None + + def __call__(self, t0, path_state, t1=None, left=True): + return self.evaluate(t0, t1, left), path_state + def evaluate(self, t0, t1=None, left=True): return jr.normal(getkey(), (3,)) @@ -86,7 +98,7 @@ def derivative(self, t, left=True): control = Control() term = diffrax.WeaklyDiagonalControlTerm(vector_field, control) args = getkey() - dx = term.contr(0, 1) + dx, state = term.contr(0, 1, None) y = jnp.array([1.0, 2.0, 3.0]) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dx) @@ -99,7 +111,7 @@ def derivative(self, t, left=True): assert tree_allclose(vf_prod, term.prod(vf, dx)) term = term.to_ode() - dt = term.contr(0, 1) + dt, state = term.contr(0, 1, None) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dt) assert vf.shape == (3,) @@ -145,7 +157,7 @@ def __call__(self, t, y, args): randlike = lambda a: jr.normal(getkey(), a.shape) a_term = jtu.tree_map(randlike, eqx.filter(term, eqx.is_array)) aug = (y, a_y, a_args, a_term) - dt = adjoint_term.contr(t, t + 1) + dt, state = adjoint_term.contr(t, t + 1, None) vf_prod1 = adjoint_term.vf_prod(t, aug, args, dt) vf = adjoint_term.vf(t, aug, args) diff --git a/test/test_typing.py b/test/test_typing.py index 4c4f3db1..2c3a8b0f 100644 --- a/test/test_typing.py +++ b/test/test_typing.py @@ -277,20 +277,24 @@ class X9(X3, X2[int, str]): def test_abstract_term(): - assert _abstract_args(dfx.AbstractTerm) == (Any, Any) - assert _abstract_args(dfx.AbstractTerm[int, str]) == (int, str) + assert _abstract_args(dfx.AbstractTerm) == (Any, Any, Any) + assert _abstract_args(dfx.AbstractTerm[int, str, int]) == (int, str, int) def test_ode_term(): - assert _abstract_args(dfx.ODETerm) == (Any, RealScalarLike) - assert _abstract_args(dfx.ODETerm[int]) == (int, RealScalarLike) + assert _abstract_args(dfx.ODETerm) == (Any, RealScalarLike, type(None)) + assert _abstract_args(dfx.ODETerm[int]) == (int, RealScalarLike, type(None)) def test_control_term(): - assert _abstract_args(dfx.ControlTerm) == (Any, Any) - assert _abstract_args(dfx.ControlTerm[int, str]) == (int, str) + assert _abstract_args(dfx.ControlTerm) == (Any, Any, Any) + assert _abstract_args(dfx.ControlTerm[int, str, int]) == (int, str, int) def test_weakly_diagonal_control_term(): - assert _abstract_args(dfx.WeaklyDiagonalControlTerm) == (Any, Any) - assert _abstract_args(dfx.WeaklyDiagonalControlTerm[int, str]) == (int, str) + assert _abstract_args(dfx.WeaklyDiagonalControlTerm) == (Any, Any, Any) + assert _abstract_args(dfx.WeaklyDiagonalControlTerm[int, str, int]) == ( + int, + str, + int, + ) From 7fb9baa85ff3246f01f7150a4932323a365d4e4c Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 31 Dec 2024 23:19:03 -0700 Subject: [PATCH 06/50] work --- diffrax/_solver/runge_kutta.py | 14 ++++++++++---- diffrax/_term.py | 2 +- examples/underdamped_langevin_example.ipynb | 18 +++++++++++------- test/test_solver.py | 8 ++++---- 4 files changed, 26 insertions(+), 16 deletions(-) diff --git a/diffrax/_solver/runge_kutta.py b/diffrax/_solver/runge_kutta.py index e7491693..95327b18 100644 --- a/diffrax/_solver/runge_kutta.py +++ b/diffrax/_solver/runge_kutta.py @@ -611,13 +611,13 @@ def _fn(tableau, *_trees): return jtu.tree_map(_fn, tableaus, *trees) def t_map_contr(fn, *trees, control, implicit_val=sentinel): - def _fn(tableau, *_trees): + def _fn(tableau, _control, *_trees): if tableau.implicit and implicit_val is not sentinel: return implicit_val else: - return fn(*_trees, control) + return fn(*_trees, _control) - return jtu.tree_map(_fn, tableaus, *trees) + return jtu.tree_map(_fn, tableaus, control, *trees) # Structure of `y` and `k`. def y_map(fn, *trees): @@ -655,11 +655,17 @@ def _get_implicit_impl(term, x): return value dt = t1 - t0 - control, new_path_state = t_map_contr( + tableau_mapped = t_map_contr( lambda term_i, path_i: term_i.contr(t0, t1, path_i), terms, control=path_state, ) + # control, new_path_state = jtu.tree_map(lambda x) + if isinstance(tableaus, ButcherTableau): + control, new_path_state = tableau_mapped + else: # tuple of butchers + control, new_path_state = tuple(i[0] for i in tableau_mapped), tuple(i[1] for i in tableau_mapped) + if implicit_tableau is None: implicit_control = _unused else: diff --git a/diffrax/_term.py b/diffrax/_term.py index 2d0d44b1..d3027f39 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -626,7 +626,7 @@ def contr( t1: RealScalarLike, control_state: PyTree, **kwargs, - ) -> tuple[tuple[PyTree[ArrayLike], ...], tuple[PyTree]]: + ) -> tuple[tuple[PyTree[ArrayLike], ...], tuple[PyTree, ...]]: contrs = [ term.contr(t0, t1, state, **kwargs) for term, state in zip(self.terms, control_state) diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index 1cce9734..fd37447e 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "9deba250066ddc39", "metadata": { "ExecuteTime": { @@ -66,8 +66,8 @@ "# Parameters\n", "gamma = jnp.array([2, 0.5], dtype=jnp.float32)\n", "u = jnp.array([0.5, 2], dtype=jnp.float32)\n", - "x0 = jnp.zeros((2,), dtype=jnp.float32)\n", - "v0 = jnp.zeros((2,), dtype=jnp.float32)\n", + "x0 = jnp.ones((2,), dtype=jnp.float32)\n", + "v0 = jnp.ones((2,), dtype=jnp.float32)\n", "y0 = (x0, v0)\n", "\n", "# Brownian motion\n", @@ -81,9 +81,13 @@ "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", "terms = drift_term #diffrax.MultiTerm(drift_term, diffusion_term)\n", + "terms = diffrax.MultiTerm(\n", + " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x),\n", + " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: - x)\n", + ")\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", - "solver = diffrax.Euler()\n", + "solver = diffrax.Tsit5()\n", "\n", "\n", "def _path_init(term):\n", @@ -120,14 +124,14 @@ "\n", "\n", "sol = diffrax.diffeqsolve(\n", - " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat, adjoint=diffrax.BacksolveAdjoint()\n", + " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat#, adjoint=diffrax.BacksolveAdjoint()\n", ")\n", "xs, vs = sol.ys" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "62da2ddbaaf98f47", "metadata": { "ExecuteTime": { @@ -138,7 +142,7 @@ "outputs": [ { "data": { - "image/png": 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", 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FQmkeZKyEDoObJKZbR3fnhy37WLb9ALe8uZJ3bxxOiEO/CYiIiIgEFWeE1TLlr3PXw+bNm8nIyMDj8bBt2zb69u1b7fX6zDvcVPTtt4XL37kWgB1GPUZMrGJ3QOfTrPUmGD2xisNu48lLBxIb7uSnXXn8a+76JitbRERERJqJYVjdA/3xMOo4AB1QXl7OFVdcwcUXX8yDDz7I9ddfz549e7yv13fe4aaiRKyFK8+2kpzC6PS6j5h4qCYcxv5Q7ePCefh865eI5xdu4ZsNOU1avoiIiIgIwP/93/+Rl5fHU089xR133EGPHj249tprgbrNO+wrSsRaOOcB6x4xs033hhVQNbHzzqVQmt9EUVnGH9+Oy4d2BGDqOz+RU1DWpOWLiIiISOu2YMECnnjiCV577TViYmKw2Wy89tprfPvttzz77LN1mnfYV3SPWEvmriCmZBcAUe3rOWJilfh0iO8MB7bCrqXQrWn7zN7zmz78uG0/G7IL+fO7P/HSNSc2rOVOREREROQwI0eOxOVyVduWnp7ubfG66aab/BEWoBaxli13Ow4qKDZDSe3YteHlJB9nLfdtbpq4DhHmtPPMZScQ6rCxcEMOM7/f2uTnEBEREREJNErEWjD3nl8B2Gqm0D05tuEFJXSxlj5IxAB6JEcz7ew+ADw8+1d+2eX7PrkiIiIiIv6kRKwFO7BzHQDbSa3/iImHalPZmrbfN4kYwGVDOnLm8Sm43CZ/eHMFhWUVPjuXiIiIiIi/KRFrwUoyrRax3MjOjbvvKqEyEfNRixiAYRj847f9SI0NY9u+YqZ9tNpn5xIRERER8TclYi2Yfd9GANwJjbg/DA62iOXuALfr6Ps2QmyEkycvHYjNgPdX7OaDlbt8di4REREREX9SItaCRRdtAyAspVcjC2pnzV5uuuHA9sYHdhQnpifwx1E9APi/D1azJafQp+cTERERkfoxTdPfIfhdU9SBErGWquQA0e5cANp26tO4sgzj4IAdPrxPrMqUM7oxrEsbisvd3DxrJaUut8/PKSIiIiJHZ7fbASgvL/dzJP5XXFwMgNPpbHAZmkeshfLkbAAgw0ygS/uUxheY0BmyV/v0PrEqdpvBk5cM4Mwnv2VdZj4PfrqWv5/X1+fnFREREZHaORwOIiIiyMnJwel0YrMFRpuOx+OhvLyc0tJSn8dkmibFxcXs2bOHuLg4b3LaEErEWqgDO9eRgjVi4tD4RoyYWCXB9yMnHiopJozHLx7A1S8t5Y0lOxjWtQ2/6ZfaLOcWERERkSMZhkG7du3YunUr27f79naV+jBNk5KSEsLDwzGMRgxQVw9xcXGkpDSusUOJWAtVuNsaun5fWHrjRkys4h3Cfkvjy6qjU3u05fcjuzLj683c+d4v9G0fS6c2kc12fhERERGpLiQkhO7duwdU90SXy8U333zDqaee2qiugnXldDob1RJWRYlYS7VvEwDlcV2aprxmGMK+JreN7sHSrfv5cdsBbp61gvduGk6oo/EXvoiIiIg0jM1mIywszN9heNntdioqKggLC2uWRKypBEbHTmlykYXbAHAm92yaAqtaxPJ2QkXz/QLisNt46tKBxEc4Wb07n+mf/9ps5xYRERER8RUlYi2QYbpJLN8NQFzacU1TaFQyhESB6YED25qmzDpqFxvOYxcNAODlRduYvTqrWc8vIiIiItLUlIi1QGFlOTipoMQMoVPn7k1TqGFYIydCsw3YcajTeyXxu1Otbpa3/+8ndu4vbvYYRERERESaihKxFsgsyARgK6l0SGjCwS38dJ9YlT+P68nAjnHkl1YwZdYKyio0v5iIiIiIBCclYi2QrdBKxPaGpjXNiIlV2jTvEPaHc9ptPH3pQGLDnfy0K4+/fbrOL3GIiIiIiDSWErEWKKLUuoeqJKaJRkys4ucWMYAO8RE8cckADANe+2E7H6zc5bdYREREREQaSolYCxRfngGA0bZH0xbsh7nEanJ6zyT+cIZ179td7//Cr1n5fo1HRERERKS+lIi1QO08VotYbIc+TVtwVYtY3i5wlTZt2fX0x1HdObVHW0pdHm56fQX5pS6/xiMiIiIiUh9KxFoYd9F+4rFaiNp1Ob5pC49MhNAYwIQDW5u27Hqy2wyeuHgA7ePC2bq3iL+8+xOmafo1JhERERGRulIi1sLkbFsNQKaZQPvktk1b+KFD2PvxPrEqCZEh/PvyEwix25izJpsXvvVvl0kRERERkbpSItbCHNhpjSSY5UzD3pQjJlZJCIz7xKr0T4tj2tlWF8yHZ6/nhy37/ByRiIiIiMixBVUi9s0333D22WeTmpqKYRh8+OGHxzxmwYIFnHDCCYSGhtKtWzdefvnlI/aZMWMG6enphIWFMXToUJYuXdr0wTeT8uwNABRGpfvmBH4ewr4mlw/tyG8HtsftMZkyayXZ+f69f01ERERE5FiCKhErKiqif//+zJgxo077b926lbPOOovTTz+dVatWceutt3L99dczZ84c7z5vv/02U6dO5d5772XFihX079+fcePGsWfPHl+9DZ9yHrASJE9CN9+cIACGsD+cYRj8/by+9EqJZm9hGTe/sYLyCo+/wxIRERERqVVQJWJnnnkmf/vb3zjvvPPqtP9zzz1H586d+de//kXv3r2ZMmUKF1xwAY8//rh3n8cee4zJkyczadIk+vTpw3PPPUdERAQzZ8701dvwqfiS7QBEtOvlmxMEyBD2hwsPsfPsFYOIDnWwbPsB7v9kjb9DEhERERGpVVAlYvW1ePFiRo8eXW3buHHjWLx4MQDl5eUsX7682j42m43Ro0d79wkm7goXKW5rDrG26cf55iRVLWL5u6G82DfnaKDOiZE8eak12fMbS3bw+g/b/R2SiIiIiEiNHP4OwJeysrJITk6uti05OZn8/HxKSko4cOAAbre7xn1+/fXXWsstKyujrKzM+zw/3xou3uVy4XL5bz6rjM1r6GS4KTFDaJPS0TexOKNxhMVilObhytkISU08V1kjndI1gT+N7s6jczdy38dr6NImnBPT45us/Ko69effuSVT/fqW6te3VL++pfr1LdWvb6l+fSvQ6reucbToRMxXpk+fzv3333/E9i+//JKIiAg/RGQpPbCbHFs/3NjJ+uorn53nVFsb4slj5bx3yYw70WfnaagOJgxsY2PlPhs3vLKUP/VzkxDatOeYO3du0xYo1ah+fUv161uqX99S/fqW6te3VL++FSj1W1xct15jLToRS0lJITs7u9q27OxsYmJiCA8Px263Y7fba9wnJSWl1nLvuusupk6d6n2en59PWloaY8eOJSYmpmnfRD25XNcwd+5cxowZg9Pp9Mk57OUfwZotDEqPxzN8gk/O0Vinl1dwyQs/si6rgP9lxfPmdUMID7E3ulyXy+Xz+m3NVL++pfr1LdWvb6l+fUv161uqX98KtPqt6i13LC06ERs2bBiff/55tW1z585l2LBhAISEhDBo0CDmz5/PxIkTAfB4PMyfP58pU6bUWm5oaCihoUc2sTidzoD444OPY0nsDoA9dyv2AHm/h4t1Onnh6sGc88z3rMko4O6P1/HkJQMwjKaZWy2Q/tYtkerXt1S/vqX69S3Vr2+pfn1L9etbgVK/dY0hqAbrKCwsZNWqVaxatQqwhqdftWoVO3bsAKyWqquuusq7/4033siWLVu4/fbb+fXXX/n3v//NO++8w2233ebdZ+rUqbzwwgu88sorrFu3jptuuomioiImTZrUrO8tqAToyImH6xAfwb8vPwGHzeDjnzL4zzeBHa+IiIiItB5B1SK2bNkyTj/9dO/zqu6BV199NS+//DKZmZnepAygc+fOfPbZZ9x22208+eSTdOjQgf/+97+MGzfOu8/FF19MTk4O06ZNIysriwEDBjB79uwjBvCQQwTgXGK1OalLG+49uw/3fLSGf8z+lZ4p0YzsmeTvsERERESklQuqRGzkyJGYplnr6y+//HKNx6xcufKo5U6ZMuWoXRHlMG26WMvCLCgrhNAo/8ZzDFec1Im1mfm8uXQnf3hzJR/8fjjdkqL9HZaIiIiItGJB1TVRAkR4vPUAOLDVv7HUgWEY3H/O8ZyYHk9BaQWTXv6RfYVlxz5QRERERMRHlIhJwwRR90SAEIeN564YRMeECHbuL+GG15ZT6nL7OywRERERaaWUiEnDeAfsCI5EDKBNVCgzrzmRmDAHy7cf4C//+/moXV1FRERERHxFiZg0jLdFLLhGIuyWFMVzVwzCYTP45KcMHp+30d8hiYiIiEgrpERMGiYIW8SqDO+WyEO/7QvAU/M38v6KXX6OSERERERaGyVi0jAJlSMnBsk9Yoe7aHAaN420ksk73vuZJVv2+TkiEREREWlNlIhJw1S1iBXtgdJ8/8bSQH8Z25MJfVNwuU1+9/pytu0t8ndIIiIiItJKKBGThgmLhYhEa31/cN0nVsVmM3jsogH0T4sjt9jFpJd/ZH9Rub/DEhEREZFWQImYNFwQ3ydWJcxp54WrBtE+Lpyte4u47pUfKS6v8HdYIiIiItLCKRGThgvSkRMPlxQdxivXnkhsuJOVO3KZMmslFW6Pv8MSERERkRZMiZg0XJvKATuCuEWsSrekaGZeM5hQh42vft3DXz/4RXOMiYiIiIjPKBGThvO2iAV/IgYwqFMCz1x2AjYD3lm2i8fmbvB3SCIiIiLSQikRk4bz3iMW3F0TDzWmTzIPnWfNMfb0V5t4bfE2/wYkIiIiIi2SEjFpuKq5xIr3Qmmef2NpQpcM6cjUMT0AmPbxGmavzvRzRCIiIiLS0igRk4YLjYbIJGu9hXRPrPKHM7px+dCOmCbc8tYqftCEzyIiIiLShJSISeO0wO6JAIZh8MC5xzO2TzLlFR4mv7qMtRnBOXG1iIiIiAQeJWLSOPHp1jJ3h1/D8AW7zeCpSwcyJD2BgtIKrnxxCVtyivwdloiIiIi0AErEpHFiO1jLvF3+jcNHwpx2Xrh6MMelxrCvqJyrX17GvlJ/RyUiIiIiwU6JmDROTHtr2UITMYDYcCevXjuEbklRZOWX8e+1dvYUlPk7LBEREREJYkrEpHFi06xl/m7/xuFjbaJCeeP6oaTFh7O3zODql5axv6jc32GJiIiISJBSIiaN4+2auNO/cTSD5JgwXpk0iNgQk005RVw1cwn5pS5/hyUiIiIiQUiJmDRObGXXxNI8KG35owqmxUdwcx83CZFOVu/O59qXfqS4vMLfYYmIiIhIkFEiJo0TGg1hcdZ6C++eWCU5HF66ehAxYQ6WbT/A715bTlmF299hiYiIiEgQUSImjVd1n1gLHrDjcH3axfDSpCFEhNj5duNeblQyJiIiIiL1oERMGi+25Y+cWJNBneL579WDCXPa+Hp9jpIxEREREakzJWLSeC18LrGjGd41kZlXn6hkTERERETqRYmYNF4rTsQAhndTMiYiIiIi9aNETBqvFd4jdjglYyIiIiJSH0rEpPFa0VxiR6NkTERERETqSomYNF5M5WAd+Rng8fg3Fj9TMiYiIiIidaFETBovuh0YNvC4oGiPv6Pxu8OTsetfWaZJn0VERESkmqBLxGbMmEF6ejphYWEMHTqUpUuX1rrvyJEjMQzjiMdZZ53l3eeaa6454vXx48c3x1tpOewOiE611lvxfWKHGt4tkZnXnOidZ+zqmUvJL3X5OywRERERCRBBlYi9/fbbTJ06lXvvvZcVK1bQv39/xo0bx549NbfCvP/++2RmZnofq1evxm63c+GFF1bbb/z48dX2e/PNN5vj7bQsuk/sCMO7JvLadUOJDnPw47YDXPbCD+wvKvd3WCIiIiISAIIqEXvssceYPHkykyZNok+fPjz33HNEREQwc+bMGvdPSEggJSXF+5g7dy4RERFHJGKhoaHV9ouPj2+Ot9OytPIh7GszqFM8b91wEm0iQ1i9O5+Ln19Mdn6pv8MSERERET9z+DuAuiovL2f58uXcdddd3m02m43Ro0ezePHiOpXx4osvcskllxAZGVlt+4IFC0hKSiI+Pp4zzjiDv/3tb7Rp06bWcsrKyigrK/M+z8/PB8DlcuFy+bf7WdX5mzsOW3Q77ID7wE48fq4DX2pI/fZoG8Eb153I1S8vY+OeQi54dhGvTBpEWnyEr8IMWv66flsL1a9vqX59S/XrW6pf31L9+lag1W9d4zBM0zR9HEuTyMjIoH379ixatIhhw4Z5t99+++0sXLiQJUuWHPX4pUuXMnToUJYsWcKQIUO829966y0iIiLo3Lkzmzdv5q9//StRUVEsXrwYu91eY1n33Xcf999//xHbZ82aRURE6/xy3TlnHv12vUpG7CB+7PJHf4cTkPaVwoy1dvaVGcSGmNzcx01yuL+jEhEREZGmVFxczGWXXUZeXh4xMTG17tdqErHf/e53LF68mJ9//vmo+23ZsoWuXbsyb948Ro0aVeM+NbWIpaWlsXfv3qNWdnNwuVzMnTuXMWPG4HQ6m+28xobZON69Ak9Kf9zXzW+28za3xtZvVn4p17y8nM05RSREOpl51SCOS/XvNRNI/HX9thaqX99S/fqW6te3VL++pfr1rUCr3/z8fBITE4+ZiAVN18TExETsdjvZ2dnVtmdnZ5OSknLUY4uKinjrrbd44IEHjnmeLl26kJiYyKZNm2pNxEJDQwkNDT1iu9PpDIg/PvghljbpANjyd2MLkDrwpYbWb1obJ+/eOJyrZi5h9e58rpi5jOevHMTJ3RJ9EGXwCqR/Sy2R6te3VL++pfr1LdWvb6l+fStQ6reuMQTNYB0hISEMGjSI+fMPtrZ4PB7mz59frYWsJu+++y5lZWVcccUVxzzPrl272LdvH+3atWt0zK1K1WAdxXvBVeLfWAJcQmQIsyafxNDOCRSWVXDNS0v5+KcMf4clIiIiIs0oaBIxgKlTp/LCCy/wyiuvsG7dOm666SaKioqYNGkSAFdddVW1wTyqvPjii0ycOPGIATgKCwv5y1/+wg8//MC2bduYP38+5557Lt26dWPcuHHN8p5ajLA4cFYOgpKvpOJYYsKcvHLtECb0TcHlNrnlzZW8+N1Wf4clIiIiIs0kaLomAlx88cXk5OQwbdo0srKyGDBgALNnzyY5ORmAHTt2YLNVzy3Xr1/Pd999x5dffnlEeXa7nZ9//plXXnmF3NxcUlNTGTt2LA8++GCNXQ/lKAzDahXbu96aS6xNV39HFPDCnHaevvQE2kat4ZXF23nw07XsyS/ljvG9sNkMf4cnIiIiIj4UVIkYwJQpU5gyZUqNry1YsOCIbT179qS28UjCw8OZM2dOU4bXunkTMc0lVld2m8F95xxHcmwYj8xez/PfbCE7v5RHLuhPiCOoGqxFREREpB70TU+ajiZ1bhDDMPj9yG48emF/7DaDD1dlcN0rP1JYVuHv0ERERETER5SISdOJTbOWSsQa5IJBHXjx6sFEhNj5duNeLnpuMZl5GvhEREREpCVSIiZNJ7a9tVQi1mAjeybx5uSTSIwKYW1mPhNnfM8vu/L8HZaIiIiINDElYtJ01DWxSfRPi+OD359Mj+QosvPLuOj5xcxZk+XvsERERESkCSkRk6ZzaCJWywApUjdpCRH876bhnNI9kRKXmxtfX84L32ypdeAZEREREQkuSsSk6cRUdk2sKIHi/f6NpQWICXPy0jUncsVJHTFN+Pvn6/jrB7/gcnv8HZqIiIiINJISMWk6jlCIsuZ0I1/dE5uCw27jwXOPZ9pv+mAz4M2lO7nmpaXkFbv8HZqIiIiINIISMWlaMRqwo6kZhsG1IzrzwlXWiIrfb9rHef/+nk17Cv0dmoiIiIg0kBIxaVoasMNnRvVO5n83Dic1Nowte4uYOON75q3N9ndYIiIiItIASsSkaXnnEtvp3zhaqD6pMXw0ZQRD0hMoLKtg8mvLeHr+RjweDeIhIiIiEkyUiEnTUouYz7WNDuX164dy1bBOmCb8a+4Gfv/GCgrLKvwdmoiIiIjUkRIxaVreRGy3f+No4UIcNh4493j+8du+hNhtzF6TxW///T3b9xX5OzQRERERqQMlYtK0YjVYR3O6ZEhH3rzhJJKiQ9mQXcjZT3/Hwg05/g5LRERERI5BiZg0rap7xAoywa0h1pvDoE7xfPKHEQxIiyO/tIJrXlrKE/M24NZ9YyIiIiIBS4mYNK2IRLCHAibkZ/g7mlYjOSaMt393EpcOScM04Yl5G7nmpaXsKyzzd2giIiIiUgMlYtK0bDZ1T/STUIed6b/tx78u7E+Y08a3G/dy1lPfsWzbfn+HJiIiIiKHUSImTa9qwI58DdjhD+cP6sBHN4+gS9tIsvJLueQ/P/Dfb7dgmuqqKCIiIhIolIhJ04upGjlRc4n5S8+UaD6eMoKz+6dS4TH522fruPH15eSV6L49ERERkUCgREyanuYSCwhRoQ6eumQAD5x7HE67wZw12Zz99Hes2pnr79BEREREWj0lYtL0lIgFDMMwuGpYOv+7cTjt48LZsb+YC55dxL8XbMKjURVFRERE/EaJmDQ9TeoccPqnxfH5LadwVt92VHhMHpm9niteXEJ2fqm/QxMRERFplZSISdOrmktMLWIBJTbCyTOXDeSR8/sR7rSzaPM+xj/xDfPWZvs7NBEREZFWR4mYNL2q4evL8qA0z7+xSDWGYXDRiWl8essI+rSL4UCxi+tfXca0j1ZT6nL7OzwRERGRVkOJmDS9kEgIj7fW1T0xIHVtG8UHNw/nuhGdAXh18XbOfeZ71mbk+zkyERERkdZBiZj4hgbsCHihDjv3/KYPL086kcSoENZnF3DujO+Y8fUmKtwef4cnIiIi0qIpERPfqLpPLF+JWKAb2TOJ2beeypg+ybjcJv+cs56Lnl/M1r1F/g5NREREpMVSIia+oRaxoJIYFcp/rhzEoxf2JzrUwYoduUx48lteXbxNw9yLiIiI+IASMfGNmMoBO5SIBQ3DMLhgUAdm33YqJ3drQ4nLzbSP1nDVzKVk5Jb4OzwRERGRFkWJmPiGWsSCVvu4cF67dij3nd2HMKeN7zbtZdwT3/D2jzswTbWOiYiIiDQFJWLiG965xHb6Nw5pEJvN4JqTO/P5LacwIC2OgtIK7njvF654cQk79hX7OzwRERGRoBd0idiMGTNIT08nLCyMoUOHsnTp0lr3ffnllzEMo9ojLCys2j6maTJt2jTatWtHeHg4o0ePZuPGjb5+Gy1fVYtYfiZ4ND9VsOrSNor/3TiMv07oRajDxveb9jHuiW948butuHXvmIiIiEiDBVUi9vbbbzN16lTuvfdeVqxYQf/+/Rk3bhx79uyp9ZiYmBgyMzO9j+3bt1d7/ZFHHuGpp57iueeeY8mSJURGRjJu3DhKS0t9/XZatugUMOzgcUFh7X8fCXwOu40bTu3KnFtP5aQuCZS43Dz46VrOf3YRG7IL/B2eiIiISFBqUCJWVFTEPffcw/Dhw+nWrRtdunSp9vCVxx57jMmTJzNp0iT69OnDc889R0REBDNnzqz1GMMwSElJ8T6Sk5O9r5mmyRNPPMHdd9/NueeeS79+/Xj11VfJyMjgww8/9Nn7aBVsdg3Y0cKkJ0Yy6/qTmP7bvkSHOli1M5eznvqWJ+dtpLxC846JiIiI1IejIQddf/31LFy4kCuvvJJ27dphGEZTx3WE8vJyli9fzl133eXdZrPZGD16NIsXL671uMLCQjp16oTH4+GEE07goYce4rjjjgNg69atZGVlMXr0aO/+sbGxDB06lMWLF3PJJZfUWGZZWRllZWXe5/n5+QC4XC5cLlej3mdjVZ3f33EA2GNSseXtoGL/NsyUAf4Op0kEUv36ywUD2zGiazz3fryOr9bn8Pi8DXy0ajcPnNOboZ0TGlW26te3VL++pfr1LdWvb6l+fUv161uBVr91jcMwGzAMWlxcHJ999hknn3xyvQNrqIyMDNq3b8+iRYsYNmyYd/vtt9/OwoULWbJkyRHHLF68mI0bN9KvXz/y8vJ49NFH+eabb1izZg0dOnRg0aJFnHzyyWRkZNCuXTvvcRdddBGGYfD222/XGMt9993H/ffff8T2WbNmERER0QTvtmU4YduzpB1YzOrUS9icPMHf4UgTM01Yuc/gvW02Cl3WjzEnJno4N91DtNPPwYmIiIj4SXFxMZdddhl5eXnExMTUul+DWsTi4+NJSGjcL9/NYdiwYdWStuHDh9O7d2+ef/55HnzwwQaXe9dddzF16lTv8/z8fNLS0hg7duxRK7s5uFwu5s6dy5gxY3A6/ftt2Pb1cli0mN7tY+g5tmUkYoFUv4HgLOCWEhePzdvErB938uNeG+sLQ/jTmO5cMrgDNlv9WstVv76l+vUt1a9vqX59S/XrW6pf3wq0+q3qLXcsDUrEHnzwQaZNm8Yrr7zSbC1AiYmJ2O12srOzq23Pzs4mJSWlTmU4nU4GDhzIpk2bALzHZWdnV2sRy87OZsCAAbWWExoaSmhoaI3lB8IfHwIkljhrCHt7QQZ2f8fSxAKifgNEG6eTv/+2Hxee2JG7P/yF1bvzufeTdby/KpO/Tzye49vH1rtM1a9vqX59S/XrW6pf31L9+pbq17cCpX7rGkODBuv417/+xZw5c0hOTqZv376ccMIJ1R6+EBISwqBBg5g/f753m8fjYf78+dVavY7G7Xbzyy+/eJOuzp07k5KSUq3M/Px8lixZUucy5Sg0qXOrMiAtjo9uHsF9Z/chKtTBTztzOeeZ75j20Wpyi8v9HZ6IiIhIQGlQi9jEiRObOIy6mTp1KldffTWDBw9myJAhPPHEExQVFTFp0iQArrrqKtq3b8/06dMBeOCBBzjppJPo1q0bubm5/POf/2T79u1cf/31gDWi4q233srf/vY3unfvTufOnbnnnntITU3123tsUZSItTr2yomgz+zbjgc/XcunP2fy6uLtfPxTBn8a04NLh3TEYQ+qWTNEREREfKJBidi9997b1HHUycUXX0xOTg7Tpk0jKyuLAQMGMHv2bO+Q9Dt27MBmO/gl78CBA0yePJmsrCzi4+MZNGgQixYtok+fPt59br/9doqKirjhhhvIzc1lxIgRzJ49+4iJn6UBqoavL94LrhJwhvs3Hmk2yTFhPHPZCVw2ZC/3f7KW9dkF3PPRGt5YsoN7zz6OYV3b+DtEEREREb9qUCJWZfny5axbtw6A4447joEDBzZJUEczZcoUpkyZUuNrCxYsqPb88ccf5/HHHz9qeYZh8MADD/DAAw80VYhSJTwenJHgKoL8DGjT1d8RSTMb3i2Rz24ZwRtLdvDY3A38mlXApS/8wIS+Kfx1Qm86xGuUUREREWmdGpSI7dmzh0suuYQFCxYQFxcHQG5uLqeffjpvvfUWbdu2bcoYJVgZBsS2h70brO6JSsRaJYfdxtXD0zmnfyr/mrueWUt28PkvWcxft4cbTu3C707rSlRoo34TEhEREQk6DbpZ4w9/+AMFBQWsWbOG/fv3s3//flavXk1+fj633HJLU8cowazqPrH83f6NQ/wuPjKEv03sy2e3nMJJXRIoq/Dw9FebGPnPr3nth+243B5/hygiIiLSbBr0M/Ts2bOZN28evXv39m7r06cPM2bMYOzYsU0WnLQAVfeJacAOqdS7XQxvTj6JOWuyeHj2erbuLeKeD1fz0vdb+cuY7tR/inkRERGR4NOgRMzj8dQ4Pr7T6cTj0a/acohYay4xJWJyKMMwGH98O0b1TmbWkh08OX8jW3KKuGnWKrpG2+nQP4/BnRP9HaaIiIiIzzSoa+IZZ5zBH//4RzIyMrzbdu/ezW233caoUaOaLDhpAWLVIia1c1beP7bgLyP5/ciuhDpsbC4wuOD5Jdz8xgo25xT6O0QRERERn2hQIvbMM8+Qn59Peno6Xbt2pWvXrnTu3Jn8/Hyefvrppo5RgllV10TdIyZHERPm5PbxvZh76wiGtPVgGPDZL5mMeWwht//vJ3YdKPZ3iCIiIiJNqkFdE9PS0lixYgXz5s3j119/BaB3796MHj26SYOTFuDQrommaY2kKFKLdrFhXN7Nwz0XncyTX21h3rps3lm2iw9XZnDZ0I78/vSuJEVrjj8REREJfg0eM9owDMaMGcOYMWOaMh5paWJSrWV5IZTmQXicX8OR4NArJZr/Xj2YFTsO8Oic9SzavI+XF23j7R93cs3J6fzu1C7ERYT4O0wRERGRBqtzIvbUU09xww03EBYWxlNPPXXUfTWEvXiFREBEGyjeZ7WKKRGTejihYzyzJp/E95v28s8561m1M5dnF2zm9cXbuebkdK4b0VkJmYiIiASlOidijz/+OJdffjlhYWE8/vjjte5nGIYSMakupr2ViOXvhpTj/R2NBKGTuyUyvGsb5q3bw7++XM+vWQU8/dUmZn63lauHp3P9KV1IiFRCJiIiIsGjzonY1q1ba1wXOabYNMj6WSMnSqMYhsGYPsmM6pXEl2uzeHL+JtZl5vPvBZt5edE2rhzWiRtO6UKbqFB/hyoiIiJyTA0aNfGBBx6guPjIUcxKSkp44IEHGh2UtDAawl6akM1mzUH2+S0j+M+Vgzi+fQzF5W6eX7iFEQ9/zd8/W0t2fqm/wxQRERE5qgYlYvfffz+FhUfO71NcXMz999/f6KCkhYntYC01hL00IcMwGHtcCp9MGcGLVw+mX4dYSlxuXvh2K6c8/DV3vvczWzQPmYiIiASoBo2aaJomRg3DkP/0008kJCQ0OihpYWLUIia+YxgGo3onc0avJBZsyOHZrzezdNt+3vpxJ28v28mZx6dw42ld6dchzt+hioiIiHjVKxGLj4/HMAwMw6BHjx7VkjG3201hYSE33nhjkwcpQe7QucREfMQwDE7vmcTpPZNYtm0/zy3czLx1e/j8lyw+/yWLk7u14cbTujKiW2KNPySJiIiINKd6JWJPPPEEpmly7bXXcv/99xMbG+t9LSQkhPT0dIYNG9bkQUqQq7pHLD8DPB6wNahHrEidDU5P4L/pCazPKuD5hZv56KcMvt+0j+837aNXSjTXjejMOQNSCXXY/R2qiIiItFL1SsSuvvpqADp37szw4cNxOp0+CUpamKgUMGzgcUHRHohO8XdE0kr0TInmsYsHMHVsD/777Vbe/nEnv2YV8Jf//czDs9dz9bBOXH5SJw19LyIiIs2uzk0T+fn53vWBAwdSUlJCfn5+jQ+RauwOiE611tU9UfygQ3wE951zHD/cNYo7xvciJSaMvYVl/GvuBoZNn89d7//Cpj0F/g5TREREWpE6t4jFx8eTmZlJUlIScXFxNd5jUTWIh9vtbtIgpQWIbQ/5u6xErMNgf0cjrVRshJObRnbl+lM68/kvmbzw7RZW787nzaU7eHPpDkZ0S+SqYZ0Y1TsZu033kYmIiIjv1DkR++qrr7wjIn799dc+C0haqNgOsHOJWsQkIDjtNs4d0J5z+qeydOt+/vvdVuaty+a7TXv5btNe2seFc/lJHbnkxI7qtigiIiI+UedE7LTTTqtxXaROqoaw11xiEkAMw2BolzYM7dKGnfuLeX3Jdt7+cSe7c0t4ZPZ6npi3kd/0a8fVw9Lpnxbn73BFRESkBWnQ8HWzZ8/mu+++8z6fMWMGAwYM4LLLLuPAgQNNFpy0IBrCXgJcWkIEd53Zmx/uGsU/L+hH3/axlFd4eH/Fbs6d8T2/efpb3liynYJSl79DFRERkRagQYnYX/7yF++gHL/88gtTp05lwoQJbN26lalTpzZpgNJCxGpSZwkOYU47Fw5O4+MpJ/PB74dz3sD2hNhtrN6dz/99sJqhD83nzvd+5qeduZim6e9wRUREJEjVa/j6Klu3bqVPnz4AvPfee5x99tk89NBDrFixggkTJjRpgNJCxHawluqaKEHCMAwGdoxnYMd47vlNH95fsYs3l+5gc04Rb/24k7d+3EmfdjFcOrQj5/RPJTZc03mIiIhI3TWoRSwkJITi4mIA5s2bx9ixYwFISEjQ8PVSs5jKRKwwGyrK/BuLSD0lRIZw/SldmDf1NN753TCrlcxhY21mPvd8uJohf5/HLW+u5JsNObg9aiUTERGRY2tQi9iIESOYOnUqJ598MkuXLuXtt98GYMOGDXTo0KFJA5QWIiIBHGFQUQr5GZDQ2d8RidSbYRgM6ZzAkM4J3Ht2H95fsZu3f9zJ+uwCPv4pg49/yqBdbBjnn9CB8wd1oHNipL9DFhERkQDVoBaxZ555BofDwf/+9z+effZZ2re37v/54osvGD9+fJMGKC2EYRzsnqj7xKQFiIsI4doRnZl96yl8POVkrjypEzFhDjLzSnnm602c/ugCLnxuEW8u3UFesQb4EBERkeoa1CLWsWNHPv300yO2P/74440OSFqwmPawb5PuE5MWxTAM+nWIo1+HOP7vrN7MW5fNu8t28e3GHH7cdoAftx3g3o/WcHqvtkwc0J7TeyUR5rT7O2wRERHxswYlYgBut5sPP/yQdevWAXDcccdxzjnnYLfrC4bUwjuE/U7/xiHiI2FOO7/pl8pv+qWSlVfKByt389Gq3fyaVcCcNdnMWZNNdJiDs/q249wB7RnaOQGbzfB32CIiIuIHDUrENm3axIQJE9i9ezc9e/YEYPr06aSlpfHZZ5/RtWvXJg1SWgjvEPZqEZOWLyU2jJtGduWmkV1Zl5nPh6t28/GqDDLzSr2jLibHhDKhbzt+0y+VEzrGYRhKykRERFqLBt0jdsstt9C1a1d27tzJihUrWLFiBTt27KBz587ccsstTR1jNTNmzCA9PZ2wsDCGDh3K0qVLa933hRde4JRTTiE+Pp74+HhGjx59xP7XXHMNhmFUe+g+Nx/RPWLSSvVuF8NdZ/bm+zvO4M3JJ3Hx4DSiwxxk55fx0vfbOP/ZRYx4+Gse+nyd5icTERFpJRrUIrZw4UJ++OEHEhISvNvatGnDP/7xD04++eQmC+5wb7/9NlOnTuW5555j6NChPPHEE4wbN47169eTlJR0xP4LFizg0ksvZfjw4YSFhfHwww8zduxY1qxZ4x1gBGD8+PG89NJL3uehoaE+ew+tWkxlnesesaZXUQ4ZK2D799ZUAX0vBFuDfmcRH7LZDIZ1bcOwrm14YOJxfLthL5/+nMHctdnszi3hP99s4T/fbKFjQgRn9k3hzOPb0a99rLovioiItEANSsRCQ0MpKCg4YnthYSEhISGNDqo2jz32GJMnT2bSpEkAPPfcc3z22WfMnDmTO++884j933jjjWrP//vf//Lee+8xf/58rrrqKu/20NBQUlJSfBa3VPLeI6ZErNE8bsj6GbZ+Yz22LwZX0cHXl78ME2dAQhe/hShHF+qwM7pPMqP7JFPqcrNgfQ6f/pzB/HV72LG/mOcXbuH5hVtoFxvGuONSGHdcCiemx+OwK8EWERFpCRqUiP3mN7/hhhtu4MUXX2TIkCEALFmyhBtvvJFzzjmnSQOsUl5ezvLly7nrrru822w2G6NHj2bx4sV1KqO4uBiXy1WtJQ+slrOkpCTi4+M544wz+Nvf/kabNm1qLaesrIyysoOTEldNYu1yuXC5/DtMddX5/R1HjSKScAKU5eEq3A+h0f6OqN78Xr8Vpdhn34Gx/lOM0rxqL5kRbTA7DMHY+g3GjkWYz47Ac8a9eAZdA0ZwfHn3e/36iR0Y1bMNo3q2obi8goUb9jJn7R4WrM8hM6+Ulxdt4+VF24iPcDK6dxJjeicxrEtCvUdfbK3121xUv76l+vUt1a9vqX59K9Dqt65xGGYDbkbIzc3lmmuu4ZNPPsHhsHK5iooKzjnnHF5++WViY2PrW+QxZWRk0L59exYtWsSwYcO822+//XYWLlzIkiVLjlnG73//e+bMmcOaNWsICwsD4K233iIiIoLOnTuzefNm/vrXvxIVFcXixYtrHQHyvvvu4/777z9i+6xZs4iIiGjgO2wdzvz5JkLcRXzV6yEKwjX5d3312/kynfd+BYDLFs7e6F7sjerN3ug+5Id1AMNGRFkOA3b8l7aF1oimOVF9WNnpekpCEv0ZujSAywPr8wx+3mfwywGD4oqDXRRDbCY9Y02OTzDpE2cS47vOCCIiIlIPxcXFXHbZZeTl5RETE1PrfvVKxDweD//85z/5+OOPKS8vp2PHjlx99dUYhkHv3r3p1q1bkwRfk8YmYv/4xz945JFHWLBgAf369at1vy1bttC1a1fmzZvHqFGjatynphaxtLQ09u7de9TKbg4ul4u5c+cyZswYnE6nX2OpieOFkRh7VlNx8VuY3Ub7O5x682f9Gr9+guM9q1tuxXn/xez1G7DV0qhterAtexHbVw9gVJRghkThHv0g5oArrMm1A1SgX7/+VOH28OP2A8xZs4f5v+4hK//gZ5BhwIAOsZzRsy1n9GpL96SoGkdgVP36lurXt1S/vqX69S3Vr28FWv3m5+eTmJh4zESsXl0T//73v3PfffcxevRowsPD+fzzz4mNjWXmzJmNDvhYEhMTsdvtZGdnV9uenZ19zPu7Hn30Uf7xj38wb968oyZhAF26dCExMZFNmzbVmoiFhobWOKCH0+kMiD8+BFYs1cR1gD2rcRRlQSDGV0fNXr8HtsGnt1rrJ9+Ko/+Fxz5m+O+h5zj48CaMnUtwfH4bbPoSLnoFHIE9IE3AXr9+5HTCqT1TOLVnCn8zTdZk5DN/3R7mrcvml915rNxpPf41bxPt48I5rWdbTu+ZxPCubYgMdRxWlurXl1S/vqX69S3Vr2+pfn0rUOq3rjHUKxF79dVX+fe//83vfvc7AObNm8dZZ53Ff//7X2w+HqEtJCSEQYMGMX/+fCZOnAhYLXTz589nypQptR73yCOP8Pe//505c+YwePDgY55n165d7Nu3j3bt2jVV6HIoDWFffxXl8L9roSwPOgyBM+6u+7FtusKkL+CHf8P8B2HDFzB3Gpz5sO/iFZ8zDIPj28dyfPtY/ji6O5l5Jd6kbNHmfezOLWHWkh3MWrKDELuNoV0SOK1HW0Z0TUAj44uIiASGeiViO3bsYMKECd7no0ePxjAMMjIy6NDB9/f7TJ06lauvvprBgwczZMgQnnjiCYqKiryjKF511VW0b9+e6dOnA/Dwww8zbdo0Zs2aRXp6OllZWQBERUURFRVFYWEh999/P+effz4pKSls3ryZ22+/nW7dujFu3Difv59WSUPY199XD8Du5RAWCxe8CPZ6/tJjs8PwP0BiD5h1ESx5DtJHQO+zfROvNLt2seFccVInrjipEyXlbn7Yso+v1+/h6/V72Lm/hG837uXbjXsBiA+xs8i1htN6JnNytzbERejmMhEREX+oVyJWUVHhHeSiitPpbLYRSi6++GJycnKYNm0aWVlZDBgwgNmzZ5OcnAxYieKhLXPPPvss5eXlXHDBBdXKuffee7nvvvuw2+38/PPPvPLKK+Tm5pKamsrYsWN58MEHNZeYr3iHsFeLWJ1snAuLnrbWz/03xHVseFk9xlkJ2aKn4aObIaUfxHdqmjglYISH2Dm9VxKn90rCNE227C3i61+tERiXbN3HgXJ4Z/lu3lm+G8OAfu1jOaV7W07pnsjAjvGEOIJjhE0REZFgV69EzDRNrrnmmmpJSmlpKTfeeCORkZHebe+//37TRXiYKVOm1NoVccGCBdWeb9u27ahlhYeHM2fOnCaKTOoktrJFTInYseVnwAdWN2CG3AC9f9P4MkfdCzt+gF0/wv8mwaTZ4FCLSEtlGAZd20bRtW0U15/ShfyiUv797peUJ3Th+837WZ9dwE+78vhpVx7PfL2JMKeNE9MTGN41keFd23B8+1jsmkxaRETEJ+qViF199dVHbLviiiuaLBhpBaruEcvPAI8HfHxvYdDyuOH9G6B4H6T0hTEPNk25didcMBOeO8Xq7jjvPhj/UNOULQEvPMRO73iTCWf2xOl0kp1fWtltMYfvN+1lb2F5tW6M0WEOhnZuw/CubRjaJYHeKTHYlJiJiIg0iXolYi+99JKv4pDWIrodYIC7DIr3QlSSvyMKTN/8E7Z9CyFRcMHL4Aw75iF1FtcRJv4b3roMfphh3S/Wa8Kxj5MWJzkmjAsGdeCCQR0wTZONewpZtGkvizbv44ct+8gvrWDeumzmrbNGq40JczCkcwJDOicwtHMbjkuNwWHXjykiIiINUa9ETKTR7E6IToGCTKt7ohKxI2X+DAsrRzX8zeOQ6IP5+XqdBSfdbCViH94EN37buPvPJOgZhkGP5Gh6JEdzzcmdcXtM1mTksWjzPhZv3seybfsrE7M9zFu3B4DIEDuD0hMYkh7PoE4JDEiLIzzE7ud3IiIiEhyUiEnzi+1wMBFrf4K/owk8S58H0wN9zoV+F/nuPKPvg50/WF0U/3etNcx9fUdklBbLbjPo1yGOfh3iuPG0rlS4PazJyGfp1v0s2bqPpVutxOybDTl8syEHAIfN4LjUGAanJzC4UzyD0uNJim7C1lwREZEWRImYNL+Y9sCPGsK+JiUH4Jf3rPWTbvbtuRwhlfeLnWoN3jH/fhj7N9+eU4KWw26jf1oc/dPimHxqF9wek/VZBSzZuo9l2w+wbNt+svPLvIN/vPjdVgDSEsIZmBbPwI5xnNAxnt7tYjQyo4iICErExB80qXPtfnobKkog6ThIG+L788Wnw8QZ8PYV1rD23cdB51N8f14JenabQZ/UGPqkxjDp5M6YpsmuAyUs336AZdv3s2zbAdZnF7Bzfwk795fw8U8ZAIQ4bPRtH8vAtDgGdIyjf4c4OsSHYxgaBERERFoXJWLS/JSI1cw0YdlMa/3Ea6G5vpj2PhsGXQPLX4YvbofffaMuilJvhmGQlhBBWkIEEwda01Tkl7r4eWceK3YcYOWOA6zcmUtusYvl2w+wfPsB77EJkSH06xBLvw5x9K9cto3WXI4iItKyKRGT5qdErGbbv4e968EZCX19eG9YTUbdC2s/hj1r4cf/wkk3Ne/5pUWKCXMyonsiI7onAtZclNv2FbNyxwFW7DjAz7vyWJeZz/6ichasz2HB+hzvse1iwzi+fSzHp8bSt0MMx7eP1f1mIiLSoigRk+YXUzmps+4Rq+7HF61lvwshLKZ5zx2RAKOmwae3wtcPwfHna0RLaXKGYdA5MZLOiZH89gTrB5myCjfrMgv4eVcuP+3M46dduWzOKSQzr5TMvFLmrs32Hp8UHUrf9rEcV9klsk+7WNIS1K1RRESCkxIxaX6xadayIAsqyq1BI1q7wj2w7hNrffC1/onhhKus7omZq6yJnif+2z9xSKsS6rAzIC2OAWlxMMzaVlDqYm1GPqsz8lm9O4/Vu/PYnFPInoIy5v+6h/m/7vEeHx3qoHe7qsQsht7tYuieHEWYU8Poi4hIYFMiJs0vMhHsodakzgWZEN/J3xH538rXweOC9oOhXX//xGCzw4RH4cXRsOoNGDQJ0k70TyzSqkWHORnapQ1Du7Txbisur2BdZj6/7MpjbWY+azPz2ZBVSEFZBUu37Wfptv3efW0GpLeJpFe7aHomx9AzJZpeKdF0TIjAZlPrmYiIBAYlYtL8DANiUuHAVqt7YmtPxDxuWP6StX7idf6NJe1EGHC5lYh9/meY/JWVoIn4WUSIg0GdEhjUKcG7zeX2sDmnkHWZ+azNsJKzdZkF7C8qZ8veIrbsLeLzX7K8+4c77XRLiqJ7clTl5NVRdE+Kpn1cuBI0ERFpdkrExD9iO1iJmAbsgM1fQe4OCIuF487zdzTWRM/rPrG6KK54FQZP8ndEIjVy2m30SomhV0oM5w20tpmmSU5hGeuzClifVcC6zALWZ+ezMbuQEpebX3bn8cvuvGrlRIRYCVq3tlF0TYqia9souiVF0alNBE675jwTERHfUCIm/qGREw+qGqRjwOXgDPdvLGAN0nH6X2H2ndYkz33OtQbzEAkChmGQFB1GUnQYp3Rv693u9phs21fExuwCNmYXsmFPIRuzC9iSU0RxuZufd+Xx867qCZrDZtCpTQTdkqLo0jaKzomRdG0bSefEKBIidW+riIg0jhIx8Q8lYpbcnbBxjrXur0E6anLiZKs1bM9a+Opv8JvH/B2RSKPYbQZd21qtXeOPP7i9wu1h275iNu0pYNOeQjbnFFUuCykud7M5p4jNOUVAdrXy4iKcdE6MpEtiFJ0TI+jUJpK0uFBKK5r3fYmISPBSIib+EdfRWuZu928c/rbiVTA9kH4KJHb3dzQH2R1w5iPwym+sSaYHXe2/QUREfMhht1ndEpOiqm33eEyy8ku9SdnWvUVsySliS04hGXml5Ba7WLkjl5U7cg8vkYfXfk3nRKtrY3qbSDomRNCxTQQdEyJoExmi4fZFRARQIib+Ep9uLQ9s82cU/uV2WYkYBFZrWJXOp1jzia1+Dz7/C1w7xxpoRaQVsNkMUuPCSY0L59Qebau9VlLuZuveosrkrJBt+4rZvs96vq+onP1FLvYXHWD59gNHlBsZYqdjm0g6JoTTMSGCDvERpCWEkxYfQfv4cCJC9N+yiEhroU988Y+qRCx3hzVqYGscmW/951CYBZFJ0Os3/o6mZmMehPWzYecS+Plt6H+JvyMS8bvwELs1b1lq9YnXXS4X73/8OT0Hj2BXXhnb9haxY38x2/cVs3N/MZn5pRSVu1mXmc+6zPway06MCqF9fAQd4sPpEBdO+/hwOsSH0z7OStSiQvXftohIS6FPdPGPmPZgc4C73JpLrOqesdakapCOE64M3EmtY9vDqX+2Bu2Ye6+VMIZGHfs4kVYqzAHHpcYwoJPziNdKXW5255awo7IFbdeBEnYeKGbnfmtZUFrB3sJy9haW89PO3BrLjw130j4unNS4MNrFhle22lWth5EcE6aRHkVEgoQSMfEPm926T2z/Fqt7YmtLxPZugq0LAQMGXePvaI7upN/Dilesv9O3/4LR9/o7IpGgFOa0ewcMqUlesasyMStmd24Juw6UsDu3hN2Vy7wSl/extpYWNcOAtlGhtIu1krJ2sWGkxIaTEhtKSkw4yTGhpMSGqQukiEgA0Cex+E98+sFELH2Ev6NpXqvfs5bdRh8cuCRQOcNg3EPw1mWw+Bk44SpI6OzvqERanNgIJ7ERsRzfPrbG1wvLKth9oISMvBIyckvIzC0lI7fqeSlZeaWUuz3sKShjT0EZkFdjOQDRoQ6SY8NIjgklOTqM5NgwkqJDaRsdWjn8fyhJMaFK2EREfEifsOI/rXnAjg1fWMvjJvo1jDrrOQG6nA5bvoYv74ZL3vB3RCKtTlSog54p0fRMia7xdY/HZG9RGdl5ZWTll5KVV0JmnpWgZeaVkpVfSnZ+KcXlbgrKKijYU8imPYXHPGdSdCiJlUla2yhrmRgVUvk8jDZRIbSJCiHU0Qrv9RURaQQlYuI/rTURK8iCjJXWevex/o2lrgwDxv8Dnh0Ov34Km7+Grqf7OyoROYTNdnAy677U3KoGVstaVl4pe/JLyS4oJTu/jKy8UnIKythTUGq1qOWXUeJyU1hWQWFZBVv2Fh3z/NFhDtpGhdImKoTEymWbSGuZEFl9PT4iBLtNo7CKSOumREz8p7UmYhu/tJbtB0FUkn9jqY+kXjBkMix5DmbfCTd+b803JiJBJSrUUePcaYcyTZPCsgpvUra3sIycgoPLnKplQRn7i8qp8JgUlFZQUFq3pM0wIC7cSXxkCAkRVnKWEBnifR4X4SQ+IoT4SCdxEVbiFqGPGxFpYfSxJv4TX3mfUWtLxDbMsZY9xvs3joYYeSf8/A7k/ArLXoShv/N3RCLiA4ZhEB3mJDrMWevgIlU8HpP8Uhd7C8vYW1jOvsJy9haWsa+wjH1F1vP9ReXsK7KStgPFLkwTDhS7OFDsYgvHTtysmCDcZuex9d8RFxliJXIRVqIWG+4kLsJJbLiz2npM5XN1mxSRQKRETPwnvpO1LMqBssLWMSy6qxQ2f2Wt9xjn31gaIjwezrgbPpsKX/8djr8AItv4O6rgYpqwdwNs/QYKs6E0z3qU5B5cL82z/j3EdYTYNGt56CMyCWwaolwCg81mEBcRQlxECN3q0Mhf4fZUJmFWgnagqJx9lcv9VduKXeQWl3OguJzcIhcFZRWYJhS7DbbvL2b7/uJ6xRjmtFmJWZiVnMWEObyJmrXNUZl4OoipXEZXbo8JcxLqsGFoQnsRaWJKxMR/wmIhPAFK9lutYinH+zsi39v2HbiKIToVUvr5O5qGGXQNLJsJ2autZOw3j/k7osBXmm9NV7BpHmyaD3k7j31MAVbCVpPweEg/BTqfCp1Pg8TuVnOBSBBw2G3WQB/RoXU+xuX2sDe/mI9nz6fv4GEUuUxyi8vJK3GRW5nU5Za4yCt2VRvmP7/Uan0rdXkodZWRnV/WoJiddoOo0IPJWtV6TJiDqDAHkaFV26xl1SMy9OBrkaF2IkMc2HRvnIhUUiIm/hWf3roSsQ2zrWWPccH7xdlmhzMfhpfPguUvweBrW8ffrr6K9sLK12DjXNi5BDwVB1+zh0Cn4dCme+UPEnHWMqxqGWMlb7k7rEfezoPr+buh5ACs+9h6AES3q0zKToUuI1vfvHzS4jntNhKjQkmJgBPT43E6j5wwuyYej0lBWQX5VYlZZXKWX1JBfunBbXklLu89bvmlLu+ysLIlzuU2vV0pGysixO5NzqrWI0PsRIQ6iApxEFGZsEWE2olwWtsjQ6x9rYeD8BA7kaF2IpzWeohDLeQiwUiJmPhXfDpkrGgd94mZZnDfH3ao9BHQZyKs/dAauOPqT4I3sWxqhTmw6Cn48UVwHXLvS0JXa964bqMh/WQIiWxY+RXlkLnKamHb+g3sWAIFmfDz29YDoP1g6HOu9ajqAizSCtlshve+sbQGHO/xmBSWV1BYao0eWVB6MGGrem695qawzFW5rYKiQ5aFZRUUlbtxe0wAisvdFJe7ySloWOtcTRw2g/BDErUwp7Ue7rQTXrmMCLETdsjzcKedsEPWHTaTDXkG7XbkEhkeYr1e+Vqo00aYw67WPJEmpkRM/Ks1jZy4Zy3k7QBHmNVyEezGPmi18G37Fta8D8ef7++I/KtwD3z/pNVt01V5/0q7/jDwSug2ChK6NM15HCGQNsR6nPoXcJXAzqVWUrZ1IexaBrsrH3PvgdSBVtLc51yIVkuZSH3YbIZ1D1lY3VrgamOaJmUVHispq0zOisut6QGKy9wUlVvbD26r8CZsReVV69a+3vVyNxWVyd2ho1ZCYxI8OzPWLq311RC7zUrKnHbCKpOzqiQtzGkn1GHzLkO9Sxuhjsr1qu32qu02QhzW6yFVr1euVz0Pcdis8+o+PWmBgi4RmzFjBv/85z/Jysqif//+PP300wwZMqTW/d99913uuecetm3bRvfu3Xn44YeZMGGC93XTNLn33nt54YUXyM3N5eSTT+bZZ5+le/fuzfF2pDUlYlXdEjufBiER/o2lKcR1hBG3wYLp8Pnt1vuKTPR3VM2vIPtgAlZRYm1LHQin3dk8XVCd4dDlNOvBPdY8des+gbUfwfbvrTnrMlbCvHtxpPSjm6035PWFxCZKDEXkmAzDqExe7CRG1f3euGMpr/BQUu6m2GUlZiXlbkpc7kPWKygp91BcXkFJuZvSCjcl5R5KXG5KXQf3L3G5KS2vYM/+PJxhEZRWeCit3MflNg+ez+2h3O2pTPiaX4jd5k3SnHbDm6SFOOyEVD132HDare1Oh41Qu/Xc6TAIsdsrl5Xb7AfLqfbcbsNxyLrTYcNhM6rt47TbcFQunTZr3WE3cNpsajmUOguqROztt99m6tSpPPfccwwdOpQnnniCcePGsX79epKSjhyqadGiRVx66aVMnz6d3/zmN8yaNYuJEyeyYsUKjj/euqflkUce4amnnuKVV16hc+fO3HPPPYwbN461a9cSFhbW3G+x9WlViVhlt8SeQd4t8VAjbrO+8O9ZC5//BS58yd8RNR93BSx6EhY+AhWl1rb2g6wErPsY/3XVjE6x5nsbMtlqpVv3idWFdNt3GFk/cxw/wzNvQ4cTrVbMPhMhpp1/Ym2JTBPcLqDCui/Q5rDuCbRp+HRpelWJRyyNa7EDcLlcfP7550yYcEq1e/Aq3B7KqhKzymWZy0Nphdu7XlbhprSWpfV65XqFx3tseYW1vbzyterPrWW521MtxqpEsFGNfs3AbjO8iZvDbuCw2XDYwFVm5/EN31VutxI6h63ydbuBveoYm3HIcdZ2h/3gutNuYD/0tcNeryrLem5tt3mfH1zavc9t2G1gt9mwG9Vfq9rfZjNqfM1uGNhs4LDZsBmo1bKegioRe+yxx5g8eTKTJk0C4LnnnuOzzz5j5syZ3HnnnUfs/+STTzJ+/Hj+8pe/APDggw8yd+5cnnnmGZ577jlM0+SJJ57g7rvv5txzzwXg1VdfJTk5mQ8//JBLLrmk+d5ca1WViOVuB4+n5Q7JXbTP6j4G0D0Ih62vjSMUJv4bXhhldU88bqLVBa6l2/MrfHiTdX8jWEnNaXdaXRAD6T+hqCQ48TrrUZiDe82H7P/2RRILf8XY9SPs+hFm3wWdTrb+dj0nQGx7f0ftX+XF1j13hdnWsiDLSmhL86As3xpEpSwfygoq1wvA4wJPBQ5PBeeaHlhVU8GGlZDZQ8DutB7OcAiJtqYqCImEkKjK9WgIja4cxCXuyAFdwuOsfQPpWpMWy1GZNESGNv9XRo/H9CZf5RWHPNyHLSs8uA7b5nKblFdYLXrllclkhfvQ/Uxchzx3HfK8ovKYw59XHLJP1f5V3UMP5faYuD1Wd9TqDPaV1W/qhWBjMzgsSTOwVSZw1hLv9kP3ObiN6sdVJnqHllF1DsM4mBximmRl2Qhdt4fx/YLn/7GgScTKy8tZvnw5d911l3ebzWZj9OjRLF68uMZjFi9ezNSpU6ttGzduHB9++CEAW7duJSsri9GjR3tfj42NZejQoSxevFiJWHOIaW/9Yuwut770tNQvgRu/BExI6dvy3mPqQKtl7NtH4bM/QacRLXduMY8bFj8DX/0d3GXWF+MzH4F+Fwf+l+KotnhOuIZFWUlMOGUgzg2fW8nzziWw/Tvr8fmfrWkVep5pDSjTbkDL+3GkvMhqga967N9qLXN3WElXWV6Diz76FWBa14y7iX7KtzkOScziDyZt4XEQGnNw9M3QmMpkrnI9JPLgwx4SeNetaVotiRWl1sA0FaWVjzIoKyKhcAPG1ijAXVmf5dZ+7rLKZeXDU2G1THpc1tJtJct4KsB0Wz/8mW7r33TVEtM6/6GxcOiXbONgfRnGweeGDQx75dJmtX4axsFtNkflNrv178mwH9xmc1Tfx+Y4uJ933X7I0nZweeg5DVtl2bbqcVEVn1E9/kOv1sptRkUF8UWbMHYvA7vj4HuvqodjLj2VD6yld5t5yHPzyNeg1v1smISZJmE1nc8b22HXz8En1rfco37TPbIeqr98WH3V8NzEwG2aeDzgNsFtmrir1j3gMU3cptWNdP36DXTu1h0MGx6PeXB/00rePObBJM5Tebzb9OA2qSzfrLafx7Re93ioLM/adnDdxOOp2uaxlt4yrKVZ9RpU7nvwONMEj+nxlmlibT9Ye9Xr31uDJhjuw1+rZd8mlA4c2GYHJWJNb+/evbjdbpKTk6ttT05O5tdff63xmKysrBr3z8rK8r5eta22fWpSVlZGWdnB/0zz8/MBq1nf5Wr80LaNUXV+f8dRH47YNIwDW6nYuwkzog6zgfpRQ+vXvv4LbIC761g8QfS3qbPht+H49VOMnF/xfPYn3Oe90KBiAvr63bcR+yd/wLZ7GQCerqNxT3jc6tZX4Z/7JerLW79hiTDoOuuRtwvbug8xfv0MY/cyjKyfIetnWPgwZlQyZrcxeLqNxew4zPrCHwzcLsjdhrFvk/fB/s0Y+7dgFO055uGmIxyiUzCjkq1lZLI3sTFDKxOaUKvVygyNBnso2By43B4WfvMdp50xCmdImPWF2eM+LCGoTBIqyjAqSqzJ7MsLobwIo7xqvRBK8zHK8qAkD8ryMEpzrRa40lyMqjKK91mPBjJtDishc0ZYD0copj3Eej+OELCFWEt7SPUv/N4v9pVL0wQ8B5Ma01OZ6Hgq33cFeMq9CZHhrYtDkqeK0spl2RFf2Ko4gVMANjb4LctROIBTAWqZvlBqZlD3L9PpAMf+CAoslTk8QfKb3A5PJC7XGH+HUefvMUGTiAWS6dOnc//99x+x/csvvyQiIjAGYZg7d66/Q6izYRWRJAE/L/yEnW1y/R1OndSnfg1PBWeu/xIb8H1OFAc+/9x3gflRXMIlnJLzALa1H7CspAOZcSc2uKyAun5ND11yvqRPxrvYTBcuWzirO1zOjuhT4LuVwEp/R1hvR9ZvF0j6AyHx+STn/0RK3kqSClbjKMzGWPU6tlWvA1AQ2o79kd04ENmN/ZHdKQhLrfzV3Q9MD2GuA0SVZRFVlk1kaSZRZdlElWURUZaDDXeth5bbIykOaUtRaBJFoUkUh7SlOKQtJSEJlDrjqLCFV/91vAIorHx4FVU+DvvRzhnD3G9/rOebcQCxlY9DhFQ+DmWa2M1ynBVFON3FON1FhFQUHlx3F+Fwl+B0F+F0l+B0F+NwF3vX7Z5y7Kb1BcHwVFhdLksPtgIGWPsYbsOBx3DgsTlxG048Nqf13HDisTmsbYYDj61ym+HAY9gxvUs7HsOOx3BgGjZMw46JzVrHhmkY3vWqFqNDf+8HMDEOSQ5Na2tlq4y1hwfDNDGwWnWMQ54bpufgstq6++B+3vXD9qFyvxrKArPyuXnYc09l5GZlLAdbkQ7+bQ9rNap8p4e/7+p1QLXWINNbV1WvV34OeLdVbjdsB/ep/KwwsYFxyDEYmJUtdt5jjYO1e/B8VfEYh0TNIX+zprh6a6gF87C//SHXQtWqt54PifjgU/OQfY48l3F4q95hcRyuth8qjnVc1dHV9z5andX+mmkceY3UHM3hr9Xjb9TAP2d2bikrA+B7VnFx3bqgBk0ilpiYiN1uJzs7u9r27OxsUlJSajwmJSXlqPtXLbOzs2nXrl21fQYMGFBrLHfddVe1Lo/5+fmkpaUxduxYYmJi6vW+mprL5WLu3LmMGTOmzhNe+pvti69gxWr6p8XQd+SEYx/gRw2pX2PbNzh+KsGMbMuwC2723xfXZmB+nQuLnuDEPW9RMfEPEJFQr+MD7votysH+4e+w7f4GAE+X0+GsJzg+pj3BOIV13erX6pJtVpRRsf17jI1zsG39GmP/FqLLMokuy6TT/m+tfUKjMVNPwEzoBnEdMeM6YcZ1hLhOVgtSY7hdUJiFkb8b8ndXLjMw8ndj5G6H/VutFqVamM4ISOiK2aYrZkI3zDbdrOfxnTHC44gEGjiTW60C7vqtgQfwuF3WFAvlxeCqbI1zFVt1XlHZ3c9d5n1uuF3Wkebhj8quZIe3ltnsWK1lNky74+B9cTZn9aUjFOwhmFUtcI6wyn0PeV75eWkD3C4X8wO8foNZMFy/wUz161uH1u+AAKjfqt5yxxI0iVhISAiDBg1i/vz5TJw4EbD6tM6fP58pU6bUeMywYcOYP38+t956q3fb3LlzGTZsGACdO3cmJSWF+fPnexOv/Px8lixZwk033VRrLKGhoYSGHjn8rNPpDJh/XIEUyzG1sYbRtufvwB4kMderfjfPB8DoPg5nSNMNWxyQzvgrbJyNkfMrzrl/hQtebFAxAXH97vgB3r3GunfRGQnjH8J2wtXYAu2emgaoU/06ndBrnPUAKNprDe6xc6m13L0co6wAY+tCa/6yw4XGWlMchMVaX7i9j7DKL+Ch1pf9skJrwIvywspBMCqfF+/jmL/u2hzWgD8JXaFNN+uzpE03aNMdIyYVDMMvLTwBcf0ejdMJYYHRe6MhAr5+g5zq17dUv74VKPVb1xiCJhEDmDp1KldffTWDBw9myJAhPPHEExQVFXlHUbzqqqto374906dPB+CPf/wjp512Gv/6178466yzeOutt1i2bBn/+c9/AGuIzVtvvZW//e1vdO/e3Tt8fWpqqjfZk2bQkoewN03Y8IW13qMFjZZYm6pRFP87Glb/zxqJr/fZ/o6qfkwTFs+AudOs+10Se8JFr0JSL39H5l+RidYgHj3PtJ67K2DPGti9whr19MD2g8vivdagF9m/NO6c9hCISYWYDtYgNzHtredxnaBNV2tpD6r/xkRERLyC6n+wiy++mJycHKZNm0ZWVhYDBgxg9uzZ3sE2duzYge2QEb6GDx/OrFmzuPvuu/nrX/9K9+7d+fDDD71ziAHcfvvtFBUVccMNN5Cbm8uIESOYPXu25hBrTi05Edu3CfZvsb5Qdj3d39E0j/aD4OQ/wnePw6dTraHR69lF0W9K8+Cjm625t8CaZ+vsp6whxaU6uwPa9bcehysvskYhzN0JriKru1vVyHfeZZmVuIdEVQ58UbmsGs49si1EJLa8URtFREQqBVUiBjBlypRauyIuWLDgiG0XXnghF154Ya3lGYbBAw88wAMPPNBUIUp9VSViRTlWt6SW9KV3w2xrmT7C+pLZWoy8C9Z/ATm/wnvXwaVvW/d8BLKsX+Cdq6zE2eaE8dPhxOsDb3jvYBASCUm9rYeIiIjUSD81iv+FxR4cFjt3u39jaWob5ljLHuP9G0dzc4TCec9bQ2Jv/go+nmINZx2ITBNWvm51p9y/BWLT4No5MGSykjARERHxGSViEhhaYvfEkgOwfZG13n2sf2Pxh9QB1r1Vhh1+fhvm3uPviI5UVggf3Gh1R6wohW5j4HffQIdB/o5MREREWjglYhIYWmIitmm+NdhD216Q0Nnf0fhH9zFw7gxrffEz8P1T/o3nUNlr4IXT4ee3rCGyz7gbLnsneO5nExERkaAWdPeISQvVEhOxzV9Zy9bYGnaoAZda9//Nvcd6RLa1tvmLacKKV+CLO6xWsOh2cP6LkH6y/2ISERGRVkeJmASGlpaImSZstSYBpstIv4YSEE6+BQqzrVaxj26GiDbQww8JalkBfHKrNbQ+QLfR1r1skYnNH4uIiIi0auqaKIGhpSViB7ZC3k5r9L2OJ/k7msAw5kHod7HVXfOdq2Dnj817/syf4PnTrCTMsMPo++Cyd5WEiYiIiF8oEZPAEF95D9WB7YE7ul59bFloLTucaA3lLdZ8UOfOsFqhKkpg1oXWfVq+VrwfPv8L/Gck7N9sTQo86XMYcZvmqBIRERG/0bcQCQwx7cHmAHcZFGT6O5rG83ZLPM2/cQQauxMufMWa9LnkAPzndFjwD3CVNv25PG5YNhOeHgRL/wOmB/pMhBu/UyuliIiI+J0SMQkMdoc1fxMEf/fEQ+8P63yqf2MJRKFRVpfArqOsxHvBdHh2OGz+uunOseMHqwXs09ugZD+07Q1XfQwXvaJREUVERCQgKBGTwNFS7hPbsw6K94IjHNoP9nc0gSmyDVzxHlzwEkSlWF0GX5uI/cMbCHXlNrzcvF3w/g0wcxxk/QyhsTD+YasVTK2TIiIiEkA0aqIEjpaSiFW1hnUaBo4Q/8YSyAwDjv+tdc/Y13+Hpf/BtuZ9RtlnY2tfAkMng81+9DI8bshYCRu/tB4ZK6sKhxOuhDOmQVRbn78VERERkfpSIiaBo6UlYuqWWDdhMXDmw9D/Ejyf3IozcxXMuQPm32t1V43vBHGdDi7jOsL+LVbitWkeFO+rXl6nk2Hsg9Z9aCIiIiIBSomYBI6WkIh53LDtO2tdiVj9pA7Efc0cVr/2F/ru/QijNA/2bbQeRxMaA13PsCbO7jYaopObJ14RERGRRlAiJoGjJSRimT9BWZ51b1K7Af6OJvjY7GxtO5reVzyMszgbcrdbUxocuszdARGJ0H2MlXylDbFGYxQREREJIkrEJHBUJWJFe6C8KDjn39paOX9Y+ohj398ktbM7IaGz9RARERFpgTRqogSO8DgIi7PWD2z3ZyQNp/vDRERERKQOlIhJYAnm7okV5bB9sbWuRExEREREjkKJmASWqq5oB7b6N46G2L0MKkqs+5eSevs7GhEREREJYErEJLAEc4vYod0SDcO/sYiIiIhIQFMiJoGlpSRiIiIiIiJHoURMAkuwJmLlxbBzqbWuRExEREREjkGJmAQWbyK2HTwev4ZSLzt/AI8LYjpAQhd/RyMiIiIiAU6JmASWmA5g2MFdBoVZ/o6m7rZUzh/W5TTdHyYiIiIix6RETAKL3QFxadZ6MHVP1P1hIiIiIlIPSsQk8ATbfWIluZC5ylpPP8WfkYiIiIhIkFAiJoEn2BKx7YvA9ECbbhDb3t/RiIiIiEgQUCImgSfYEjF1SxQRERGRelIiJoGnatTBvRv8G0ddKRETERERkXpSIiaBJ+k4a7lnHbgr/BvLsRTmwJ411rruDxMRERGROlIiJoEnoTM4I6CiFPZv9nc0R7etsjUsuS9EJvo3FhEREREJGkrEJPDY7JDUx1rP+sW/sRyLuiWKiIiISAMETSK2f/9+Lr/8cmJiYoiLi+O6666jsLDwqPv/4Q9/oGfPnoSHh9OxY0duueUW8vLyqu1nGMYRj7feesvXb0eOJeV4a5m9xr9xHEvVRM5KxERERESkHhz+DqCuLr/8cjIzM5k7dy4ul4tJkyZxww03MGvWrBr3z8jIICMjg0cffZQ+ffqwfft2brzxRjIyMvjf//5Xbd+XXnqJ8ePHe5/HxcX58q1IXSRXJWKr/RvH0RzYDge2gmGHTsP9HY2IiIiIBJGgSMTWrVvH7Nmz+fHHHxk8eDAATz/9NBMmTODRRx8lNTX1iGOOP/543nvvPe/zrl278ve//50rrriCiooKHI6Dbz0uLo6UlBTfvxGpu5S+1jIrgBOxrZWtYe0HQViMf2MRERERkaASFInY4sWLiYuL8yZhAKNHj8Zms7FkyRLOO++8OpWTl5dHTExMtSQM4Oabb+b666+nS5cu3HjjjUyaNAnDMGotp6ysjLKyMu/z/Px8AFwuFy6Xqz5vrclVnd/fcTRaQg+cAAUZuPKyISLB3xEB1evXvvlrbIA7/RQ8wV7fAaLFXL8BSvXrW6pf31L9+pbq17dUv74VaPVb1ziCIhHLysoiKSmp2jaHw0FCQgJZWVl1KmPv3r08+OCD3HDDDdW2P/DAA5xxxhlERETw5Zdf8vvf/57CwkJuueWWWsuaPn06999//xHbv/zySyIiIuoUj6/NnTvX3yE02uiQJCLL97D0k5nsje7j73Cqmfvll4xbP58wYHFWKPs+/9zfIbUoLeH6DWSqX99S/fqW6te3VL++pfr1rUCp3+Li4jrt59dE7M477+Thhx8+6j7r1q1r9Hny8/M566yz6NOnD/fdd1+11+655x7v+sCBAykqKuKf//znUROxu+66i6lTp1YrPy0tjbFjxxIT498uai6Xi7lz5zJmzBicTqdfY2kse/HbsP4zTkqPxDN0gr/DAQ7W79iBHQlblYfpCGfo+TeDI9TfobUILen6DUSqX99S/fqW6te3VL++pfr1rUCr36recsfi10TsT3/6E9dcc81R9+nSpQspKSns2bOn2vaKigr2799/zHu7CgoKGD9+PNHR0XzwwQfH/OMMHTqUBx98kLKyMkJDa/5yHRoaWuNrTqczIP74EFixNFi7frD+M+w567AH2HsJ2fU9AEan4TjDo/wcTcvTIq7fAKb69S3Vr2+pfn1L9etbql/fCpT6rWsMfk3E2rZtS9u2bY+537Bhw8jNzWX58uUMGjQIgK+++gqPx8PQoUNrPS4/P59x48YRGhrKxx9/TFhY2DHPtWrVKuLj42tNwqQZeUdODLy5xIyq+cO6nObfQEREREQkKAXFPWK9e/dm/PjxTJ48meeeew6Xy8WUKVO45JJLvCMm7t69m1GjRvHqq68yZMgQ8vPzGTt2LMXFxbz++uvk5+d7mwnbtm2L3W7nk08+ITs7m5NOOomwsDDmzp3LQw89xJ///Gd/vl2pUjWXWM56cLvA7v9fOAAM042xY5H1pLMSMRERERGpv6BIxADeeOMNpkyZwqhRo7DZbJx//vk89dRT3tddLhfr16/33hy3YsUKlixZAkC3bt2qlbV161bS09NxOp3MmDGD2267DdM06datG4899hiTJ09uvjcmtYvrBKExUJYPezdA8nH+jgiAuOItGOWFEB4PKf38HY6IiIiIBKGgScQSEhJqnbwZID09HdM0vc9HjhxZ7XlNxo8fX20iZwkwhmElXzsWW/OJBUgi1rZgrbXS+VSw2fwbjIiIiIgEJX2LlMAWgPeJtS1YY62oW6KIiIiINJASMQlsVfeJZa32bxxVXMXEF22y1ruM9GsoIiIiIhK8lIhJYEvuay2zAyMRM3YuwW5WYMa0h4Qu/g5HRERERIKUEjEJbEm9wbBBUQ4UZPs7Goxt1rD1Zvpp1j1sIiIiIiINoERMAltIBCR0tdYD4D4x29aFAHg6n+LnSEREREQkmCkRk8AXKPeJFe+HLCsZNDspERMRERGRhlMiJoHPO3KinxOxbd9iYJIf1h6iU/wbi4iIiIgENSViEvhSKgfs8HeL2BarW2JOdGDMZyYiIiIiwUuJmAS+qhaxvRvAVeq/OLYssMKI7uO/GERERESkRVAiJoEvJhXC48F0Q86v/okhbxfs34xp2Ngb1cs/MYiIiIhIi6FETAKfYfj/PrHKbolmu4FU2CP8E4OIiIiItBhKxCQ4+Ps+scph683Op/nn/CIiIiLSoigRk+DgzxYx0/TeH2ama9h6EREREWk8JWISHLxzif1iJUbNKWc9FGaDIwyzw4nNe24RERERaZGUiElwaNsLbA4ozYX83c177spuiXQcBo6w5j23iIiIiLRISsQkODhCIbGHtd7c94lVdkuki+4PExEREZGmoURMgof3PrFfmu+cZYWw+Strvdvo5juviIiIiLRoSsQkeHjvE2vGFrGNX0JFKSR0OZgIioiIiIg0khIxCR7+GDlx7UfWss+51nxmIiIiIiJNQImYBI+qucT2bYbyIt+fr7zYahEDKxETEREREWkiSsQkeEQlQWQSYMKedb4/36Z54CqGuI7QboDvzyciIiIirYYSMQkuh84n5mvqligiIiIiPqJETIJLc90n5iqFDbOt9T4TfXsuEREREWl1lIhJcKm6T8zXLWKbv4LyQojpAO0H+fZcIiIiItLqKBGT4NJhsLXctQyK9/vuPN5uieeoW6KIiIiINDklYhJcErpYrWKmG9Z94ptzVJTB+i+sdY2WKCIiIiI+oERMgs9x51nLNR/4pvwtC6EsD6JSoMMQ35xDRERERFo1JWISfKoSsa3fQNHepi//0G6JNv0TEREREZGmp2+ZEnwSuljzepluWPdx05btdsGvn1rr6pYoIiIiIj6iREyCk6+6J279BkpzIbItdBzWtGWLiIiIiFQKmkRs//79XH755cTExBAXF8d1111HYWHhUY8ZOXIkhmFUe9x4443V9tmxYwdnnXUWERERJCUl8Ze//IWKigpfvhVpCsdNtJbbvoPCPU1XblW3xN5ng83edOWKiIiIiBwiaBKxyy+/nDVr1jB37lw+/fRTvvnmG2644YZjHjd58mQyMzO9j0ceecT7mtvt5qyzzqK8vJxFixbxyiuv8PLLLzNt2jRfvhVpCvHpkHoCmJ6DyVNjuSvULVFEREREmkVQJGLr1q1j9uzZ/Pe//2Xo0KGMGDGCp59+mrfeeouMjIyjHhsREUFKSor3ERMT433tyy+/ZO3atbz++usMGDCAM888kwcffJAZM2ZQXl7u67cljXX8b63lmg+bprzt30PxPghPgE4jmqZMEREREZEaOPwdQF0sXryYuLg4Bg8e7N02evRobDYbS5Ys4bzzzqv12DfeeIPXX3+dlJQUzj77bO655x4iIiK85fbt25fk5GTv/uPGjeOmm25izZo1DBw4sMYyy8rKKCsr8z7Pz88HwOVy4XK5GvVeG6vq/P6Oo1n0OAvnl3djbv+eiv07ITqlUcXZVn+AHfD0nIDbY4LnyDpsVfXrB6pf31L9+pbq17dUv76l+vUt1a9vBVr91jWOoEjEsrKySEpKqrbN4XCQkJBAVlZWrcdddtlldOrUidTUVH7++WfuuOMO1q9fz/vvv+8t99AkDPA+P1q506dP5/777z9i+5dffulN8vxt7ty5/g6hWZwS0ZWE4s2se/8fbG07tuEFmR7GrX4fO/BDQQo5n39+1N1bS/36i+rXt1S/vqX69S3Vr2+pfn1L9etbgVK/xcXFddrPr4nYnXfeycMPP3zUfdatW9fg8g+9h6xv3760a9eOUaNGsXnzZrp27drgcu+66y6mTp3qfZ6fn09aWhpjx46t1vXRH1wuF3PnzmXMmDE4nU6/xtIcbG22w7x7ON7YRO8JTzS4HGPHIhyr8jDD4jjxwj+Bvea6a23129xUv76l+vUt1a9vqX59S/XrW6pf3wq0+q3qLXcsfk3E/vSnP3HNNdccdZ8uXbqQkpLCnj3VR8arqKhg//79pKTUvTva0KFDAdi0aRNdu3YlJSWFpUuXVtsnOzsb4KjlhoaGEhoaesR2p9MZEH98CKxYfKrv+TDvHmw7f8BWkgMxqQ0rZ/1nABi9zsIZduxWzVZTv36i+vUt1a9vqX59S/XrW6pf31L9+lag1G9dY/BrIta2bVvatm17zP2GDRtGbm4uy5cvZ9CgQQB89dVXeDweb3JVF6tWrQKgXbt23nL//ve/s2fPHm/Xx7lz5xITE0OfPn3q+W7EL2LbQ9pJsPMHa/TEk26qfxlFe+GXd6z13uc0bXwiIiIiIjUIilETe/fuzfjx45k8eTJLly7l+++/Z8qUKVxyySWkplotILt376ZXr17eFq7Nmzfz4IMPsnz5crZt28bHH3/MVVddxamnnkq/fv0AGDt2LH369OHKK6/kp59+Ys6cOdx9993cfPPNNbZ4SYCqmtx59fsNO372XVByAJKPh26jmy4uEREREZFaBEUiBtboh7169WLUqFFMmDCBESNG8J///Mf7usvlYv369d6b40JCQpg3bx5jx46lV69e/OlPf+L888/nk08+8R5jt9v59NNPsdvtDBs2jCuuuIKrrrqKBx54oNnfnzRCn3MBA3Ythdyd9Tt207zK1jADznkK7EExfo2IiIiIBLmg+daZkJDArFmzan09PT0d0zS9z9PS0li4cOExy+3UqROfH2OEPAlwMe2g03BrHrC1H8HwKXU7rrwIPr3NWh96I7Qf5LsYRUREREQOETQtYiJHVdU9cU09uicumA65OyA2Dc642zdxiYiIiIjUQImYtAy9zwEM2L0cDmw/9v4Zq2DxDGv9rMcgNMqX0YmIiIiIVKNETFqG6GRIH2Gtr/3w6Pu6K+CTW8D0wHG/hR6NmAhaRERERKQBlIhJy1HVPXH5y5D5c+37LXkWMn+CsDg48+gTiouIiIiI+IISMWk5+pxrJVf7t8Dzp8DbV0L22ur7HNgGXz9krY/9G0QlNXeUIiIiIiJKxKQFiUyE6+fD8ecDBqz7GJ4dDv+7FnI2gGnCp1PBVQzpp8DAK/wdsYiIiIi0UkrEpGVJ7AYXzISbFlXOL2bC6vfg30PhlbNh83ywh8LZT4Jh+DtaEREREWmllIhJy5TcBy56FW78DnqeZQ3Mse1b67XTboc2Xf0bn4iIiIi0akEzobNIg6T0hUtnQcZK+O5xqzVs+C3+jkpEREREWjklYtI6pA60WshERERERAKAuiaKiIiIiIg0MyViIiIiIiIizUyJmIiIiIiISDNTIiYiIiIiItLMlIiJiIiIiIg0MyViIiIiIiIizUyJmIiIiIiISDNTIiYiIiIiItLMlIiJiIiIiIg0MyViIiIiIiIizUyJmIiIiIiISDNTIiYiIiIiItLMlIiJiIiIiIg0M4e/A2gJTNMEID8/38+RgMvlori4mPz8fJxOp7/DaXFUv76l+vUt1a9vqX59S/XrW6pf31L9+lag1W9VTlCVI9RGiVgTKCgoACAtLc3PkYiIiIiISCAoKCggNja21tcN81ipmhyTx+MhIyOD6OhoDMPwayz5+fmkpaWxc+dOYmJi/BpLS6T69S3Vr2+pfn1L9etbql/fUv36lurXtwKtfk3TpKCggNTUVGy22u8EU4tYE7DZbHTo0MHfYVQTExMTEBdiS6X69S3Vr2+pfn1L9etbql/fUv36lurXtwKpfo/WElZFg3WIiIiIiIg0MyViIiIiIiIizUyJWAsTGhrKvffeS2hoqL9DaZFUv76l+vUt1a9vqX59S/XrW6pf31L9+law1q8G6xAREREREWlmahETERERERFpZkrEREREREREmpkSMRERERERkWamRExERERERKSZKRELQjNmzCA9PZ2wsDCGDh3K0qVLj7r/u+++S69evQgLC6Nv3758/vnnzRRpcJk+fTonnngi0dHRJCUlMXHiRNavX3/UY15++WUMw6j2CAsLa6aIg8t99913RF316tXrqMfo2q279PT0I+rXMAxuvvnmGvfXtXt033zzDWeffTapqakYhsGHH35Y7XXTNJk2bRrt2rUjPDyc0aNHs3HjxmOWW9/P75bqaPXrcrm444476Nu3L5GRkaSmpnLVVVeRkZFx1DIb8hnTUh3r+r3mmmuOqKvx48cfs1xdvwcdq45r+jw2DIN//vOftZapa9hSl+9jpaWl3HzzzbRp04aoqCjOP/98srOzj1puQz+3fUmJWJB5++23mTp1Kvfeey8rVqygf//+jBs3jj179tS4/6JFi7j00ku57rrrWLlyJRMnTmTixImsXr26mSMPfAsXLuTmm2/mhx9+YO7cubhcLsaOHUtRUdFRj4uJiSEzM9P72L59ezNFHHyOO+64anX13Xff1bqvrt36+fHHH6vV7dy5cwG48MILaz1G127tioqK6N+/PzNmzKjx9UceeYSnnnqK5557jiVLlhAZGcm4ceMoLS2ttcz6fn63ZEer3+LiYlasWME999zDihUreP/991m/fj3nnHPOMcutz2dMS3as6xdg/Pjx1erqzTffPGqZun6rO1YdH1q3mZmZzJw5E8MwOP/8849arq7hun0fu+222/jkk0949913WbhwIRkZGfz2t789arkN+dz2OVOCypAhQ8ybb77Z+9ztdpupqanm9OnTa9z/oosuMs8666xq24YOHWr+7ne/82mcLcGePXtMwFy4cGGt+7z00ktmbGxs8wUVxO69916zf//+dd5f127j/PGPfzS7du1qejyeGl/XtVt3gPnBBx94n3s8HjMlJcX85z//6d2Wm5trhoaGmm+++Wat5dT387u1OLx+a7J06VITMLdv317rPvX9jGktaqrfq6++2jz33HPrVY6u39rV5Ro+99xzzTPOOOOo++gartnh38dyc3NNp9Npvvvuu9591q1bZwLm4sWLayyjoZ/bvqYWsSBSXl7O8uXLGT16tHebzWZj9OjRLF68uMZjFi9eXG1/gHHjxtW6vxyUl5cHQEJCwlH3KywspFOnTqSlpXHuueeyZs2a5ggvKG3cuJHU1FS6dOnC5Zdfzo4dO2rdV9duw5WXl/P6669z7bXXYhhGrfvp2m2YrVu3kpWVVe36jI2NZejQobVenw35/JaD8vLyMAyDuLi4o+5Xn8+Y1m7BggUkJSXRs2dPbrrpJvbt21frvrp+Gyc7O5vPPvuM66677pj76ho+0uHfx5YvX47L5ap2Pfbq1YuOHTvWej025HO7OSgRCyJ79+7F7XaTnJxcbXtycjJZWVk1HpOVlVWv/cXi8Xi49dZbOfnkkzn++ONr3a9nz57MnDmTjz76iNdffx2Px8Pw4cPZtWtXM0YbHIYOHcrLL7/M7NmzefbZZ9m6dSunnHIKBQUFNe6va7fhPvzwQ3Jzc7nmmmtq3UfXbsNVXYP1uT4b8vktltLSUu644w4uvfRSYmJiat2vvp8xrdn48eN59dVXmT9/Pg8//DALFy7kzDPPxO1217i/rt/GeeWVV4iOjj5m1zldw0eq6ftYVlYWISEhR/wwc6zvw1X71PWY5uDw25lFAtjNN9/M6tWrj9k3e9iwYQwbNsz7fPjw4fTu3Zvnn3+eBx980NdhBpUzzzzTu96vXz+GDh1Kp06deOedd+r0K6HU3YsvvsiZZ55Jampqrfvo2pVg4HK5uOiiizBNk2efffao++ozpu4uueQS73rfvn3p168fXbt2ZcGCBYwaNcqPkbVMM2fO5PLLLz/mgEi6ho9U1+9jwUotYkEkMTERu91+xKgw2dnZpKSk1HhMSkpKvfYXmDJlCp9++ilff/01HTp0qNexTqeTgQMHsmnTJh9F13LExcXRo0ePWutK127DbN++nXnz5nH99dfX6zhdu3VXdQ3W5/psyOd3a1eVhG3fvp25c+cetTWsJsf6jJGDunTpQmJiYq11peu34b799lvWr19f789k0DVc2/exlJQUysvLyc3Nrbb/sb4PV+1T12OagxKxIBISEsKgQYOYP3++d5vH42H+/PnVftk+1LBhw6rtDzB37txa92/NTNNkypQpfPDBB3z11Vd07ty53mW43W5++eUX2rVr54MIW5bCwkI2b95ca13p2m2Yl156iaSkJM4666x6Hadrt+46d+5MSkpKteszPz+fJUuW1Hp9NuTzuzWrSsI2btzIvHnzaNOmTb3LONZnjBy0a9cu9u3bV2td6fptuBdffJFBgwbRv3//eh/bWq/hY30fGzRoEE6ns9r1uH79enbs2FHr9diQz+1m4bdhQqRB3nrrLTM0NNR8+eWXzbVr15o33HCDGRcXZ2ZlZZmmaZpXXnmleeedd3r3//77702Hw2E++uij5rp168x7773XdDqd5i+//OKvtxCwbrrpJjM2NtZcsGCBmZmZ6X0UFxd79zm8fu+//35zzpw55ubNm83ly5ebl1xyiRkWFmauWbPGH28hoP3pT38yFyxYYG7dutX8/vvvzdGjR5uJiYnmnj17TNPUtdsU3G632bFjR/OOO+444jVdu/VTUFBgrly50ly5cqUJmI899pi5cuVK76h9//jHP8y4uDjzo48+Mn/++Wfz3HPPNTt37myWlJR4yzjjjDPMp59+2vv8WJ/frcnR6re8vNw855xzzA4dOpirVq2q9nlcVlbmLePw+j3WZ0xrcrT6LSgoMP/85z+bixcvNrdu3WrOmzfPPOGEE8zu3bubpaWl3jJ0/R7dsT4jTNM08/LyzIiICPPZZ5+tsQxdwzWry/exG2+80ezYsaP51VdfmcuWLTOHDRtmDhs2rFo5PXv2NN9//33v87p8bjc3JWJB6OmnnzY7duxohoSEmEOGDDF/+OEH72unnXaaefXVV1fb/5133jF79OhhhoSEmMcdd5z52WefNXPEwQGo8fHSSy959zm8fm+99Vbv3yI5OdmcMGGCuWLFiuYPPghcfPHFZrt27cyQkBCzffv25sUXX2xu2rTJ+7qu3cabM2eOCZjr168/4jVdu/Xz9ddf1/h5UFWHHo/HvOeee8zk5GQzNDTUHDVq1BH13qlTJ/Pee++ttu1on9+tydHqd+vWrbV+Hn/99dfeMg6v32N9xrQmR6vf4uJic+zYsWbbtm1Np9NpdurUyZw8efIRCZWu36M71meEaZrm888/b4aHh5u5ubk1lqFruGZ1+T5WUlJi/v73vzfj4+PNiIgI87zzzjMzMzOPKOfQY+ryud3cDNM0Td+0tYmIiIiIiEhNdI+YiIiIiIhIM1MiJiIiIiIi0syUiImIiIiIiDQzJWIiIiIiIiLNTImYiIiIiIhIM1MiJiIiIiIi0syUiImIiIiIiDQzJWIiIiJ1dM011zBx4kR/hyEiIi2Aw98BiIiIBALDMI76+r333suTTz6JaZrNFJGIiLRkSsRERESAzMxM7/rbb7/NtGnTWL9+vXdbVFQUUVFR/ghNRERaIHVNFBERAVJSUryP2NhYDMOoti0qKuqIrokjR47kD3/4A7feeivx8fEkJyfzwgsvUFRUxKRJk4iOjqZbt2588cUX1c61evVqzjzzTKKiokhOTubKK69k7969zfyORUTEn5SIiYiINMIrr7xCYmIiS5cu5Q9/+AM33XQTF154IcOHD2fFihWMHTuWK6+8kuLiYgByc3M544wzGDhwIMuWLWP27NlkZ2dz0UUX+fmdiIhIc1IiJiIi0gj9+/fn7rvvpnv37tx1112EhYWRmJjI5MmT6d69O9OmTWPfvn38/PPPADzzzDMMHDiQhx56iF69ejFw4EBmzpzJ119/zYYNG/z8bkREpLnoHjEREZFG6Nevn3fdbrfTpk0b+vbt692WnJwMwJ49ewD46aef+Prrr2u832zz5s306NHDxxGLiEggUCImIiLSCE6ns9pzwzCqbasajdHj8QBQWFjI2WefzcMPP3xEWe3atfNhpCIiEkiUiImIiDSjE044gffee4/09HQcDv03LCLSWukeMRERkWZ08803s3//fi699FJ+/PFHNm/ezJw5c5g0aRJut9vf4YmISDNRIiYiItKMUlNT+f7773G73YwdO5a+ffty6623EhcXh82m/5ZFRFoLwzRN099BiIiIiIiItCb66U1ERERERKSZKRETERERERFpZkrEREREREREmpkSMRERERERkWamRExERERERKSZKRETERERERFpZkrEREREREREmpkSMRERERERkWamRExERERERKSZKRETERERERFpZkrEREREREREmpkSMRERERERkWamRExERERERKSZKRETERERERFpZkrEREREREREmpkSMRERERERkWamRExERERERKSZKRETERERERFpZkrEREREREREmpkSMRERERERkWamRExERP6/vTuPj6q6/z/+urNk30lIwr7KouwIYtWqrIJb9auitCJV+GpFa7GL+HO3lVqpO5W2iuhXEbVVW5cCAUWtIiiLCyIKssiSsISQPZnM3N8fNxkSspPczJL38/GYx71z59x7P/OZMc6Hc+85IiIi0sZUiImIiIiIiLQxFWIiIiIiIiJtTIWYiIiIiIhIG1MhJiIiIiIi0sZUiImIiIiIiLQxFWIiIiIiIiJtTIWYiIiIiIhIG3MFOoBw4PP52LdvH/Hx8RiGEehwREREREQkQEzTpKCggE6dOuFw1N/vpUKsFezbt4+uXbsGOgwREREREQkSP/zwA126dKn3dRVirSA+Ph6wkp2QkBDQWDweDytWrGDChAm43e6AxhKOlF97Kb/2Un7tpfzaS/m1l/JrL+XXXsGW3/z8fLp27eqvEeqjQqwVVF2OmJCQEBSFWExMDAkJCUHxRQw3yq+9lF97Kb/2Un7tpfzaS/m1l/Jrr2DNb2O3LGmwDhERERERkTamQkxERERERKSNqRATERERERFpY7pHTEREREREmszr9eLxeAIdhp/H48HlclFaWorX67X9fE6nE5fL1eJpq1SIiYiIiIhIkxQWFrJnzx5M0wx0KH6maZKRkcEPP/zQZnP6xsTEkJmZSURExAkfQ4WYiIiIiIg0yuv1smfPHmJiYkhLS2uzoqcxPp+PwsJC4uLiGpxAuTWYpkl5eTkHDx5kx44d9O3b94TPqUJMREREREQa5fF4ME2TtLQ0oqOjAx2On8/no7y8nKioKNsLMYDo6Gjcbje7du3yn/dEaLAOERERERFpsmDpCQuk1ij4QqoQ++CDD7jgggvo1KkThmHwxhtvNLrP6tWrGT58OJGRkfTp04fFixfXarNgwQJ69OhBVFQUo0ePZt26da0fvIiIiIiISKWQKsSKiooYMmQICxYsaFL7HTt2MGXKFM455xw2bdrELbfcwnXXXcfy5cv9bV5++WXmzJnD3XffzYYNGxgyZAgTJ07kwIEDdr0NERERERFp50LqHrHzzjuP8847r8ntFy5cSM+ePfnzn/8MwIABA/jvf//LI488wsSJEwF4+OGHmTlzJjNmzPDv8/bbb7No0SJuu+221n8TIiIiIiLS7oVUj1hzrVmzhnHjxtXYNnHiRNasWQNAeXk569evr9HG4XAwbtw4f5tQcuTgfta/8QQV378f6FBERERERILe/v37ueqqqzjppJNwOBzccsstbXbukOoRa67s7GzS09NrbEtPTyc/P5+SkhKOHDmC1+uts80333xT73HLysooKyvzP8/PzweskWQCObndoX07OG3zvRw2E/B4fh+wOMJZ1ecbTJMYhhPl117Kr72UX3spv/ZSfu0VLvmtGjXR5/Ph8/kCHY5f1ZxmVbE1R0lJCampqdx+++089thjTT6Gz+fDNE08Hg9Op7PGa039nMO6ELPLvHnzuPfee2ttX7FiBTExMQGIyFJemEtfIJFC3lq+AsOhEW3skpWVFegQwpryay/l117Kr72UX3spv/YK9fy6XC4yMjIoLCykvLwc0zQp9QSmIItyO2qN3lhQUFCr3eLFi3nwwQfZvHlzjZEOr7rqKlJSUnjyySe57777AHj66acpLy/3d7I0pLy8nJKSEj744AMqKipqvFZcXNyk9xDWhVhGRgY5OTk1tuXk5JCQkEB0dDROpxOn01lnm4yMjHqPO3fuXObMmeN/np+fT9euXZkwYQIJCQmt+yaaoaSoEB69BZfh44wxp5LYoWPAYglXHo+HrKwsxo8fj9vtDnQ4YUf5tZfyay/l117Kr72UX3uFS35LS0v54YcfiIuLIyoqiuLyCoY9GJji8qt7xhMTYZUypmlSUFBAfHx8reLsZz/7Gb/73e9Yv349Y8eOBSA3N5dVq1bx1ltv1fjt7nK5iIiIaNLv+dLSUqKjoznrrLNqzSPWlEIOwrwQGzNmDO+8806NbVlZWYwZMwaAiIgIRowYwapVq7j44osBq5tx1apVzJ49u97jRkZGEhkZWWu72+0O6H9crsQkSswIoo1ySouOkJrROWCxhLtAf9bhTvm1l/JrL+XXXsqvvZRfe4V6fr1eL4Zh4HA4/I9AqX7+qksJq2KrrkOHDpx33nksXbqU8ePHA/Daa6+RmprK2LFja7Wv6xj1nd8wjDo/06Z+xiFViBUWFrJt2zb/8x07drBp0yZSUlLo1q0bc+fOZe/evTz//PMAXH/99Tz55JP89re/5ec//znvvvsur7zyCm+//bb/GHPmzGH69OmMHDmSUaNG8eijj1JUVOQfRTGUGIZBvhFHNLkUHz0U6HBEREREJIxFu518fd/EgJ27qaZNm8bMmTP5y1/+QmRkJC+++CJTp04NaCEJIVaIffbZZ5xzzjn+51WXB06fPp3Fixezf/9+du/e7X+9Z8+evP322/zqV7/iscceo0uXLjz99NP+oesBrrjiCg4ePMhdd91FdnY2Q4cOZdmyZbUG8AgVRY548OVSmn840KGIiIiISBgzDMN/eWAwu+CCCzBNk7fffptTTz2VDz/8kEceeSTQYYVWIXb22Wf7R0Wpy+LFi+vcZ+PGjQ0ed/bs2Q1eihhKip0J4ANPYW6gQxERERERCbioqCguueQSXnzxRbZt20a/fv0YPnx4oMMKrUJMGlfqTgQPeIvUIyYiIiIiAtblieeffz6bN2/mpz/9aY3XNm3aBFi3QR08eJBNmzYRERHBwIEDbY1JhViY8UQkQjH4io8EOhQRERERkaBw7rnnkpKSwtatW7nqqqtqvDZs2DD/+vr161myZAndu3dn586dtsakQizMeCMSATBKVYiJiIiIiIA1yuG+ffvqfK2hW5/sFNihQqTVmdEpALjK8gIbiIiIiIiI1EuFWJgxopMBcJcfDXAkIiIiIiJSHxViYcYZa/WIRXqaNqO3iIiIiIi0PRViYcYdbxViMV71iImIiIiIBCsVYmEmKj4VgDhfQYAjERERERGR+qgQCzMxiVYhFm8WQoBGgBERERERkYapEAszsUlWIeYyfHiK8wIbjIiIiIiI1EmFWJhJiIunxIwAoPDIwQBHIyIiIiIidVEhFmZcTgdHiQWg6OihAEcjIiIiIiJ1USEWhvKJB6AkXz1iIiIiIiL1ee211xg/fjxpaWkkJCQwZswYli9f3ibnViEWhgoNq0esvOBwgCMREREREQleH3zwAePHj+edd95h/fr1nHPOOVxwwQVs3LjR9nO7bD+DtLkiRxz4oKIwN9ChiIiIiIgEzN/+9jfuuece9uzZg8NxrA/qoosuokOHDixatKhG+wceeIB//etfvPnmmwwbNszW2FSIhaGSyh4xX5F6xERERETEJqYJnuLAnNsdA4bRaLPLLruMm266iffee4+xY8cCkJuby7Jly3jnnXdqtff5fBQUFJCSktLqIR9PhVgYKnXGgRcoORLoUEREREQkXHmK4YFOgTn37fsgIrbRZsnJyZx33nksWbLEX4j94x//IDU1lXPOOadW+/nz51NYWMjll1/e6iEfT/eIhaFyZxwAjtK8wAYiIiIiIhJg06ZN45///CdlZWUAvPjii0ydOrXGpYoAS5Ys4d577+WVV16hY8eOtselHrEw5HFZ/zrgKs8LbCAiIiIiEr7cMVbPVKDO3UQXXHABpmny9ttvc+qpp/Lhhx/yyCOP1GizdOlSrrvuOl599VXGjRvX2tHWSYVYGKpwWT1ikZ6jAY5ERERERMKWYTTp8sBAi4qK4pJLLuHFF19k27Zt9OvXj+HDh/tff+mll/j5z3/O0qVLmTJlSpvFpUIsDHnd1n8Q0RX5AY5ERERERCTwpk2bxvnnn8/mzZv56U9/6t++ZMkSpk+fzmOPPcbo0aPJzs4GIDo6msTERFtj0j1i4SjC6hGL8RUEOBARERERkcA799xzSUlJYevWrVx11VX+7X/729+oqKjgxhtvJDMz0//45S9/aXtM6hELQ2ZlF3G8WWANK9qEoT1FRERERMKVw+Fg377a97OtXr267YOppB6xMOSsLMRc+DBLdZ+YiIiIiEiwUSEWhqIiIigxIwAoK8gNcDQiIiIiInI8FWJhKMIBeVj3iRUeyQlwNCIiIiIicjwVYmHIMKDAiAeg+OihAEcjIiIiIiLHUyEWpoqcViFWVnA4wJGIiIiIiMjxVIiFqRKnNe+BR4WYiIiIiLQi0zQDHULAtUYOVIiFqXJ3AgC+Yg3WISIiIiIt53Q6ASgvLw9wJIFXXFwMgNvtPuFjhFwhtmDBAnr06EFUVBSjR49m3bp19bY9++yzMQyj1mPKlCn+Ntdcc02t1ydNmtQWb8VWFZFJAJglKsREREREpOVcLhcxMTEcPHiQ4uJiSktLg+ZRXl7eJucpKSnh8OHDHDhwgKSkJH9xekL5bMXPxnYvv/wyc+bMYeHChYwePZpHH32UiRMnsnXrVjp27Fir/WuvvVajYj98+DBDhgzhsssuq9Fu0qRJPPvss/7nkZGR9r2JNuKrLMQcJUcCG4iIiIiIhAXDMMjMzGTHjh3s2rUr0OH4maZJSUkJ0dHRGIbRJudMSkoiIyOjRccIqULs4YcfZubMmcyYMQOAhQsX8vbbb7No0SJuu+22Wu1TUlJqPF+6dCkxMTG1CrHIyMgWJzLYmNHJADjLNKGziIiIiLSOiIgI+vbtG1SXJ3o8Hj744APOOuusFl0q2FRut7tFPWFVQqYQKy8vZ/369cydO9e/zeFwMG7cONasWdOkYzzzzDNMnTqV2NjYGttXr15Nx44dSU5O5txzz+X3v/89HTp0qPc4ZWVllJWV+Z/n5+cD1pfA4/E05221Ov/5o5MAiPDkBTymcFKVS+XUHsqvvZRfeym/9lJ+7aX82isc89sahUhr8fl8VFRU4HQ62yQun8+Hz+er9/Wmfs6GGSLDnuzbt4/OnTvz8ccfM2bMGP/23/72t7z//vusXbu2wf3XrVvH6NGjWbt2LaNGjfJvr+ol69mzJ9u3b+f2228nLi6ONWvW1PtB3nPPPdx77721ti9ZsoSYmJgTfIet6+Ceb7ju4AP8YGSyYeiDgQ5HRERERKRdKC4u5qqrruLo0aMkJCTU2y5kesRa6plnnmHQoEE1ijCAqVOn+tcHDRrE4MGD6d27N6tXr2bs2LF1Hmvu3LnMmTPH/zw/P5+uXbsyYcKEBpPdFjweD1lZWfQbNBLehQSKmDx5ckBjCidV+R0/fnybdH23N8qvvZRfeym/9lJ+7aX82kv5tVew5bfqarnGhEwhlpqaitPpJCcnp8b2nJycRu/vKioqYunSpdx3332NnqdXr16kpqaybdu2eguxyMjIOgf0cLvdQfHhA8QmpwMQZxZaPXuOkBsgM6gF02cdjpRfeym/9lJ+7aX82kv5tZfya69gyW9TYwiZX+cRERGMGDGCVatW+bf5fD5WrVpV41LFurz66quUlZXx05/+tNHz7Nmzh8OHD5OZmdnimAMpNjEVACc+KC8IcDQiIiIiIlJdyBRiAHPmzOHvf/87zz33HFu2bOGGG26gqKjIP4ri1VdfXWMwjyrPPPMMF198ca0BOAoLC/nNb37DJ598ws6dO1m1ahUXXXQRffr0YeLEiW3ynuySGB9HsWn12nmLNJeYiIiIiEgwCZlLEwGuuOIKDh48yF133UV2djZDhw5l2bJlpKdbl+Ht3r0bx3GX4G3dupX//ve/rFixotbxnE4nX3zxBc899xx5eXl06tSJCRMmcP/994f8XGIJ0W7yiCWGMorzDhLfoWegQxIRERERkUohVYgBzJ49m9mzZ9f52urVq2tt69evH/UNDBkdHc3y5ctbM7yg4XY6KCAOyKU4/yDxgQ5IRERERET8QurSRGmeQoc1gmPp0UMBjkRERERERKpTIRbGSlxWIVZeeDjAkYiIiIiISHUqxMJYmTsR0GAdIiIiIiLBRoVYGPNEJAFgqhATEREREQkqKsTCmC8qCQCj9EhgAxERERERkRpUiIUxMzoZAGdZXmADERERERGRGlSIhTFHjFWIRZQfDXAkIiIiIiJSnQqxMOaK6wBAZIUKMRERERGRYKJCLIxFxKUCEOMtCHAkIiIiIiJSnQqxMBadYBVisb4C8PkCHI2IiIiIiFRRIRbGYpOsQsyJD8ryAxyNiIiIiIhUUSEWxhLi4yk2I60nJRrCXkREREQkWKgQC2OJMW7yiAWgrOBwgKMREREREZEqKsTCWHykizwzHoCSowcDHI2IiIiIiFRRIRbGDMOg0BEHQEn+oQBHIyIiIiIiVVSIhbkSZwIA5bo0UUREREQkaKgQC3Ml7iQAKgpViImIiIiIBAsVYmGuIiIRAG9xboAjERERERGRKirEwpw3MgkAQ8PXi4iIiIgEDRViYc4XnQyAszQvsIGIiIiIiIifCrEw56gsxNzleYENRERERERE/FSIhTlnbAoAEZ78AEciIiIiIiJVVIiFuYj4VABivEcDHImIiIiIiFRRIRbmIhMqCzFfIfh8AY5GRERERERAhVjYi020CjEnPijT5YkiIiIiIsFAhViYS4iPo9iMtJ6UaC4xEREREZFgoEIszCVFuzlCHAC+Is0lJiIiIiISDFSIhbmEaDdHTasQKyk4FOBoREREREQEVIiFvSi3k3zDKsRKj6oQExEREREJBirE2oEiRwIAZeoRExEREREJCiFXiC1YsIAePXoQFRXF6NGjWbduXb1tFy9ejGEYNR5RUVE12pimyV133UVmZibR0dGMGzeO7777zu630aZK3VYh5inUYB0iIiIiIsEgpAqxl19+mTlz5nD33XezYcMGhgwZwsSJEzlw4EC9+yQkJLB//37/Y9euXTVe/9Of/sTjjz/OwoULWbt2LbGxsUycOJHS0lK7306bKXcnAuArUiEmIiIiIhIMQqoQe/jhh5k5cyYzZsxg4MCBLFy4kJiYGBYtWlTvPoZhkJGR4X+kp6f7XzNNk0cffZQ77riDiy66iMGDB/P888+zb98+3njjjTZ4R22jIjLZWtHw9SIiIiIiQcEV6ACaqry8nPXr1zN37lz/NofDwbhx41izZk29+xUWFtK9e3d8Ph/Dhw/ngQce4OSTTwZgx44dZGdnM27cOH/7xMRERo8ezZo1a5g6dWqdxywrK6OsrMz/PD/fmijZ4/Hg8Xha9D5bqur81ePwRVo9YkbJkYDHF+rqyq+0HuXXXsqvvZRfeym/9lJ+7aX82ivY8tvUOEKmEDt06BBer7dGjxZAeno633zzTZ379OvXj0WLFjF48GCOHj3K/PnzOf3009m8eTNdunQhOzvbf4zjj1n1Wl3mzZvHvffeW2v7ihUriImJae5bs0VWVpZ/PSe/HICK/BzeeeedQIUUVqrnV1qf8msv5ddeyq+9lF97Kb/2Un7tFSz5LS4ublK7kCnETsSYMWMYM2aM//npp5/OgAED+Otf/8r9999/wsedO3cuc+bM8T/Pz8+na9euTJgwgYSEhBbF3FIej4esrCzGjx+P2+0G4A1PCXwFic5SJk+eHND4Ql1d+ZXWo/zaS/m1l/JrL+XXXsqvvZRfewVbfquulmtMyBRiqampOJ1OcnJyamzPyckhIyOjScdwu90MGzaMbdu2Afj3y8nJITMzs8Yxhw4dWu9xIiMjiYyMrPP4wfDhQ81YIhPTAIiuyA+a+EJdMH3W4Uj5tZfyay/l117Kr72UX3spv/YKlvw2NYaQGawjIiKCESNGsGrVKv82n8/HqlWravR6NcTr9fLll1/6i66ePXuSkZFR45j5+fmsXbu2yccMBZEJHQCI8RWCzxfgaEREREREJGR6xADmzJnD9OnTGTlyJKNGjeLRRx+lqKiIGTNmAHD11VfTuXNn5s2bB8B9993HaaedRp8+fcjLy+Ohhx5i165dXHfddYA1ouItt9zC73//e/r27UvPnj2588476dSpExdffHGg3mari0lIBcCBD8qOQnRygCMSEREREWnfQqoQu+KKKzh48CB33XUX2dnZDB06lGXLlvkH29i9ezcOx7FOviNHjjBz5kyys7NJTk5mxIgRfPzxxwwcONDf5re//S1FRUXMmjWLvLw8zjjjDJYtW1Zr4udQlhAXR5EZSaxRBiVHVIiJiIiIiARYSBViALNnz2b27Nl1vrZ69eoazx955BEeeeSRBo9nGAb33Xcf9913X2uFGHSSYtzkEUcslYWYiIiIiIgEVMjcIyYnLik6gqNmHACewsMBjkZERERERFSItQPxUS6OYBViJUcPBTgaERERERFRIdYOOBwGRY54AMoK1CMmIiIiIhJoKsTaiVKXNdG0p0A9YiIiIiIigaZCrJ0ocycB4C3ODWwgIiIiIiKiQqy9qIhIBMAs1qiJreq/j8JjQ+Cjx8HrCXQ0IiIiIhIiVIi1E96oJACMUhVircI04d3fw8q74chOyLoT/noW7FoT6MhEREREJASoEGsvYlIAcJXmBTaOcGCaON67Hz54yHo+9KcQnQIHvoZnJ8Ebv4Ai3YsnIiIiIvVTIdZOOGKSAYjwHA1wJCHONDl570s41zxuPZ/0R7h4Ady0HkZcY23b9CI8MQI+exZ8voCFKiIiIiLBS4VYOxERmwpAZEV+gCMJYaaJY8Vc+hxcZj2fPB9Ou8Faj0mBCx6Da1dCxiAozYO3boFnxsORXYGKWERERESClAqxdiIi3irEYrwF6qU5ET4fvPUrnJ89jYlBxeRHYNTM2u26ngozV8OkByEiHvZ+Bv++ybqnTERERESkkgqxdiIqqQMADnxWb400nc8Lb94E65/FxGBjt+swh/2s/vZOF5x2Pfzv++CMgB3vw7aVbReviIiIiAQ9FWLtREJsLPlmtPVEc4k1z8p7YOMLYDjwXvQXfuhwZtP269AbRs2y1rPusgo6ERERERFUiLUbSTFujpjx1pPiw4ENJpSUF8Gnz1jrFz+Fecplzdv/rF9DVJI1ouKmJa0enoiIiIiEJhVi7URSdARHsAoxs1hDqzfZN2+DpwiSe8LgK5q/f3QynPUba/29P1iFnYiIiIi0eyrE2omkGDe5lT1ixXkHAhxNCPl8qbUcfAUYxokdY9RMSOoGBfthzV9aLzYRERERCVkqxNqJKLeTUncSAHmHsgMbTKgoyIbv37PWB19+4sdxRcLYu631jx6FwoMtDk1EREREQpsKsXbEjEkBoPBIToAjCRFfvgqmD7qOtgbeaImTL4FOw6C8EN7/Y+vEJyIiIiIhS4VYO+KKSwPAk68emSb5/GVreSL3hh3P4YDx91vrnz0Lh75r+TFFREREJGSpEGtHYpI6AmBq1MTG5WyGnC+tecBO/knrHLPnmXDSJDC91pD4IiIiItJuqRBrRxJT0wFwlWoesUZVDdLRdwJUXtLZKsbdC4YDvnkLdq1pveOKiIiISEhRIdaOpHbsDEBMxVF8PjPA0QQxn9e6PwxgyNTWPXbH/jDsZ9Z61p1g6nMQERERaY9UiLUjHdM7AZBEPjkFpQGOJojt+MAaaj4qyeoRa23n3A7uGNjzKXz9RusfX0RERESCngqxdqRqsI5Eo5idOXmBDSaYVV2WeMol1tDzrS0+A06/yVr/6LHWP76IiIiIBD0VYu1JdBI+rEmJ9+7fH+BgglR5EWx501of3MqXJVY3ahY43LBvI2R/ad95RERERCQoqRBrTxxOSl0JABw6sC/AwQSpLW+BpwiSe0LXUfadJzYV+k+x1tc/Z995RERERCQoqRBrZzyRyQDkHc4OcCRB6ovKyxKHTAXDsPdcI6ZXnvMV8JTYey4RERERCSoqxNoZI6YDACV5OQGOJAgVZMP3q631wZfbf76eZ0NSNyg7Cl//y/7ziYiIiEjQUCHWzkQkWAN2eAsP4fH6AhxNkPnyVTB90HU0pPSy/3wOBwy72lrX5YkiIiIi7YoKsXYmsrIQSzIL+CG3OMDRBJnPX7aWg69ou3MOm2ZN8Lz7Yzj4bdudV0REREQCKuQKsQULFtCjRw+ioqIYPXo069atq7ft3//+d84880ySk5NJTk5m3Lhxtdpfc801GIZR4zFp0iS730bAGLGpAKQYBew4VBTgaIJI9leQ8yU4I+Dkn7TdeRM6Qd+J1vrG59vuvCIiIiISUCFViL388svMmTOHu+++mw0bNjBkyBAmTpzIgQMH6my/evVqrrzySt577z3WrFlD165dmTBhAnv37q3RbtKkSezfv9//eOmll9ri7QRG5T1iyUYB3x9UIeZXNUhH3wkQk9K25x5eeXnippegorxtzy0iIiIiARFShdjDDz/MzJkzmTFjBgMHDmThwoXExMSwaNGiOtu/+OKL/OIXv2Do0KH079+fp59+Gp/Px6pVq2q0i4yMJCMjw/9ITk5ui7cTGJWFWAoFfK8esWOqBssYYuPcYfXpOwHiM6H4EGx9u+3PLyIiIiJtzhXoAJqqvLyc9evXM3fuXP82h8PBuHHjWLNmTZOOUVxcjMfjISWlZo/H6tWr6dixI8nJyZx77rn8/ve/p0OHDvUep6ysjLKyMv/z/Px8ADweDx6Ppzlvq9VVnb++OIyIRFxU9YgVBDzeoJC/D3febkzDQUW3M6CBnDSW3xPlGHwlzo8exrf+Obwnnd+qxw4lduVXLMqvvZRfeym/9lJ+7aX82ivY8tvUOAzTNE2bY2kV+/bto3Pnznz88ceMGTPGv/23v/0t77//PmvXrm30GL/4xS9Yvnw5mzdvJioqCoClS5cSExNDz5492b59O7fffjtxcXGsWbMGp9NZ53Huuece7r333lrblyxZQkxMzAm+w7aRXLSds769lz1mKlN8j3HfSG+gQwq4TkfWcerOJ8mL7s77/e8PSAwxZQcY//WvMTHIGjifksi0gMQhIiIiIi1TXFzMVVddxdGjR0lISKi3Xcj0iLXUH//4R5YuXcrq1av9RRjA1KnHLkUbNGgQgwcPpnfv3qxevZqxY8fWeay5c+cyZ84c//P8/Hz//WcNJbsteDwesrKyGD9+PG63u3aDIzvg23tJoYCjHoMfj51AbGS7+RrUyZH1EeyE+IHjmDxpcoNtG81vC/hK3sSx433GJu/Dd/b0Vj12qLAzv6L82k35tZfyay/l117Kr72CLb9VV8s1JmR+gaempuJ0OsnJqTkRcU5ODhkZGQ3uO3/+fP74xz+ycuVKBg8e3GDbXr16kZqayrZt2+otxCIjI4mMjKy13e12B8WHDw3EkpAOQIxRRhRl7Dlazimdo9s4uiCzdz0Azu5jcDbx87Plsx5xDex4H+cXL+E893Zwhsx/nq0umP5bCkfKr72UX3spv/ZSfu2l/NorWPLb1BhCZrCOiIgIRowYUWOgjaqBN6pfqni8P/3pT9x///0sW7aMkSNHNnqePXv2cPjwYTIzM1sl7qATmQAO6wd+MoUawt5TAvs/t9a7jgpsLP2nQHQKFOyDbSsDG4uIiIiI2CpkCjGAOXPm8Pe//53nnnuOLVu2cMMNN1BUVMSMGTMAuPrqq2sM5vHggw9y5513smjRInr06EF2djbZ2dkUFhYCUFhYyG9+8xs++eQTdu7cyapVq7jooovo06cPEydODMh7tJ1hHBs5UXOJwb5N4PNAXAYkdQtsLK5IGHqVtb7hucDGIiIiIiK2CqlC7IorrmD+/PncddddDB06lE2bNrFs2TLS063L7Xbv3s3+/fv97Z966inKy8v5n//5HzIzM/2P+fPnA+B0Ovniiy+48MILOemkk7j22msZMWIEH374YZ2XHoaNGGtS52QVYvBD5SAvXUdZRWqgVc0p9u1yyN/fcFsRERERCVkhdxPK7NmzmT17dp2vrV69usbznTt3Nnis6Oholi9f3kqRhZDKCYs1lxjwwzprGejLEquk9YOup8EPn8CmF+GsXwc6IhERERGxQUj1iEkrqbw0MdkoYMfBQkJkBoPWZ5rVesRGBzaW6kZUjpi48QUrRhEREREJOyrE2qNq94jll1aQW1Qe4IACJPd7KD4EzgjIHBLoaI4ZeBG4Y62pBvZ8FuhoRERERMQGKsTao8pCrEtkMUD7vU+s6rLETsOsgTKCRUQsDDjfWv9iaWBjERERERFbqBBrj6oKsQirEGu394lVH6gj2Ay+wlp+9U+oaKc9liIiIiJhTIVYe1RZiKU5rWH8222P2J5PrWUw3R9WpdfZEJcOJUc0p5iIiIhIGGp2IdajRw/uu+8+du/ebUc80hYqR01MogCAHQfbYSFWmg85m631LkHYI+ZwwqDLrHVdnigiIiISdppdiN1yyy289tpr9OrVi/Hjx7N06VLKysrsiE3sUtkjFus9CrTTHrG9nwEmJPeA+PRAR1O3qssTty6DkryAhiIiIiIireuECrFNmzaxbt06BgwYwE033URmZiazZ89mw4YNdsQorS3WmtA5ouwIYLLjcBE+XzsbJr1qoI5g7A2rkjEIOg4Ebxl8/UagoxERERGRVnTC94gNHz6cxx9/nH379nH33Xfz9NNPc+qppzJ06FAWLVrUfuemCgXR1qWJhs9DsrOU8gof+46WBDioNhbMA3VUMQwYfLm1/sUrgY1FRERERFrVCRdiHo+HV155hQsvvJBbb72VkSNH8vTTT3PppZdy++23M23atNaMU1pTRAy4YwA4JdkLtLPLE33eY/NzBeNAHdUNuhwwYNdHcGRXoKMRERERkVbiau4OGzZs4Nlnn+Wll17C4XBw9dVX88gjj9C/f39/m5/85CeceuqprRqotLKYDnC0mAEJHj48ZBViZ/ZNC3RUbePgN1CWDxFx1qV/wSyxM/Q8E3Z8AF++Amf9JtARiYiIiEgraHaP2Kmnnsp3333HU089xd69e5k/f36NIgygZ8+eTJ06tdWCFBtUjpzYJ64UgO/b08iJVZcldh4Bzmb/W0Tbqxq044tXQJf8ioiIiISFZv8K/f777+nevXuDbWJjY3n22WdPOChpA5UjJ3aPtu4Na1eTOlcN1BHslyVWGXAhvH0rHPoW9m2EzsMDHZGIiIiItFCze8TOOeccDh8+XGt7Xl4evXr1apWgpA1UFmKZ7mIAdhwqDGQ0bSvUCrGoBOg/xVr/4uXAxiIiIiIiraLZhdjOnTvxer21tpeVlbF3795WCUraQGUhluqwCrA9R0ooq6j9uYadokOQu91a7zIisLE0R9XliV/+A7yewMYiIiIiIi3W5EsT//3vf/vXly9fTmJiov+51+tl1apV9OjRo1WDExtVFmLRnjziI10UlFWw+3AxfdPjAxyYzap6w9L6Q3RyYGNpjt7nQkwqFB+C7e/BSRMCHZGIiIiItECTC7GLL74YAMMwmD59eo3X3G43PXr04M9//nOrBic2qizEjJJceqbF8sWeo3x/qKgdFGIhMH9YXZxuGPQ/sHYhfLFUhZiIiIhIiGvypYk+nw+fz0e3bt04cOCA/7nP56OsrIytW7dy/vnn2xmrtKbKQoziw/RMjQXayVxioXZ/WHVVkzt/8zaU5gc2FhERERFpkWbfI7Zjxw5SU1PtiEXaUl2FWLgPYV9RDvs2WOuhWIh1Gg4d+kJFKWx5M9DRiIiIiEgLNOnSxMcff5xZs2YRFRXF448/3mDbm2++uVUCE5u1xx6x7C+tIiY6GTr0CXQ0zWcYMOQKePf31uWJw6YFOqLQZpqQ+z3sXQ97PoP8vRCXDgmZEF/9kWF9Zwwj0BGLiIhIGGlSIfbII48wbdo0oqKieOSRR+ptZxiGCrFQ4S/EcumVEg20g7nE/PeHjQ7dH9WDLod3/wA7PoDcHZDSM9ARhY6yQti9xiq69n5mFWAlR5q2b0Q89JsEp/yPNXCKK8LeWEVERCTsNakQ27FjR53rEsJiUipXTHrGlQNwqLCM/FIPCVHuwMVlp1AdqKO65O7Q62z4/j3Y+AKMvTPQEQU/bwWsfxbeewBKcmu+5oyEzMHQeSSk9IKig1Cwv/KRDfn7rH3KC+DLV61HVBIMvAhOuRR6nAEOZ0DeloiIiIS2Jo+aKGHG6YbIRCg7Spw3n85J0ezNK+HTHbmMHZAe6OjssedTa9klhAsxgBHTrUJs04tw9lxw6j/jOpkmbFsJy/8fHNpqbUvsCt1/BF1GQucRkH5K471bnlLrstav/gmbX4PCHNjwnPWIy4BTLoFTr4MOve1/TyIiIhI2mj1Yx6WXXsqDDz5Ya/uf/vQnLrvsslYJStpIVa9Y8WHGD7SKr3e+zA5gQDYqPGjdA4QBnYYFOpqW6TfFmlOsYD98tyLQ0QSnnK/hhUvgxf+xirDoFJg8H27eCJf8FUbNhM7Dm3aJoTsKup4K5/0R5myBq/8Nw6+2esYKs+GTv8CTp8Lr18Ph7ba/NREREQkPzS7EPvjgAyZPnlxr+3nnnccHH3zQKkFJG6k2YMfkQZkAZH2dTXmFL4BB2eTAZmuZ3AMi4wIaSou5ImDoldb6hucCG0uwKTwIb94CC38E298FhxtOv8kqwEbNtHqCW8LhhF4/hgufgF9/B1cuhb4TwPTC5y/BkyPhtVlw6LtWeTsiIiISvppdiBUWFhIRUftfkd1uN/n5mtsopFQrxEZ0TyYtPpL80go+3n4osHHZIedra5l+cmDjaC3DKydV/24FHN0b2FiCxZ718JfTrPvBTB8MuBBmr4MJv4fopNY/nysC+p0H016Fme/CSZOs837xMiwYBf+8Dg5ubf3zioiISFhodiE2aNAgXn755Vrbly5dysCBA1slKGkjsZXzwRUfxukwmHRyBgD/CcfLE6t6xMKlEEvta93rZPpg05JARxN43y6H586H4kPQ8WSY8R+44v+sATjaQucRcNXLMGs19JtsfS5fvgoLRsNr/wt5u9smDhEREQkZzb7L/8477+SSSy5h+/btnHvuuQCsWrWKl156iVdffbXVAxQbVbtHDOC8QRn83ye7WP51Nr/3noLb2ew6PXhV9Yh1DKN/LBh+Nez6CDY+D2feCo4w+ryaY8Pz1uWIphd6j4XLnw/c5aedhsGVL8H+z+H9P8E3b1lzvm1+HUbPsj6n6OTAxCYiIiJBpdm/3C644ALeeOMNtm3bxi9+8QtuvfVW9uzZw8qVK7n44ottCFFsU20uMYBRPVLoEBtBXrGHtd/nNrBjiPH54OA31nq49IiBNYR6VKLV2/L9e4GOpu2ZJqx+EP59k1WEDbnK6pUKhnsAM4fA1Bdh5nvQ40zwlsHHT8BjQ62lpzTQEYqIiEiAndA/oU+ZMoWPPvqIoqIiDh06xLvvvsuPf/zj1o5N7OYvxKx7wlxOBxMqL09856v9gYqq9R3ZAZ5icEW13aVqbcEdDYOvsNbb26Ad3gp485ew+gHr+Zm3wsV/aflgHK2t83CY/iZM+4fVG1uaByvusAb1+Pxl6x8JREREpF064QmI1q9fz5YtWwA4+eSTGTasbYYEX7BgAQ899BDZ2dkMGTKEJ554glGj6p8X6tVXX+XOO+9k586d9O3blwcffLDGqI+maXL33Xfz97//nby8PH70ox/x1FNP0bdv37Z4O4FVbbCOKlMGZfLSut0s/yqb+y86BafDCFBwrehA5WWJaf3Cb/Ld4dNh3d/gm3esEQPj0gIdkf3Ki+EfP4dv/wMYMPkha0TEYGUY0Hc89D7XGlnx3T/A0R/g9Vnw0WNw9m3Q//z2e2lpY0wTig5B/h44uscanKYkF8qLoLywcllt3VsODpc1YqbDZf0376xcd0ZYA7dEJVVbJh9bxnWEuPSmTWsgIiLSQs0uxA4cOMDUqVNZvXo1SUlJAOTl5XHOOeewdOlS0tLs+yH48ssvM2fOHBYuXMjo0aN59NFHmThxIlu3bqVjx4612n/88cdceeWVzJs3j/PPP58lS5Zw8cUXs2HDBk455RTAmv/s8ccf57nnnqNnz57ceeedTJw4ka+//pqoqCjb3ktQqKMQG90rheQYN4eLylm3I5cxvTsEKLhW5L8/LIwuS6yScYo1UMTe9fD5EvjRLwMdkb3KCuH/fgJ71lk9nJc+DQMuCHRUTeNwwrCfwsmXwNqF8N9HrEFkXvkZZAyyJufuN9kq3Nqj4lzrH01yNkPOV3Bkp1V45e+Dija+lDM6xSrI4tOtSbvjOkJCZ0jItJbxmdbrmkxdRERaoNn/F7npppsoKChg8+bNDBgwAICvv/6a6dOnc/PNN/PSSy+1epBVHn74YWbOnMmMGTMAWLhwIW+//TaLFi3itttuq9X+scceY9KkSfzmN78B4P777ycrK4snn3yShQsXYpomjz76KHfccQcXXXQRAM8//zzp6em88cYbTJ061bb3EhSOu0cMwO10MGFgBi9/9gP/+Wp/eBRi/hETw2igjuqGT7cKsQ3Pw+k3h+8Pea8HXp1uFWFRSdb9YN1OC3RUzRcRA2fOgZEzYM0C+GQhZH8JS6+CzKFwzu3W3GTh+jmaptUj+MM6q+DK2Ww98huahsGwCp/ELtYjNhUi4iofMRARW7kea/WEmV7r++KrqHx4wecBTwmUHrUuES3Jq7ksPgKFOVa7klzrcXBLAyE5rJgSOlmFWXwGjpiOdDt8AGN7FCR3sYq46OTw6u00Tagosy73Li+0eqg9xVax7Ck5tqxa95Zb7b0ea91bfmzdV2F9VqZpfUamz3ru8x47n2EA1n8LTtNkxP79ON/4l9VraTit3BpO6x86HK5jvaAOd2VPaPV1t1U8OyOObXO6K5+7qq1Xtqvexn9sV7VzVR6/Ko5AM00rh/5cVubTv82sY5vv2MNTTlzpPmvaDafj2Haq9jOtB9WXvprnt1aaGbhR7e9dXetG5VegcrthWP/91Xi9KUtqP6+rDce19a8fF7N/tZ6/1eZxefCUE+nJg4JscLtr56tGe7OB1+tpX9c569qvKbG2lkb/P9aEPDb1uBUVxJTlWH/T3aFzdVCzC7Fly5axcuVKfxEGMHDgQBYsWMCECRNaNbjqysvLWb9+PXPnzvVvczgcjBs3jjVr1tS5z5o1a5gzZ06NbRMnTuSNN94AYMeOHWRnZzNu3Dj/64mJiYwePZo1a9bUW4iVlZVRVlbmf141f5rH48Hj8ZzQ+2stVedvUhzuBNwAZfl4Sous/wEBEwamWYXYl/v5f5NOwhHilye6cjZjABUd+mG28PNpVn7bSv8LcS2fi3F4GxXff4DZ7fRAR3TC6s2vaeJ8+xYc21ZiuqLxTn0FM3M4BNPn0FyuODjzdzBiJo61f8Hx6d8x9m+CJZfj6zQc3+m3YPadYP3gayUB+f5WlGHkfImx59PKxzqMwrqnyDATu2F2HIjZ8WTMDr0hoTNmYher0HG2weWCpgklVkFmFOZA0QEr1oIcjIL9ULAfo2AfFGRjmF6o3FbFCQwD2P30sUMaTmuE2pgOmNEpEJOKWfmcqETMiHhrgJmIOIiMx6xc4oqqViBEWMWA0YQf+z6vNThMRflxhVAZRkVpZQFVWTx5ijGqPy8vwigvBE+R/5JPo/rln1VtTG/jcdjAAXQBOBKQ0zfKNJyYhgMMh/W5GwYmjmpLMLF+3JtVn6X/9+9xP4RNE/BhVCt8jMrCx6h6bnr92xy0/H5TNzAWoIF/f5AT5wYmAXwV4EDClBsYD3xcvJNTp90b6HCa/P/ZZv8f3ufz4XbXviHe7Xbjs/HG80OHDuH1eklPT6+xPT09nW+++abOfbKzs+tsn52d7X+9alt9beoyb9487r239oe8YsUKYmJiGn8zbSArK6vxRqaPCzEwMFn11j8ocycBUOGDaKeTg4XlLHjlP/ROsDdWOzl85Zx/eDsAK7/cT9k377TKcZuU3zY0JP5Uehxezf63/siGHtcHOpwWOz6/J+1/gwHZr2FisLbb9eR8ng2ft85nGRyGE9HvQfoc+A89D2Xh2rcBxz+upsSdzK4OP2Z3h7MoiUhttbPZ+f11eUtIKfqODoVb6VD4DUnFO3GaNf+H5MPJ0Zhu5MX0JD+qK/nRXcmP7kKFs/LvZ3Hlg6OVj822xduwOKCP9YjGenQETB+RFflEl+cS5TlS+cir9jhCVEUekRUFVtFSdBCKDtLSf9Ly4cBX+WO/6ge6gVm57sPR7J6IlinHTZkRSRmRlBtuyomwHkYEZVjPy3BTgQtPjYcTDy4qcOLDgdd04MWoXDfw4rDeWeW/0pv+f+23yhXDNDHw4cSLAx+OykLEUbnNiReX6cVpeHFR/VGBu3LdTQVuo9q6//Vj26u/5qw8tsuo/3eOYXoDVqQ2h9c0OJYxA6u0M2o891V9Blap5/92+SrHeDPNY/sd628xaiybomrvY8tj242qstUwa287bp16thvHRVVzW+3nUP1cNWM8Pubq8dbn+P8ij8/Nsejqer3+16rvd3zej4+2qfE053NriuPz1tDrtc9c/761+yZrts0+dIR33gn874Pi4uImtWt2IXbuuefyy1/+kpdeeolOnToBsHfvXn71q18xduzY5h4uJM2dO7dGT1t+fj5du3ZlwoQJJCQEtmrxeDxkZWUxfvz4OgvmWr7tAMWHGDtmaI05tv5b/hWvb9xHXnwvJk/ub1/Adtv/OcbnJmZ0CmMvvLLFl3s1O79txNibAYtX06VgAxnnnG4NRBCC6sqv8fkSXBtfA8B33kOMGH5NACO021TMooN41z6F4/MXiS4+TP/sN+iX/S/M3mPxDZ+O2Wf8CfeS2fL9LT6M8cNajN0fY+xeY/V+mTV/rJoxHTA7n4rZpfKROZQ4dwx2TTTg85l4fCYer6/yUW29wsTjO7atonJZXrle4d/PpKKyXUW1Y1T4TKud04fHZW3zVp6v3ONl7/79pGZ0xGuCWVFOTMVRYiryiKnII96bR6w3n3hvHgm+o0SbxUSbxcSYJcSYxcRQSqxZTCwlROLBYdT8gVFVdDS13vKZBmW4K4siN2Wmm2IiKSGSYjOy2noUJURQRBRFZhRFRFNMJEVmFMWV24qJqrbN2s9LmA1+VI3DAIdhWFfBGQbOyucOTNyGDzc+XIYPl+HFbZi4DROn4cNZuXRhWsWbAQ7DtI5H5dIAJz4wHBhV5wEwDByV5wMwHA5MHNbz4y/Hq+p5wwEOZ2Ubp/XVMJyYDmdlW0flutUzZzicVkHiMCqLnGPnN00fe/fupWuXLjidDgyseDCOtbWWldsr14+9ZtRoc2zdeo3qr1Xm2Kj2/2T/2zzuOBx37uqxVI8Njt/X2lJz/2Nt/K8ef8xq56H6satiqeP9HjtY7XZV616vl40bNjJ8+DBcbleteKl23OrnrRHDca/VyNHxcRzbvVab4/NFjbbV1uuKpZ6Y6jp3jc+3ruMff4wmnqdWzICnooIPP/yQyePOISU+mkCrulquMc3+v/mTTz7JhRdeSI8ePejatSsAP/zwA6eccgovvPBCcw/XZKmpqTidTnJycmpsz8nJISMjo859MjIyGmxftczJySEzM7NGm6FDh9YbS2RkJJGRkbW2u93uoPlx3uRYYqxCzF1+1LpmudL5gzvx+sZ9rPj6APdceEroXp6Y+y0ARvrJuCNa79KmYPqsAeg+CtJPwcj5CveW12D0/wY6ohbx53fbSnj7V9bGM2/FOXpmGP/0q5TUCSbeD2PvsCaEXr8YY8cHGNtX4ti+0rpMb9D/QM+zrXvkTmDetBP+/norrHsu93wKez6zloe31WrmSexBccZo8tNHkpc6gvzobpR7TcoqvJQV+Cg/kk9ZRR7lFVYBVF7hq7FeVuO5F4/X9Lcp8/rwVPj8xZOnwke51/S3qyqWAscBhw9Ve24AyZWP5h7JV9kjU1HZr+Qlggochg+Hw4nD4cAwHBgOB06HE6PyfimvIwJf5WWNLqcDp8OB0wFOw8DpMHA5HDgcVC4NXA4Dh2EtnQ4Dh8MqPKIdDuIcWNsq961aHlvHOn7lurVvzbbHtlUVNce2OwwrLke14zmMmudzGFYMPq+XT9Z8zBln/IgIt9t6rfJ9GZXtDay2hnEs7qpiw1F5bsN/Dmq87qxWeLVHHo+Hd975gcmTTwmu/7+FCY/HQ+kOk3MGZCi/NvB4PGyOhJT46KDIb1NjaHYh1rVrVzZs2MDKlSv9lwQOGDCgxn1WdoiIiGDEiBGsWrXKP3G0z+dj1apVzJ49u859xowZw6pVq7jlllv827KyshgzZgwAPXv2JCMjg1WrVvkLr/z8fNauXcsNN9xg59sJHlUDdhQdqrH5jL6pxEW6yM4vZeMPeYzo3vwfEUEhp/KSpo5hOlBHFcOwBu34z29g/XMwalaLe/8Cbt8meGW6dWP54Klw7p2BjqhtuSLhlEutx6Ft1lxxm1607kn6+Anr4XBBp+HQ8yzoeSZ0HY3PGUVZhY8Sj5dSj5cSj5eSci9lFV4KSsr5KtfA/DIbjw9KK3yUVb5eWuGl1OOj1OOlrMKHUXqUpJLddCjdRUbZ9/Qu20rfim+JoqxWqN/6OrPO1591vgGs9fUnpzQF/P8Gll35CBzDgAinA7fTgdtpVC4dRLis5y7Hse2uaq+7HAZulwO3w8BVbf+q9q4a6w4cmGzd8jWDB51CVIQLl8Phb2Md18DpsI7nrDymy3HsOE7HsWLI7az23GngrvZ6yP7DWAt5PB72fwWDOicGxQ8tEZGWOqHrWwzDYPz48YwfP76142nQnDlzmD59OiNHjmTUqFE8+uijFBUV+UdRvPrqq+ncuTPz5s0D4Je//CU//vGP+fOf/8yUKVNYunQpn332GX/729/87+OWW27h97//PX379vUPX9+pUyd/sRf2YlKsZbUh7AEiXU7GDejIG5v28Z8v94duIVY1h1i4jphY3eDLYeXdVo/FdyvgpImBjujE5e2GJZdbo7L1/DFc+EToF5bVVHh9FJV7KS6voKjMKoSKyiv8y+Jya5u1tJ4Xey6nvOsFnJT3If0L19K/ZCMdfQesUST3rIMP51NuuthidiPHTOaAmUSOmUwO1voBM5kCoommnHXf7iDGKCOaMqIpJ8YoJZkC+hjZ9HLsp5exjzSj7ssq8s0YNvl6s9Hsy0ZfHzb5epNHvP91t9MgzuUk0mUVOpEuB5Eup389ourhdBDpdhLhdNR8zVmzzfHb3DW2GdUKKkfNYst17HlbzYfo8Xh4J28zk0d1VaEgIiKNalIh9vjjjzf5gDfffPMJB9OYK664goMHD3LXXXeRnZ3N0KFDWbZsmX+wjd27d+OoNoTs6aefzpIlS7jjjju4/fbb6du3L2+88YZ/DjGA3/72txQVFTFr1izy8vI444wzWLZsWfjPIValjiHsq5w3KNMqxL7K5v9NGRCal2uE8xxix4tOglOvtXpKVs8L2SHQ3RVFuJZeYQ0l3vFkuOL/Aj7BboXXR1GZl8LyCgpLKygssx5FZdbzovLK9TIvRZXbiyqLrKLyCorLvBSWVViFV7mX8oqWDGzUv/IxnS7GAcY4vmaM42tOd2wmwzjCEOP7VnrXUByZRlF8D4rje1OcNpiSjsMxU/uS4nYz2eXgJy4nkW4HUZXLCKej3fbWiIiINFeTCrFHHnmkSQczDMPWQgxg9uzZ9V6KuHr16lrbLrvsMi677LJ6j2cYBvfddx/33Xdfa4UYWuqY1LnKj09KIzbCyd68Er7Yc5QhXZPaNraWKs6FqmGyO4bwgCPNcfov4dNnYN9G+HY59JsU6Iiax+dlxM6/YBR8Z02cO+1ViEps0SHLKrwUlFaQX+Ihv7SCglIPBdWW1bcVllZQUOapXFb4t5V47BkJzeUwiIlwEhvpIjrCSWyEtYypth7ttp5XbY+OcB3b5nYSHXEh0W4n+W4HZtEuYvK3E1V6AHfxARxFOdacNZUPs6yAclxExCZhuGOsObjcsdYyMgE69IYOfSG1D6T0JiYqgeAYB1ZERCT8NKkQ27Fjh91xSKA0UIhFuZ2cOyCdNz/fxztf7Q+9Qqzq/rCk7ta8PO1BXBqMmgkfPWb1ip00MaR6xRzv3Ud6wZeYrmiMK5dCYmdM06S43MvREg95xR7ySsrJL/FwtNojv6Ti2Hqpx1905Zd4KGtR71NNES4HcZEu4iJdxEa6iI90ERtpFVJV22IjXcRGHLctwknMccvoCCeRrtYeemRQ5aNuFR4Py955h8mTJ+vSORERkQA74ZlCy8vL2bFjB71798blar0JR6WNNVCIAUw+JYM3P9/Hf77M5rZJ/UPr8kT//WHt4LLE6k7/Jax7GvZvgq3/gf6TAx2RX1mFl7xiD7lF5RwpKudIZWGVV+yh8+5/c/GOBQA8HPNL/vPSEfKKV3K0pByPt+Wj4MVHuUiIctdYWg9rPa5qPdLaHhdZuS3STWykk7golw2Fk4iIiLRXza6giouLuemmm3juuecA+Pbbb+nVqxc33XQTnTt35rbbbmv1IMVGjRRiZ/frSLTbye7cYv677RBn9k1rw+BaqL2MmHi82A4wehb89xGrV6zfebb0ipmmSX5pBYcLy8gtKvc/Dldbzy0q50ixtcwr9lBYVlHnsQYb23k14gEw4MmKi3jiwGCgsEYbt9MgMTqCxGgXSTERJEa7SYx2kxDlspZVz/3b3SREW8VVXKSrzQZsEBEREWmKZhdic+fO5fPPP2f16tVMmnTs/pNx48Zxzz33qBALNbH1D9YBEB3h5NIRnXnhk93MeeVz3rn5TNLia8+hFpTa04iJxzv9Zlj3d8j+Ar55Gwac36TdvD6T3KJyDhaUcajQehwsKONwUTmHCss4XFjO4aLKZWE55d7mX/bnMCA5JoKkGDcpsRF0jyjgzn2PEVnhYUfKmeyJvIQnRg8hNT6axGg3STHWI9rtDK0eWREREZEGNLsQe+ONN3j55Zc57bTTavwoOvnkk9m+fXurBidtoJEeMYDbJw9g3Y5cvs0p5JdLN/J/144O/t4Fnw8ObLHW28OIiceLSbEmdf7wz7D6jxT3mkBOgYcD+aUcKLCKq4OFZRzIr1qWcqiyZ6u5c+HGRjhJiYsgJTaSDrERpBz/iIkgudp6fJTr2Mh6FWWw+HyoOASp/ehyzfOMWfUhk05O1z1MIiIiEtaaXYgdPHiQjh071tpeVFSkf60ORVWFWEUJlBdBRGztJhEu/jJtOBc++REfbz/MYyu/Zc6Efm0caDMd3W3NQeWMsEaCC1Mer4+DBWVk55eSfdR65BSUciC/jKIjp/EI0cTmfMmv7v0Dy32jmnRMw4AOsRGkxkWSGhdJWrxVYKVWLeMi6RAXQYc463mU+wTvmzJNeGuONQdWVCJc+VL7GVRFRERE2r1mF2IjR47k7bff5qabbgLwF19PP/00Y8aMad3oxH4RcVax4i23esXqKMQA+nSMZ94lg/jl0k088d42RvRI4ccnBfH9YlXzh6X2A2do9qx4vD5y8kvZf7SUfXkl7D9ayv7KZXbl9kOFZZgN9GAtck3kJtcb3OL6J/9lNGnx0XSMjyItIZK0uEg6Vi7T4o89UmIicDkd9R+0taz9K2x6AQwH/M+zVsHs8dh/XhEREZEg0ORC7KuvvuKUU05h3rx5TJo0ia+//hqPx8Njjz3G119/zccff8z7779vZ6xiB8OwesUK9luFWFK3epteNLQz63bk8uLa3fzq5U28ffMZZCZGt2GwzXCgcqCOIL4/7GiJh71HStiXV8LevGPLqvUDBQ0XWVVcDoP0hCgyEqPISIiiY0IkGQlRpCdE0SmyH943VjGg/Ae+uqwc4+QgGUFx20pYfru1Pv5+6DM2sPGIiIiItLEmF2KDBw/m1FNP5brrruOjjz7iiSeeYPDgwaxYsYLhw4ezZs0aBg2qf/4aCWLVC7FG3Hn+QDb9kMfmffnctGQjL806DXdb9J40VxCMmFhQ6uGH3BJ+OFLMniMl7PEvrfWC0rpHEKzO7TTISIwiMzGaTolRZCZFk1n5PCMhivTESFJjI4/dc1WX7Bvggz9hvP8gDLgQHAH+vPZthJevBtMLQ66EMTcGNh4RERGRAGhyIfb+++/z7LPPcuutt+Lz+bj00kuZP38+Z511lp3xSVuISbGW9YycWF2U28lfpg3n/Mf/y2e7jvDQ8q3cPnmAzQGegBz75xCr8PrYf7SUXYeL2Z1bzA9HKpeVjyPFjV9mlxIbQaekKDonRdMpKZrOlY/MpGg6JUU1XmQ1xZhfWJcBHvgatvwLTv5Jy47XErk74MXLwFMEPX8MFzweUhNOi4iIiLSWJhdiZ555JmeeeSZPPPEEr7zyCosXL+bss8+mT58+XHvttUyfPp2MjAw7YxW7NGHkxOq6d4jlocsGc/0LG/jbB98zsnsyE04Oos++ogwOb7PWW9gjVlbh5YfcYnYeKmZXbjE7DhawfquDR779L3uOlFDRyBCDKbERdE2OpktyDF2Soysf1nrn5GhiItpgMvToZDjtBnj/j7D6Qeh/ATgDMAl70WF44VIoOgjpg+CKF8AV0fZxiIiIiASBZv8ai42NZcaMGcyYMYNt27bx7LPPsmDBAu68804mTZrEv//9bzviFDs1sxADmHRKJtee0ZNn/ruDX7/6OX82DMYN6BgcI2ce3Gpd9haVCAmdGm3u8frYc6SEHYcK2XGomJ2Hith5uIgdh4rYl1dSx3DuDqAYgAingy4p0XRPiaFbSgxdKx9V63GRASh46nLaDbD2KTi4BbLugkkPtO35y4thyeWQux0Su8G0VyEqoW1jEBEREQkiLfqV2KdPH26//Xa6d+/O3Llzefvtt1srLmlLManWshmFGMDvJvVnw+4jbNydx8znP2NIl0R+Nf4kfnxSWmALsqqJnDue7L/szTRNsvNL2XGwiB2Hi6zlIeuxO7e4wZ6t2Agn3TvE0iM1hi5JUeTv3c6UH4+mV3oCGQlRwT+nGkB0Elz4JLzyM/hkAWQOhiFT2+bc3gr4x89h72dW79xP/wkJmW1zbhEREZEgdcKF2AcffMCiRYv45z//icPh4PLLL+faa69tzdikrVT1iBUdatZuES4Hz/18FE+t3s7ij3by+Z6jXPPsp4zonsyt40/i9D6pNgRbP9M0OVLsofy7DWQAG8s68fSLG/j+UBE7DxVR4vHWu2+020mP1Fh6psbQo0MsPVJjK5cxpMVF+gtLj8fDO+9s47ReKaE34fDAC+Gs38AHD8Gbv4S0ftBpmL3nNE14ew58+x9wRcGVSyHtJHvPKSIiIhICmlWI7du3j8WLF7N48WK2bdvG6aefzuOPP87ll19ObGzd809JCGjGYB3HS4hy87tJ/fn5j3ry1/e383+f7GL9riNc9fRaTuuVwi3jTmJk9+RWm5fK4/VxoKCM3YeL2Z1bxK7Dxdajcr2gtILF7k/IcMI/9iTwtne/f1+Xw6BrSgy9UmPpmRpLzzRr2Ss1jvSEyOC4rNJuZ98O2V/Ct8tg6U9h1mqIs3E+uA8egg3PAQZc+jR0O82+c4mIiIiEkCYXYueddx4rV64kNTWVq6++mp///Of069fPztikrZzAPWLHS4uP5I7zBzLzrF785b1tvLTuBz75Ppepf/sEl8OgS3I03TrE0qODdf9U9w6xdEmOxjCgzOOj1OOlrOLYssTj5WBBmX9C45wmTmAMMND5AwDdBozkju4D6JUWS8/UOLokRwfnUPttyeGAS/4Gfz/XGtDk1elw9b9af9Jr04QP58N7f7CeT34IBlzQuucQERERCWFNLsTcbjf/+Mc/OP/883E6nXbGJG2tFQqxKukJUdx70SnM+nFvnnx3G69v3EOpx8fOw8XsPFzMBy0+gzW3Vuek2oVdjw4xdI0uJ+phq2fvf//nfGvADqkpKhGmLoG/j4VdH1kTK09+qPWOX1YAr18P37xlPT/z1zBqZusdX0RERCQMNLkQ02iIYax6IWaarTKvU+ekaOZdMog/XHwK2flVc23VvJRw75ESnA6DSJeTSLfDWrocRFWud4iLIDMxiozKyYut9ShSYiLqn1tr50fWMrGrirCGpPWzesaWXgnr/gaZQ2DYT1t+3EPfwdJpcGgrOCNg8nwYMb3lxxUREREJM0EytrYEVNU9YqYXSo9aI+y1EofDoFPlZMVjendotePWyz9iYsvmD2sX+k+Gs+fC6nnw1q8grT90GXnix9v6H3htFpTlQ3wnuOL/WnY8ERERkTDWzm+YEQDc0eCuHGylFS5PDKiczdYyXYVYk5z1W+g3BbzlVk/WF6+A19O8Y/h88N48eGmqVYR1Ox3+930VYSIiIiINUCEmlvh0a1mYE9g4Wqr6HGLSOIcDfrLQ6g0rzIbXZsLjw2DNAuter4aYJhz4xrq88f0/WttG/S9M/zfEdbQ/dhEREZEQpksTxRKfCbnfQ8H+xtsGK9OEA1usdfWINV1UAvx8Oaz7O6z7Kxz9wRrA4/0HYeS1MPp6q1A3TTj0Lez8EHZ8CDv/C8WVc885I+GCR2HoVQF9KyIiIiKhQoWYWOIzrGVBdmDjaImje6xL4xwu6NA30NGElugk+PFv4PSb4POXYM2T1vD2/33YWu9xBmR/BUUHau7niobuY2DsXfZPDi0iIiISRlSIiSU+01qGco/Yoa3WMqU3uCICG0uockfByBkwfDpsfQc+egz2rIPt71qvu6Kg6yjocab16DwcXJGBjVlEREQkBKkQE0tc5T1iodwjdrCyEEvTROMt5nDAgPOtx+5PYO8GyBwMnUdaxZqIiIiItIgKMbH4e8RUiMlxup1mPURERESk1WjURLH47xEL5UsTv7WWqSrERERERCS4qRATS1j1iJ0U2DhERERERBqhQkwsVfOIlRc2Pn9UMCo6BCW5gKERE0VEREQk6KkQE0tkPETEW+uh2Ct28BtrmdQNImICG4uIiIiISCNCphDLzc1l2rRpJCQkkJSUxLXXXkthYWGD7W+66Sb69etHdHQ03bp14+abb+bo0aM12hmGUeuxdOlSu99OcArl+8Q0UIeIiIiIhJCQGTVx2rRp7N+/n6ysLDweDzNmzGDWrFksWbKkzvb79u1j3759zJ8/n4EDB7Jr1y6uv/569u3bxz/+8Y8abZ999lkmTZrkf56UlGTnWwle8Rlw+LvQ7BHzD9Sh+8NEREREJPiFRCG2ZcsWli1bxqeffsrIkSMBeOKJJ5g8eTLz58+nU6dOtfY55ZRT+Oc//+l/3rt3b/7whz/w05/+lIqKClyuY289KSmJjIwM+99IsAvlSZ3VIyYiIiIiISQkCrE1a9aQlJTkL8IAxo0bh8PhYO3atfzkJz9p0nGOHj1KQkJCjSIM4MYbb+S6666jV69eXH/99cyYMQPDMOo9TllZGWVlZf7n+fn5AHg8HjweT3PeWqurOv+JxOGI7YgT8B7diy/A76O5XAe3YgAVyb0xbYy9JfmVxim/9lJ+7aX82kv5tZfyay/l117Blt+mxhEShVh2djYdO3assc3lcpGSkkJ2dtMuozt06BD3338/s2bNqrH9vvvu49xzzyUmJoYVK1bwi1/8gsLCQm6++eZ6jzVv3jzuvffeWttXrFhBTExwDBSRlZXV7H16HchlEJD97UY+87zT+kHZxOUtYUrBPgBWbNiF54tDtp/zRPIrTaf82kv5tZfyay/l117Kr72UX3sFS36Li4ub1C6ghdhtt93Ggw8+2GCbLVu2tPg8+fn5TJkyhYEDB3LPPffUeO3OO+/0rw8bNoyioiIeeuihBguxuXPnMmfOnBrH79q1KxMmTCAhIaHF8baEx+MhKyuL8ePH43a7m7Wv8XU5vL6EzHgHkydPtinC1mfs3QBfgBnbkfEXXmbruVqSX2mc8msv5ddeyq+9lF97Kb/2Un7tFWz5rbparjEBLcRuvfVWrrnmmgbb9OrVi4yMDA4cOFBje0VFBbm5uY3e21VQUMCkSZOIj4/n9ddfb/TDGT16NPfffz9lZWVERkbW2SYyMrLO19xud1B8+HCCsSR1AcBRmI0jSN5Hk+RtB8BI69dm+Q+mzzocKb/2Un7tpfzaS/m1l/JrL+XXXsGS36bGENBCLC0tjbS0tEbbjRkzhry8PNavX8+IESMAePfdd/H5fIwePbre/fLz85k4cSKRkZH8+9//JioqqtFzbdq0ieTk5HqLsLDmH74+G0wTGrhPLqhooA4RERERCTEhcY/YgAEDmDRpEjNnzmThwoV4PB5mz57N1KlT/SMm7t27l7Fjx/L8888zatQo8vPzmTBhAsXFxbzwwgvk5+f7uwnT0tJwOp28+eab5OTkcNpppxEVFUVWVhYPPPAAv/71rwP5dgOnqhCrKIXSPIhODmg4TeYful6FmIiIiIiEhpAoxABefPFFZs+ezdixY3E4HFx66aU8/vjj/tc9Hg9bt2713xy3YcMG1q5dC0CfPn1qHGvHjh306NEDt9vNggUL+NWvfoVpmvTp04eHH36YmTNntt0bCybuaIhKsoqwguzQKcQOfmMt1SMmIiIiIiEiZAqxlJSUeidvBujRowemafqfn3322TWe12XSpEk1JnIWrLnESvOsucQ6Dgh0NI3zlMKRnda6CjERERERCRGOQAcgQab6fWKhIHc7mD6ITIS49EBHIyIiIiLSJCrEpKb4TGtZsD+wcTSVf6COk0JncBERERERafdUiElNodYjpoE6RERERCQEqRCTmkKuR0wDdYiIiIhI6FEhJjWFWo/YwcoeMRViIiIiIhJCVIhJTf4esRAoxHxeOLzNWk89KbCxiIiIiIg0gwoxqal6j5jPF9hYGnNkJ3jLwBUFSd0CHY2IiIiISJOpEJOaqoaA93mgJDewsTSmaqCODn3B4QxsLCIiIiIizaBCTGpyRUBMqrUe7AN2aKAOEREREQlRKsSktlC5T0wDdYiIiIhIiFIhJrX57xML8h6xQ5WTOWugDhEREREJMSrEpLZQGMLeNNUjJiIiIiIhS4WY1BYKkzoX7IfyAjCckNI70NGIiIiIiDSLCjGpLRR6xKoG6kjpZQ0wIiIiIiISQlSISW2h0COmyxJFREREJISpEJPaQqFHTAN1iIiIiEgIUyEmtVX1iBUeAJ83sLHURz1iIiIiIhLCVIhJbbFpYDjA9ELRoUBHUzf1iImIiIhICFMhJrU5XRDb0VoPxvvEinOh6KC1rkJMREREREKQCjGpWzDfJ3awsjcssStExgU2FhERERGRE6BCTOoWzCMn6rJEEREREQlxKsSkbkHdI6aBOkREREQktKkQk7qpR0xERERExDYqxKRuQd0jVlmIpfUPbBwiIiIiIidIhZjULVh7xMoK4egP1rouTRQRERGREKVCTOoWrD1ih7+zljGpEJMS2FhERERERE6QCjGpW1WPWNFB8HoCG0t1B7ZYy44DAhuHiIiIiEgLqBCTusV0AIcLMKHwQKCjOebA19ay48DAxiEiIiIi0gIqxKRuDgfEBeHliTlVhZh6xEREREQkdIVMIZabm8u0adNISEggKSmJa6+9lsLCwgb3OfvsszEMo8bj+uuvr9Fm9+7dTJkyhZiYGDp27MhvfvMbKioq7HwrocN/n1gQDdhRdWli+smBjUNEREREpAVcgQ6gqaZNm8b+/fvJysrC4/EwY8YMZs2axZIlSxrcb+bMmdx3333+5zExMf51r9fLlClTyMjI4OOPP2b//v1cffXVuN1uHnjgAdveS8gItkKs5AgU7LPWNXS9iIiIiISwkCjEtmzZwrJly/j0008ZOXIkAE888QSTJ09m/vz5dOrUqd59Y2JiyMjIqPO1FStW8PXXX7Ny5UrS09MZOnQo999/P7/73e+45557iIiIsOX9hAz/EPZBcmliVW9YYleISghsLCIiIiIiLRASlyauWbOGpKQkfxEGMG7cOBwOB2vXrm1w3xdffJHU1FROOeUU5s6dS3FxcY3jDho0iPT0dP+2iRMnkp+fz+bNm1v/jYSaYBvCPqfyM9FAHSIiIiIS4kKiRyw7O5uOHTvW2OZyuUhJSSE7u/4i4aqrrqJ79+506tSJL774gt/97nds3bqV1157zX/c6kUY4H/e0HHLysooKyvzP8/PzwfA4/Hg8QR2qPeq87dGHEZMR1yAL38f3gC/LwBH9macgDe1H74AxdOa+ZXalF97Kb/2Un7tpfzaS/m1l/Jrr2DLb1PjCGghdtttt/Hggw822GbLli0nfPxZs2b51wcNGkRmZiZjx45l+/bt9O7d+4SPO2/ePO69995a21esWFHjHrRAysrKavEx0vJ3czpQsO87Vr/zTsuDaqEzvv2IDsCmfeXsCXA8rZFfqZ/yay/l117Kr72UX3spv/ZSfu0VLPmtfgVeQwJaiN16661cc801Dbbp1asXGRkZHDhQcy6riooKcnNz673/qy6jR48GYNu2bfTu3ZuMjAzWrVtXo01OTg5Ag8edO3cuc+bM8T/Pz8+na9euTJgwgYSEwN675PF4yMrKYvz48bjd7pYd7EBP2P4QCUYRkydPbp0AT5Rp4tpyEwCDx09lcPopAQmjVfMrtSi/9lJ+7aX82kv5tZfyay/l117Blt+qq+UaE9BCLC0tjbS0tEbbjRkzhry8PNavX8+IESMAePfdd/H5fP7iqik2bdoEQGZmpv+4f/jDHzhw4ID/0sesrCwSEhIYOLD++5AiIyOJjIystd3tdgfFhw+tFEtyFwCMklzchg9ctd9zmzm6F0qPguHEnTEQXIHNczB91uFI+bWX8msv5ddeyq+9lF97Kb/2Cpb8NjWGkBisY8CAAUyaNImZM2eybt06PvroI2bPns3UqVP9Iybu3buX/v37+3u4tm/fzv3338/69evZuXMn//73v7n66qs566yzGDx4MAATJkxg4MCB/OxnP+Pzzz9n+fLl3HHHHdx44411FlrtTnQyOCvzEOgBO6pGTOzQJ7AFoYiIiIhIKwiJQgys0Q/79+/P2LFjmTx5MmeccQZ/+9vf/K97PB62bt3qvyYzIiKClStXMmHCBPr378+tt97KpZdeyptvvunfx+l08tZbb+F0OhkzZgw//elPufrqq2vMO9auGUbwjJx4oHLExHSNmCgiIiIioS8kRk0ESElJaXDy5h49emCapv95165def/99xs9bvfu3XknCAaiCFrxmZC3K/CTOlf1iGnoehEREREJAyHTIyYBEiw9YppDTERERETCiAoxaVi8NbBJQHvEfF44uNVa7zggcHGIiIiIiLQSFWLSsGDoEcv9Hrxl4IqG5J6Bi0NEREREpJWoEJOGBUOP2IGvrWXH/uDQV1ZEREREQp9+1UrDgqFHLKeqEDs5cDGIiIiIiLQiFWLSMH+PWAALMX+PmO4PExEREZHwoEJMGlbVI1Z2FMqLAhNDVSGmOcREREREJEyoEJOGRcaDO9ZaD0SvmKfEGqwDNHS9iIiIiIQNFWLSMMOodp9YAAbsOLgVTB9Ep0BcetufX0RERETEBirEpHHJPazl4W1tf+4DW6xlx4FWUSgiIiIiEgZUiEnj0vpby6pJldvSgc3WUveHiYiIiEgYUSEmjetYVYh90/bn9veIacREEREREQkfKsSkcVU9YgcCUIhpDjERERERCUMqxKRxqSdZy4J9UHq07c5bcsQ6JxzrlRMRERERCQMqxKRx0UnHJnY++G3bnbfqssTErhCV2HbnFRERERGxmQoxaZq0ANwnVjWRs+4PExEREZEwo0JMmiYQhZj//jCNmCgiIiIi4UWFmDRNWj9r2aY9YtXmEBMRERERCSMqxKRp2nouMdPUHGIiIiIiErZUiEnTVPWIHf0BygrsP1/BfmuERsN5bNRGEREREZEwoUJMmiYmBeLSrfW2GDmx6v6wDn3AFWn/+URERERE2pAKMWm6trxPTCMmioiIiEgYUyEmTdeWIydWFWLpJ9t/LhERERGRNqZCTJrO3yPWBgN2qEdMRERERMKYCjFpurTKosjuHjGf91ixp6HrRURERCQMqRCTpqu6NDFvF5QX2Xee3B1QUQquaEjuYd95REREREQCRIWYNF1sB4hJtdYP2ThyYs6X1jKtHzic9p1HRERERCRAVIhJ87TFxM6711rLziPsO4eIiIiISACpEJPm6dgGIyfu/thadj/dvnOIiIiIiASQCjFpnqoesQM2FWKl+ZBdeWmiCjERERERCVMhU4jl5uYybdo0EhISSEpK4tprr6WwsLDe9jt37sQwjDofr776qr9dXa8vXbq0Ld5SaLJ7Uucf1oHpswbpSOhkzzlERERERALMFegAmmratGns37+frKwsPB4PM2bMYNasWSxZsqTO9l27dmX//v01tv3tb3/joYce4rzzzqux/dlnn2XSpEn+50lJSa0ef9io6hE7shM8JeCObt3j7/rIWnb/UeseV0REREQkiIREIbZlyxaWLVvGp59+ysiRIwF44oknmDx5MvPnz6dTp9o9J06nk4yMjBrbXn/9dS6//HLi4uJqbE9KSqrVVuoRmwbRKVCSC4e+g8zBrXv83WusZbcxrXtcEREREZEgEhKF2Jo1a0hKSvIXYQDjxo3D4XCwdu1afvKTnzR6jPXr17Np0yYWLFhQ67Ubb7yR6667jl69enH99dczY8YMDMOo91hlZWWUlZX5n+fn5wPg8XjweDzNeWutrur8dsbhTD0Jxw+fUJH9FWbqgNY7cEUprr3rMQBP51EQ4FzWpS3y254pv/ZSfu2l/NpL+bWX8msv5ddewZbfpsYREoVYdnY2HTt2rLHN5XKRkpJCdnZ2k47xzDPPMGDAAE4/veYAEPfddx/nnnsuMTExrFixgl/84hcUFhZy880313usefPmce+999bavmLFCmJiYpoUj92ysrJsO/bg4mh6At9/8g5bdse22nE7FH7DGd5ySl2JLF+zBQwbR2ZsITvzK8qv3ZRfeym/9lJ+7aX82kv5tVew5Le4uLhJ7QJaiN122208+OCDDbbZsmVLi89TUlLCkiVLuPPOO2u9Vn3bsGHDKCoq4qGHHmqwEJs7dy5z5szxP8/Pz6dr165MmDCBhISEFsfbEh6Ph6ysLMaPH4/b7bblHI5P98CK9+iT6KXn5Mmtd9z/boHvIKLv2UyeMqXVjtua2iK/7Znyay/l117Kr72UX3spv/ZSfu0VbPmtulquMQEtxG699VauueaaBtv06tWLjIwMDhw4UGN7RUUFubm5Tbq36x//+AfFxcVcffXVjbYdPXo0999/P2VlZURGRtbZJjIyss7X3G53UHz4YHMs6QMBcBz+FkdrnmOPNZGzo8cZrXtcGwTTZx2OlF97Kb/2Un7tpfzaS/m1l/Jrr2DJb1NjCGghlpaWRlpaWqPtxowZQ15eHuvXr2fEiBEAvPvuu/h8PkaPHt3o/s888wwXXnhhk861adMmkpOT6y3CBOhYeV9Y7vfgKQV3VMuP6a2whq4HzR8mIiIiImEvJO4RGzBgAJMmTWLmzJksXLgQj8fD7NmzmTp1qn/ExL179zJ27Fief/55Ro0a5d9327ZtfPDBB7zzzju1jvvmm2+Sk5PDaaedRlRUFFlZWTzwwAP8+te/brP3FpLi0iEqEUqPwuFtkHFKy4+Z/QWUF1rH7Tiw5ccTEREREQliIVGIAbz44ovMnj2bsWPH4nA4uPTSS3n88cf9r3s8HrZu3Vrr5rhFixbRpUsXJkyYUOuYbrebBQsW8Ktf/QrTNOnTpw8PP/wwM2fOtP39hDTDsOYT+2GtNbFzaxRiuz62lt3GgCNk5hkXERERETkhIVOIpaSk1Dt5M0CPHj0wTbPW9gceeIAHHnigzn0mTZpUYyJnaYa0fpWF2NbWOZ7mDxMRERGRdkRdD3Ji0irvEzvYCkPM+3zHesS6/6jlxxMRERERCXIqxOTEpPWzlq1RiB36FkpywRUNmUNafjwRERERkSCnQkxOTFp/a3l4O1SUt+xYuz6yll1PBVdEy44lIiIiIhICVIjJiUnoBJEJYHohd3vLjuW/P0zD1ouIiIhI+6BCTE6MYbTO5YmmWe3+MBViIiIiItI+qBCTE1dViB1oQSGWtxvy94LDBV1ObZ24RERERESCnAoxOXFV94m1pEesqjes0zCIiGl5TCIiIiIiIUCFmJw4fyHWgrnEduuyRBERERFpf1SIyYnzj5y4DbyeEztGVY+YBuoQERERkXZEhZicuMQuEBEHPg/kft/8/QsPWEUcBnQb3erhiYiIiIgEKxVicuIMA1JPstazv2z+/lW9YeknQ3Ry68UlIiIiIhLkVIhJy/T4kbX8bFHz99Ww9SIiIiLSTqkQk5YZfQM43LDrI9i1pnn7Vg3U0W1M68clIiIiIhLEVIhJyyR2hqFXWusfzm/6fiV5kP2Vta4eMRERERFpZ1SIScv96BYwHLBtJezb1LR9flgHmJDSC+IzbAxORERERCT4qBCTluvQG0651Fr/8M+Nt/d54YM/Wes9z7IvLhERERGRIKVCTFrHGXOs5ZY3G5/g+ePHYc+nEJkAZ/7a/thERERERIKMCjFpHekDof/5gAkfPlx/u5zN8N4D1vqkP0JS1zYJT0REREQkmKgQk9ZzZmWv2JevQu6O2q9XlMPr14O3HE46D4Ze1bbxiYiIiIgECRVi0no6j4Be54DptS4/PN6H8yH7C2vy5gsesyaEFhERERFph1SISes6q/Ker40vQP7+Y9v3boAPKoe3n/IwxKe3fWwiIiIiIkFChZi0ru4/gq6nWZcfrnnS2uYptS5JNL3W6IqnXBLYGEVEREREAkyFmLQuwzjWK/bZIig6DO/9Hg5thbh0mNyMSZ9FRERERMKUK9ABSBjqMw4yh8D+z+GNG+C7Fdb2Cx6HmJTAxiYiIiIiEgTUIyatzzDgzFut9e+WAyYM+yn0mxTQsEREREREgoUKMbFH/wsg9SRrPbErTJwX2HhERERERIKICjGxh8MBU/4MXUbB/yyCqIRARyQiIiIiEjR0j5jYp+dZcF1WoKMQEREREQk66hETERERERFpYyrERERERERE2ljIFGJ/+MMfOP3004mJiSEpKalJ+5imyV133UVmZibR0dGMGzeO7777rkab3Nxcpk2bRkJCAklJSVx77bUUFhba8A5EREREREQsIVOIlZeXc9lll3HDDTc0eZ8//elPPP744yxcuJC1a9cSGxvLxIkTKS0t9beZNm0amzdvJisri7feeosPPviAWbNm2fEWREREREREgBAarOPee+8FYPHixU1qb5omjz76KHfccQcXXXQRAM8//zzp6em88cYbTJ06lS1btrBs2TI+/fRTRo4cCcATTzzB5MmTmT9/Pp06dbLlvYiIiIiISPsWMoVYc+3YsYPs7GzGjRvn35aYmMjo0aNZs2YNU6dOZc2aNSQlJfmLMIBx48bhcDhYu3YtP/nJT+o8dllZGWVlZf7n+fn5AHg8Hjwej03vqGmqzh/oOMKV8msv5ddeyq+9lF97Kb/2Un7tpfzaK9jy29Q4wrYQy87OBiA9Pb3G9vT0dP9r2dnZdOzYscbrLpeLlJQUf5u6zJs3z99DV92KFSuIiYlpaeitIitLw8bbSfm1l/JrL+XXXsqvvZRfeym/9lJ+7RUs+S0uLm5Su4AWYrfddhsPPvhgg222bNlC//792yiippk7dy5z5szxP8/Pz6dr165MmDCBhITATlzs8XjIyspi/PjxuN3ugMYSjpRfeym/9lJ+7aX82kv5tZfyay/l117Blt+qq+UaE9BC7NZbb+Waa65psE2vXr1O6NgZGRkA5OTkkJmZ6d+ek5PD0KFD/W0OHDhQY7+Kigpyc3P9+9clMjKSyMjIWtvdbndQfPgQXLGEI+XXXsqvvZRfeym/9lJ+7aX82kv5tVew5LepMQS0EEtLSyMtLc2WY/fs2ZOMjAxWrVrlL7zy8/NZu3atf+TFMWPGkJeXx/r16xkxYgQA7777Lj6fj9GjR9sSl4iIiIiISMgMX7979242bdrE7t278Xq9bNq0iU2bNtWY86t///68/vrrABiGwS233MLvf/97/v3vf/Pll19y9dVX06lTJy6++GIABgwYwKRJk5g5cybr1q3jo48+Yvbs2UydOlUjJoqIiIiIiG1CZrCOu+66i+eee87/fNiwYQC89957nH322QBs3bqVo0eP+tv89re/paioiFmzZpGXl8cZZ5zBsmXLiIqK8rd58cUXmT17NmPHjsXhcHDppZfy+OOPt82bEhERERGRdilkCrHFixc3OoeYaZo1nhuGwX333cd9991X7z4pKSksWbKkNUIUERERERFpkpApxIJZVQHY1BFS7OTxeCguLiY/Pz8oblYMN8qvvZRfeym/9lJ+7aX82kv5tZfya69gy29VTXB8J9HxVIi1goKCAgC6du0a4EhERERERCQYFBQUkJiYWO/rhtlYqSaN8vl87Nu3j/j4eAzDCGgsVXOa/fDDDwGf0ywcKb/2Un7tpfzaS/m1l/JrL+XXXsqvvYItv6ZpUlBQQKdOnXA46h8bUT1ircDhcNClS5dAh1FDQkJCUHwRw5Xyay/l117Kr72UX3spv/ZSfu2l/NormPLbUE9YlZAZvl5ERERERCRcqBATERERERFpYyrEwkxkZCR33303kZGRgQ4lLCm/9lJ+7aX82kv5tZfyay/l117Kr71CNb8arENERERERKSNqUdMRERERESkjakQExERERERaWMqxERERERERNqYCjEREREREZE2pkIsBC1YsIAePXoQFRXF6NGjWbduXYPtX331Vfr3709UVBSDBg3inXfeaaNIQ8u8efM49dRTiY+Pp2PHjlx88cVs3bq1wX0WL16MYRg1HlFRUW0UcWi55557auWqf//+De6j727T9ejRo1Z+DcPgxhtvrLO9vrsN++CDD7jgggvo1KkThmHwxhtv1HjdNE3uuusuMjMziY6OZty4cXz33XeNHre5f7/DVUP59Xg8/O53v2PQoEHExsbSqVMnrr76avbt29fgMU/kb0y4auz7e80119TK1aRJkxo9rr6/xzSW47r+HhuGwUMPPVTvMfUdtjTl91hpaSk33ngjHTp0IC4ujksvvZScnJwGj3uif7ftpEIsxLz88svMmTOHu+++mw0bNjBkyBAmTpzIgQMH6mz/8ccfc+WVV3LttdeyceNGLr74Yi6++GK++uqrNo48+L3//vvceOONfPLJJ2RlZeHxeJgwYQJFRUUN7peQkMD+/fv9j127drVRxKHn5JNPrpGr//73v/W21Xe3eT799NMauc3KygLgsssuq3cffXfrV1RUxJAhQ1iwYEGdr//pT3/i8ccfZ+HChaxdu5bY2FgmTpxIaWlpvcds7t/vcNZQfouLi9mwYQN33nknGzZs4LXXXmPr1q1ceOGFjR63OX9jwllj31+ASZMm1cjVSy+91OAx9f2tqbEcV8/t/v37WbRoEYZhcOmllzZ4XH2Hm/Z77Fe/+hVvvvkmr776Ku+//z779u3jkksuafC4J/J323amhJRRo0aZN954o/+51+s1O3XqZM6bN6/O9pdffrk5ZcqUGttGjx5t/u///q+tcYaDAwcOmID5/vvv19vm2WefNRMTE9suqBB29913m0OGDGlye313W+aXv/yl2bt3b9Pn89X5ur67TQeYr7/+uv+5z+czMzIyzIceesi/LS8vz4yMjDRfeumleo/T3L/f7cXx+a3LunXrTMDctWtXvW2a+zemvagrv9OnTzcvuuiiZh1H39/6NeU7fNFFF5nnnntug230Ha7b8b/H8vLyTLfbbb766qv+Nlu2bDEBc82aNXUe40T/bttNPWIhpLy8nPXr1zNu3Dj/NofDwbhx41izZk2d+6xZs6ZGe4CJEyfW216OOXr0KAApKSkNtissLKR79+507dqViy66iM2bN7dFeCHpu+++o1OnTvTq1Ytp06axe/fuetvqu3viysvLeeGFF/j5z3+OYRj1ttN398Ts2LGD7OzsGt/PxMRERo8eXe/380T+fssxR48exTAMkpKSGmzXnL8x7d3q1avp2LEj/fr144YbbuDw4cP1ttX3t2VycnJ4++23ufbaaxttq+9wbcf/Hlu/fj0ej6fG97F///5069at3u/jifzdbgsqxELIoUOH8Hq9pKen19ienp5OdnZ2nftkZ2c3q71YfD4ft9xyCz/60Y845ZRT6m3Xr18/Fi1axL/+9S9eeOEFfD4fp59+Onv27GnDaEPD6NGjWbx4McuWLeOpp55ix44dnHnmmRQUFNTZXt/dE/fGG2+Ql5fHNddcU28bfXdPXNV3sDnfzxP5+y2W0tJSfve733HllVeSkJBQb7vm/o1pzyZNmsTzzz/PqlWrePDBB3n//fc577zz8Hq9dbbX97dlnnvuOeLj4xu9dE7f4drq+j2WnZ1NRERErX+Yaez3cFWbpu7TFlwBO7NIELvxxhv56quvGr02e8yYMYwZM8b//PTTT2fAgAH89a9/5f7777c7zJBy3nnn+dcHDx7M6NGj6d69O6+88kqT/pVQmu6ZZ57hvPPOo1OnTvW20XdXQoHH4+Hyyy/HNE2eeuqpBtvqb0zTTZ061b8+aNAgBg8eTO/evVm9ejVjx44NYGThadGiRUybNq3RAZH0Ha6tqb/HQpV6xEJIamoqTqez1qgwOTk5ZGRk1LlPRkZGs9oLzJ49m7feeov33nuPLl26NGtft9vNsGHD2LZtm03RhY+kpCROOumkenOl7+6J2bVrFytXruS6665r1n767jZd1XewOd/PE/n73d5VFWG7du0iKyurwd6wujT2N0aO6dWrF6mpqfXmSt/fE/fhhx+ydevWZv9NBn2H6/s9lpGRQXl5OXl5eTXaN/Z7uKpNU/dpCyrEQkhERAQjRoxg1apV/m0+n49Vq1bV+Jft6saMGVOjPUBWVla97dsz0zSZPXs2r7/+Ou+++y49e/Zs9jG8Xi9ffvklmZmZNkQYXgoLC9m+fXu9udJ398Q8++yzdOzYkSlTpjRrP313m65nz55kZGTU+H7m5+ezdu3aer+fJ/L3uz2rKsK+++47Vq5cSYcOHZp9jMb+xsgxe/bs4fDhw/XmSt/fE/fMM88wYsQIhgwZ0ux92+t3uLHfYyNGjMDtdtf4Pm7dupXdu3fX+308kb/bbSJgw4TICVm6dKkZGRlpLl682Pz666/NWbNmmUlJSWZ2drZpmqb5s5/9zLztttv87T/66CPT5XKZ8+fPN7ds2WLefffdptvtNr/88stAvYWgdcMNN5iJiYnm6tWrzf379/sfxcXF/jbH5/fee+81ly9fbm7fvt1cv369OXXqVDMqKsrcvHlzIN5CULv11lvN1atXmzt27DA/+ugjc9y4cWZqaqp54MAB0zT13W0NXq/X7Natm/m73/2u1mv67jZPQUGBuXHjRnPjxo0mYD788MPmxo0b/aP2/fGPfzSTkpLMf/3rX+YXX3xhXnTRRWbPnj3NkpIS/zHOPfdc84knnvA/b+zvd3vSUH7Ly8vNCy+80OzSpYu5adOmGn+Py8rK/Mc4Pr+N/Y1pTxrKb0FBgfnrX//aXLNmjbljxw5z5cqV5vDhw82+ffuapaWl/mPo+9uwxv5GmKZpHj161IyJiTGfeuqpOo+h73DdmvJ77Prrrze7detmvvvuu+Znn31mjhkzxhwzZkyN4/Tr18987bXX/M+b8ne7rakQC0FPPPGE2a1bNzMiIsIcNWqU+cknn/hf+/GPf2xOnz69RvtXXnnFPOmkk8yIiAjz5JNPNt9+++02jjg0AHU+nn32WX+b4/N7yy23+D+L9PR0c/LkyeaGDRvaPvgQcMUVV5iZmZlmRESE2blzZ/OKK64wt23b5n9d392WW758uQmYW7durfWavrvN895779X596Aqhz6fz7zzzjvN9PR0MzIy0hw7dmytvHfv3t28++67a2xr6O93e9JQfnfs2FHv3+P33nvPf4zj89vY35j2pKH8FhcXmxMmTDDT0tJMt9ttdu/e3Zw5c2atgkrf34Y19jfCNE3zr3/9qxkdHW3m5eXVeQx9h+vWlN9jJSUl5i9+8QszOTnZjImJMX/yk5+Y+/fvr3Wc6vs05e92WzNM0zTt6WsTERERERGRuugeMRERERERkTamQkxERERERKSNqRATERERERFpYyrERERERERE2pgKMRERERERkTamQkxERERERKSNqRATERERERFpYyrEREREmuiaa67h4osvDnQYIiISBlyBDkBERCQYGIbR4Ot33303jz32GKZptlFEIiISzlSIiYiIAPv37/evv/zyy9x1111s3brVvy0uLo64uLhAhCYiImFIlyaKiIgAGRkZ/kdiYiKGYdTYFhcXV+vSxLPPPpubbrqJW265heTkZNLT0/n73/9OUVERM2bMID4+nj59+vCf//ynxrm++uorzjvvPOLi4khPT+dnP/sZhw4dauN3LCIigaRCTEREpAWee+45UlNTWbduHTfddBM33HADl112GaeffjobNmxgwoQJ/OxnP6O4uBiAvLw8zj33XIYNG8Znn33GsmXLyMnJ4fLLLw/wOxERkbakQkxERKQFhgwZwh133EHfvn2ZO3cuUVFRpKamMnPmTPr27ctdd93F4cOH+eKLLwB48sknGTZsGA888AD9+/dn2LBhLFq0iPfee49vv/02wO9GRETaiu4RExERaYHBgwf7151OJx06dGDQoEH+benp6QAcOHAAgM8//5z33nuvzvvNtm/fzkknnWRzxCIiEgxUiImIiLSA2+2u8dwwjBrbqkZj9Pl8ABQWFnLBBRfw4IMP1jpWZmamjZGKiEgwUSEmIiLShoYPH84///lPevTogcul/w2LiLRXukdMRESkDd14443k5uZy5ZVX8umnn7J9+3aWL1/OjBkz8Hq9gQ5PRETaiAoxERGRNtSpUyc++ugjvF4vEyZMYNCgQdxyyy0kJSXhcOh/yyIi7YVhmqYZ6CBERERERETaE/3Tm4iIiIiISBtTISYiIiIiItLGVIiJiIiIiIi0MRViIiIiIiIibUyFmIiIiIiISBtTISYiIiIiItLGVIiJiIiIiIi0MRViIiIiIiIibUyFmIiIiIiISBtTISYiIiIiItLGVIiJiIiIiIi0MRViIiIiIiIibez/A64Jbm9avODhAAAAAElFTkSuQmCC", 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" ] diff --git a/test/test_solver.py b/test/test_solver.py index d49d3a4c..74625576 100644 --- a/test/test_solver.py +++ b/test/test_solver.py @@ -100,7 +100,7 @@ def test_multiple_tableau_single_step(vf_expensive): None, solver_state=solver_state1, made_jump=False, - path_state=None, + path_state=(None, None), ) out2 = solver2.step( terms, @@ -110,7 +110,7 @@ def test_multiple_tableau_single_step(vf_expensive): None, solver_state=solver_state2, made_jump=False, - path_state=None, + path_state=(None, None), ) out2[2]["k"] = out2[2]["k"][0] + out2[2]["k"][1] assert tree_allclose(out1, out2) @@ -412,10 +412,10 @@ def f2(t, y, args): state = solver.init(terms, t0, t1, y0, args, None) out = solver.step( - terms, t0, t1, y0, args, solver_state=state, made_jump=False, path_state=None + terms, t0, t1, y0, args, solver_state=state, made_jump=False, path_state=(None, None) ) reference_out = reference_solver.step( - terms, t0, t1, y0, args, solver_state=None, made_jump=False, path_state=None + terms, t0, t1, y0, args, solver_state=None, made_jump=False, path_state=(None, None) ) assert tree_allclose(out, reference_out) From 35dc705845c25a79ac1e45403b376564c70321b1 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Fri, 3 Jan 2025 12:28:01 -0700 Subject: [PATCH 07/50] work --- benchmarks/stateful_paths.py | 7 +- diffrax/_brownian/path.py | 23 ++--- diffrax/_brownian/tree.py | 1 - diffrax/_global_interpolation.py | 1 - diffrax/_integrate.py | 31 +------ diffrax/_local_interpolation.py | 1 - diffrax/_path.py | 3 +- diffrax/_solution.py | 1 - diffrax/_solver/foster_langevin_srk.py | 1 + diffrax/_solver/runge_kutta.py | 7 +- diffrax/_solver/srk.py | 6 +- diffrax/_term.py | 99 ++++++++++++++++++--- examples/underdamped_langevin_example.ipynb | 53 ++++++----- test/test_solver.py | 18 +++- test/test_term.py | 4 +- test/test_underdamped_langevin.py | 1 + 16 files changed, 159 insertions(+), 98 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 97b35853..9d825290 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -1,5 +1,5 @@ import math -from typing import cast, Union +from typing import cast, Optional, Union import diffrax import equinox as eqx @@ -23,14 +23,14 @@ class OldBrownianPath(diffrax.AbstractBrownianPath): ] ] = eqx.field(static=True) key: PRNGKeyArray - precompute: bool = eqx.field(static=True) + precompute: Optional[int] = eqx.field(static=True) def __init__( self, shape, key, levy_area=diffrax.BrownianIncrement, - precompute=False, + precompute=None, ): self.shape = ( jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) @@ -61,7 +61,6 @@ def init( t1, y0, args, - max_steps, ): return None diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 32586437..beafe5ff 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -77,7 +77,7 @@ class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = eqx.field(static=True) key: PRNGKeyArray - precompute: bool = eqx.field(static=True) + precompute: Optional[int] = eqx.field(static=True) def __init__( self, @@ -86,7 +86,7 @@ def __init__( levy_area: type[ Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = BrownianIncrement, - precompute: bool = False, + precompute: Optional[int] = None, ): self.shape = ( jax.ShapeDtypeStruct(shape, lxi.default_floating_dtype()) @@ -119,16 +119,9 @@ def _generate_noise( ) -> Float[Array, "levy_dims shape"]: # TODO: merge into a single jr.normal call if self.levy_area is SpaceTimeTimeLevyArea: - key_w, key_hh, key_kk = jr.split(key, 3) - w = jr.normal(key_w, (max_steps, *shape.shape), shape.dtype) - hh = jr.normal(key_hh, (max_steps, *shape.shape), shape.dtype) - kk = jr.normal(key_kk, (max_steps, *shape.shape), shape.dtype) - noise = jnp.stack([w, hh, kk], axis=1) + noise = jr.normal(key, (3, max_steps, *shape.shape), shape.dtype) elif self.levy_area is SpaceTimeLevyArea: - key_w, key_hh = jr.split(key, 2) - w = jr.normal(key_w, (max_steps, *shape.shape), shape.dtype) - hh = jr.normal(key_hh, (max_steps, *shape.shape), shape.dtype) - noise = jnp.stack([w, hh], axis=1) + noise = jr.normal(key, (2, max_steps, *shape.shape), shape.dtype) elif self.levy_area is BrownianIncrement: noise = jr.normal(key, (max_steps, *shape.shape), shape.dtype) else: @@ -142,9 +135,9 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> _BrownianState: - if max_steps is not None and self.precompute: + if self.precompute is not None: + max_steps = self.precompute subkey = split_by_tree(self.key, self.shape) noise = jtu.tree_map( lambda subkey, shape: self._generate_noise(subkey, shape, max_steps), @@ -196,7 +189,7 @@ def __call__( out = levy_tree_transpose(self.shape, out) assert isinstance(out, self.levy_area) # if a solver needs to call .evaluate twice, but wants access to the same - # brownian motion, the solver could just decrease the counter + # brownian motion, the solver could just use the same original state return out, (None, noises, counter + 1) else: assert noises is None and counter is None and key is not None @@ -339,7 +332,7 @@ def _evaluate_leaf( - `key`: A random key. - `levy_area`: Whether to additionally generate Lévy area. This is required by some SDE solvers. -- `precompute`: Whether or not to precompute the brownian motion (if possible). +- `precompute`: Size of array to precompute the brownian motion (if possible). Precomputing requires additional memory at initialization time, but can result in faster integrations. Some thought may be required before enabling this, as solvers which require multiple brownian increments may result in index out of bounds diff --git a/diffrax/_brownian/tree.py b/diffrax/_brownian/tree.py index fc550629..d3ef0fd6 100644 --- a/diffrax/_brownian/tree.py +++ b/diffrax/_brownian/tree.py @@ -309,7 +309,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> _BrownianState: return None diff --git a/diffrax/_global_interpolation.py b/diffrax/_global_interpolation.py index 270c3986..2ad4f22a 100644 --- a/diffrax/_global_interpolation.py +++ b/diffrax/_global_interpolation.py @@ -63,7 +63,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> _PathState: return None diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index b3b65959..81fd296f 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -41,7 +41,6 @@ from ._global_interpolation import DenseInterpolation from ._heuristics import is_sde, is_unsafe_sde from ._misc import linear_rescale, static_select -from ._path import AbstractPath from ._progress_meter import ( AbstractProgressMeter, NoProgressMeter, @@ -67,11 +66,9 @@ StepTo, ) from ._term import ( - _AbstractControlTerm, AbstractTerm, MultiTerm, ODETerm, - UnderdampedLangevinDiffusionTerm, WrapTerm, ) from ._typing import better_isinstance, get_args_of, get_origin_no_specials @@ -348,6 +345,7 @@ def body_fun_aux(state): # Actually do some differential equation solving! Make numerical steps, adapt # step sizes, all that jazz. # + (y, y_error, dense_info, solver_state, path_state, solver_result) = solver.step( terms, state.tprev, @@ -1106,27 +1104,8 @@ def _promote(yi): ) terms = MultiTerm(*terms) - def _path_init(term, end): - if isinstance(term, _AbstractControlTerm) or isinstance( - term, UnderdampedLangevinDiffusionTerm - ): - if isinstance(term.control, AbstractPath): - return term.control.init(t0, end, y0, args, max_steps) - return None - elif isinstance(term, MultiTerm): - return jax.tree.map( - lambda x: _path_init(x, end), - term.terms, - is_leaf=lambda x: isinstance(x, AbstractTerm), - ) - return None - if path_state is None: - path_state = jtu.tree_map( - lambda x: _path_init(x, t1), - terms, - is_leaf=lambda x: isinstance(x, AbstractTerm), - ) + path_state = terms.init(t0, t1, y0, args) # Error checking for term compatibility _assert_term_compatible( @@ -1273,11 +1252,7 @@ def _subsaveat_direction_fn(x): if path_state is None: passed_path_state = False - path_state = jtu.tree_map( - lambda x: _path_init(x, tnext), - terms, - is_leaf=lambda x: isinstance(x, AbstractTerm), - ) + path_state = terms.init(t0, tnext, y0, args) else: passed_path_state = True diff --git a/diffrax/_local_interpolation.py b/diffrax/_local_interpolation.py index 7e0e598b..3902e562 100644 --- a/diffrax/_local_interpolation.py +++ b/diffrax/_local_interpolation.py @@ -32,7 +32,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> _PathState: return None diff --git a/diffrax/_path.py b/diffrax/_path.py index d9e4a2bc..7ac1939b 100644 --- a/diffrax/_path.py +++ b/diffrax/_path.py @@ -55,7 +55,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> _PathState: """Initialises any hidden state for the path. @@ -139,6 +138,8 @@ def evaluate( The increment of the path between `t0` and `t1`. """ + # make a stateful derivative or just make user do this with jvp? + # idk where this is used, hard for me to say def derivative(self, t: RealScalarLike, left: bool = True) -> _Control: r"""Evaluate the derivative of the path. Essentially equivalent to `jax.jvp(self.evaluate, (t,), (jnp.ones_like(t),))` (and indeed this is its diff --git a/diffrax/_solution.py b/diffrax/_solution.py index 65ab53c1..cf9aa82b 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -130,7 +130,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> None: return None diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index 759198c0..4f648722 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -390,6 +390,7 @@ def step( drift, diffusion = terms.terms drift_path, diffusion_path = path_state + h, drift_path = drift.contr(t0, t1, drift_path) h_prev = st.h tay: PyTree[_Coeffs] = st.taylor_coeffs diff --git a/diffrax/_solver/runge_kutta.py b/diffrax/_solver/runge_kutta.py index 2b336bb4..704ded30 100644 --- a/diffrax/_solver/runge_kutta.py +++ b/diffrax/_solver/runge_kutta.py @@ -663,8 +663,11 @@ def _get_implicit_impl(term, x): # control, new_path_state = jtu.tree_map(lambda x) if isinstance(tableaus, ButcherTableau): control, new_path_state = tableau_mapped - else: # tuple of butchers - control, new_path_state = tuple(i[0] for i in tableau_mapped), tuple(i[1] for i in tableau_mapped) + else: # tuple of butchers + control, new_path_state = ( + tuple(i[0] for i in tableau_mapped), + tuple(i[1] for i in tableau_mapped), + ) if implicit_tableau is None: implicit_control = _unused diff --git a/diffrax/_solver/srk.py b/diffrax/_solver/srk.py index 7877e319..c24d1126 100644 --- a/diffrax/_solver/srk.py +++ b/diffrax/_solver/srk.py @@ -344,6 +344,8 @@ def step( dtype = jnp.result_type(*jtu.tree_leaves(y0)) drift, diffusion = terms.terms + drift_path, diffusion_path = path_state + if self.tableau.ignore_stage_f is None: ignore_stage_f = None else: @@ -380,7 +382,7 @@ def make_zeros_aux(leaf): # Now the diffusion related stuff # Brownian increment (and space-time Lévy area) - bm_inc, path_state = diffusion.contr(t0, t1, path_state, use_levy=True) + bm_inc, diffusion_path = diffusion.contr(t0, t1, diffusion_path, use_levy=True) if not isinstance(bm_inc, self.minimal_levy_area): raise ValueError( f"The Brownian increment {bm_inc} does not have the " @@ -661,7 +663,7 @@ def compute_and_insert_kg_j(_w_kgs_in, _levylist_kgs_in): y1 = (y0**ω + drift_result**ω + diffusion_result**ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, error, dense_info, None, path_state, RESULTS.successful + return y1, error, dense_info, None, (drift_path, diffusion_path), RESULTS.successful def func( self, diff --git a/diffrax/_term.py b/diffrax/_term.py index d3027f39..31d44dce 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -31,6 +31,8 @@ _VF = TypeVar("_VF", bound=VF) _Control = TypeVar("_Control", bound=Control) _ControlState = TypeVar("_ControlState") +_PathState: TypeAlias = PyTree +# should probably make the typing of this better/more consistent class AbstractTerm(eqx.Module, Generic[_VF, _Control, _ControlState]): @@ -62,6 +64,23 @@ def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: """ pass + @abc.abstractmethod + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + """Initialises any hidden state for the path. + + **Arguments** as [`diffrax.diffeqsolve`][]. + + **Returns:** + + The initial path state. + """ + @abc.abstractmethod def contr( self, @@ -197,6 +216,15 @@ class ODETerm(AbstractTerm[_VF, RealScalarLike, None]): vector_field: Callable[[RealScalarLike, Y, Args], _VF] + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> None: + return None + def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: out = self.vector_field(t, y, args) if jtu.tree_structure(out) != jtu.tree_structure(y): @@ -247,6 +275,7 @@ def _mul(v): [`diffrax.diffeqsolve`][]. """ + # question over stateful custom functions comes up here too class _CallableToPath(AbstractPath[_Control, None]): fn: Callable @@ -265,7 +294,6 @@ def init( t1: RealScalarLike, y0: Y, args: Args, - max_steps: Optional[int], ) -> None: return None @@ -284,12 +312,16 @@ def evaluate( return self.fn(t0, t1) +# probably be consistent with path/control naming +_MaybePathState: TypeAlias = Union[PyTree, None] + + def _callable_to_path( x: Union[ AbstractPath[_Control, _ControlState], Callable[[RealScalarLike, RealScalarLike], _Control], ], -) -> AbstractPath[_Control, _ControlState]: +) -> AbstractPath[_Control, _MaybePathState]: if isinstance(x, AbstractPath): return x else: @@ -316,6 +348,17 @@ class _AbstractControlTerm(AbstractTerm[_VF, _Control, _ControlState]): Callable[[RealScalarLike, RealScalarLike], _Control], ] = eqx.field(converter=_callable_to_path) # pyright: ignore + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + if isinstance(self.control, AbstractPath): + return self.control.init(t0, t1, y0, args) + return None + def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: return self.vector_field(t, y, args) @@ -620,6 +663,11 @@ def __init__(self, *terms: AbstractTerm): def vf(self, t: RealScalarLike, y: Y, args: Args) -> tuple[PyTree[ArrayLike], ...]: return tuple(term.vf(t, y, args) for term in self.terms) + def init( + self, t0: RealScalarLike, t1: RealScalarLike, y0: Y, args: Args + ) -> tuple[PyTree, ...]: + return tuple(term.init(t0, t1, y0, args) for term in self.terms) + def contr( self, t0: RealScalarLike, @@ -627,6 +675,7 @@ def contr( control_state: PyTree, **kwargs, ) -> tuple[tuple[PyTree[ArrayLike], ...], tuple[PyTree, ...]]: + contrs = [ term.contr(t0, t1, state, **kwargs) for term, state in zip(self.terms, control_state) @@ -673,6 +722,15 @@ def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: t = t * self.direction return self.term.vf(t, y, args) + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + return self.term.init(t0, t1, y0, args) + def contr( self, t0: RealScalarLike, @@ -726,6 +784,15 @@ def is_vf_expensive( else: return True + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + return self.term.init(t0, t1, y0, args) + def vf( self, t: RealScalarLike, @@ -753,15 +820,7 @@ def vf( # The value of `control` is never actually used -- just its shape, dtype, and # PyTree structure. (This is because `self.vf_prod` is linear in `control`.) - contr_state_struct = None - if isinstance(self.term, _AbstractControlTerm) or isinstance( - self.term, UnderdampedLangevinDiffusionTerm - ): - if isinstance(self.term.control, AbstractPath): - # contr_state_struct = eqx.filter_eval_shape( - # self.term.control.init, t, t, y, args, None - # ) - contr_state_struct = self.term.control.init(t, t, y, args, None) + contr_state_struct = self.init(t, t, y, args) control, _ = self.contr(t, t, contr_state_struct) y_size = sum(np.size(yi) for yi in jtu.tree_leaves(y)) @@ -957,6 +1016,15 @@ def __init__( self.u = u self.control = bm + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + return self.control.init(t0, t1, y0, args) + def vf( self, t: RealScalarLike, y: UnderdampedLangevinTuple, args: Args ) -> UnderdampedLangevinX: @@ -1036,6 +1104,15 @@ def __init__( self.u = u self.grad_f = grad_f + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> None: + return None + def vf( self, t: RealScalarLike, y: UnderdampedLangevinTuple, args: Args ) -> UnderdampedLangevinTuple: diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index fd37447e..ca01dda1 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "9deba250066ddc39", "metadata": { "ExecuteTime": { @@ -46,14 +46,21 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(None, None)\n" + ] + } + ], "source": [ "from warnings import simplefilter\n", "\n", "\n", "simplefilter(action=\"ignore\", category=FutureWarning)\n", "import diffrax\n", - "import jax\n", "import jax.numpy as jnp\n", "import jax.random as jr\n", "import matplotlib.pyplot as plt\n", @@ -75,39 +82,23 @@ " t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", ")\n", "# bm = diffrax.UnsafeBrownianPath(\n", - "# shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, precompute=True\n", + "# shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea,\n", + "# precompute=1000\n", "# )\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", - "terms = drift_term #diffrax.MultiTerm(drift_term, diffusion_term)\n", + "terms = drift_term # diffrax.MultiTerm(drift_term, diffusion_term)\n", "terms = diffrax.MultiTerm(\n", " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x),\n", - " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: - x)\n", + " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: -x),\n", ")\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", "solver = diffrax.Tsit5()\n", "\n", - "\n", - "def _path_init(term):\n", - " if isinstance(term, diffrax.ControlTerm) or isinstance(\n", - " term, diffrax.UnderdampedLangevinDiffusionTerm\n", - " ):\n", - " return term.control.init(t0, t1, y0, None, 4096)\n", - " elif isinstance(term, diffrax.MultiTerm):\n", - " return jax.tree.map(\n", - " _path_init,\n", - " term.terms,\n", - " is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm),\n", - " )\n", - " return None\n", - "\n", - "\n", - "state = jax.tree.map(\n", - " _path_init, terms, is_leaf=lambda x: isinstance(x, diffrax.AbstractTerm)\n", - ")\n", - "# print(state)\n", + "state = terms.init(t0, t1, y0, None)\n", + "print(state)\n", "# print(terms.contr(t0, t1, state))\n", "\n", "# @eqx.filter_jit\n", @@ -124,14 +115,22 @@ "\n", "\n", "sol = diffrax.diffeqsolve(\n", - " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat#, adjoint=diffrax.BacksolveAdjoint()\n", + " terms,\n", + " solver,\n", + " t0,\n", + " t1,\n", + " dt0=dt0,\n", + " y0=y0,\n", + " args=None,\n", + " saveat=saveat,\n", + " # , adjoint=diffrax.BacksolveAdjoint()\n", ")\n", "xs, vs = sol.ys" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "62da2ddbaaf98f47", "metadata": { "ExecuteTime": { diff --git a/test/test_solver.py b/test/test_solver.py index 74625576..8f12e082 100644 --- a/test/test_solver.py +++ b/test/test_solver.py @@ -412,10 +412,24 @@ def f2(t, y, args): state = solver.init(terms, t0, t1, y0, args, None) out = solver.step( - terms, t0, t1, y0, args, solver_state=state, made_jump=False, path_state=(None, None) + terms, + t0, + t1, + y0, + args, + solver_state=state, + made_jump=False, + path_state=(None, None), ) reference_out = reference_solver.step( - terms, t0, t1, y0, args, solver_state=None, made_jump=False, path_state=(None, None) + terms, + t0, + t1, + y0, + args, + solver_state=None, + made_jump=False, + path_state=(None, None), ) assert tree_allclose(out, reference_out) diff --git a/test/test_term.py b/test/test_term.py index 0e3db380..66fa6ec4 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -34,7 +34,7 @@ class Control(diffrax.AbstractPath[Shaped[Array, "2"], None]): t0 = 0 t1 = 1 - def init(self, t0, t1, y0, args, max_steps): + def init(self, t0, t1, y0, args): return None def __call__(self, t0, path_state: None, t1=None, left=True): @@ -83,7 +83,7 @@ class Control(diffrax.AbstractPath): t0 = 0 t1 = 1 - def init(self, t0, t1, y0, args, max_steps): + def init(self, t0, t1, y0, args): return None def __call__(self, t0, path_state, t1=None, left=True): diff --git a/test/test_underdamped_langevin.py b/test/test_underdamped_langevin.py index e945cad5..84f7316f 100644 --- a/test/test_underdamped_langevin.py +++ b/test/test_underdamped_langevin.py @@ -91,6 +91,7 @@ def test_shape(solver, dtype): sde = get_pytree_uld(t0, t1, dtype) bm = sde.get_bm(jr.key(5678), diffrax.SpaceTimeTimeLevyArea, tol=0.2) terms = sde.get_terms(bm) + print(terms) sol = diffeqsolve( terms, solver, t0, t1, dt0=dt0, y0=sde.y0, args=None, saveat=saveat From 427a594a38481c6d1847be89374cc25256e0557d Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Fri, 3 Jan 2025 15:26:53 -0700 Subject: [PATCH 08/50] some fixes --- diffrax/_integrate.py | 13 ++++++++--- diffrax/_solver/foster_langevin_srk.py | 4 ++-- diffrax/_solver/milstein.py | 32 ++++++++++++++++++++------ diffrax/_solver/semi_implicit_euler.py | 14 ++++++++--- diffrax/_solver/srk.py | 9 +++++++- diffrax/_term.py | 1 - test/test_solver.py | 3 +++ test/test_underdamped_langevin.py | 1 - 8 files changed, 59 insertions(+), 18 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 81fd296f..dfb2a268 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -340,7 +340,6 @@ def cond_fun(state): def body_fun_aux(state): state = _handle_static(state) - # # Actually do some differential equation solving! Make numerical steps, adapt # step sizes, all that jazz. @@ -1105,7 +1104,11 @@ def _promote(yi): terms = MultiTerm(*terms) if path_state is None: - path_state = terms.init(t0, t1, y0, args) + path_state = jax.tree.map( + lambda term: term.init(t0, t1, y0, args), + terms, + is_leaf=lambda x: isinstance(x, AbstractTerm), + ) # Error checking for term compatibility _assert_term_compatible( @@ -1252,7 +1255,11 @@ def _subsaveat_direction_fn(x): if path_state is None: passed_path_state = False - path_state = terms.init(t0, tnext, y0, args) + path_state = jax.tree.map( + lambda term: term.init(t0, tnext, y0, args), + terms, + is_leaf=lambda x: isinstance(x, AbstractTerm), + ) else: passed_path_state = True diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index 4f648722..47e89ee7 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -260,6 +260,7 @@ def init( evaluation of grad_f. """ drift, diffusion = terms.terms + drift_path, diffusion_path = path_state ( gamma_drift, u_drift, @@ -271,7 +272,7 @@ def init( # is this the only solver class that has `init` depend on the path state? # feels irksome to change everything for one class, but I'm going to make # `init` now depend on path state for the sake of generality - h, _ = drift.contr(t0, t1, path_state) + h, _ = drift.contr(t0, t1, drift_path) x0, v0 = y0 gamma = broadcast_underdamped_langevin_arg(gamma_drift, x0, "gamma") @@ -390,7 +391,6 @@ def step( drift, diffusion = terms.terms drift_path, diffusion_path = path_state - h, drift_path = drift.contr(t0, t1, drift_path) h_prev = st.h tay: PyTree[_Coeffs] = st.taylor_coeffs diff --git a/diffrax/_solver/milstein.py b/diffrax/_solver/milstein.py index 945300a2..175c8514 100644 --- a/diffrax/_solver/milstein.py +++ b/diffrax/_solver/milstein.py @@ -69,6 +69,8 @@ def init( ) -> _SolverState: return None + # TODO, a bunch of these solvers have tuple requirements, we can type the + # _PathState to be the same pytree. def step( self, terms: MultiTerm[ @@ -84,9 +86,10 @@ def step( ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump drift, diffusion = terms.terms - # should these be same path state? - dt, _ = drift.contr(t0, t1, path_state) - dw, path_state = diffusion.contr(t0, t1, path_state) + drift_path, diffusion_path = path_state + + dt, drift_path = drift.contr(t0, t1, drift_path) + dw, diffusion_path = diffusion.contr(t0, t1, diffusion_path) f0_prod = drift.vf_prod(t0, y0, args, dt) g0_prod = diffusion.vf_prod(t0, y0, args, dw) @@ -98,7 +101,14 @@ def _to_jvp(_y0): y1 = (y0**ω + f0_prod**ω + g0_prod**ω + 0.5 * v0_prod**ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, path_state, RESULTS.successful + return ( + y1, + None, + dense_info, + None, + (drift_path, diffusion_path), + RESULTS.successful, + ) def func( self, @@ -167,8 +177,9 @@ def step( ) -> tuple[Y, _ErrorEstimate, DenseInfo, _SolverState, _PathState, RESULTS]: del solver_state, made_jump drift, diffusion = terms.terms - Δt, path_state = drift.contr(t0, t1, path_state) - Δw, path_state = diffusion.contr(t0, t1, path_state) + drift_path, diffusion_path = path_state + Δt, drift_path = drift.contr(t0, t1, drift_path) + Δw, diffusion_path = diffusion.contr(t0, t1, diffusion_path) # # So this is a bit involved, largely because of the generality that the rest of @@ -379,7 +390,14 @@ def _dot(_, _v0): # dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, path_state, RESULTS.successful + return ( + y1, + None, + dense_info, + None, + (drift_path, diffusion_path), + RESULTS.successful, + ) def func( self, diff --git a/diffrax/_solver/semi_implicit_euler.py b/diffrax/_solver/semi_implicit_euler.py index 376cd409..8e4c7433 100644 --- a/diffrax/_solver/semi_implicit_euler.py +++ b/diffrax/_solver/semi_implicit_euler.py @@ -62,16 +62,24 @@ def step( del solver_state, made_jump term_1, term_2 = terms + path_state1, path_state2 = path_state y0_1, y0_2 = y0 - control1, path_state = term_1.contr(t0, t1, path_state) - control2, path_state = term_2.contr(t0, t1, path_state) + control1, path_state1 = term_1.contr(t0, t1, path_state1) + control2, path_state2 = term_2.contr(t0, t1, path_state2) y1_1 = (y0_1**ω + term_1.vf_prod(t0, y0_2, args, control1) ** ω).ω y1_2 = (y0_2**ω + term_2.vf_prod(t0, y1_1, args, control2) ** ω).ω y1 = (y1_1, y1_2) dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, path_state, RESULTS.successful + return ( + y1, + None, + dense_info, + None, + (path_state1, path_state2), + RESULTS.successful, + ) def func( self, diff --git a/diffrax/_solver/srk.py b/diffrax/_solver/srk.py index c24d1126..96f802b3 100644 --- a/diffrax/_solver/srk.py +++ b/diffrax/_solver/srk.py @@ -663,7 +663,14 @@ def compute_and_insert_kg_j(_w_kgs_in, _levylist_kgs_in): y1 = (y0**ω + drift_result**ω + diffusion_result**ω).ω dense_info = dict(y0=y0, y1=y1) - return y1, error, dense_info, None, (drift_path, diffusion_path), RESULTS.successful + return ( + y1, + error, + dense_info, + None, + (drift_path, diffusion_path), + RESULTS.successful, + ) def func( self, diff --git a/diffrax/_term.py b/diffrax/_term.py index 31d44dce..7a2f5f5f 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -675,7 +675,6 @@ def contr( control_state: PyTree, **kwargs, ) -> tuple[tuple[PyTree[ArrayLike], ...], tuple[PyTree, ...]]: - contrs = [ term.contr(t0, t1, state, **kwargs) for term, state in zip(self.terms, control_state) diff --git a/test/test_solver.py b/test/test_solver.py index 8f12e082..8f0f08ca 100644 --- a/test/test_solver.py +++ b/test/test_solver.py @@ -205,6 +205,9 @@ def test_everything_pytree(implicit, vf_expensive, adaptive): class Term(diffrax.AbstractTerm): coeff: float + def init(self, t0, t1, y0, args): + return None + def vf(self, t, y, args): return {"f": -self.coeff * y["y"]} diff --git a/test/test_underdamped_langevin.py b/test/test_underdamped_langevin.py index 84f7316f..e945cad5 100644 --- a/test/test_underdamped_langevin.py +++ b/test/test_underdamped_langevin.py @@ -91,7 +91,6 @@ def test_shape(solver, dtype): sde = get_pytree_uld(t0, t1, dtype) bm = sde.get_bm(jr.key(5678), diffrax.SpaceTimeTimeLevyArea, tol=0.2) terms = sde.get_terms(bm) - print(terms) sol = diffeqsolve( terms, solver, t0, t1, dt0=dt0, y0=sde.y0, args=None, saveat=saveat From 29138ed0d365c191a117d2a636bcec0d544b3989 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Fri, 3 Jan 2025 15:52:47 -0700 Subject: [PATCH 09/50] testing work --- benchmarks/stateful_paths.py | 4 ++-- diffrax/_integrate.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 9d825290..180e88ba 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -163,7 +163,7 @@ def _evaluate_leaf( brownian_motion = diffrax.VirtualBrownianTree(t0, t1, tol=1e-3, shape=(), key=key) ubp = OldBrownianPath(shape=(), key=key) new_ubp = diffrax.UnsafeBrownianPath(shape=(), key=key) -new_ubp_pre = diffrax.UnsafeBrownianPath(shape=(), key=key, precompute=True) +new_ubp_pre = diffrax.UnsafeBrownianPath(shape=(), key=key, precompute=ndt + 10) solver = diffrax.Euler() terms = diffrax.MultiTerm( diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, brownian_motion) @@ -177,7 +177,7 @@ def _evaluate_leaf( terms_new_precompute = diffrax.MultiTerm( diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp_pre) ) -saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, ndt)) +saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, 1000)) @jax.jit diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index dfb2a268..0ea1f76e 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -387,7 +387,7 @@ def body_fun_aux(state): tprev = jnp.minimum(tprev, t1) tnext = _clip_to_end(tprev, tnext, t1, keep_step) - + progress_meter_state = progress_meter.step( state.progress_meter_state, linear_rescale(t0, tprev, t1) ) @@ -862,7 +862,7 @@ class SaveAt(eqx.Module): # noqa: F811 t1: bool -# @eqx.filter_jit +@eqx.filter_jit @eqxi.doc_remove_args("discrete_terminating_event") def diffeqsolve( terms: PyTree[AbstractTerm], From 7f76cddfa31fcfd191bfdcaeef612c046be02d87 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Fri, 3 Jan 2025 17:33:24 -0700 Subject: [PATCH 10/50] fixes --- diffrax/_adjoint.py | 3 ++- diffrax/_integrate.py | 32 +++++++++++++++++++++----------- diffrax/_solver/euler_heun.py | 14 +++++++++++--- diffrax/_term.py | 4 +++- test/test_integrate.py | 22 ++++++++++++++++------ 5 files changed, 53 insertions(+), 22 deletions(-) diff --git a/diffrax/_adjoint.py b/diffrax/_adjoint.py index 728ae4ac..66a24b94 100644 --- a/diffrax/_adjoint.py +++ b/diffrax/_adjoint.py @@ -909,9 +909,10 @@ def loop( throw, passed_solver_state, passed_controller_state, + passed_path_state, **kwargs, ): - del throw, passed_solver_state, passed_controller_state + del throw, passed_solver_state, passed_controller_state, passed_path_state inner_while_loop = eqx.Partial(_inner_loop, kind="lax") outer_while_loop = eqx.Partial(_outer_loop, kind="lax") # Support forward-mode autodiff. diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 0ea1f76e..c164156a 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -181,6 +181,7 @@ def _check(term_cls, term, term_contr_kwargs, yi): if not vf_type_compatible: raise ValueError(f"Vector field term {term} is incompatible.") + term_contr_kwargs["control_state"] = term.init(0.0, 0.0, y, args) contr = ft.partial(term.contr, **term_contr_kwargs) # Work around https://github.com/google/jax/issues/21825 try: @@ -387,7 +388,7 @@ def body_fun_aux(state): tprev = jnp.minimum(tprev, t1) tnext = _clip_to_end(tprev, tnext, t1, keep_step) - + progress_meter_state = progress_meter.step( state.progress_meter_state, linear_rescale(t0, tprev, t1) ) @@ -1111,17 +1112,26 @@ def _promote(yi): ) # Error checking for term compatibility + + # try: + # contr_kwargs = jtu.tree_map( + # lambda _, x, y: jtu.tree_map( + # lambda a, b: a | {"control_state": b}, + # x, + # y, + # is_leaf=lambda v: isinstance(v, dict), + # ), + # solver.term_structure, + # solver.term_compatible_contr_kwargs, + # path_state, + # is_leaf=lambda z: isinstance(z, AbstractTerm) + # and not isinstance(z, MultiTerm), + # ) + # except Exception as e: + # raise ValueError("Terms are not compatible with solver!") from e + _assert_term_compatible( - y0, - args, - terms, - solver.term_structure, - jtu.tree_map( - lambda x, y: x | {"control_state": y}, - solver.term_compatible_contr_kwargs, - path_state, - is_leaf=lambda x: isinstance(x, dict), - ), + y0, args, terms, solver.term_structure, solver.term_compatible_contr_kwargs ) if is_sde(terms): diff --git a/diffrax/_solver/euler_heun.py b/diffrax/_solver/euler_heun.py index dc78fe13..70855b62 100644 --- a/diffrax/_solver/euler_heun.py +++ b/diffrax/_solver/euler_heun.py @@ -68,8 +68,9 @@ def step( del solver_state, made_jump drift, diffusion = terms.terms - dt, path_state = drift.contr(t0, t1, path_state) - dW, path_state = diffusion.contr(t0, t1, path_state) + drift_path, diffusion_path = path_state + dt, drift_path = drift.contr(t0, t1, drift_path) + dW, diffusion_path = diffusion.contr(t0, t1, diffusion_path) f0 = drift.vf_prod(t0, y0, args, dt) g0 = diffusion.vf_prod(t0, y0, args, dW) @@ -80,7 +81,14 @@ def step( y1 = (y0**ω + f0**ω + 0.5 * (g0**ω + g_prime**ω)).ω dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, path_state, RESULTS.successful + return ( + y1, + None, + dense_info, + None, + (drift_path, diffusion_path), + RESULTS.successful, + ) def func( self, diff --git a/diffrax/_term.py b/diffrax/_term.py index 7a2f5f5f..f6bf4822 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -777,7 +777,9 @@ def is_vf_expensive( ], args: Args, ) -> bool: - control_struct = eqx.filter_eval_shape(self.contr, t0, t1) + control_struct = eqx.filter_eval_shape( + self.contr, t0, t1, self.term.init(t0, t1, y, args) + ) if sum(c.size for c in jtu.tree_leaves(control_struct)) in (0, 1): return False else: diff --git a/test/test_integrate.py b/test/test_integrate.py index 388b1a51..a84b78a8 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -14,7 +14,7 @@ import scipy.stats from diffrax import ControlTerm, MultiTerm, ODETerm from equinox.internal import ω -from jaxtyping import Array, ArrayLike, Float +from jaxtyping import Array, ArrayLike, Float, PyTree from .helpers import ( all_ode_solvers, @@ -638,6 +638,10 @@ class TestSolver(diffrax.Euler): def test_term_compatibility_pytree(): + class _TestState(eqx.Module): + y: PyTree + state: PyTree + class TestSolver(diffrax.AbstractSolver): term_structure = { "a": diffrax.ODETerm, @@ -661,14 +665,20 @@ def init(self, terms, t0, t1, y0, args, path_state): return None def step(self, terms, t0, t1, y0, args, solver_state, made_jump, path_state): - def _step(_term, _y): - control = _term.contr(t0, t1) - return _y + _term.vf_prod(t0, _y, args, control) + def _step(_term, _y, state): + control, new_state = _term.contr(t0, t1, state) + return _TestState(_y + _term.vf_prod(t0, _y, args, control), new_state) _is_term = lambda x: isinstance(x, diffrax.AbstractTerm) - y1 = jtu.tree_map(_step, terms, y0, is_leaf=_is_term) + output = jtu.tree_map(_step, terms, y0, path_state, is_leaf=_is_term) + y1 = jtu.tree_map( + lambda x: x.y, output, is_leaf=lambda x: isinstance(x, _TestState) + ) + path_state = jtu.tree_map( + lambda x: x.state, output, is_leaf=lambda x: isinstance(x, _TestState) + ) dense_info = dict(y0=y0, y1=y1) - return y1, None, dense_info, None, None, diffrax.RESULTS.successful + return y1, None, dense_info, None, path_state, diffrax.RESULTS.successful def func(self, terms, t0, y0, args): assert False From d24e6f10ff84ca0fe2eed407ecc2606cda63cf79 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sat, 4 Jan 2025 11:12:30 -0700 Subject: [PATCH 11/50] add test --- benchmarks/stateful_paths.py | 54 ++++++++++++--- test/test_adjoint.py | 127 +++++++++++++++++++++++++++++++++++ 2 files changed, 170 insertions(+), 11 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 180e88ba..fbca5ea7 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -152,19 +152,30 @@ def _evaluate_leaf( # https://github.com/patrick-kidger/diffrax/issues/517 key = jax.random.key(42) +# t0 = 0 +# t1 = 100 +# y0 = 1.0 +# ndt = 4000 +# dt = (t1 - t0) / (ndt - 1) +# drift = lambda t, y, args: -y +# diffusion = lambda t, y, args: 0.2 t0 = 0 -t1 = 100 +t1 = 1 y0 = 1.0 -ndt = 4000 +ndt = 40010 dt = (t1 - t0) / (ndt - 1) drift = lambda t, y, args: -y diffusion = lambda t, y, args: 0.2 +# saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, ndt)) +saveat = diffrax.SaveAt(steps=True) brownian_motion = diffrax.VirtualBrownianTree(t0, t1, tol=1e-3, shape=(), key=key) ubp = OldBrownianPath(shape=(), key=key) new_ubp = diffrax.UnsafeBrownianPath(shape=(), key=key) new_ubp_pre = diffrax.UnsafeBrownianPath(shape=(), key=key, precompute=ndt + 10) + solver = diffrax.Euler() + terms = diffrax.MultiTerm( diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, brownian_motion) ) @@ -177,39 +188,52 @@ def _evaluate_leaf( terms_new_precompute = diffrax.MultiTerm( diffrax.ODETerm(drift), diffrax.ControlTerm(diffusion, new_ubp_pre) ) -saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, 1000)) @jax.jit def diffrax_vbt(): - return diffrax.diffeqsolve(terms, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat).ys + return diffrax.diffeqsolve( + terms, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat, throw=False + ).ys @jax.jit def diffrax_old(): return diffrax.diffeqsolve( - terms_old, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + terms_old, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat, throw=False ).ys @jax.jit def diffrax_new(): return diffrax.diffeqsolve( - terms_new, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + terms_new, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat, throw=False ).ys @jax.jit def diffrax_new_pre(): return diffrax.diffeqsolve( - terms_new_precompute, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat + terms_new_precompute, solver, t0, t1, dt0=dt, y0=y0, saveat=saveat, throw=False ).ys +@jax.jit +def homemade_simu(): + dWs = jnp.sqrt(dt) * jax.random.normal(key, (ndt,)) + + def step(y, dW): + dy = drift(None, y, None) * dt + diffusion(None, y, None) * dW + return y + dy, y + + return jax.lax.scan(step, y0, dWs)[-1] + + _ = diffrax_vbt().block_until_ready() _ = diffrax_old().block_until_ready() _ = diffrax_new().block_until_ready() _ = diffrax_new_pre().block_until_ready() +_ = homemade_simu().block_until_ready() from timeit import Timer @@ -232,12 +256,17 @@ def diffrax_new_pre(): total_time = timer.timeit(number=num_runs) print(f"New UBP + Precompute: {total_time / num_runs:.6f}") +timer = Timer(stmt="_ = homemade_simu().block_until_ready()", globals=globals()) +total_time = timer.timeit(number=num_runs) +print(f"Pure Jax: {total_time / num_runs:.6f}") + """ Results on Mac M1 CPU: -VBT: 0.282765 -Old UBP: 0.015823 -New UBP: 0.013105 -New UBP + Precompute: 0.002506 +VBT: 0.184882 +Old UBP: 0.016347 +New UBP: 0.013731 +New UBP + Precompute: 0.002430 +Pure Jax: 0.002799 Results on A100 GPU: VBT: 3.881952 @@ -245,6 +274,9 @@ def diffrax_new_pre(): New UBP: 0.364158 New UBP + Precompute: 0.325521 +For small ndt (e.g. 100) the pure jax is faster, but the diffrax overhead +becomes less important as the time increases. + GPU being much slower isn't unsurprising and is a common trend for small-medium sized SDEs with VFs that are relatively cheap to evaluate (i.e. not neural networks). diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 12e6ee27..8584bbba 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -214,6 +214,133 @@ def _convert_float0(x): assert tree_allclose(direct_grads, backsolve_grads, atol=1e-5) assert tree_allclose(direct_grads, forward_grads, atol=1e-5) +@pytest.mark.slow +def test_direct_brownian(): + key = jax.random.key(42) + key, subkey = jax.random.split(key) + driftkey, diffusionkey, ykey = jr.split(subkey, 3) + drift_mlp = eqx.nn.MLP( + in_size=3, + out_size=3, + width_size=8, + depth=2, + activation=jax.nn.swish, + final_activation=jnp.tanh, + key=driftkey, + ) + diffusion_mlp = eqx.nn.MLP( + in_size=3, + out_size=3, + width_size=8, + depth=2, + activation=jax.nn.swish, + final_activation=jnp.tanh, + key=diffusionkey, + ) + y0 = jr.normal(ykey, (3,)) + + k1, k2, k3 = jax.random.split(key, 3) + + vbt = diffrax.VirtualBrownianTree( + 0.3, 9.5, 1e-4, (3,), k1, levy_area=diffrax.SpaceTimeLevyArea + ) + dbp = diffrax.UnsafeBrownianPath((3,), k2, levy_area=diffrax.SpaceTimeLevyArea) + dbp_pre = diffrax.UnsafeBrownianPath( + (3,), k3, levy_area=diffrax.SpaceTimeLevyArea, precompute=int(9.5 / 0.1) + ) + + vbt_terms = diffrax.MultiTerm( + diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ControlTerm( + lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), vbt + ), + ) + dbp_terms = diffrax.MultiTerm( + diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ControlTerm( + lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), dbp + ), + ) + dbp_pre_terms = diffrax.MultiTerm( + diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ControlTerm( + lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), dbp_pre + ), + ) + + solver = diffrax.GeneralShARK() + + y0_args_term0 = (y0, None, vbt_terms) + y0_args_term1 = (y0, None, dbp_terms) + y0_args_term2 = (y0, None, dbp_pre_terms) + + def _run(y0__args__term, saveat, adjoint): + y0, args, term = y0__args__term + ys = diffrax.diffeqsolve( + term, + solver, + 0.3, + 9.5, + 0.1, + y0, + args, + saveat=saveat, + adjoint=adjoint, + ).ys + return jnp.sum(cast(Array, ys)) + + # Only does gradients with respect to y0 + def _run_finite_diff(y0__args__term, saveat, adjoint): + y0, args, term = y0__args__term + y0_a = y0 + jnp.array([1e-5, 0, 0]) + y0_b = y0 + jnp.array([0, 1e-5, 0]) + y0_c = y0 + jnp.array([0, 0, 1e-5]) + val = _run((y0, args, term), saveat, adjoint) + val_a = _run((y0_a, args, term), saveat, adjoint) + val_b = _run((y0_b, args, term), saveat, adjoint) + val_c = _run((y0_c, args, term), saveat, adjoint) + out_a = (val_a - val) / 1e-5 + out_b = (val_b - val) / 1e-5 + out_c = (val_c - val) / 1e-5 + return jnp.stack([out_a, out_b, out_c]) + + for t0 in (True, False): + for t1 in (True, False): + for ts in (None, [0.3], [2.0], [9.5], [1.0, 7.0], [0.3, 7.0, 9.5]): + for y0__args__term in (y0_args_term0,):#, y0_args_term1, y0_args_term2): + if t0 is False and t1 is False and ts is None: + continue + + saveat = diffrax.SaveAt(t0=t0, t1=t1, ts=ts) + + inexact, static = eqx.partition( + y0__args__term, eqx.is_inexact_array + ) + + def _run_inexact(inexact, saveat, adjoint): + return _run(eqx.combine(inexact, static), saveat, adjoint) + + _run_grad = eqx.filter_jit(jax.grad(_run_inexact)) + _run_fwd_grad = eqx.filter_jit(jax.jacfwd(_run_inexact)) + + fd_grads = _run_finite_diff( + y0__args__term, saveat, diffrax.RecursiveCheckpointAdjoint() + ) + recursive_grads = _run_grad( + inexact, saveat, diffrax.RecursiveCheckpointAdjoint() + ) + # backsolve_grads = _run_grad( + # inexact, saveat, diffrax.BacksolveAdjoint() + # ) + forward_grads = _run_fwd_grad( + inexact, saveat, diffrax.ForwardMode() + ) + # direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) + # assert tree_allclose(fd_grads, direct_grads[0]) + assert tree_allclose(fd_grads, recursive_grads, atol=1e-5) + # assert tree_allclose(fd_grads, backsolve_grads, atol=1e-5) + assert tree_allclose(fd_grads, forward_grads, atol=1e-5) + def test_adjoint_seminorm(): vector_field = lambda t, y, args: -y From a1374f90ac79e66bfba84ce486ff51f46e02192c Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sat, 4 Jan 2025 17:15:45 -0700 Subject: [PATCH 12/50] tests + examples --- benchmarks/stateful_paths.py | 1 + diffrax/_adjoint.py | 8 ++- diffrax/_brownian/base.py | 3 +- diffrax/_brownian/path.py | 9 +-- examples/underdamped_langevin_example.ipynb | 61 +++------------------ test/test_adjoint.py | 45 ++++++++++----- 6 files changed, 53 insertions(+), 74 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index fbca5ea7..9551ce29 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -268,6 +268,7 @@ def step(y, dW): New UBP + Precompute: 0.002430 Pure Jax: 0.002799 +(these are out of date) Results on A100 GPU: VBT: 3.881952 Old UBP: 0.337173 diff --git a/diffrax/_adjoint.py b/diffrax/_adjoint.py index 66a24b94..6ecd1fd0 100644 --- a/diffrax/_adjoint.py +++ b/diffrax/_adjoint.py @@ -377,6 +377,9 @@ def loop( # "Cannot reverse-mode autodifferentiate when using " # "`UnsafeBrownianPath`." # ) + # if is_unsafe_sde(terms): + # kind = "lax" + # msg = None if max_steps is None: kind = "lax" msg = ( @@ -836,7 +839,10 @@ def loop( raise NotImplementedError( "Cannot use `adjoint=BacksolveAdjoint()` with `saveat=SaveAt(fn=...)`." ) - # is this still true with DirectAdjoint? + # is this still true with DirectBP? + # it seems to give inaccurate results, so not currently, but seems doable + # might just require more careful thinking about path state management + # and more knowledge about continuous adjoints than I have currently if is_unsafe_sde(terms): raise ValueError( "`adjoint=BacksolveAdjoint()` does not support `UnsafeBrownianPath`. " diff --git a/diffrax/_brownian/base.py b/diffrax/_brownian/base.py index e9496960..9dd40e80 100644 --- a/diffrax/_brownian/base.py +++ b/diffrax/_brownian/base.py @@ -9,6 +9,7 @@ BrownianIncrement, RealScalarLike, SpaceTimeLevyArea, + SpaceTimeTimeLevyArea ) from .._path import AbstractPath @@ -20,7 +21,7 @@ class AbstractBrownianPath(AbstractPath[_Control, _BrownianState]): """Abstract base class for all Brownian paths.""" - levy_area: AbstractVar[type[Union[BrownianIncrement, SpaceTimeLevyArea]]] + levy_area: AbstractVar[type[Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea]]] @abc.abstractmethod def __call__( diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index beafe5ff..9bab7eb1 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -17,6 +17,7 @@ BrownianIncrement, levy_tree_transpose, RealScalarLike, + IntScalarLike, SpaceTimeLevyArea, SpaceTimeTimeLevyArea, Y, @@ -31,7 +32,7 @@ _Control = Union[PyTree[Array], AbstractBrownianIncrement] _BrownianState: TypeAlias = Union[ - tuple[None, PyTree[Array], int], tuple[PRNGKeyArray, None, None] + tuple[None, PyTree[Array], IntScalarLike], tuple[PRNGKeyArray, None, None] ] @@ -73,10 +74,10 @@ class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): """ shape: PyTree[jax.ShapeDtypeStruct] = eqx.field(static=True) + key: PRNGKeyArray levy_area: type[ Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ] = eqx.field(static=True) - key: PRNGKeyArray precompute: Optional[int] = eqx.field(static=True) def __init__( @@ -116,7 +117,7 @@ def _generate_noise( key: PRNGKeyArray, shape: jax.ShapeDtypeStruct, max_steps: int, - ) -> Float[Array, "levy_dims shape"]: + ) -> Float[Array, "..."]: # TODO: merge into a single jr.normal call if self.levy_area is SpaceTimeTimeLevyArea: noise = jr.normal(key, (3, max_steps, *shape.shape), shape.dtype) @@ -254,7 +255,7 @@ def _evaluate_leaf_precomputed( Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea] ], use_levy: bool, - noises: Float[Array, "levy_dims shape"], + noises: Float[Array, "..."], ): w_std = jnp.sqrt(t1 - t0).astype(shape.dtype) dt = jnp.asarray(t1 - t0, dtype=complex_to_real_dtype(shape.dtype)) diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index ca01dda1..ffde137e 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -46,15 +46,7 @@ "start_time": "2024-09-01T17:24:06.215228Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(None, None)\n" - ] - } - ], + "outputs": [], "source": [ "from warnings import simplefilter\n", "\n", @@ -68,62 +60,25 @@ "\n", "t0, t1 = 0.0, 20.0\n", "dt0 = 0.05\n", - "saveat = diffrax.SaveAt(ts=jnp.linspace(t0, t1, 100))\n", + "saveat = diffrax.SaveAt(steps=True)\n", "\n", "# Parameters\n", "gamma = jnp.array([2, 0.5], dtype=jnp.float32)\n", "u = jnp.array([0.5, 2], dtype=jnp.float32)\n", - "x0 = jnp.ones((2,), dtype=jnp.float32)\n", - "v0 = jnp.ones((2,), dtype=jnp.float32)\n", + "x0 = jnp.zeros((2,), dtype=jnp.float32)\n", + "v0 = jnp.zeros((2,), dtype=jnp.float32)\n", "y0 = (x0, v0)\n", "\n", "# Brownian motion\n", - "bm = diffrax.VirtualBrownianTree(\n", - " t0, t1, tol=0.01, shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", - ")\n", - "# bm = diffrax.UnsafeBrownianPath(\n", - "# shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea,\n", - "# precompute=1000\n", - "# )\n", + "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea)\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", - "terms = drift_term # diffrax.MultiTerm(drift_term, diffusion_term)\n", - "terms = diffrax.MultiTerm(\n", - " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x),\n", - " diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: -x),\n", - ")\n", + "terms = diffrax.MultiTerm(drift_term, diffusion_term)\n", "\n", "solver = diffrax.QUICSORT(100.0)\n", - "solver = diffrax.Tsit5()\n", - "\n", - "state = terms.init(t0, t1, y0, None)\n", - "print(state)\n", - "# print(terms.contr(t0, t1, state))\n", - "\n", - "# @eqx.filter_jit\n", - "# def f():\n", - "# return diffrax._integrate._assert_term_compatible(\n", - "# y0,\n", - "# None,\n", - "# terms,\n", - "# solver.term_structure,\n", - "# solver.term_compatible_contr_kwargs | {\"control_state\": state},\n", - "# )\n", - "\n", - "# f()\n", - "\n", - "\n", "sol = diffrax.diffeqsolve(\n", - " terms,\n", - " solver,\n", - " t0,\n", - " t1,\n", - " dt0=dt0,\n", - " y0=y0,\n", - " args=None,\n", - " saveat=saveat,\n", - " # , adjoint=diffrax.BacksolveAdjoint()\n", + " terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat\n", ")\n", "xs, vs = sol.ys" ] @@ -141,7 +96,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 8584bbba..f1a41161 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -237,6 +237,18 @@ def test_direct_brownian(): final_activation=jnp.tanh, key=diffusionkey, ) + class Field(eqx.Module): + force: eqx.nn.MLP + + def __call__(self, t, y, args): + return self.force(y) + + class DiffusionField(eqx.Module): + force: eqx.nn.MLP + + def __call__(self, t, y, args): + return lx.DiagonalLinearOperator(self.force(y)) + y0 = jr.normal(ykey, (3,)) k1, k2, k3 = jax.random.split(key, 3) @@ -250,25 +262,25 @@ def test_direct_brownian(): ) vbt_terms = diffrax.MultiTerm( - diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ODETerm(Field(drift_mlp)), diffrax.ControlTerm( - lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), vbt + DiffusionField(diffusion_mlp), vbt ), ) dbp_terms = diffrax.MultiTerm( - diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ODETerm(Field(drift_mlp)), diffrax.ControlTerm( - lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), dbp + DiffusionField(diffusion_mlp), dbp ), ) dbp_pre_terms = diffrax.MultiTerm( - diffrax.ODETerm(lambda t, y, args: drift_mlp(y)), + diffrax.ODETerm(Field(drift_mlp)), diffrax.ControlTerm( - lambda t, y, args: lx.DiagonalLinearOperator(diffusion_mlp(y)), dbp_pre + DiffusionField(diffusion_mlp), dbp_pre ), ) - solver = diffrax.GeneralShARK() + solver = diffrax.Heun() y0_args_term0 = (y0, None, vbt_terms) y0_args_term1 = (y0, None, dbp_terms) @@ -307,7 +319,7 @@ def _run_finite_diff(y0__args__term, saveat, adjoint): for t0 in (True, False): for t1 in (True, False): for ts in (None, [0.3], [2.0], [9.5], [1.0, 7.0], [0.3, 7.0, 9.5]): - for y0__args__term in (y0_args_term0,):#, y0_args_term1, y0_args_term2): + for i, y0__args__term in enumerate((y0_args_term0, y0_args_term1, y0_args_term2)): if t0 is False and t1 is False and ts is None: continue @@ -329,17 +341,20 @@ def _run_inexact(inexact, saveat, adjoint): recursive_grads = _run_grad( inexact, saveat, diffrax.RecursiveCheckpointAdjoint() ) - # backsolve_grads = _run_grad( - # inexact, saveat, diffrax.BacksolveAdjoint() - # ) + if i == 0: + backsolve_grads = _run_grad( + inexact, saveat, diffrax.BacksolveAdjoint() + ) + assert tree_allclose(fd_grads, backsolve_grads[0], atol=1e-3) + forward_grads = _run_fwd_grad( inexact, saveat, diffrax.ForwardMode() ) + # TODO: fix via https://github.com/patrick-kidger/equinox/issues/923 # direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) - # assert tree_allclose(fd_grads, direct_grads[0]) - assert tree_allclose(fd_grads, recursive_grads, atol=1e-5) - # assert tree_allclose(fd_grads, backsolve_grads, atol=1e-5) - assert tree_allclose(fd_grads, forward_grads, atol=1e-5) + # assert tree_allclose(fd_grads, direct_grads[0], atol=1e-3) + assert tree_allclose(fd_grads, recursive_grads[0], atol=1e-3) + assert tree_allclose(fd_grads, forward_grads[0], atol=1e-3) def test_adjoint_seminorm(): From 919abf9a5198612afc536e2439d10466870d9101 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sat, 4 Jan 2025 17:18:59 -0700 Subject: [PATCH 13/50] format --- diffrax/_brownian/base.py | 6 ++++-- diffrax/_brownian/path.py | 2 +- examples/underdamped_langevin_example.ipynb | 4 +++- test/test_adjoint.py | 20 +++++++++----------- 4 files changed, 17 insertions(+), 15 deletions(-) diff --git a/diffrax/_brownian/base.py b/diffrax/_brownian/base.py index 9dd40e80..a4f69045 100644 --- a/diffrax/_brownian/base.py +++ b/diffrax/_brownian/base.py @@ -9,7 +9,7 @@ BrownianIncrement, RealScalarLike, SpaceTimeLevyArea, - SpaceTimeTimeLevyArea + SpaceTimeTimeLevyArea, ) from .._path import AbstractPath @@ -21,7 +21,9 @@ class AbstractBrownianPath(AbstractPath[_Control, _BrownianState]): """Abstract base class for all Brownian paths.""" - levy_area: AbstractVar[type[Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea]]] + levy_area: AbstractVar[ + type[Union[BrownianIncrement, SpaceTimeLevyArea, SpaceTimeTimeLevyArea]] + ] @abc.abstractmethod def __call__( diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 9bab7eb1..522205bd 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -15,9 +15,9 @@ AbstractBrownianIncrement, Args, BrownianIncrement, + IntScalarLike, levy_tree_transpose, RealScalarLike, - IntScalarLike, SpaceTimeLevyArea, SpaceTimeTimeLevyArea, Y, diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index ffde137e..faa02fac 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -70,7 +70,9 @@ "y0 = (x0, v0)\n", "\n", "# Brownian motion\n", - "bm = diffrax.UnsafeBrownianPath(shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea)\n", + "bm = diffrax.UnsafeBrownianPath(\n", + " shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", + ")\n", "\n", "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", diff --git a/test/test_adjoint.py b/test/test_adjoint.py index f1a41161..63e37d77 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -214,6 +214,7 @@ def _convert_float0(x): assert tree_allclose(direct_grads, backsolve_grads, atol=1e-5) assert tree_allclose(direct_grads, forward_grads, atol=1e-5) + @pytest.mark.slow def test_direct_brownian(): key = jax.random.key(42) @@ -237,6 +238,7 @@ def test_direct_brownian(): final_activation=jnp.tanh, key=diffusionkey, ) + class Field(eqx.Module): force: eqx.nn.MLP @@ -263,21 +265,15 @@ def __call__(self, t, y, args): vbt_terms = diffrax.MultiTerm( diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm( - DiffusionField(diffusion_mlp), vbt - ), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), vbt), ) dbp_terms = diffrax.MultiTerm( diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm( - DiffusionField(diffusion_mlp), dbp - ), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp), ) dbp_pre_terms = diffrax.MultiTerm( diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm( - DiffusionField(diffusion_mlp), dbp_pre - ), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp_pre), ) solver = diffrax.Heun() @@ -319,7 +315,9 @@ def _run_finite_diff(y0__args__term, saveat, adjoint): for t0 in (True, False): for t1 in (True, False): for ts in (None, [0.3], [2.0], [9.5], [1.0, 7.0], [0.3, 7.0, 9.5]): - for i, y0__args__term in enumerate((y0_args_term0, y0_args_term1, y0_args_term2)): + for i, y0__args__term in enumerate( + (y0_args_term0, y0_args_term1, y0_args_term2) + ): if t0 is False and t1 is False and ts is None: continue @@ -346,7 +344,7 @@ def _run_inexact(inexact, saveat, adjoint): inexact, saveat, diffrax.BacksolveAdjoint() ) assert tree_allclose(fd_grads, backsolve_grads[0], atol=1e-3) - + forward_grads = _run_fwd_grad( inexact, saveat, diffrax.ForwardMode() ) From 37640edc8c8b0b9303fc771c1a34f450419d0fa4 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Wed, 8 Jan 2025 14:30:44 -0700 Subject: [PATCH 14/50] remove todo --- diffrax/_brownian/path.py | 1 - 1 file changed, 1 deletion(-) diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 522205bd..131352bc 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -118,7 +118,6 @@ def _generate_noise( shape: jax.ShapeDtypeStruct, max_steps: int, ) -> Float[Array, "..."]: - # TODO: merge into a single jr.normal call if self.levy_area is SpaceTimeTimeLevyArea: noise = jr.normal(key, (3, max_steps, *shape.shape), shape.dtype) elif self.levy_area is SpaceTimeLevyArea: From cc0d4bca8fb4ad4a3892a89eb8d3e30cfccba833 Mon Sep 17 00:00:00 2001 From: Riccardo Orsi <104301293+ricor07@users.noreply.github.com> Date: Fri, 27 Dec 2024 11:52:13 +0100 Subject: [PATCH 15/50] Allowing args into grad_f for ULD --- diffrax/_term.py | 8 ++++---- test/test_term.py | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 40 insertions(+), 4 deletions(-) diff --git a/diffrax/_term.py b/diffrax/_term.py index efa28d29..d13d430b 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -925,13 +925,13 @@ class UnderdampedLangevinDriftTerm(AbstractTerm): gamma: PyTree[ArrayLike] u: PyTree[ArrayLike] - grad_f: Callable[[UnderdampedLangevinX], UnderdampedLangevinX] + grad_f: Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX] def __init__( self, gamma: PyTree[ArrayLike], u: PyTree[ArrayLike], - grad_f: Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + grad_f: Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ): r""" **Arguments:** @@ -942,7 +942,7 @@ def __init__( a scalar or a PyTree of the same shape as the position vector $x$. - `grad_f`: A callable representing the gradient of the potential function $f$. This callable should take a PyTree of the same shape as $x$ and - return a PyTree of the same shape. + an optional `args` argument, returning a PyTree of the same shape. """ self.gamma = gamma self.u = u @@ -963,7 +963,7 @@ def fun(_gamma, _u, _v, _f_x): vf_x = v try: - f_x = self.grad_f(x) + f_x = self.grad_f(x, args) # Pass args to grad_f vf_v = jtu.tree_map(fun, gamma, u, v, f_x) except ValueError: raise RuntimeError( diff --git a/test/test_term.py b/test/test_term.py index 5260db2c..8e8bf8be 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -158,3 +158,39 @@ def test_weaklydiagonal_deprecate(): _ = diffrax.WeaklyDiagonalControlTerm( lambda t, y, args: 0.0, lambda t0, t1: jnp.array(t1 - t0) ) + + +def test_underdamped_langevin_drift_term_args(): + """ + Test that the UnderdampedLangevinDriftTerm handles `args` in grad_f correctly. + """ + + # Mock gradient function that uses args + def mock_grad_f(x, args): + return jtu.tree_map(lambda xi, ai: xi + ai, x, args) + + # Mock data + gamma = jnp.array([0.1, 0.2, 0.3]) + u = jnp.array([0.4, 0.5, 0.6]) + x = jnp.array([1.0, 2.0, 3.0]) + v = jnp.array([0.1, 0.2, 0.3]) + args = jnp.array([0.7, 0.8, 0.9]) + y = (x, v) + + # Create instance of the drift term + term = diffrax.UnderdampedLangevinDriftTerm(gamma=gamma, u=u, grad_f=mock_grad_f) + + # Compute the vector field + vf_y = term.vf(0.0, y, args) + + # Extract results + vf_x, vf_v = vf_y + + # Expected results + expected_vf_x = v # By definition, vf_x = v + f_x = x + args # Output of mock_grad_f + expected_vf_v = -gamma * v - u * f_x # Drift term calculation + + # Assertions + assert jnp.allclose(vf_x, expected_vf_x), "vf_x does not match expected results" + assert jnp.allclose(vf_v, expected_vf_v), "vf_v does not match expected results" From d304d9f4e56f5d16ca1df9a752c2aaaba2a341c4 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Wed, 15 Jan 2025 20:07:10 -0800 Subject: [PATCH 16/50] clean up --- diffrax/_integrate.py | 27 ++------------------------- diffrax/_solver/base.py | 3 ++- diffrax/_solver/milstein.py | 28 ++++++++-------------------- test/test_adjoint.py | 13 +++++++++++-- 4 files changed, 23 insertions(+), 48 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 0acb5ef6..ea16c0da 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -1115,32 +1115,8 @@ def _promote(yi): ) terms = MultiTerm(*terms) - if path_state is None: - path_state = jax.tree.map( - lambda term: term.init(t0, t1, y0, args), - terms, - is_leaf=lambda x: isinstance(x, AbstractTerm), - ) - # Error checking for term compatibility - # try: - # contr_kwargs = jtu.tree_map( - # lambda _, x, y: jtu.tree_map( - # lambda a, b: a | {"control_state": b}, - # x, - # y, - # is_leaf=lambda v: isinstance(v, dict), - # ), - # solver.term_structure, - # solver.term_compatible_contr_kwargs, - # path_state, - # is_leaf=lambda z: isinstance(z, AbstractTerm) - # and not isinstance(z, MultiTerm), - # ) - # except Exception as e: - # raise ValueError("Terms are not compatible with solver!") from e - _assert_term_compatible( y0, args, terms, solver.term_structure, solver.term_compatible_contr_kwargs ) @@ -1286,7 +1262,8 @@ def _subsaveat_direction_fn(x): if solver_state is None: passed_solver_state = False - solver_state = solver.init(terms, t0, tnext, y0, args, path_state) + # pyright says it can't be PyTree | None, but None is a PyTree, so it can? + solver_state = solver.init(terms, t0, tnext, y0, args, path_state) # pyright: ignore[reportArgumentType] else: passed_solver_state = True diff --git a/diffrax/_solver/base.py b/diffrax/_solver/base.py index 992fe72b..38287f06 100644 --- a/diffrax/_solver/base.py +++ b/diffrax/_solver/base.py @@ -9,6 +9,7 @@ Optional, Type, TYPE_CHECKING, + TypeAlias, TypeVar, ) @@ -39,7 +40,7 @@ # (thus it was totally general for all solvers, which was like, why is it a type # var then?) In Term it makes sense because control/ode terms are specific # parameterizations of the type var -_PathState = PyTree +_PathState: TypeAlias = PyTree def vector_tree_dot(a, b): diff --git a/diffrax/_solver/milstein.py b/diffrax/_solver/milstein.py index 175c8514..cb2a4e00 100644 --- a/diffrax/_solver/milstein.py +++ b/diffrax/_solver/milstein.py @@ -17,7 +17,7 @@ _ErrorEstimate: TypeAlias = None _SolverState: TypeAlias = None -_PathState: TypeAlias = PyTree +_PathState: TypeAlias = tuple[None, PyTree] # # The best online reference I've found for commutative-noise Milstein is @@ -44,7 +44,7 @@ class StratonovichMilstein(AbstractStratonovichSolver): """ # noqa: E501 term_structure: ClassVar = MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm] ] interpolation_cls: ClassVar[ Callable[..., LocalLinearInterpolation] @@ -58,9 +58,7 @@ def strong_order(self, terms): def init( self, - terms: MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] - ], + terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm]], t0: RealScalarLike, t1: RealScalarLike, y0: Y, @@ -69,13 +67,9 @@ def init( ) -> _SolverState: return None - # TODO, a bunch of these solvers have tuple requirements, we can type the - # _PathState to be the same pytree. def step( self, - terms: MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] - ], + terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm]], t0: RealScalarLike, t1: RealScalarLike, y0: Y, @@ -137,7 +131,7 @@ class ItoMilstein(AbstractItoSolver): """ # noqa: E501 term_structure: ClassVar = MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] + tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm] ] interpolation_cls: ClassVar[ Callable[..., LocalLinearInterpolation] @@ -151,9 +145,7 @@ def strong_order(self, terms): def init( self, - terms: MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] - ], + terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm]], t0: RealScalarLike, t1: RealScalarLike, y0: Y, @@ -164,9 +156,7 @@ def init( def step( self, - terms: MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] - ], + terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm]], t0: RealScalarLike, t1: RealScalarLike, y0: Y, @@ -401,9 +391,7 @@ def _dot(_, _v0): def func( self, - terms: MultiTerm[ - tuple[AbstractTerm[Any, RealScalarLike, _PathState], AbstractTerm] - ], + terms: MultiTerm[tuple[AbstractTerm[Any, RealScalarLike, None], AbstractTerm]], t0: RealScalarLike, y0: Y, args: Args, diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 1e2a6730..e121a939 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -294,6 +294,7 @@ def _run(y0__args__term, saveat, adjoint): args, saveat=saveat, adjoint=adjoint, + max_steps=250, ).ys return jnp.sum(cast(Array, ys)) @@ -349,8 +350,16 @@ def _run_inexact(inexact, saveat, adjoint): inexact, saveat, diffrax.ForwardMode() ) # TODO: fix via https://github.com/patrick-kidger/equinox/issues/923 - # direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) - # assert tree_allclose(fd_grads, direct_grads[0], atol=1e-3) + # turns out this actually only fails for steps >256. Which is weird, + # because thats means 3 vs 2 calls in the base 16. But idk why that + # matter and yields some opaque assertion error. Maybe something to + # do with shapes? AssertionError + # ... + # assert all(all(map(core.typematch, + # j.out_avals, branches_known[0].out_avals)) + # for j in branches_known[1:]) + direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) + assert tree_allclose(fd_grads, direct_grads[0], atol=1e-3) assert tree_allclose(fd_grads, recursive_grads[0], atol=1e-3) assert tree_allclose(fd_grads, forward_grads[0], atol=1e-3) From 1ad8dad58b3afe3bfe9c4d605661c3c84251b313 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Wed, 15 Jan 2025 20:16:09 -0800 Subject: [PATCH 17/50] int --- diffrax/_integrate.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index a2350c93..c9ab6064 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -187,10 +187,10 @@ def _check(term_cls, term, term_contr_kwargs, yi): contr = ft.partial(term.contr, **term_contr_kwargs) # Work around https://github.com/google/jax/issues/21825 try: - control_type = eqx.filter_eval_shape(contr, t, t) + control_type, path_type = eqx.filter_eval_shape(contr, t, t) except Exception as e: raise ValueError(f"Error while tracing {term}.contr: " + str(e)) - control_type_compatible = eqx.filter_eval_shape( + control_type_compatible, path_type_expected = eqx.filter_eval_shape( better_isinstance, control_type, control_type_expected ) if not control_type_compatible: From d0f161c73e96e68e3dd06253d8192b9bf34c4815 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 21 Jan 2025 19:06:50 -0800 Subject: [PATCH 18/50] fix --- diffrax/_brownian/path.py | 4 ++-- test/test_brownian.py | 4 +++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 131352bc..2efbc89f 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -119,9 +119,9 @@ def _generate_noise( max_steps: int, ) -> Float[Array, "..."]: if self.levy_area is SpaceTimeTimeLevyArea: - noise = jr.normal(key, (3, max_steps, *shape.shape), shape.dtype) + noise = jr.normal(key, (max_steps, 3, *shape.shape), shape.dtype) elif self.levy_area is SpaceTimeLevyArea: - noise = jr.normal(key, (2, max_steps, *shape.shape), shape.dtype) + noise = jr.normal(key, (max_steps, 2, *shape.shape), shape.dtype) elif self.levy_area is BrownianIncrement: noise = jr.normal(key, (max_steps, *shape.shape), shape.dtype) else: diff --git a/test/test_brownian.py b/test/test_brownian.py index 3a265019..a534005a 100644 --- a/test/test_brownian.py +++ b/test/test_brownian.py @@ -131,11 +131,13 @@ def test_statistics(ctr, levy_area, use_levy): def _eval(key): if ctr is diffrax.UnsafeBrownianPath: path = ctr(shape=(), key=key, levy_area=levy_area) + state = path.init(t0, t1, None, None) elif ctr is diffrax.VirtualBrownianTree: path = ctr(t0=0, t1=5, tol=2**-5, shape=(), key=key, levy_area=levy_area) + state = path.init(t0, t1, None, None) else: assert False - return path.evaluate(t0, t1, use_levy=use_levy) + return path(t0, state, t1, use_levy=use_levy)[0] values = jax.vmap(_eval)(keys) if use_levy: From 16fedb25640c2cb8bb66de1714ac81483f433931 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 21 Jan 2025 19:16:10 -0800 Subject: [PATCH 19/50] ULD fix --- diffrax/_solver/foster_langevin_srk.py | 17 ++++++++++++----- test/test_underdamped_langevin.py | 4 ++-- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index 47e89ee7..044b7438 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -13,6 +13,7 @@ from .._custom_types import ( AbstractBrownianIncrement, + Args, BoolScalarLike, DenseInfo, RealScalarLike, @@ -50,7 +51,7 @@ def _get_args_from_terms( PyTree, PyTree, PyTree, - Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ]: drift, diffusion = terms.terms if isinstance(drift, WrapTerm): @@ -320,7 +321,7 @@ def shape_check_fun(_x, _g, _u, _fx): coeffs = self._recompute_coeffs(h, gamma, tay_coeffs) rho = jtu.tree_map(lambda c, _u: jnp.sqrt(2 * c * _u), gamma, u) - prev_f = grad_f(x0) if self._is_fsal else None + prev_f = grad_f(x0, args) if self._is_fsal else None state_out = SolverState( gamma=gamma, @@ -386,7 +387,6 @@ def step( _PathState, RESULTS, ]: - del args st = solver_state drift, diffusion = terms.terms drift_path, diffusion_path = path_state @@ -422,12 +422,19 @@ def step( prev_f = st.prev_f else: prev_f = lax.cond( - eqxi.unvmap_any(made_jump), lambda: grad_f(x0), lambda: st.prev_f + eqxi.unvmap_any(made_jump), lambda: grad_f(x0, args), lambda: st.prev_f ) # The actual step computation, handled by the subclass x_out, v_out, f_fsal, error = self._compute_step( - h, levy, x0, v0, (gamma, u, grad_f), coeffs, rho, prev_f + h, + levy, + x0, + v0, + (gamma, u, lambda inp: grad_f(inp, args)), + coeffs, + rho, + prev_f, ) def check_shapes_dtypes(arg, *args): diff --git a/test/test_underdamped_langevin.py b/test/test_underdamped_langevin.py index e945cad5..53a43a24 100644 --- a/test/test_underdamped_langevin.py +++ b/test/test_underdamped_langevin.py @@ -59,7 +59,7 @@ def make_pytree(array_factory): "qq": jnp.ones((), dtype), } - def grad_f(x): + def grad_f(x, _): xa = x["rr"] xb = x["qq"] return {"rr": jtu.tree_map(lambda _x: 0.2 * _x, xa), "qq": xb} @@ -242,7 +242,7 @@ def test_different_args(): u1 = (jnp.array([1, 2]), 1) g2 = (jnp.array([1, 2]), jnp.array([1, 3])) u2 = (jnp.array([1, 2]), jnp.ones((2,))) - grad_f = lambda x: x + grad_f = lambda x, _: x w_shape = ( jax.ShapeDtypeStruct((2,), jnp.float64), From 9a19d68ee62fd308bf323eb653db6c0481cce866 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 21 Jan 2025 20:25:54 -0800 Subject: [PATCH 20/50] more langevin fixes --- diffrax/_integrate.py | 2 +- diffrax/_solver/foster_langevin_srk.py | 2 +- examples/underdamped_langevin_example.ipynb | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index c9ab6064..ec402cb6 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -190,7 +190,7 @@ def _check(term_cls, term, term_contr_kwargs, yi): control_type, path_type = eqx.filter_eval_shape(contr, t, t) except Exception as e: raise ValueError(f"Error while tracing {term}.contr: " + str(e)) - control_type_compatible, path_type_expected = eqx.filter_eval_shape( + control_type_compatible = eqx.filter_eval_shape( better_isinstance, control_type, control_type_expected ) if not control_type_compatible: diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index 044b7438..39435b33 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -297,7 +297,7 @@ def compare_args_fun(arg1, arg2): u = jtu.tree_map(compare_args_fun, u, u_diffusion) try: - grad_f_shape = jax.eval_shape(grad_f, x0) + grad_f_shape = jax.eval_shape(grad_f, x0, args) except ValueError: raise RuntimeError( "The function `grad_f` in the Underdamped Langevin term must be" diff --git a/examples/underdamped_langevin_example.ipynb b/examples/underdamped_langevin_example.ipynb index faa02fac..85d31ff7 100644 --- a/examples/underdamped_langevin_example.ipynb +++ b/examples/underdamped_langevin_example.ipynb @@ -74,7 +74,7 @@ " shape=(2,), key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea\n", ")\n", "\n", - "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x: 2 * x)\n", + "drift_term = diffrax.UnderdampedLangevinDriftTerm(gamma, u, lambda x, _: 2 * x)\n", "diffusion_term = diffrax.UnderdampedLangevinDiffusionTerm(gamma, u, bm)\n", "terms = diffrax.MultiTerm(drift_term, diffusion_term)\n", "\n", @@ -98,7 +98,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 499498269153f86af823170b58e78b7d6965c0b1 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Fri, 24 Jan 2025 12:11:22 -0800 Subject: [PATCH 21/50] adjoit --- test/test_adjoint.py | 25 +++++++++++-------------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/test/test_adjoint.py b/test/test_adjoint.py index e121a939..5bba08fe 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -221,8 +221,8 @@ def test_direct_brownian(): key, subkey = jax.random.split(key) driftkey, diffusionkey, ykey = jr.split(subkey, 3) drift_mlp = eqx.nn.MLP( - in_size=3, - out_size=3, + in_size=2, + out_size=2, width_size=8, depth=2, activation=jax.nn.swish, @@ -230,8 +230,8 @@ def test_direct_brownian(): key=driftkey, ) diffusion_mlp = eqx.nn.MLP( - in_size=3, - out_size=3, + in_size=2, + out_size=2, width_size=8, depth=2, activation=jax.nn.swish, @@ -251,16 +251,16 @@ class DiffusionField(eqx.Module): def __call__(self, t, y, args): return lx.DiagonalLinearOperator(self.force(y)) - y0 = jr.normal(ykey, (3,)) + y0 = jr.normal(ykey, (2,)) k1, k2, k3 = jax.random.split(key, 3) vbt = diffrax.VirtualBrownianTree( - 0.3, 9.5, 1e-4, (3,), k1, levy_area=diffrax.SpaceTimeLevyArea + 0.3, 9.5, 1e-4, (2,), k1, levy_area=diffrax.SpaceTimeLevyArea ) - dbp = diffrax.UnsafeBrownianPath((3,), k2, levy_area=diffrax.SpaceTimeLevyArea) + dbp = diffrax.UnsafeBrownianPath((2,), k2, levy_area=diffrax.SpaceTimeLevyArea) dbp_pre = diffrax.UnsafeBrownianPath( - (3,), k3, levy_area=diffrax.SpaceTimeLevyArea, precompute=int(9.5 / 0.1) + (2,), k3, levy_area=diffrax.SpaceTimeLevyArea, precompute=int(9.5 / 0.1) ) vbt_terms = diffrax.MultiTerm( @@ -301,17 +301,14 @@ def _run(y0__args__term, saveat, adjoint): # Only does gradients with respect to y0 def _run_finite_diff(y0__args__term, saveat, adjoint): y0, args, term = y0__args__term - y0_a = y0 + jnp.array([1e-5, 0, 0]) - y0_b = y0 + jnp.array([0, 1e-5, 0]) - y0_c = y0 + jnp.array([0, 0, 1e-5]) + y0_a = y0 + jnp.array([1e-5, 0]) + y0_b = y0 + jnp.array([0, 1e-5]) val = _run((y0, args, term), saveat, adjoint) val_a = _run((y0_a, args, term), saveat, adjoint) val_b = _run((y0_b, args, term), saveat, adjoint) - val_c = _run((y0_c, args, term), saveat, adjoint) out_a = (val_a - val) / 1e-5 out_b = (val_b - val) / 1e-5 - out_c = (val_c - val) / 1e-5 - return jnp.stack([out_a, out_b, out_c]) + return jnp.stack([out_a, out_b]) for t0 in (True, False): for t1 in (True, False): From 80fef544dbef14d1b4322f77b0f02910804bb942 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 27 Jan 2025 09:39:23 -0800 Subject: [PATCH 22/50] shorten test --- test/test_adjoint.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 5bba08fe..f9dd1f5d 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -216,8 +216,8 @@ def _convert_float0(x): @pytest.mark.slow -def test_direct_brownian(): - key = jax.random.key(42) +def test_direct_brownian(getkey): + key = getkey() key, subkey = jax.random.split(key) driftkey, diffusionkey, ykey = jr.split(subkey, 3) drift_mlp = eqx.nn.MLP( @@ -289,12 +289,12 @@ def _run(y0__args__term, saveat, adjoint): solver, 0.3, 9.5, - 0.1, + 1.0, y0, args, saveat=saveat, adjoint=adjoint, - max_steps=250, + max_steps=250, # see note below ).ys return jnp.sum(cast(Array, ys)) From 1d34946318663d3f032cd27fbdb44b448cd40e26 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 26 Jan 2025 21:00:39 +0100 Subject: [PATCH 23/50] Test fixes for v0.5.0 + args for langevin --- diffrax/_solver/align.py | 4 +++- diffrax/_solver/foster_langevin_srk.py | 19 +++++++++++-------- diffrax/_solver/quicsort.py | 4 +++- diffrax/_solver/should.py | 4 +++- test/helpers.py | 6 ++++-- test/test_brownian.py | 12 ++++++------ test/test_integrate.py | 2 +- test/test_progress_meter.py | 6 ++++++ test/test_sde1.py | 7 +++---- test/test_underdamped_langevin.py | 11 ++++++----- 10 files changed, 46 insertions(+), 29 deletions(-) diff --git a/diffrax/_solver/align.py b/diffrax/_solver/align.py index c6bc6105..433b2779 100644 --- a/diffrax/_solver/align.py +++ b/diffrax/_solver/align.py @@ -6,6 +6,7 @@ from .._custom_types import ( AbstractSpaceTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -156,6 +157,7 @@ def _compute_step( coeffs: _ALIGNCoeffs, rho: UnderdampedLangevinX, prev_f: UnderdampedLangevinX, + args: Args, ) -> tuple[ UnderdampedLangevinX, UnderdampedLangevinX, @@ -176,7 +178,7 @@ def _compute_step( - coeffs.b1**ω * uh**ω * f0**ω + rho**ω * (coeffs.b1**ω * w**ω + coeffs.chh**ω * hh**ω) ).ω - f1 = f(x1) + f1 = f(x1, args) v1 = ( coeffs.beta**ω * v0**ω - u**ω * ((coeffs.a1**ω - coeffs.b1**ω) * f0**ω + coeffs.b1**ω * f1**ω) diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index dbdf3939..47ae3090 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -13,6 +13,7 @@ from .._custom_types import ( AbstractBrownianIncrement, + Args, BoolScalarLike, DenseInfo, RealScalarLike, @@ -37,7 +38,7 @@ UnderdampedLangevinArgs = tuple[ UnderdampedLangevinX, UnderdampedLangevinX, - Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ] @@ -48,7 +49,7 @@ def _get_args_from_terms( PyTree, PyTree, PyTree, - Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ]: drift, diffusion = terms.terms if isinstance(drift, WrapTerm): @@ -255,6 +256,7 @@ def init( evaluation of grad_f. """ drift, diffusion = terms.terms + del diffusion ( gamma_drift, u_drift, @@ -265,6 +267,7 @@ def init( h = drift.contr(t0, t1) x0, v0 = y0 + del v0 gamma = broadcast_underdamped_langevin_arg(gamma_drift, x0, "gamma") u = broadcast_underdamped_langevin_arg(u_drift, x0, "u") @@ -287,7 +290,7 @@ def compare_args_fun(arg1, arg2): u = jtu.tree_map(compare_args_fun, u, u_diffusion) try: - grad_f_shape = jax.eval_shape(grad_f, x0) + grad_f_shape = jax.eval_shape(grad_f, x0, args) except ValueError: raise RuntimeError( "The function `grad_f` in the Underdamped Langevin term must be" @@ -300,7 +303,7 @@ def shape_check_fun(_x, _g, _u, _fx): if not jtu.tree_all(jtu.tree_map(shape_check_fun, x0, gamma, u, grad_f_shape)): raise RuntimeError( - "The shapes and PyTree structures of x0, gamma, u, and grad_f(x0)" + "The shapes and PyTree structures of x0, gamma, u, and grad_f(x0, args)" " must match." ) @@ -311,7 +314,7 @@ def shape_check_fun(_x, _g, _u, _fx): coeffs = self._recompute_coeffs(h, gamma, tay_coeffs) rho = jtu.tree_map(lambda c, _u: jnp.sqrt(2 * c * _u), gamma, u) - prev_f = grad_f(x0) if self._is_fsal else None + prev_f = grad_f(x0, args) if self._is_fsal else None state_out = SolverState( gamma=gamma, @@ -336,6 +339,7 @@ def _compute_step( coeffs: _Coeffs, rho: UnderdampedLangevinX, prev_f: Optional[UnderdampedLangevinX], + args: Args, ) -> tuple[ UnderdampedLangevinX, UnderdampedLangevinX, @@ -369,7 +373,6 @@ def step( ) -> tuple[ UnderdampedLangevinTuple, _ErrorEstimate, DenseInfo, SolverState, RESULTS ]: - del args st = solver_state drift, diffusion = terms.terms @@ -404,12 +407,12 @@ def step( prev_f = st.prev_f else: prev_f = lax.cond( - eqxi.unvmap_any(made_jump), lambda: grad_f(x0), lambda: st.prev_f + eqxi.unvmap_any(made_jump), lambda: grad_f(x0, args), lambda: st.prev_f ) # The actual step computation, handled by the subclass x_out, v_out, f_fsal, error = self._compute_step( - h, levy, x0, v0, (gamma, u, grad_f), coeffs, rho, prev_f + h, levy, x0, v0, (gamma, u, grad_f), coeffs, rho, prev_f, args ) def check_shapes_dtypes(arg, *args): diff --git a/diffrax/_solver/quicsort.py b/diffrax/_solver/quicsort.py index 4f21bd6f..dd7c47f6 100644 --- a/diffrax/_solver/quicsort.py +++ b/diffrax/_solver/quicsort.py @@ -10,6 +10,7 @@ from .._custom_types import ( AbstractSpaceTimeTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -199,6 +200,7 @@ def _compute_step( coeffs: _QUICSORTCoeffs, rho: UnderdampedLangevinX, prev_f: Optional[UnderdampedLangevinX], + args: Args, ) -> tuple[UnderdampedLangevinX, UnderdampedLangevinX, None, None]: del prev_f dtypes = jtu.tree_map(jnp.result_type, x0) @@ -235,7 +237,7 @@ def _extract_coeffs(coeff, index): def fn(carry): x, _f, _ = carry - fx_uh = (f(x) ** ω * uh**ω).ω + fx_uh = (f(x, args) ** ω * uh**ω).ω return x, _f, fx_uh def compute_x2(carry): diff --git a/diffrax/_solver/should.py b/diffrax/_solver/should.py index caab54d3..4999b9de 100644 --- a/diffrax/_solver/should.py +++ b/diffrax/_solver/should.py @@ -6,6 +6,7 @@ from .._custom_types import ( AbstractSpaceTimeTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -198,6 +199,7 @@ def _compute_step( coeffs: _ShOULDCoeffs, rho: UnderdampedLangevinX, prev_f: UnderdampedLangevinX, + args: Args, ) -> tuple[UnderdampedLangevinX, UnderdampedLangevinX, UnderdampedLangevinX, None]: dtypes = jtu.tree_map(jnp.result_type, x0) w: UnderdampedLangevinX = jtu.tree_map(jnp.asarray, levy.W, dtypes) @@ -225,7 +227,7 @@ def _compute_step( def fn(carry): x, _f, _ = carry - fx = f(x) + fx = f(x, args) return x, _f, fx def compute_x2(carry): diff --git a/test/helpers.py b/test/helpers.py index 3eba28a4..67be343f 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -500,7 +500,7 @@ def make_underdamped_langevin_term(gamma, u, grad_f, bm): def get_bqp(t0=0.3, t1=15.0, dtype=jnp.float32): - grad_f_bqp = lambda x: 4 * x * (jnp.square(x) - 1) + grad_f_bqp = lambda x, _: 4 * x * (jnp.square(x) - 1) gamma, u = dtype(0.8), dtype(0.2) y0_bqp = (dtype(0), dtype(0)) w_shape_bqp = () @@ -520,7 +520,9 @@ def get_harmonic_oscillator(t0=0.3, t1=15.0, dtype=jnp.float32): w_shape_hosc = (2,) def get_terms_hosc(bm): - return make_underdamped_langevin_term(gamma_hosc, u_hosc, lambda x: 2 * x, bm) + return make_underdamped_langevin_term( + gamma_hosc, u_hosc, lambda x, _: 2 * x, bm + ) return SDE(get_terms_hosc, None, y0_hosc, t0, t1, w_shape_hosc) diff --git a/test/test_brownian.py b/test/test_brownian.py index 3a265019..126ea245 100644 --- a/test/test_brownian.py +++ b/test/test_brownian.py @@ -123,7 +123,7 @@ def is_tuple_of_ints(obj): def test_statistics(ctr, levy_area, use_levy): # Deterministic key for this test; not using getkey() key = jr.PRNGKey(5678) - num_samples = 60000 + num_samples = 600000 keys = jr.split(key, num_samples) t0, t1 = 0.0, 5.0 dt = t1 - t0 @@ -279,14 +279,14 @@ def _true_cond_stats_whk(bm_s, bm_u, s, r, u): def _conditional_statistics( levy_area, use_levy: bool, tol, spacing, spline: _Spline, min_num_points ): - key = jr.PRNGKey(5678) + key = jr.PRNGKey(5680) bm_key, sample_key, permute_key = jr.split(key, 3) # Get some randomly selected points; not too close to avoid discretisation error. t0 = 0.0 t1 = 8.7 boundary = 0.1 ts = jr.uniform( - sample_key, shape=(100,), minval=t0 + boundary, maxval=t1 - boundary + sample_key, shape=(10000,), minval=t0 + boundary, maxval=t1 - boundary ) sorted_ts = jnp.sort(ts) ts = [] @@ -581,7 +581,7 @@ def test_whk_interpolation(tol, spline): u = jnp.array(5.7, dtype=jnp.float64) bound = 0.0 rs = jr.uniform( - r_key, (100,), dtype=jnp.float64, minval=s + bound, maxval=u - bound + r_key, (1000,), dtype=jnp.float64, minval=s + bound, maxval=u - bound ) path = diffrax.VirtualBrownianTree( t0=s, @@ -672,8 +672,8 @@ def eval_paths(t): assert jnp.all(_pvals_w > 0.1 / _pvals_w.shape[0]) assert jnp.all(_pvals_h > 0.1 / _pvals_h.shape[0]) assert jnp.all(_pvals_k > 0.1 / _pvals_k.shape[0]) - assert jnp.all(jnp.abs(total_mean_err) < 0.005) - assert jnp.all(jnp.abs(total_cov_err) < 0.005) + assert jnp.all(jnp.abs(total_mean_err) < 0.01) + assert jnp.all(jnp.abs(total_cov_err) < 0.01) def test_levy_area_reverse_time(): diff --git a/test/test_integrate.py b/test/test_integrate.py index 555d6ade..424146e5 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -319,7 +319,7 @@ def get_dt_and_controller(level): levy_area=None, ref_solution=None, ) - assert -0.2 < order - theoretical_order < 0.2 + assert -0.3 < order - theoretical_order < 0.3 # Step size deliberately chosen not to divide the time interval diff --git a/test/test_progress_meter.py b/test/test_progress_meter.py index 1c87b035..a9613c9e 100644 --- a/test/test_progress_meter.py +++ b/test/test_progress_meter.py @@ -57,21 +57,25 @@ def solve(t0): ) solve(2.0) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.33%\n20.67%\n31.00%\n41.33%\n51.67%\n62.00%\n72.33%\n82.67%\n93.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.vmap(solve)(jnp.arange(3.0)) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.00%\n20.00%\n30.00%\n40.00%\n50.20%\n60.40%\n70.60%\n80.80%\n91.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.jit(solve)(2.0) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.33%\n20.67%\n31.00%\n41.33%\n51.67%\n62.00%\n72.33%\n82.67%\n93.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.jit(jax.vmap(solve))(jnp.arange(3.0)) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.00%\n20.00%\n30.00%\n40.00%\n50.20%\n60.40%\n70.60%\n80.80%\n91.00%\n100.00%\n" # noqa: E501 assert captured.out == expected @@ -98,6 +102,7 @@ def solve(p): capfd.readouterr() jax.grad(solve)(jnp.array(1.0)) + jax.effects_barrier() captured = capfd.readouterr() if isinstance(progress_meter, diffrax.TextProgressMeter): @@ -108,3 +113,4 @@ def solve(p): assert captured.out == true_out jax.jit(jax.grad(solve))(jnp.array(1.0)) + jax.effects_barrier() diff --git a/test/test_sde1.py b/test/test_sde1.py index b4504872..b50d014f 100644 --- a/test/test_sde1.py +++ b/test/test_sde1.py @@ -89,10 +89,9 @@ def get_dt_and_controller(level): levy_area=None, ref_solution=None, ) - # The upper bound needs to be 0.25, otherwise we fail. - # This still preserves a 0.05 buffer between the intervals - # corresponding to the different orders. - assert -0.2 < order - theoretical_order < 0.25 + # TODO: this is a pretty wide range to check. Maybe fixable by being better about + # the randomness (e.g. average over multiple original seeds)? + assert -0.4 < order - theoretical_order < 0.4 # Make variables to store the correct solutions in. diff --git a/test/test_underdamped_langevin.py b/test/test_underdamped_langevin.py index e945cad5..246506bb 100644 --- a/test/test_underdamped_langevin.py +++ b/test/test_underdamped_langevin.py @@ -59,7 +59,7 @@ def make_pytree(array_factory): "qq": jnp.ones((), dtype), } - def grad_f(x): + def grad_f(x, _): xa = x["rr"] xb = x["qq"] return {"rr": jtu.tree_map(lambda _x: 0.2 * _x, xa), "qq": xb} @@ -218,7 +218,7 @@ def test_reverse_solve(solver_cls): key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, ) - terms = make_underdamped_langevin_term(gamma, u, lambda x: 2 * x, bm) + terms = make_underdamped_langevin_term(gamma, u, lambda x, _: 2 * x, bm) solver = solver_cls(0.01) sol = diffeqsolve(terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat) @@ -234,7 +234,8 @@ def test_reverse_solve(solver_cls): # Here we check that if the drift and diffusion term have different arguments, # an error is thrown. -def test_different_args(): +@pytest.mark.parametrize("solver_cls", _only_uld_solvers_cls()) +def test_different_args(solver_cls): x0 = (jnp.ones(2), jnp.zeros(2)) v0 = (jnp.zeros(2), jnp.zeros(2)) y0 = (x0, v0) @@ -242,7 +243,7 @@ def test_different_args(): u1 = (jnp.array([1, 2]), 1) g2 = (jnp.array([1, 2]), jnp.array([1, 3])) u2 = (jnp.array([1, 2]), jnp.ones((2,))) - grad_f = lambda x: x + grad_f = lambda x, args: x w_shape = ( jax.ShapeDtypeStruct((2,), jnp.float64), @@ -267,7 +268,7 @@ def test_different_args(): diffusion_term_b = diffrax.UnderdampedLangevinDiffusionTerm(g1, u2, bm) terms_b = diffrax.MultiTerm(drift_term, diffusion_term_b) - solver = diffrax.ShOULD(0.01) + solver = solver_cls(0.01) with pytest.raises(Exception): diffeqsolve(terms_a, solver, 0, 1, 0.1, y0, args=None) diffeqsolve(terms_b, solver, 0, 1, 0.1, y0, args=None) From 1067c1078da995d9697d36c8dd4a7a9835a2fbb4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lukas=20K=C3=B6nig?= Date: Tue, 28 Jan 2025 18:29:38 +0100 Subject: [PATCH 24/50] Fix for making vmap over diffeqsolve possible (#578) * _integrate.py * Added new test checking gradient of vmapped diffeqsolve * Import optimistix * Fixed issue * added .any() * diffrax root finder --- diffrax/_integrate.py | 3 ++- test/test_integrate.py | 47 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 1 deletion(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index cacc1070..5f6d05d5 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -649,7 +649,8 @@ def body_fun(state): event_mask = final_state.event_mask flat_mask = jtu.tree_leaves(event_mask) assert all(jnp.shape(x) == () for x in flat_mask) - event_happened = jnp.any(jnp.stack(flat_mask)) + float_mask = jnp.array(flat_mask).astype(jnp.float32) + event_happened = jnp.max(float_mask) > 0.0 def _root_find(): _interpolator = solver.interpolation_cls( diff --git a/test/test_integrate.py b/test/test_integrate.py index 424146e5..dbfeee03 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -792,3 +792,50 @@ def func(self, terms, t0, y0, args): ValueError, match=r"Terms are not compatible with solver!" ): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, y0) + + +def test_vmap_backprop(): + def dynamics(t, y, args): + param = args + return param - y + + def event_fn(t, y, args, **kwargs): + return y - 1.5 + + def single_loss_fn(param): + solver = diffrax.Euler() + root_finder = diffrax.VeryChord(rtol=1e-3, atol=1e-6) + event = diffrax.Event(event_fn, root_finder) + term = diffrax.ODETerm(dynamics) + sol = diffrax.diffeqsolve( + term, + solver=solver, + t0=0.0, + t1=2.0, + dt0=0.1, + y0=0.0, + args=param, + event=event, + max_steps=1000, + ) + assert sol.ys is not None + final_y = sol.ys[-1] + return param**2 + final_y**2 + + def batched_loss_fn(params: jnp.ndarray) -> jnp.ndarray: + return jax.vmap(single_loss_fn)(params) + + def grad_fn(params: jnp.ndarray) -> jnp.ndarray: + return jax.grad(lambda p: jnp.sum(batched_loss_fn(p)))(params) + + batch = jnp.array([1.0, 2.0, 3.0]) + + try: + grad = grad_fn(batch) + except NotImplementedError as e: + pytest.fail(f"NotImplementedError was raised: {e}") + except Exception as e: + pytest.fail(f"An unexpected exception was raised: {e}") + + assert not jnp.isnan(grad).any(), "Gradient should not be NaN." + assert not jnp.isinf(grad).any(), "Gradient should not be infinite." From 5366e65170ab4c4de8e721026ea9cde7179b1a46 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 18:30:50 +0100 Subject: [PATCH 25/50] Tweak test name --- test/test_integrate.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/test_integrate.py b/test/test_integrate.py index dbfeee03..d8ca4360 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -794,7 +794,8 @@ def func(self, terms, t0, y0, args): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, y0) -def test_vmap_backprop(): +# Test that we don't hit a JAX bug: https://github.com/patrick-kidger/diffrax/issues/568 +def test_vmap_backprop_with_event(): def dynamics(t, y, args): param = args return param - y From 54e9e779837990a5abd4d92a421e9f2df0540225 Mon Sep 17 00:00:00 2001 From: joharkit <98756257+joharkit@users.noreply.github.com> Date: Tue, 28 Jan 2025 18:54:12 +0100 Subject: [PATCH 26/50] Update pyproject.toml to meet poetry conventions in python-poetry ~=3.9 is interpreted as >=3.9<3.10 [2], though it should be >=3.9,<4.0 [2] https://python-poetry.org/docs/dependency-specification/ --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 01cacf52..0d56b739 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "diffrax" version = "0.6.2" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" -requires-python ="~=3.9" +requires-python =">=3.9,<4.0" license = {file = "LICENSE"} authors = [ {name = "Patrick Kidger", email = "contact@kidger.site"}, From 92fb93dd4d91fe09c506ddaa2f5c5840c6016f6b Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 12 Jan 2025 21:45:09 +0100 Subject: [PATCH 27/50] Fixed a major source of bugs: ControlTerms no longer broadcast. --- diffrax/_term.py | 295 +++++++++++++++++++++++++++++------------ docs/api/terms.md | 4 +- pyproject.toml | 2 +- test/test_adjoint.py | 3 +- test/test_integrate.py | 63 ++++++--- test/test_sde2.py | 4 +- test/test_term.py | 8 +- test/test_typing.py | 5 - 8 files changed, 266 insertions(+), 118 deletions(-) diff --git a/diffrax/_term.py b/diffrax/_term.py index d13d430b..0ea97301 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -256,7 +256,7 @@ def _callable_to_path( x: Union[ AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] ], -) -> AbstractPath[_Control]: +) -> AbstractPath: if isinstance(x, AbstractPath): return x else: @@ -270,55 +270,7 @@ def _prod(vf, control): return jnp.tensordot(jnp.conj(vf), control, axes=jnp.ndim(control)) -# This class exists for backward compatibility with `WeaklyDiagonalControlTerm`. If we -# were writing things again today it would be folded into just `ControlTerm`. -class _AbstractControlTerm(AbstractTerm[_VF, _Control]): - vector_field: Callable[[RealScalarLike, Y, Args], _VF] - control: Union[ - AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] - ] = eqx.field(converter=_callable_to_path) # pyright: ignore - - def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: - return self.vector_field(t, y, args) - - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: - return self.control.evaluate(t0, t1, **kwargs) # pyright: ignore - - def to_ode(self) -> ODETerm: - r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ - may be thought of as an ODE as - - $f(t, y(t), args) \frac{\mathrm{d}x}{\mathrm{d}t}\mathrm{d}t$. - - This method converts this `ControlTerm` into the corresponding - [`diffrax.ODETerm`][] in this way. - """ - vector_field = _ControlToODE(self) - return ODETerm(vector_field=vector_field) - - -_AbstractControlTerm.__init__.__doc__ = """**Arguments:** - -- `vector_field`: A callable representing the vector field. This callable takes three - arguments `(t, y, args)`. `t` is a scalar representing the integration time. `y` is - the evolving state of the system. `args` are any static arguments as passed to - [`diffrax.diffeqsolve`][]. This `vector_field` can either be - - 1. a function that returns a PyTree of JAX arrays, or - 2. it can return a - [Lineax linear operator](https://docs.kidger.site/lineax/api/operators), - as described above. - -- `control`: The control. Should either be - - 1. a [`diffrax.AbstractPath`][], in which case its `.evaluate(t0, t1)` method - will be used to give the increment of the control over a time interval - `[t0, t1]`, or - 2. a callable `(t0, t1) -> increment`, which returns the increment directly. -""" - - -class ControlTerm(_AbstractControlTerm[_VF, _Control]): +class ControlTerm(AbstractTerm[_VF, _Control]): r"""A term representing the general case of $f(t, y(t), args) \mathrm{d}x(t)$, in which the vector field ($f$) - control ($\mathrm{d}x$) interaction is a matrix-vector product. @@ -380,6 +332,7 @@ def vector_field(t, y, args): diffusion_term = ControlTerm(vector_field, control) diffeqsolve(terms=diffusion_term, y0=y0, ...) ``` + !!! Example In this example we consider an SDE with a one-dimensional state @@ -451,14 +404,182 @@ def vector_field(t, y, args): ``` """ # noqa: E501 + vector_field: Callable[[RealScalarLike, Y, Args], _VF] + control: AbstractPath[_Control] + + def __init__( + self, + vector_field: Callable[[RealScalarLike, Y, Args], _VF], + control: Union[ + AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] + ], + ): + self.vector_field = vector_field + self.control = _callable_to_path(control) + + def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: + return self.vector_field(t, y, args) + + def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: + return self.control.evaluate(t0, t1, **kwargs) + def prod(self, vf: _VF, control: _Control) -> Y: if isinstance(vf, lx.AbstractLinearOperator): return vf.mv(control) else: return jtu.tree_map(_prod, vf, control) + def vf_prod(self, t: RealScalarLike, y: Y, args: Args, control: _Control) -> Y: + vf = self.vf(t, y, args) + out = self.prod(vf, control) + + def _raise(): + # SDEs are a common special case; try to make the error message a little + # easier to understand in this case! + if isinstance(self.control, AbstractBrownianPath): + diffusion_word = "diffusion" + control_word = "Brownian motion" + diffusion_phrase = "diffusion matrix" + else: + diffusion_word = "vector field" + control_word = "control" + diffusion_phrase = "vector field in a control term" + if isinstance(vf, lx.AbstractLinearOperator): + dot_phrase = ( + f"combined with `{type(vf).__module__}.{type(vf).__qualname__}.mv`" + ) + else: + dot_phrase = "dotted together" + vf_str = eqx.tree_pformat(vf) + control_str = eqx.tree_pformat(control) + out_str = eqx.tree_pformat(out) + y_str = eqx.tree_pformat(y) + if "\n" in vf_str: + vf_str = f"\n```\n{vf_str}\n```\n" + else: + vf_str = f" `{vf_str}` " + if "\n" in control_str: + control_str = f"\n```\n{control_str}\n```\n" + else: + control_str = f" `{control_str}`, " + if "\n" in out_str: + out_str = f"\n```\n{out_str}\n```\n" + else: + out_str = f" `{out_str}`, " + if "\n" in y_str: + y_str = f"\n```\n{y_str}\n```\n" + else: + y_str = f" `{y_str}`.\n" + raise ValueError( + "The `ControlTerm` returned arrays whose output structure did not " + "match the structure of the evolving state `y`. Specifically, the " + f"{diffusion_word} had structure{vf_str}and the {control_word} " + f"had structure{control_str}which when {dot_phrase} produced an " + f"output of structure{out_str}which is different to the evolving " + f"state `y` which had structure{y_str}" + "\n" + "This became an error in Diffrax 0.7.0. In previous versions of " + "Diffrax then the output was broadcast to the shape of `y`. This " + "has been removed as it was a common source of bugs.\n" + "\n" + "To walk you through what is going on, here is a sample program " + "that now raises an error:\n" + "```\n" + "import diffrax as dfx\n" + "import jax.numpy as jnp\n" + "import jax.random as jr\n" + "\n" + "def drift(t, y, args):\n" + " return -y\n" + "\n" + "def diffusion(t, y, args):\n" + " return jnp.array([1., 0.5])\n" + "\n" + "key = jr.key(0)\n" + "bm = dfx.VirtualBrownianTree(t0=0, t1=1, tol=1e-3, shape=(2,), key=key)\n" # noqa: E501 + "terms = dfx.MultiTerm(dfx.ODETerm(drift), dfx.ControlTerm(diffusion, bm))\n" # noqa: E501 + "solver = dfx.Euler()\n" + "y0 = jnp.array([1., 1.])\n" + "dfx.diffeqsolve(terms, solver, t0=0, t1=1, dt0=0.1, y0=y0)\n" + "```\n" + "In this case, the diffusion returns an array of shape `(2,)` and " + "the Brownian motion is of shape `(2,)`. By the rules of " + "`ControlTerm`, they are then dotted together so that the " + "diffusion term returns a scalar. Under previous versions of " + "Diffrax, this would then be broadcast out to both elements of the " + "evolving state `y`, corresponding to the SDE:\n" + "```\n" + "dy₁(t) = -y₁(t) dt + dW₁ + 0.5 dW₂\n" + "dy₂(t) = -y₂(t) dt + dW₁ + 0.5 dW₂\n" + "```\n" + "or the equivalent in vector notation, with `y(t), W(t) ⋹ R²`\n" + "```\n" + "dy(t) = -y(t) dt + [[1, 0.5], [1, 0.5]] dW\n" + "```\n" + "Which may have been unexpected! Quite possibly what was actually " + "intended was an SDE with diagonal noise:\n" + "```\n" + "dy(t) = -y(t) dt + [[1, 0], [0, 0.5]] dW\n" + "```\n" + "\n" + "As of Diffrax 0.7.0, the recommended way to express the " + f"{diffusion_phrase} is to use a Lineax linear operator. " + "(https://docs.kidger.site/lineax/api/operators/) For example, to " + "represent diagonal noise in the example above:\n" + "```python\n" + "import lineax as lx\n" + "\n" + "def diffusion(t, y, args):\n" + " diagonal = jnp.array([1., 0.5])\n" + " return lx.DiagonalLinearOperator(diagonal)\n" + "```\n" + ) + + if jtu.tree_structure(y) != jtu.tree_structure(out): + _raise() + + def _check_shape(yi, out_i): + if jnp.shape(yi) != jnp.shape(out_i): + _raise() + + jtu.tree_map(_check_shape, y, out) + return out + + def to_ode(self) -> ODETerm: + r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ + may be thought of as an ODE as + + $f(t, y(t), args) \frac{\mathrm{d}x}{\mathrm{d}t}\mathrm{d}t$. + + This method converts this `ControlTerm` into the corresponding + [`diffrax.ODETerm`][] in this way. + """ + vector_field = _ControlToODE(self) + return ODETerm(vector_field=vector_field) + + +ControlTerm.__init__.__doc__ = """**Arguments:** + +- `vector_field`: A callable representing the vector field. This callable takes three + arguments `(t, y, args)`. `t` is a scalar representing the integration time. `y` is + the evolving state of the system. `args` are any static arguments as passed to + [`diffrax.diffeqsolve`][]. This `vector_field` can either be + + 1. a function that returns a PyTree of JAX arrays, or + 2. it can return a + [Lineax linear operator](https://docs.kidger.site/lineax/api/operators), + as described above. + +- `control`: The control. Should either be + + 1. a [`diffrax.AbstractPath`][], in which case its `.evaluate(t0, t1)` method + will be used to give the increment of the control over a time interval + `[t0, t1]`, or + 2. a callable `(t0, t1) -> increment`, which returns the increment directly. +""" -class WeaklyDiagonalControlTerm(_AbstractControlTerm[_VF, _Control]): + +def WeaklyDiagonalControlTerm(vector_field, control): r""" DEPRECATED. Prefer: @@ -469,6 +590,9 @@ def vector_field(t, y, args): diffrax.ControlTerm(vector_field, ...) ``` + The current implementation is a backward-compatible shim that returns something like + the code snippet the above. + --- A term representing the case of $f(t, y(t), args) \mathrm{d}x(t)$, in @@ -492,45 +616,46 @@ def vector_field(t, y, args): without the "weak". (This stronger property is useful in some SDE solvers.) """ - def __check_init__(self): - warnings.warn( - "`WeaklyDiagonalControlTerm` is now deprecated, in favour combining " - "`ControlTerm` with a `lineax.AbstractLinearOperator`. This offers a way " - "to define a vector field with any kind of structure -- diagonal or " - "otherwise.\n" - "For a diagonal linear operator, then this can be easily converted as " - "follows. What was previously:\n" - "```\n" - "def vector_field(t, y, args):\n" - " ...\n" - " return some_vector\n" - "\n" - "diffrax.WeaklyDiagonalControlTerm(vector_field)\n" - "```\n" - "is now:\n" - "```\n" - "import lineax\n" - "\n" - "def vector_field(t, y, args):\n" - " ...\n" - " return lineax.DiagonalLinearOperator(some_vector)\n" - "\n" - "diffrax.ControlTerm(vector_field)\n" - "```\n" - "Lineax is available at `https://github.com/patrick-kidger/lineax`.\n", - stacklevel=3, - ) - - def prod(self, vf: _VF, control: _Control) -> Y: - with jax.numpy_dtype_promotion("standard"): - return jtu.tree_map(operator.mul, vf, control) + warnings.warn( + "`WeaklyDiagonalControlTerm` is now deprecated, in favour combining " + "`ControlTerm` with a `lineax.AbstractLinearOperator`. This offers a way " + "to define a vector field with any kind of structure -- diagonal or " + "otherwise.\n" + "For a diagonal linear operator, then this can be easily converted as " + "follows. What was previously:\n" + "```\n" + "def vector_field(t, y, args):\n" + " ...\n" + " return some_vector\n" + "\n" + "diffrax.WeaklyDiagonalControlTerm(vector_field)\n" + "```\n" + "is now:\n" + "```\n" + "import lineax\n" + "\n" + "def vector_field(t, y, args):\n" + " ...\n" + " return lineax.DiagonalLinearOperator(some_vector)\n" + "\n" + "diffrax.ControlTerm(vector_field)\n" + "```\n" + "Lineax is available at `https://github.com/patrick-kidger/lineax`.\n", + stacklevel=2, + ) + + def new_vector_field(t, y, args): + vf = vector_field(t, y, args) + return lx.DiagonalLinearOperator(vf) + + return ControlTerm(new_vector_field, control) class _ControlToODE(eqx.Module): - control_term: _AbstractControlTerm + control_term: ControlTerm def __call__(self, t: RealScalarLike, y: Y, args: Args) -> Y: - control = self.control_term.control.derivative(t) # pyright: ignore + control = self.control_term.control.derivative(t) return self.control_term.vf_prod(t, y, args, control) diff --git a/docs/api/terms.md b/docs/api/terms.md index 0c72f9f6..6eecde1b 100644 --- a/docs/api/terms.md +++ b/docs/api/terms.md @@ -71,7 +71,7 @@ Some example term structures include: ??? note "Defining your own term types" - For advanced users: you can create your own terms if appropriate. For example if your diffusion is matrix, itself computed as a matrix-matrix product, then you may wish to define a custom term and specify its [`diffrax.AbstractTerm.vf_prod`][] method. By overriding this method you could express the contraction of the vector field - control as a matrix-(matix-vector) product, which is more efficient than the default (matrix-matrix)-vector product. + For advanced users, you can create your own terms if appropriate. See for example the [underdamped Langevin terms](#underdamped-langevin-terms), which have their own special set of solvers. --- @@ -113,7 +113,7 @@ $\gamma , u \in \mathbb{R}^{d \times d}$ are diagonal matrices governing the friction and the damping of the system. These terms enable the use of ULD-specific solvers which can be found -[here](./solvers/sde_solvers.md#underdamped-langevin-solvers). Note that these ULD solvers will only work if given +[here](./solvers/sde_solvers.md#underdamped-langevin-solvers). These ULD solvers expect terms with structure `MultiTerm(UnderdampedLangevinDriftTerm(gamma, u, grad_f), UnderdampedLangevinDiffusionTerm(gamma, u, bm))`, where `bm` is an [`diffrax.AbstractBrownianPath`][] and the same values of `gammma` and `u` are passed to both terms. diff --git a/pyproject.toml b/pyproject.toml index 0d56b739..3b9b3d3d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "diffrax" -version = "0.6.2" +version = "0.7.0" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" requires-python =">=3.9,<4.0" diff --git a/test/test_adjoint.py b/test/test_adjoint.py index c45c6286..9e17e535 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -391,7 +391,8 @@ def g_lx(t, y, args): bm = diffrax.VirtualBrownianTree(t0, t1, tol, shape, key=getkey()) drift = diffrax.ODETerm(f) if diffusion_fn == "weak": - diffusion = diffrax.WeaklyDiagonalControlTerm(g, bm) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + diffusion = diffrax.WeaklyDiagonalControlTerm(g, bm) else: diffusion = diffrax.ControlTerm(g_lx, bm) terms = diffrax.MultiTerm(drift, diffusion) diff --git a/test/test_integrate.py b/test/test_integrate.py index d8ca4360..15d83f3e 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -603,31 +603,49 @@ def test_term_compatibility(): class TestControl(eqx.Module): dt: Float[ArrayLike, ""] - def __rmul__(self, other): - return other.__mul__(self.dt) - - def __mul__(self, other): - return self.dt * other - class TestSolver(diffrax.Euler): term_structure = diffrax.AbstractTerm[ - tuple[Float[Array, "n 3"]], tuple[TestControl] + lx.AbstractLinearOperator, tuple[TestControl] ] + class TestLinearOperator(lx.AbstractLinearOperator): + def mv(self, vector): + assert ( + type(vector) is tuple + and len(vector) == 1 + and type(vector[0]) is TestControl + ) + return (jnp.ones((2, 3)) * vector[0].dt,) + + def as_matrix(self): + assert False + + def transpose(self): + assert False + + def in_structure(self): + return (jax.eval_shape(lambda: TestControl(1.0)),) + + def out_structure(self): + return (jax.ShapeDtypeStruct((2, 3), jnp.float64),) + + @lx.is_symmetric.register(TestLinearOperator) + def _(operator): + del operator + return False + solver = TestSolver() - incompatible_vf = lambda t, y, args: jnp.ones((2, 1)) - compatible_vf = lambda t, y, args: (jnp.ones((2, 3)),) + incompatible_vf = lambda t, y, args: jnp.ones((2, 3)) + compatible_vf = lambda t, y, args: TestLinearOperator() incompatible_control = lambda t0, t1: t1 - t0 compatible_control = lambda t0, t1: (TestControl(t1 - t0),) incompatible_terms = [ - diffrax.WeaklyDiagonalControlTerm(incompatible_vf, incompatible_control), - diffrax.WeaklyDiagonalControlTerm(incompatible_vf, compatible_control), - diffrax.WeaklyDiagonalControlTerm(compatible_vf, incompatible_control), + diffrax.ControlTerm(incompatible_vf, incompatible_control), + diffrax.ControlTerm(incompatible_vf, compatible_control), + diffrax.ControlTerm(compatible_vf, incompatible_control), ] - compatible_term = diffrax.WeaklyDiagonalControlTerm( - compatible_vf, compatible_control - ) + compatible_term = diffrax.ControlTerm(compatible_vf, compatible_control) for term in incompatible_terms: with pytest.raises(ValueError, match=r"Terms are not compatible with solver!"): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, (jnp.zeros((2, 1)),)) @@ -669,6 +687,10 @@ def _step(_term, _y): def func(self, terms, t0, y0, args): assert False + def weakly_diagonal(*a): + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + return diffrax.WeaklyDiagonalControlTerm(*a) + ode_term = diffrax.ODETerm(lambda t, y, args: -y) solver = TestSolver() compatible_term = { @@ -678,8 +700,9 @@ def func(self, terms, t0, y0, args): "d": ode_term, "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( - lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(5) + weakly_diagonal( + lambda t, y, args: -y, + lambda t0, t1: jnp.array(t1 - t0).repeat(5), ), ), "f": diffrax.MultiTerm( @@ -707,7 +730,7 @@ def func(self, terms, t0, y0, args): "d": ode_term, "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( + weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: t1 - t0, # wrong control shape ), @@ -727,7 +750,7 @@ def func(self, terms, t0, y0, args): # Missing "d" piece "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( + weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(3) ), ), @@ -745,7 +768,7 @@ def func(self, terms, t0, y0, args): "c": ode_term, "d": ode_term, # No MultiTerm for "e" - "e": diffrax.WeaklyDiagonalControlTerm( + "e": weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(3) ), "f": diffrax.MultiTerm( diff --git a/test/test_sde2.py b/test/test_sde2.py index 3b4a4628..077177b8 100644 --- a/test/test_sde2.py +++ b/test/test_sde2.py @@ -83,7 +83,9 @@ def _drift(t, y, args): 0.0, 1.0, 0.05, w_shape, jr.key(0), diffrax.SpaceTimeLevyArea ) - terms = MultiTerm(ODETerm(_drift), WeaklyDiagonalControlTerm(_diffusion, bm)) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + diffusion = WeaklyDiagonalControlTerm(_diffusion, bm) + terms = MultiTerm(ODETerm(_drift), diffusion) saveat = diffrax.SaveAt(t1=True) solution = diffrax.diffeqsolve( terms, solver, 0.0, 1.0, 0.1, y0, args, saveat=saveat diff --git a/test/test_term.py b/test/test_term.py index 8e8bf8be..0c75fc78 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -4,6 +4,7 @@ import jax.numpy as jnp import jax.random as jr import jax.tree_util as jtu +import lineax as lx import pytest from jaxtyping import Array, PyTree, Shaped @@ -84,15 +85,16 @@ def derivative(self, t, left=True): return jr.normal(derivkey, (3,)) control = Control() - term = diffrax.WeaklyDiagonalControlTerm(vector_field, control) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + term = diffrax.WeaklyDiagonalControlTerm(vector_field, control) args = getkey() dx = term.contr(0, 1) y = jnp.array([1.0, 2.0, 3.0]) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dx) - if isinstance(dx, jax.Array) and isinstance(vf, jax.Array): + if isinstance(dx, jax.Array) and isinstance(vf, lx.DiagonalLinearOperator): assert dx.shape == (3,) - assert vf.shape == (3,) + assert vf.diagonal.shape == (3,) else: raise TypeError("dx/vf is not an array") assert vf_prod.shape == (3,) diff --git a/test/test_typing.py b/test/test_typing.py index 4c4f3db1..705b0bcd 100644 --- a/test/test_typing.py +++ b/test/test_typing.py @@ -289,8 +289,3 @@ def test_ode_term(): def test_control_term(): assert _abstract_args(dfx.ControlTerm) == (Any, Any) assert _abstract_args(dfx.ControlTerm[int, str]) == (int, str) - - -def test_weakly_diagonal_control_term(): - assert _abstract_args(dfx.WeaklyDiagonalControlTerm) == (Any, Any) - assert _abstract_args(dfx.WeaklyDiagonalControlTerm[int, str]) == (int, str) From 7c2c720556300af7662632b424ce1e601a1182c8 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 12 Jan 2025 21:46:13 +0100 Subject: [PATCH 28/50] Now using jaxtyping.Real for prettier documentation. --- diffrax/_custom_types.py | 19 +++---------------- mkdocs.yml | 2 ++ 2 files changed, 5 insertions(+), 16 deletions(-) diff --git a/diffrax/_custom_types.py b/diffrax/_custom_types.py index 7e08aa1b..a16b4d61 100644 --- a/diffrax/_custom_types.py +++ b/diffrax/_custom_types.py @@ -1,4 +1,3 @@ -import typing from typing import Any, TYPE_CHECKING, Union import equinox as eqx @@ -13,6 +12,7 @@ Float, Int, PyTree, + Real, Shaped, ) @@ -21,27 +21,14 @@ BoolScalarLike = Union[bool, Array, np.ndarray] FloatScalarLike = Union[float, Array, np.ndarray] IntScalarLike = Union[int, Array, np.ndarray] -elif getattr(typing, "GENERATING_DOCUMENTATION", False): - # Skip the union with Array in docs. - BoolScalarLike = bool - FloatScalarLike = float - IntScalarLike = int - - # - # Because they appear in our docstrings, we also monkey-patch some non-Diffrax - # types that have similar defined-in-one-place, exported-in-another behaviour. - # - - jtu.Partial.__module__ = "jax.tree_util" - + RealScalarLike = Union[bool, int, float, Array, np.ndarray] else: BoolScalarLike = Bool[ArrayLike, ""] FloatScalarLike = Float[ArrayLike, ""] IntScalarLike = Int[ArrayLike, ""] + RealScalarLike = Real[ArrayLike, ""] -RealScalarLike = Union[FloatScalarLike, IntScalarLike] - Y = PyTree[Shaped[ArrayLike, "?*y"], "Y"] VF = PyTree[Shaped[ArrayLike, "?*vf"], "VF"] Control = PyTree[Shaped[ArrayLike, "?*control"], "C"] diff --git a/mkdocs.yml b/mkdocs.yml index b399fbd8..067cd458 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,6 +75,8 @@ plugins: setup_commands: - import pytkdocs_tweaks - pytkdocs_tweaks.main() + - import jax.tree_util + - jax.tree_util.Partial.__module__ = "jax.tree_util" selection: inherited_members: true # Allow looking up inherited methods From 583cd6d6078b84fe3977f8c4c80f3144dca0117b Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 20:11:37 +0100 Subject: [PATCH 29/50] Bumped minimum version of Python to 3.10 --- README.md | 2 +- docs/index.md | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 20d04589..bd6b94c6 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ _From a technical point of view, the internal structure of the library is pretty pip install diffrax ``` -Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.10.11+. +Requires Python 3.10+. ## Documentation diff --git a/docs/index.md b/docs/index.md index 73fed50e..7c6deaac 100644 --- a/docs/index.md +++ b/docs/index.md @@ -20,7 +20,7 @@ _From a technical point of view, the internal structure of the library is pretty pip install diffrax ``` -Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.10.11+. +Requires Python 3.10+. ## Quick example diff --git a/pyproject.toml b/pyproject.toml index 3b9b3d3d..42b77f52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "diffrax" version = "0.7.0" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" -requires-python =">=3.9,<4.0" +requires-python =">=3.10,<4.0" license = {file = "LICENSE"} authors = [ {name = "Patrick Kidger", email = "contact@kidger.site"}, From 427227039807186b6a852f9d37dd5ddc37effe5c Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 20:05:04 +0100 Subject: [PATCH 30/50] Investigating if we can drop the typeguard dependency. --- diffrax/_integrate.py | 2 +- diffrax/_typing.py | 43 +++++++++++++++++++++++++------------------ 2 files changed, 26 insertions(+), 19 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 5f6d05d5..88c014aa 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -198,7 +198,7 @@ def _check(term_cls, term, term_contr_kwargs, yi): try: with jax.numpy_dtype_promotion("standard"): jtu.tree_map(_check, term_structure, terms, contr_kwargs, y) - except Exception as e: + except ValueError as e: # ValueError may also arise from mismatched tree structures pretty_term = wl.pformat(terms) pretty_expected = wl.pformat(term_structure) diff --git a/diffrax/_typing.py b/diffrax/_typing.py index e0bfff6c..694357ed 100644 --- a/diffrax/_typing.py +++ b/diffrax/_typing.py @@ -1,5 +1,4 @@ import inspect -import sys import types from typing import ( Annotated, @@ -14,8 +13,6 @@ ) from typing_extensions import TypeAlias -import typeguard - # We don't actually care what people have subscripted with. # In practice this should be thought of as TypeLike = Union[type, types.UnionType]. Plus @@ -23,24 +20,34 @@ TypeLike: TypeAlias = Any -def better_isinstance(x, annotation) -> bool: - """As `isinstance`, but supports general type hints.""" +_T = TypeVar("_T") - @typeguard.typechecked - def f(y: annotation): - pass - try: - f(x) - except TypeError: - return False - else: - return True +class _Foo(Generic[_T]): + pass + +_generic_alias_types = (types.GenericAlias, type(_Foo[int])) +_union_origins = (Union, types.UnionType) +del _Foo, _T -_union_types: list = [Union] -if sys.version_info >= (3, 10): - _union_types.append(types.UnionType) + +def better_isinstance(x, annotation) -> bool: + """As `isinstance`, but supports a few other types that are useful to us.""" + origin = get_origin(annotation) + if origin in _union_origins: + return any(better_isinstance(x, arg) for arg in get_args(annotation)) + elif isinstance(annotation, _generic_alias_types): + assert origin is not None + return better_isinstance(x, origin) + elif annotation is Any: + return True + elif isinstance(annotation, type): + return isinstance(x, annotation) + else: + raise NotImplementedError( + f"Do not know how to check whether `{x}` is an instance of `{annotation}`." + ) def get_origin_no_specials(x, error_msg: str) -> Optional[type]: @@ -59,7 +66,7 @@ def get_origin_no_specials(x, error_msg: str) -> Optional[type]: As `get_origin`, specifically either `None` or a class. """ origin = get_origin(x) - if origin in _union_types: + if origin in _union_origins: raise NotImplementedError(f"Cannot use unions in `{error_msg}`.") elif origin is Annotated: # We do allow Annotated, just because it's easy to handle. From 96f8bf32051aff88319d00de1be1c29c56563e47 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Wed, 29 Jan 2025 23:04:11 -0800 Subject: [PATCH 31/50] fix merge --- diffrax/_solver/base.py | 5 --- diffrax/_term.py | 88 +++++++++++++++++++++-------------------- test/test_typing.py | 10 ----- 3 files changed, 45 insertions(+), 58 deletions(-) diff --git a/diffrax/_solver/base.py b/diffrax/_solver/base.py index 38287f06..bae98735 100644 --- a/diffrax/_solver/base.py +++ b/diffrax/_solver/base.py @@ -35,11 +35,6 @@ _SolverState = TypeVar("_SolverState") -# Should pathstate be a TypeVar? Originally I had it as one, but it doesn't seem -# to matter since no solver actually provides a specific type for the typevar -# (thus it was totally general for all solvers, which was like, why is it a type -# var then?) In Term it makes sense because control/ode terms are specific -# parameterizations of the type var _PathState: TypeAlias = PyTree diff --git a/diffrax/_term.py b/diffrax/_term.py index bc62170d..d4e54656 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -30,12 +30,10 @@ _VF = TypeVar("_VF", bound=VF) _Control = TypeVar("_Control", bound=Control) -_ControlState = TypeVar("_ControlState") -_PathState: TypeAlias = PyTree -# should probably make the typing of this better/more consistent +_PathState = TypeVar("_PathState") -class AbstractTerm(eqx.Module, Generic[_VF, _Control, _ControlState]): +class AbstractTerm(eqx.Module, Generic[_VF, _Control, _PathState]): r"""Abstract base class for all terms. Let $y$ solve some differential equation with vector field $f$ and control $x$. @@ -86,9 +84,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _ControlState, + control_state: _PathState, **kwargs, - ) -> tuple[_Control, _ControlState]: + ) -> tuple[_Control, _PathState]: r"""The control. Represents the $\mathrm{d}t$ in an ODE, or the $\mathrm{d}w(t)$ in an SDE, etc. @@ -312,22 +310,6 @@ def evaluate( return self.fn(t0, t1) -# probably be consistent with path/control naming -_MaybePathState: TypeAlias = Union[PyTree, None] - - -def _callable_to_path( - x: Union[ - AbstractPath[_Control, _ControlState], - Callable[[RealScalarLike, RealScalarLike], _Control], - ], -) -> AbstractPath[_Control, _MaybePathState]: - if isinstance(x, AbstractPath): - return x - else: - return _CallableToPath(x) - - # vf: Shaped[Array, "*state *control"] # control: Shaped[Array, "*control"] # return: Shaped[Array, "*state"] @@ -335,8 +317,7 @@ def _prod(vf, control): return jnp.tensordot(jnp.conj(vf), control, axes=jnp.ndim(control)) - -class ControlTerm(AbstractTerm[_VF, _Control, _ControlState]): +class ControlTerm(AbstractTerm[_VF, _Control, _PathState]): r"""A term representing the general case of $f(t, y(t), args) \mathrm{d}x(t)$, in which the vector field ($f$) - control ($\mathrm{d}x$) interaction is a matrix-vector product. @@ -471,7 +452,7 @@ def vector_field(t, y, args): """ # noqa: E501 vector_field: Callable[[RealScalarLike, Y, Args], _VF] - control: AbstractPath[_Control] + control: AbstractPath[_Control, _PathState] def __init__( self, @@ -481,17 +462,38 @@ def __init__( # not ideal, probably just be easier to have them make an abstract path? # Callable[[RealScalarLike, PyTree, RealScalarLike], tuple[_Control, PyTree]], control: Union[ - AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] + AbstractPath[_Control, _PathState], + Callable[[RealScalarLike, RealScalarLike], _Control], ], ): self.vector_field = vector_field - self.control = _callable_to_path(control) + if isinstance(control, AbstractPath): + new_control = control + else: + new_control = _CallableToPath(control) + self.control = new_control + self.vector_field = vector_field + + def init( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + args: Args, + ) -> _PathState: + return self.control.init(t0, t1, y0, args) def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: return self.vector_field(t, y, args) - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: - return self.control.evaluate(t0, t1, **kwargs) + def contr( + self, + t0: RealScalarLike, + t1: RealScalarLike, + control_state: _PathState, + **kwargs, + ) -> tuple[_Control, _PathState]: + return self.control(t0, control_state, t1, **kwargs) def prod(self, vf: _VF, control: _Control) -> Y: if isinstance(vf, lx.AbstractLinearOperator): @@ -615,6 +617,8 @@ def _check_shape(yi, out_i): jtu.tree_map(_check_shape, y, out) return out + # TODO: support stateful conversion here + # more broadly, add derivative function to path for __call__? def to_ode(self) -> ODETerm: r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ may be thought of as an ODE as @@ -648,6 +652,7 @@ def to_ode(self) -> ODETerm: 2. a callable `(t0, t1) -> increment`, which returns the increment directly. """ + def WeaklyDiagonalControlTerm(vector_field, control): r""" DEPRECATED. Prefer: @@ -816,8 +821,8 @@ def is_vf_expensive( return any(term.is_vf_expensive(t0, t1, y, args) for term in self.terms) -class WrapTerm(AbstractTerm[_VF, _Control, _ControlState]): - term: AbstractTerm[_VF, _Control, _ControlState] +class WrapTerm(AbstractTerm[_VF, _Control, _PathState]): + term: AbstractTerm[_VF, _Control, _PathState] direction: IntScalarLike def vf(self, t: RealScalarLike, y: Y, args: Args) -> _VF: @@ -837,9 +842,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _ControlState, + control_state: _PathState, **kwargs, - ) -> tuple[_Control, _ControlState]: + ) -> tuple[_Control, _PathState]: _t0 = jnp.where(self.direction == 1, t0, -t1) _t1 = jnp.where(self.direction == 1, t1, -t0) contrs = self.term.contr(_t0, _t1, control_state, **kwargs) @@ -865,11 +870,8 @@ def is_vf_expensive( return self.term.is_vf_expensive(_t0, _t1, y, args) -_AdjoingControlState: TypeAlias = Union[None, PyTree] - - -class AdjointTerm(AbstractTerm[_VF, _Control, _AdjoingControlState]): - term: AbstractTerm[_VF, _Control, _AdjoingControlState] +class AdjointTerm(AbstractTerm[_VF, _Control, _PathState]): + term: AbstractTerm[_VF, _Control, _PathState] def is_vf_expensive( self, @@ -963,9 +965,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _AdjoingControlState, + control_state: _PathState, **kwargs, - ) -> tuple[_Control, _AdjoingControlState]: + ) -> tuple[_Control, _PathState]: return self.term.contr(t0, t1, control_state, **kwargs) def prod( @@ -1078,7 +1080,7 @@ class UnderdampedLangevinDiffusionTerm( AbstractTerm[ UnderdampedLangevinX, Union[UnderdampedLangevinX, AbstractBrownianIncrement], - _ControlState, + _PathState, ] ): r"""Represents the diffusion term in the Underdamped Langevin Diffusion (ULD). @@ -1149,9 +1151,9 @@ def contr( self, t0: RealScalarLike, t1: RealScalarLike, - control_state: _ControlState, + control_state: _PathState, **kwargs, - ) -> tuple[Union[UnderdampedLangevinX, AbstractBrownianIncrement], _ControlState]: + ) -> tuple[Union[UnderdampedLangevinX, AbstractBrownianIncrement], _PathState]: # same stateless function as above return self.control(t0, control_state, t1, **kwargs) diff --git a/test/test_typing.py b/test/test_typing.py index 71b73e04..a09cdae2 100644 --- a/test/test_typing.py +++ b/test/test_typing.py @@ -289,13 +289,3 @@ def test_ode_term(): def test_control_term(): assert _abstract_args(dfx.ControlTerm) == (Any, Any, Any) assert _abstract_args(dfx.ControlTerm[int, str, int]) == (int, str, int) - - -def test_weakly_diagonal_control_term(): - assert _abstract_args(dfx.WeaklyDiagonalControlTerm) == (Any, Any, Any) - assert _abstract_args(dfx.WeaklyDiagonalControlTerm[int, str, int]) == ( - int, - str, - int, - ) - From 1946a8b0bf2d665baa44da2c0a2426c491d9ef27 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 3 Feb 2025 13:01:57 -0800 Subject: [PATCH 32/50] fix tests --- diffrax/_adjoint.py | 5 +---- diffrax/_brownian/path.py | 12 ++++++------ test/test_adjoint.py | 2 +- test/test_brownian.py | 1 + 4 files changed, 9 insertions(+), 11 deletions(-) diff --git a/diffrax/_adjoint.py b/diffrax/_adjoint.py index dbda4b92..699122e0 100644 --- a/diffrax/_adjoint.py +++ b/diffrax/_adjoint.py @@ -847,10 +847,7 @@ def loop( raise NotImplementedError( "Cannot use `adjoint=BacksolveAdjoint()` with `saveat=SaveAt(fn=...)`." ) - # is this still true with DirectBP? - # it seems to give inaccurate results, so not currently, but seems doable - # might just require more careful thinking about path state management - # and more knowledge about continuous adjoints than I have currently + # TODO: support DBP is theoretically possible, just requires more care if is_unsafe_sde(terms): raise ValueError( "`adjoint=BacksolveAdjoint()` does not support `UnsafeBrownianPath`. " diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 2efbc89f..cecabfca 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -150,7 +150,7 @@ def init( else: noise = None counter = None - key = self.key + _, key = jr.split(self.key) return key, noise, counter def __call__( @@ -193,13 +193,13 @@ def __call__( return out, (None, noises, counter + 1) else: assert noises is None and counter is None and key is not None - new_key, key = jr.split(key) - key = split_by_tree(key, self.shape) + new_key, subkey = jr.split(key) + subkeys = split_by_tree(subkey, self.shape) out = jtu.tree_map( - lambda key, shape: self._evaluate_leaf( - t0, t1, key, shape, self.levy_area, use_levy + lambda k, shape: self._evaluate_leaf( + t0, t1, k, shape, self.levy_area, use_levy ), - key, + subkeys, self.shape, ) if use_levy: diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 17c5c3e9..452516fd 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -289,7 +289,7 @@ def _run(y0__args__term, saveat, adjoint): solver, 0.3, 9.5, - 1.0, + 0.1, y0, args, saveat=saveat, diff --git a/test/test_brownian.py b/test/test_brownian.py index f4478935..88e4705e 100644 --- a/test/test_brownian.py +++ b/test/test_brownian.py @@ -132,6 +132,7 @@ def _eval(key): if ctr is diffrax.UnsafeBrownianPath: path = ctr(shape=(), key=key, levy_area=levy_area) state = path.init(t0, t1, None, None) + # return path.evaluate(t0, t1, use_levy=use_levy) elif ctr is diffrax.VirtualBrownianTree: path = ctr(t0=0, t1=5, tol=2**-5, shape=(), key=key, levy_area=levy_area) state = path.init(t0, t1, None, None) From b3bb170949c9b19c4e889a51af45d6a8aeaded6b Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 3 Feb 2025 14:35:36 -0800 Subject: [PATCH 33/50] fix test2 --- diffrax/_brownian/path.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index cecabfca..48155733 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -150,7 +150,7 @@ def init( else: noise = None counter = None - _, key = jr.split(self.key) + key = self.key return key, noise, counter def __call__( @@ -312,7 +312,8 @@ def _evaluate_leaf( hh = jr.normal(key_hh, shape.shape, shape.dtype) * hh_std levy_val = SpaceTimeLevyArea(dt=dt, W=w, H=hh) elif levy_area is BrownianIncrement: - w = jr.normal(key, shape.shape, shape.dtype) * w_std + key_w, key_hh = jr.split(key, 2) + w = jr.normal(key_w, shape.shape, shape.dtype) * w_std levy_val = BrownianIncrement(dt=dt, W=w) else: assert False From 03e5b920efc624a3caeea84c2f77f356e208ec74 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 3 Feb 2025 16:44:12 -0800 Subject: [PATCH 34/50] trying larger stepsize --- test/test_adjoint.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_adjoint.py b/test/test_adjoint.py index 452516fd..0f683f5c 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -289,7 +289,7 @@ def _run(y0__args__term, saveat, adjoint): solver, 0.3, 9.5, - 0.1, + 0.5, y0, args, saveat=saveat, From 766b4717735b6b4ec59e36abca271737f777345c Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Mon, 3 Feb 2025 20:44:44 -0800 Subject: [PATCH 35/50] does splitting it up help? (passes locally, but github actions fails) --- test/{test_adjoint.py => test_adjoint1.py} | 146 ------------------- test/test_adjoint2.py | 159 +++++++++++++++++++++ 2 files changed, 159 insertions(+), 146 deletions(-) rename test/{test_adjoint.py => test_adjoint1.py} (74%) create mode 100644 test/test_adjoint2.py diff --git a/test/test_adjoint.py b/test/test_adjoint1.py similarity index 74% rename from test/test_adjoint.py rename to test/test_adjoint1.py index 0f683f5c..9e17e535 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint1.py @@ -215,152 +215,6 @@ def _convert_float0(x): assert tree_allclose(direct_grads, forward_grads, atol=1e-5) -@pytest.mark.slow -def test_direct_brownian(getkey): - key = getkey() - key, subkey = jax.random.split(key) - driftkey, diffusionkey, ykey = jr.split(subkey, 3) - drift_mlp = eqx.nn.MLP( - in_size=2, - out_size=2, - width_size=8, - depth=2, - activation=jax.nn.swish, - final_activation=jnp.tanh, - key=driftkey, - ) - diffusion_mlp = eqx.nn.MLP( - in_size=2, - out_size=2, - width_size=8, - depth=2, - activation=jax.nn.swish, - final_activation=jnp.tanh, - key=diffusionkey, - ) - - class Field(eqx.Module): - force: eqx.nn.MLP - - def __call__(self, t, y, args): - return self.force(y) - - class DiffusionField(eqx.Module): - force: eqx.nn.MLP - - def __call__(self, t, y, args): - return lx.DiagonalLinearOperator(self.force(y)) - - y0 = jr.normal(ykey, (2,)) - - k1, k2, k3 = jax.random.split(key, 3) - - vbt = diffrax.VirtualBrownianTree( - 0.3, 9.5, 1e-4, (2,), k1, levy_area=diffrax.SpaceTimeLevyArea - ) - dbp = diffrax.UnsafeBrownianPath((2,), k2, levy_area=diffrax.SpaceTimeLevyArea) - dbp_pre = diffrax.UnsafeBrownianPath( - (2,), k3, levy_area=diffrax.SpaceTimeLevyArea, precompute=int(9.5 / 0.1) - ) - - vbt_terms = diffrax.MultiTerm( - diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm(DiffusionField(diffusion_mlp), vbt), - ) - dbp_terms = diffrax.MultiTerm( - diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp), - ) - dbp_pre_terms = diffrax.MultiTerm( - diffrax.ODETerm(Field(drift_mlp)), - diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp_pre), - ) - - solver = diffrax.Heun() - - y0_args_term0 = (y0, None, vbt_terms) - y0_args_term1 = (y0, None, dbp_terms) - y0_args_term2 = (y0, None, dbp_pre_terms) - - def _run(y0__args__term, saveat, adjoint): - y0, args, term = y0__args__term - ys = diffrax.diffeqsolve( - term, - solver, - 0.3, - 9.5, - 0.5, - y0, - args, - saveat=saveat, - adjoint=adjoint, - max_steps=250, # see note below - ).ys - return jnp.sum(cast(Array, ys)) - - # Only does gradients with respect to y0 - def _run_finite_diff(y0__args__term, saveat, adjoint): - y0, args, term = y0__args__term - y0_a = y0 + jnp.array([1e-5, 0]) - y0_b = y0 + jnp.array([0, 1e-5]) - val = _run((y0, args, term), saveat, adjoint) - val_a = _run((y0_a, args, term), saveat, adjoint) - val_b = _run((y0_b, args, term), saveat, adjoint) - out_a = (val_a - val) / 1e-5 - out_b = (val_b - val) / 1e-5 - return jnp.stack([out_a, out_b]) - - for t0 in (True, False): - for t1 in (True, False): - for ts in (None, [0.3], [2.0], [9.5], [1.0, 7.0], [0.3, 7.0, 9.5]): - for i, y0__args__term in enumerate( - (y0_args_term0, y0_args_term1, y0_args_term2) - ): - if t0 is False and t1 is False and ts is None: - continue - - saveat = diffrax.SaveAt(t0=t0, t1=t1, ts=ts) - - inexact, static = eqx.partition( - y0__args__term, eqx.is_inexact_array - ) - - def _run_inexact(inexact, saveat, adjoint): - return _run(eqx.combine(inexact, static), saveat, adjoint) - - _run_grad = eqx.filter_jit(jax.grad(_run_inexact)) - _run_fwd_grad = eqx.filter_jit(jax.jacfwd(_run_inexact)) - - fd_grads = _run_finite_diff( - y0__args__term, saveat, diffrax.RecursiveCheckpointAdjoint() - ) - recursive_grads = _run_grad( - inexact, saveat, diffrax.RecursiveCheckpointAdjoint() - ) - if i == 0: - backsolve_grads = _run_grad( - inexact, saveat, diffrax.BacksolveAdjoint() - ) - assert tree_allclose(fd_grads, backsolve_grads[0], atol=1e-3) - - forward_grads = _run_fwd_grad( - inexact, saveat, diffrax.ForwardMode() - ) - # TODO: fix via https://github.com/patrick-kidger/equinox/issues/923 - # turns out this actually only fails for steps >256. Which is weird, - # because thats means 3 vs 2 calls in the base 16. But idk why that - # matter and yields some opaque assertion error. Maybe something to - # do with shapes? AssertionError - # ... - # assert all(all(map(core.typematch, - # j.out_avals, branches_known[0].out_avals)) - # for j in branches_known[1:]) - direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) - assert tree_allclose(fd_grads, direct_grads[0], atol=1e-3) - assert tree_allclose(fd_grads, recursive_grads[0], atol=1e-3) - assert tree_allclose(fd_grads, forward_grads[0], atol=1e-3) - - def test_adjoint_seminorm(): vector_field = lambda t, y, args: -y term = diffrax.ODETerm(vector_field) diff --git a/test/test_adjoint2.py b/test/test_adjoint2.py new file mode 100644 index 00000000..396c1226 --- /dev/null +++ b/test/test_adjoint2.py @@ -0,0 +1,159 @@ +from typing import cast + +import diffrax +import equinox as eqx +import jax +import jax._src.interpreters.ad +import jax.numpy as jnp +import jax.random as jr +import lineax as lx +import pytest +from jaxtyping import Array + +from .helpers import tree_allclose + + +@pytest.mark.slow +def test_direct_brownian(getkey): + key = getkey() + key, subkey = jax.random.split(key) + driftkey, diffusionkey, ykey = jr.split(subkey, 3) + drift_mlp = eqx.nn.MLP( + in_size=2, + out_size=2, + width_size=8, + depth=2, + activation=jax.nn.swish, + final_activation=jnp.tanh, + key=driftkey, + ) + diffusion_mlp = eqx.nn.MLP( + in_size=2, + out_size=2, + width_size=8, + depth=2, + activation=jax.nn.swish, + final_activation=jnp.tanh, + key=diffusionkey, + ) + + class Field(eqx.Module): + force: eqx.nn.MLP + + def __call__(self, t, y, args): + return self.force(y) + + class DiffusionField(eqx.Module): + force: eqx.nn.MLP + + def __call__(self, t, y, args): + return lx.DiagonalLinearOperator(self.force(y)) + + y0 = jr.normal(ykey, (2,)) + + k1, k2, k3 = jax.random.split(key, 3) + + vbt = diffrax.VirtualBrownianTree( + 0.3, 9.5, 1e-4, (2,), k1, levy_area=diffrax.SpaceTimeLevyArea + ) + dbp = diffrax.UnsafeBrownianPath((2,), k2, levy_area=diffrax.SpaceTimeLevyArea) + dbp_pre = diffrax.UnsafeBrownianPath( + (2,), k3, levy_area=diffrax.SpaceTimeLevyArea, precompute=int(9.5 / 0.1) + ) + + vbt_terms = diffrax.MultiTerm( + diffrax.ODETerm(Field(drift_mlp)), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), vbt), + ) + dbp_terms = diffrax.MultiTerm( + diffrax.ODETerm(Field(drift_mlp)), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp), + ) + dbp_pre_terms = diffrax.MultiTerm( + diffrax.ODETerm(Field(drift_mlp)), + diffrax.ControlTerm(DiffusionField(diffusion_mlp), dbp_pre), + ) + + solver = diffrax.Heun() + + y0_args_term0 = (y0, None, vbt_terms) + y0_args_term1 = (y0, None, dbp_terms) + y0_args_term2 = (y0, None, dbp_pre_terms) + + def _run(y0__args__term, saveat, adjoint): + y0, args, term = y0__args__term + ys = diffrax.diffeqsolve( + term, + solver, + 0.3, + 9.5, + 0.1, + y0, + args, + saveat=saveat, + adjoint=adjoint, + max_steps=250, # see note below + ).ys + return jnp.sum(cast(Array, ys)) + + # Only does gradients with respect to y0 + def _run_finite_diff(y0__args__term, saveat, adjoint): + y0, args, term = y0__args__term + y0_a = y0 + jnp.array([1e-5, 0]) + y0_b = y0 + jnp.array([0, 1e-5]) + val = _run((y0, args, term), saveat, adjoint) + val_a = _run((y0_a, args, term), saveat, adjoint) + val_b = _run((y0_b, args, term), saveat, adjoint) + out_a = (val_a - val) / 1e-5 + out_b = (val_b - val) / 1e-5 + return jnp.stack([out_a, out_b]) + + for t0 in (True, False): + for t1 in (True, False): + for ts in (None, [0.3], [2.0], [9.5], [1.0, 7.0], [0.3, 7.0, 9.5]): + for i, y0__args__term in enumerate( + (y0_args_term0, y0_args_term1, y0_args_term2) + ): + if t0 is False and t1 is False and ts is None: + continue + + saveat = diffrax.SaveAt(t0=t0, t1=t1, ts=ts) + + inexact, static = eqx.partition( + y0__args__term, eqx.is_inexact_array + ) + + def _run_inexact(inexact, saveat, adjoint): + return _run(eqx.combine(inexact, static), saveat, adjoint) + + _run_grad = eqx.filter_jit(jax.grad(_run_inexact)) + _run_fwd_grad = eqx.filter_jit(jax.jacfwd(_run_inexact)) + + fd_grads = _run_finite_diff( + y0__args__term, saveat, diffrax.RecursiveCheckpointAdjoint() + ) + recursive_grads = _run_grad( + inexact, saveat, diffrax.RecursiveCheckpointAdjoint() + ) + if i == 0: + backsolve_grads = _run_grad( + inexact, saveat, diffrax.BacksolveAdjoint() + ) + assert tree_allclose(fd_grads, backsolve_grads[0], atol=1e-3) + + forward_grads = _run_fwd_grad( + inexact, saveat, diffrax.ForwardMode() + ) + # TODO: fix via https://github.com/patrick-kidger/equinox/issues/923 + # turns out this actually only fails for steps >256. Which is weird, + # because thats means 3 vs 2 calls in the base 16. But idk why that + # matter and yields some opaque assertion error. Maybe something to + # do with shapes? AssertionError + # ... + # assert all(all(map(core.typematch, + # j.out_avals, branches_known[0].out_avals)) + # for j in branches_known[1:]) + direct_grads = _run_grad(inexact, saveat, diffrax.DirectAdjoint()) + assert tree_allclose(fd_grads, direct_grads[0], atol=1e-3) + assert tree_allclose(fd_grads, recursive_grads[0], atol=1e-3) + assert tree_allclose(fd_grads, forward_grads[0], atol=1e-3) From 679d68ceeeef1ae9308aa13df6adb3a25a19814f Mon Sep 17 00:00:00 2001 From: Riccardo Orsi <104301293+ricor07@users.noreply.github.com> Date: Fri, 27 Dec 2024 11:52:13 +0100 Subject: [PATCH 36/50] Allowing args into grad_f for ULD --- diffrax/_term.py | 8 ++++---- test/test_term.py | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 40 insertions(+), 4 deletions(-) diff --git a/diffrax/_term.py b/diffrax/_term.py index efa28d29..d13d430b 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -925,13 +925,13 @@ class UnderdampedLangevinDriftTerm(AbstractTerm): gamma: PyTree[ArrayLike] u: PyTree[ArrayLike] - grad_f: Callable[[UnderdampedLangevinX], UnderdampedLangevinX] + grad_f: Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX] def __init__( self, gamma: PyTree[ArrayLike], u: PyTree[ArrayLike], - grad_f: Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + grad_f: Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ): r""" **Arguments:** @@ -942,7 +942,7 @@ def __init__( a scalar or a PyTree of the same shape as the position vector $x$. - `grad_f`: A callable representing the gradient of the potential function $f$. This callable should take a PyTree of the same shape as $x$ and - return a PyTree of the same shape. + an optional `args` argument, returning a PyTree of the same shape. """ self.gamma = gamma self.u = u @@ -963,7 +963,7 @@ def fun(_gamma, _u, _v, _f_x): vf_x = v try: - f_x = self.grad_f(x) + f_x = self.grad_f(x, args) # Pass args to grad_f vf_v = jtu.tree_map(fun, gamma, u, v, f_x) except ValueError: raise RuntimeError( diff --git a/test/test_term.py b/test/test_term.py index 5260db2c..8e8bf8be 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -158,3 +158,39 @@ def test_weaklydiagonal_deprecate(): _ = diffrax.WeaklyDiagonalControlTerm( lambda t, y, args: 0.0, lambda t0, t1: jnp.array(t1 - t0) ) + + +def test_underdamped_langevin_drift_term_args(): + """ + Test that the UnderdampedLangevinDriftTerm handles `args` in grad_f correctly. + """ + + # Mock gradient function that uses args + def mock_grad_f(x, args): + return jtu.tree_map(lambda xi, ai: xi + ai, x, args) + + # Mock data + gamma = jnp.array([0.1, 0.2, 0.3]) + u = jnp.array([0.4, 0.5, 0.6]) + x = jnp.array([1.0, 2.0, 3.0]) + v = jnp.array([0.1, 0.2, 0.3]) + args = jnp.array([0.7, 0.8, 0.9]) + y = (x, v) + + # Create instance of the drift term + term = diffrax.UnderdampedLangevinDriftTerm(gamma=gamma, u=u, grad_f=mock_grad_f) + + # Compute the vector field + vf_y = term.vf(0.0, y, args) + + # Extract results + vf_x, vf_v = vf_y + + # Expected results + expected_vf_x = v # By definition, vf_x = v + f_x = x + args # Output of mock_grad_f + expected_vf_v = -gamma * v - u * f_x # Drift term calculation + + # Assertions + assert jnp.allclose(vf_x, expected_vf_x), "vf_x does not match expected results" + assert jnp.allclose(vf_v, expected_vf_v), "vf_v does not match expected results" From 0217f92087ce10e9731ca09dceb07796ff76fdd9 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 26 Jan 2025 21:00:39 +0100 Subject: [PATCH 37/50] Test fixes for v0.5.0 + args for langevin --- diffrax/_solver/align.py | 4 +++- diffrax/_solver/foster_langevin_srk.py | 19 +++++++++++-------- diffrax/_solver/quicsort.py | 4 +++- diffrax/_solver/should.py | 4 +++- test/helpers.py | 6 ++++-- test/test_brownian.py | 12 ++++++------ test/test_integrate.py | 2 +- test/test_progress_meter.py | 6 ++++++ test/test_sde1.py | 7 +++---- test/test_underdamped_langevin.py | 11 ++++++----- 10 files changed, 46 insertions(+), 29 deletions(-) diff --git a/diffrax/_solver/align.py b/diffrax/_solver/align.py index c6bc6105..433b2779 100644 --- a/diffrax/_solver/align.py +++ b/diffrax/_solver/align.py @@ -6,6 +6,7 @@ from .._custom_types import ( AbstractSpaceTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -156,6 +157,7 @@ def _compute_step( coeffs: _ALIGNCoeffs, rho: UnderdampedLangevinX, prev_f: UnderdampedLangevinX, + args: Args, ) -> tuple[ UnderdampedLangevinX, UnderdampedLangevinX, @@ -176,7 +178,7 @@ def _compute_step( - coeffs.b1**ω * uh**ω * f0**ω + rho**ω * (coeffs.b1**ω * w**ω + coeffs.chh**ω * hh**ω) ).ω - f1 = f(x1) + f1 = f(x1, args) v1 = ( coeffs.beta**ω * v0**ω - u**ω * ((coeffs.a1**ω - coeffs.b1**ω) * f0**ω + coeffs.b1**ω * f1**ω) diff --git a/diffrax/_solver/foster_langevin_srk.py b/diffrax/_solver/foster_langevin_srk.py index dbdf3939..47ae3090 100644 --- a/diffrax/_solver/foster_langevin_srk.py +++ b/diffrax/_solver/foster_langevin_srk.py @@ -13,6 +13,7 @@ from .._custom_types import ( AbstractBrownianIncrement, + Args, BoolScalarLike, DenseInfo, RealScalarLike, @@ -37,7 +38,7 @@ UnderdampedLangevinArgs = tuple[ UnderdampedLangevinX, UnderdampedLangevinX, - Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ] @@ -48,7 +49,7 @@ def _get_args_from_terms( PyTree, PyTree, PyTree, - Callable[[UnderdampedLangevinX], UnderdampedLangevinX], + Callable[[UnderdampedLangevinX, Args], UnderdampedLangevinX], ]: drift, diffusion = terms.terms if isinstance(drift, WrapTerm): @@ -255,6 +256,7 @@ def init( evaluation of grad_f. """ drift, diffusion = terms.terms + del diffusion ( gamma_drift, u_drift, @@ -265,6 +267,7 @@ def init( h = drift.contr(t0, t1) x0, v0 = y0 + del v0 gamma = broadcast_underdamped_langevin_arg(gamma_drift, x0, "gamma") u = broadcast_underdamped_langevin_arg(u_drift, x0, "u") @@ -287,7 +290,7 @@ def compare_args_fun(arg1, arg2): u = jtu.tree_map(compare_args_fun, u, u_diffusion) try: - grad_f_shape = jax.eval_shape(grad_f, x0) + grad_f_shape = jax.eval_shape(grad_f, x0, args) except ValueError: raise RuntimeError( "The function `grad_f` in the Underdamped Langevin term must be" @@ -300,7 +303,7 @@ def shape_check_fun(_x, _g, _u, _fx): if not jtu.tree_all(jtu.tree_map(shape_check_fun, x0, gamma, u, grad_f_shape)): raise RuntimeError( - "The shapes and PyTree structures of x0, gamma, u, and grad_f(x0)" + "The shapes and PyTree structures of x0, gamma, u, and grad_f(x0, args)" " must match." ) @@ -311,7 +314,7 @@ def shape_check_fun(_x, _g, _u, _fx): coeffs = self._recompute_coeffs(h, gamma, tay_coeffs) rho = jtu.tree_map(lambda c, _u: jnp.sqrt(2 * c * _u), gamma, u) - prev_f = grad_f(x0) if self._is_fsal else None + prev_f = grad_f(x0, args) if self._is_fsal else None state_out = SolverState( gamma=gamma, @@ -336,6 +339,7 @@ def _compute_step( coeffs: _Coeffs, rho: UnderdampedLangevinX, prev_f: Optional[UnderdampedLangevinX], + args: Args, ) -> tuple[ UnderdampedLangevinX, UnderdampedLangevinX, @@ -369,7 +373,6 @@ def step( ) -> tuple[ UnderdampedLangevinTuple, _ErrorEstimate, DenseInfo, SolverState, RESULTS ]: - del args st = solver_state drift, diffusion = terms.terms @@ -404,12 +407,12 @@ def step( prev_f = st.prev_f else: prev_f = lax.cond( - eqxi.unvmap_any(made_jump), lambda: grad_f(x0), lambda: st.prev_f + eqxi.unvmap_any(made_jump), lambda: grad_f(x0, args), lambda: st.prev_f ) # The actual step computation, handled by the subclass x_out, v_out, f_fsal, error = self._compute_step( - h, levy, x0, v0, (gamma, u, grad_f), coeffs, rho, prev_f + h, levy, x0, v0, (gamma, u, grad_f), coeffs, rho, prev_f, args ) def check_shapes_dtypes(arg, *args): diff --git a/diffrax/_solver/quicsort.py b/diffrax/_solver/quicsort.py index 4f21bd6f..dd7c47f6 100644 --- a/diffrax/_solver/quicsort.py +++ b/diffrax/_solver/quicsort.py @@ -10,6 +10,7 @@ from .._custom_types import ( AbstractSpaceTimeTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -199,6 +200,7 @@ def _compute_step( coeffs: _QUICSORTCoeffs, rho: UnderdampedLangevinX, prev_f: Optional[UnderdampedLangevinX], + args: Args, ) -> tuple[UnderdampedLangevinX, UnderdampedLangevinX, None, None]: del prev_f dtypes = jtu.tree_map(jnp.result_type, x0) @@ -235,7 +237,7 @@ def _extract_coeffs(coeff, index): def fn(carry): x, _f, _ = carry - fx_uh = (f(x) ** ω * uh**ω).ω + fx_uh = (f(x, args) ** ω * uh**ω).ω return x, _f, fx_uh def compute_x2(carry): diff --git a/diffrax/_solver/should.py b/diffrax/_solver/should.py index caab54d3..4999b9de 100644 --- a/diffrax/_solver/should.py +++ b/diffrax/_solver/should.py @@ -6,6 +6,7 @@ from .._custom_types import ( AbstractSpaceTimeTimeLevyArea, + Args, RealScalarLike, ) from .._local_interpolation import LocalLinearInterpolation @@ -198,6 +199,7 @@ def _compute_step( coeffs: _ShOULDCoeffs, rho: UnderdampedLangevinX, prev_f: UnderdampedLangevinX, + args: Args, ) -> tuple[UnderdampedLangevinX, UnderdampedLangevinX, UnderdampedLangevinX, None]: dtypes = jtu.tree_map(jnp.result_type, x0) w: UnderdampedLangevinX = jtu.tree_map(jnp.asarray, levy.W, dtypes) @@ -225,7 +227,7 @@ def _compute_step( def fn(carry): x, _f, _ = carry - fx = f(x) + fx = f(x, args) return x, _f, fx def compute_x2(carry): diff --git a/test/helpers.py b/test/helpers.py index 3eba28a4..67be343f 100644 --- a/test/helpers.py +++ b/test/helpers.py @@ -500,7 +500,7 @@ def make_underdamped_langevin_term(gamma, u, grad_f, bm): def get_bqp(t0=0.3, t1=15.0, dtype=jnp.float32): - grad_f_bqp = lambda x: 4 * x * (jnp.square(x) - 1) + grad_f_bqp = lambda x, _: 4 * x * (jnp.square(x) - 1) gamma, u = dtype(0.8), dtype(0.2) y0_bqp = (dtype(0), dtype(0)) w_shape_bqp = () @@ -520,7 +520,9 @@ def get_harmonic_oscillator(t0=0.3, t1=15.0, dtype=jnp.float32): w_shape_hosc = (2,) def get_terms_hosc(bm): - return make_underdamped_langevin_term(gamma_hosc, u_hosc, lambda x: 2 * x, bm) + return make_underdamped_langevin_term( + gamma_hosc, u_hosc, lambda x, _: 2 * x, bm + ) return SDE(get_terms_hosc, None, y0_hosc, t0, t1, w_shape_hosc) diff --git a/test/test_brownian.py b/test/test_brownian.py index 3a265019..126ea245 100644 --- a/test/test_brownian.py +++ b/test/test_brownian.py @@ -123,7 +123,7 @@ def is_tuple_of_ints(obj): def test_statistics(ctr, levy_area, use_levy): # Deterministic key for this test; not using getkey() key = jr.PRNGKey(5678) - num_samples = 60000 + num_samples = 600000 keys = jr.split(key, num_samples) t0, t1 = 0.0, 5.0 dt = t1 - t0 @@ -279,14 +279,14 @@ def _true_cond_stats_whk(bm_s, bm_u, s, r, u): def _conditional_statistics( levy_area, use_levy: bool, tol, spacing, spline: _Spline, min_num_points ): - key = jr.PRNGKey(5678) + key = jr.PRNGKey(5680) bm_key, sample_key, permute_key = jr.split(key, 3) # Get some randomly selected points; not too close to avoid discretisation error. t0 = 0.0 t1 = 8.7 boundary = 0.1 ts = jr.uniform( - sample_key, shape=(100,), minval=t0 + boundary, maxval=t1 - boundary + sample_key, shape=(10000,), minval=t0 + boundary, maxval=t1 - boundary ) sorted_ts = jnp.sort(ts) ts = [] @@ -581,7 +581,7 @@ def test_whk_interpolation(tol, spline): u = jnp.array(5.7, dtype=jnp.float64) bound = 0.0 rs = jr.uniform( - r_key, (100,), dtype=jnp.float64, minval=s + bound, maxval=u - bound + r_key, (1000,), dtype=jnp.float64, minval=s + bound, maxval=u - bound ) path = diffrax.VirtualBrownianTree( t0=s, @@ -672,8 +672,8 @@ def eval_paths(t): assert jnp.all(_pvals_w > 0.1 / _pvals_w.shape[0]) assert jnp.all(_pvals_h > 0.1 / _pvals_h.shape[0]) assert jnp.all(_pvals_k > 0.1 / _pvals_k.shape[0]) - assert jnp.all(jnp.abs(total_mean_err) < 0.005) - assert jnp.all(jnp.abs(total_cov_err) < 0.005) + assert jnp.all(jnp.abs(total_mean_err) < 0.01) + assert jnp.all(jnp.abs(total_cov_err) < 0.01) def test_levy_area_reverse_time(): diff --git a/test/test_integrate.py b/test/test_integrate.py index 555d6ade..424146e5 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -319,7 +319,7 @@ def get_dt_and_controller(level): levy_area=None, ref_solution=None, ) - assert -0.2 < order - theoretical_order < 0.2 + assert -0.3 < order - theoretical_order < 0.3 # Step size deliberately chosen not to divide the time interval diff --git a/test/test_progress_meter.py b/test/test_progress_meter.py index 1c87b035..a9613c9e 100644 --- a/test/test_progress_meter.py +++ b/test/test_progress_meter.py @@ -57,21 +57,25 @@ def solve(t0): ) solve(2.0) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.33%\n20.67%\n31.00%\n41.33%\n51.67%\n62.00%\n72.33%\n82.67%\n93.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.vmap(solve)(jnp.arange(3.0)) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.00%\n20.00%\n30.00%\n40.00%\n50.20%\n60.40%\n70.60%\n80.80%\n91.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.jit(solve)(2.0) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.33%\n20.67%\n31.00%\n41.33%\n51.67%\n62.00%\n72.33%\n82.67%\n93.00%\n100.00%\n" # noqa: E501 assert captured.out == expected jax.jit(jax.vmap(solve))(jnp.arange(3.0)) + jax.effects_barrier() captured = capfd.readouterr() expected = "0.00%\n10.00%\n20.00%\n30.00%\n40.00%\n50.20%\n60.40%\n70.60%\n80.80%\n91.00%\n100.00%\n" # noqa: E501 assert captured.out == expected @@ -98,6 +102,7 @@ def solve(p): capfd.readouterr() jax.grad(solve)(jnp.array(1.0)) + jax.effects_barrier() captured = capfd.readouterr() if isinstance(progress_meter, diffrax.TextProgressMeter): @@ -108,3 +113,4 @@ def solve(p): assert captured.out == true_out jax.jit(jax.grad(solve))(jnp.array(1.0)) + jax.effects_barrier() diff --git a/test/test_sde1.py b/test/test_sde1.py index b4504872..b50d014f 100644 --- a/test/test_sde1.py +++ b/test/test_sde1.py @@ -89,10 +89,9 @@ def get_dt_and_controller(level): levy_area=None, ref_solution=None, ) - # The upper bound needs to be 0.25, otherwise we fail. - # This still preserves a 0.05 buffer between the intervals - # corresponding to the different orders. - assert -0.2 < order - theoretical_order < 0.25 + # TODO: this is a pretty wide range to check. Maybe fixable by being better about + # the randomness (e.g. average over multiple original seeds)? + assert -0.4 < order - theoretical_order < 0.4 # Make variables to store the correct solutions in. diff --git a/test/test_underdamped_langevin.py b/test/test_underdamped_langevin.py index e945cad5..246506bb 100644 --- a/test/test_underdamped_langevin.py +++ b/test/test_underdamped_langevin.py @@ -59,7 +59,7 @@ def make_pytree(array_factory): "qq": jnp.ones((), dtype), } - def grad_f(x): + def grad_f(x, _): xa = x["rr"] xb = x["qq"] return {"rr": jtu.tree_map(lambda _x: 0.2 * _x, xa), "qq": xb} @@ -218,7 +218,7 @@ def test_reverse_solve(solver_cls): key=jr.key(0), levy_area=diffrax.SpaceTimeTimeLevyArea, ) - terms = make_underdamped_langevin_term(gamma, u, lambda x: 2 * x, bm) + terms = make_underdamped_langevin_term(gamma, u, lambda x, _: 2 * x, bm) solver = solver_cls(0.01) sol = diffeqsolve(terms, solver, t0, t1, dt0=dt0, y0=y0, args=None, saveat=saveat) @@ -234,7 +234,8 @@ def test_reverse_solve(solver_cls): # Here we check that if the drift and diffusion term have different arguments, # an error is thrown. -def test_different_args(): +@pytest.mark.parametrize("solver_cls", _only_uld_solvers_cls()) +def test_different_args(solver_cls): x0 = (jnp.ones(2), jnp.zeros(2)) v0 = (jnp.zeros(2), jnp.zeros(2)) y0 = (x0, v0) @@ -242,7 +243,7 @@ def test_different_args(): u1 = (jnp.array([1, 2]), 1) g2 = (jnp.array([1, 2]), jnp.array([1, 3])) u2 = (jnp.array([1, 2]), jnp.ones((2,))) - grad_f = lambda x: x + grad_f = lambda x, args: x w_shape = ( jax.ShapeDtypeStruct((2,), jnp.float64), @@ -267,7 +268,7 @@ def test_different_args(): diffusion_term_b = diffrax.UnderdampedLangevinDiffusionTerm(g1, u2, bm) terms_b = diffrax.MultiTerm(drift_term, diffusion_term_b) - solver = diffrax.ShOULD(0.01) + solver = solver_cls(0.01) with pytest.raises(Exception): diffeqsolve(terms_a, solver, 0, 1, 0.1, y0, args=None) diffeqsolve(terms_b, solver, 0, 1, 0.1, y0, args=None) From 36a6b001f10129c55dac933dc5c157df94ffc86c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lukas=20K=C3=B6nig?= Date: Tue, 28 Jan 2025 18:29:38 +0100 Subject: [PATCH 38/50] Fix for making vmap over diffeqsolve possible (#578) * _integrate.py * Added new test checking gradient of vmapped diffeqsolve * Import optimistix * Fixed issue * added .any() * diffrax root finder --- diffrax/_integrate.py | 3 ++- test/test_integrate.py | 47 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 1 deletion(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index cacc1070..5f6d05d5 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -649,7 +649,8 @@ def body_fun(state): event_mask = final_state.event_mask flat_mask = jtu.tree_leaves(event_mask) assert all(jnp.shape(x) == () for x in flat_mask) - event_happened = jnp.any(jnp.stack(flat_mask)) + float_mask = jnp.array(flat_mask).astype(jnp.float32) + event_happened = jnp.max(float_mask) > 0.0 def _root_find(): _interpolator = solver.interpolation_cls( diff --git a/test/test_integrate.py b/test/test_integrate.py index 424146e5..dbfeee03 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -792,3 +792,50 @@ def func(self, terms, t0, y0, args): ValueError, match=r"Terms are not compatible with solver!" ): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, y0) + + +def test_vmap_backprop(): + def dynamics(t, y, args): + param = args + return param - y + + def event_fn(t, y, args, **kwargs): + return y - 1.5 + + def single_loss_fn(param): + solver = diffrax.Euler() + root_finder = diffrax.VeryChord(rtol=1e-3, atol=1e-6) + event = diffrax.Event(event_fn, root_finder) + term = diffrax.ODETerm(dynamics) + sol = diffrax.diffeqsolve( + term, + solver=solver, + t0=0.0, + t1=2.0, + dt0=0.1, + y0=0.0, + args=param, + event=event, + max_steps=1000, + ) + assert sol.ys is not None + final_y = sol.ys[-1] + return param**2 + final_y**2 + + def batched_loss_fn(params: jnp.ndarray) -> jnp.ndarray: + return jax.vmap(single_loss_fn)(params) + + def grad_fn(params: jnp.ndarray) -> jnp.ndarray: + return jax.grad(lambda p: jnp.sum(batched_loss_fn(p)))(params) + + batch = jnp.array([1.0, 2.0, 3.0]) + + try: + grad = grad_fn(batch) + except NotImplementedError as e: + pytest.fail(f"NotImplementedError was raised: {e}") + except Exception as e: + pytest.fail(f"An unexpected exception was raised: {e}") + + assert not jnp.isnan(grad).any(), "Gradient should not be NaN." + assert not jnp.isinf(grad).any(), "Gradient should not be infinite." From 25d25a80a69a63ca9f9e821b6d5263136b91729a Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 18:30:50 +0100 Subject: [PATCH 39/50] Tweak test name --- test/test_integrate.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/test_integrate.py b/test/test_integrate.py index dbfeee03..d8ca4360 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -794,7 +794,8 @@ def func(self, terms, t0, y0, args): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, y0) -def test_vmap_backprop(): +# Test that we don't hit a JAX bug: https://github.com/patrick-kidger/diffrax/issues/568 +def test_vmap_backprop_with_event(): def dynamics(t, y, args): param = args return param - y From 287fff37f21f0bf5ed3dcac84aa45cd53d473459 Mon Sep 17 00:00:00 2001 From: joharkit <98756257+joharkit@users.noreply.github.com> Date: Tue, 28 Jan 2025 18:54:12 +0100 Subject: [PATCH 40/50] Update pyproject.toml to meet poetry conventions in python-poetry ~=3.9 is interpreted as >=3.9<3.10 [2], though it should be >=3.9,<4.0 [2] https://python-poetry.org/docs/dependency-specification/ --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 01cacf52..0d56b739 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "diffrax" version = "0.6.2" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" -requires-python ="~=3.9" +requires-python =">=3.9,<4.0" license = {file = "LICENSE"} authors = [ {name = "Patrick Kidger", email = "contact@kidger.site"}, From 5aa502c33a76707288a0aab71bfd869645413b0e Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 12 Jan 2025 21:45:09 +0100 Subject: [PATCH 41/50] Fixed a major source of bugs: ControlTerms no longer broadcast. --- diffrax/_term.py | 295 +++++++++++++++++++++++++++++------------ docs/api/terms.md | 4 +- pyproject.toml | 2 +- test/test_adjoint.py | 3 +- test/test_integrate.py | 63 ++++++--- test/test_sde2.py | 4 +- test/test_term.py | 8 +- test/test_typing.py | 5 - 8 files changed, 266 insertions(+), 118 deletions(-) diff --git a/diffrax/_term.py b/diffrax/_term.py index d13d430b..0ea97301 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -256,7 +256,7 @@ def _callable_to_path( x: Union[ AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] ], -) -> AbstractPath[_Control]: +) -> AbstractPath: if isinstance(x, AbstractPath): return x else: @@ -270,55 +270,7 @@ def _prod(vf, control): return jnp.tensordot(jnp.conj(vf), control, axes=jnp.ndim(control)) -# This class exists for backward compatibility with `WeaklyDiagonalControlTerm`. If we -# were writing things again today it would be folded into just `ControlTerm`. -class _AbstractControlTerm(AbstractTerm[_VF, _Control]): - vector_field: Callable[[RealScalarLike, Y, Args], _VF] - control: Union[ - AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] - ] = eqx.field(converter=_callable_to_path) # pyright: ignore - - def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: - return self.vector_field(t, y, args) - - def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: - return self.control.evaluate(t0, t1, **kwargs) # pyright: ignore - - def to_ode(self) -> ODETerm: - r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ - may be thought of as an ODE as - - $f(t, y(t), args) \frac{\mathrm{d}x}{\mathrm{d}t}\mathrm{d}t$. - - This method converts this `ControlTerm` into the corresponding - [`diffrax.ODETerm`][] in this way. - """ - vector_field = _ControlToODE(self) - return ODETerm(vector_field=vector_field) - - -_AbstractControlTerm.__init__.__doc__ = """**Arguments:** - -- `vector_field`: A callable representing the vector field. This callable takes three - arguments `(t, y, args)`. `t` is a scalar representing the integration time. `y` is - the evolving state of the system. `args` are any static arguments as passed to - [`diffrax.diffeqsolve`][]. This `vector_field` can either be - - 1. a function that returns a PyTree of JAX arrays, or - 2. it can return a - [Lineax linear operator](https://docs.kidger.site/lineax/api/operators), - as described above. - -- `control`: The control. Should either be - - 1. a [`diffrax.AbstractPath`][], in which case its `.evaluate(t0, t1)` method - will be used to give the increment of the control over a time interval - `[t0, t1]`, or - 2. a callable `(t0, t1) -> increment`, which returns the increment directly. -""" - - -class ControlTerm(_AbstractControlTerm[_VF, _Control]): +class ControlTerm(AbstractTerm[_VF, _Control]): r"""A term representing the general case of $f(t, y(t), args) \mathrm{d}x(t)$, in which the vector field ($f$) - control ($\mathrm{d}x$) interaction is a matrix-vector product. @@ -380,6 +332,7 @@ def vector_field(t, y, args): diffusion_term = ControlTerm(vector_field, control) diffeqsolve(terms=diffusion_term, y0=y0, ...) ``` + !!! Example In this example we consider an SDE with a one-dimensional state @@ -451,14 +404,182 @@ def vector_field(t, y, args): ``` """ # noqa: E501 + vector_field: Callable[[RealScalarLike, Y, Args], _VF] + control: AbstractPath[_Control] + + def __init__( + self, + vector_field: Callable[[RealScalarLike, Y, Args], _VF], + control: Union[ + AbstractPath[_Control], Callable[[RealScalarLike, RealScalarLike], _Control] + ], + ): + self.vector_field = vector_field + self.control = _callable_to_path(control) + + def vf(self, t: RealScalarLike, y: Y, args: Args) -> VF: + return self.vector_field(t, y, args) + + def contr(self, t0: RealScalarLike, t1: RealScalarLike, **kwargs) -> _Control: + return self.control.evaluate(t0, t1, **kwargs) + def prod(self, vf: _VF, control: _Control) -> Y: if isinstance(vf, lx.AbstractLinearOperator): return vf.mv(control) else: return jtu.tree_map(_prod, vf, control) + def vf_prod(self, t: RealScalarLike, y: Y, args: Args, control: _Control) -> Y: + vf = self.vf(t, y, args) + out = self.prod(vf, control) + + def _raise(): + # SDEs are a common special case; try to make the error message a little + # easier to understand in this case! + if isinstance(self.control, AbstractBrownianPath): + diffusion_word = "diffusion" + control_word = "Brownian motion" + diffusion_phrase = "diffusion matrix" + else: + diffusion_word = "vector field" + control_word = "control" + diffusion_phrase = "vector field in a control term" + if isinstance(vf, lx.AbstractLinearOperator): + dot_phrase = ( + f"combined with `{type(vf).__module__}.{type(vf).__qualname__}.mv`" + ) + else: + dot_phrase = "dotted together" + vf_str = eqx.tree_pformat(vf) + control_str = eqx.tree_pformat(control) + out_str = eqx.tree_pformat(out) + y_str = eqx.tree_pformat(y) + if "\n" in vf_str: + vf_str = f"\n```\n{vf_str}\n```\n" + else: + vf_str = f" `{vf_str}` " + if "\n" in control_str: + control_str = f"\n```\n{control_str}\n```\n" + else: + control_str = f" `{control_str}`, " + if "\n" in out_str: + out_str = f"\n```\n{out_str}\n```\n" + else: + out_str = f" `{out_str}`, " + if "\n" in y_str: + y_str = f"\n```\n{y_str}\n```\n" + else: + y_str = f" `{y_str}`.\n" + raise ValueError( + "The `ControlTerm` returned arrays whose output structure did not " + "match the structure of the evolving state `y`. Specifically, the " + f"{diffusion_word} had structure{vf_str}and the {control_word} " + f"had structure{control_str}which when {dot_phrase} produced an " + f"output of structure{out_str}which is different to the evolving " + f"state `y` which had structure{y_str}" + "\n" + "This became an error in Diffrax 0.7.0. In previous versions of " + "Diffrax then the output was broadcast to the shape of `y`. This " + "has been removed as it was a common source of bugs.\n" + "\n" + "To walk you through what is going on, here is a sample program " + "that now raises an error:\n" + "```\n" + "import diffrax as dfx\n" + "import jax.numpy as jnp\n" + "import jax.random as jr\n" + "\n" + "def drift(t, y, args):\n" + " return -y\n" + "\n" + "def diffusion(t, y, args):\n" + " return jnp.array([1., 0.5])\n" + "\n" + "key = jr.key(0)\n" + "bm = dfx.VirtualBrownianTree(t0=0, t1=1, tol=1e-3, shape=(2,), key=key)\n" # noqa: E501 + "terms = dfx.MultiTerm(dfx.ODETerm(drift), dfx.ControlTerm(diffusion, bm))\n" # noqa: E501 + "solver = dfx.Euler()\n" + "y0 = jnp.array([1., 1.])\n" + "dfx.diffeqsolve(terms, solver, t0=0, t1=1, dt0=0.1, y0=y0)\n" + "```\n" + "In this case, the diffusion returns an array of shape `(2,)` and " + "the Brownian motion is of shape `(2,)`. By the rules of " + "`ControlTerm`, they are then dotted together so that the " + "diffusion term returns a scalar. Under previous versions of " + "Diffrax, this would then be broadcast out to both elements of the " + "evolving state `y`, corresponding to the SDE:\n" + "```\n" + "dy₁(t) = -y₁(t) dt + dW₁ + 0.5 dW₂\n" + "dy₂(t) = -y₂(t) dt + dW₁ + 0.5 dW₂\n" + "```\n" + "or the equivalent in vector notation, with `y(t), W(t) ⋹ R²`\n" + "```\n" + "dy(t) = -y(t) dt + [[1, 0.5], [1, 0.5]] dW\n" + "```\n" + "Which may have been unexpected! Quite possibly what was actually " + "intended was an SDE with diagonal noise:\n" + "```\n" + "dy(t) = -y(t) dt + [[1, 0], [0, 0.5]] dW\n" + "```\n" + "\n" + "As of Diffrax 0.7.0, the recommended way to express the " + f"{diffusion_phrase} is to use a Lineax linear operator. " + "(https://docs.kidger.site/lineax/api/operators/) For example, to " + "represent diagonal noise in the example above:\n" + "```python\n" + "import lineax as lx\n" + "\n" + "def diffusion(t, y, args):\n" + " diagonal = jnp.array([1., 0.5])\n" + " return lx.DiagonalLinearOperator(diagonal)\n" + "```\n" + ) + + if jtu.tree_structure(y) != jtu.tree_structure(out): + _raise() + + def _check_shape(yi, out_i): + if jnp.shape(yi) != jnp.shape(out_i): + _raise() + + jtu.tree_map(_check_shape, y, out) + return out + + def to_ode(self) -> ODETerm: + r"""If the control is differentiable then $f(t, y(t), args) \mathrm{d}x(t)$ + may be thought of as an ODE as + + $f(t, y(t), args) \frac{\mathrm{d}x}{\mathrm{d}t}\mathrm{d}t$. + + This method converts this `ControlTerm` into the corresponding + [`diffrax.ODETerm`][] in this way. + """ + vector_field = _ControlToODE(self) + return ODETerm(vector_field=vector_field) + + +ControlTerm.__init__.__doc__ = """**Arguments:** + +- `vector_field`: A callable representing the vector field. This callable takes three + arguments `(t, y, args)`. `t` is a scalar representing the integration time. `y` is + the evolving state of the system. `args` are any static arguments as passed to + [`diffrax.diffeqsolve`][]. This `vector_field` can either be + + 1. a function that returns a PyTree of JAX arrays, or + 2. it can return a + [Lineax linear operator](https://docs.kidger.site/lineax/api/operators), + as described above. + +- `control`: The control. Should either be + + 1. a [`diffrax.AbstractPath`][], in which case its `.evaluate(t0, t1)` method + will be used to give the increment of the control over a time interval + `[t0, t1]`, or + 2. a callable `(t0, t1) -> increment`, which returns the increment directly. +""" -class WeaklyDiagonalControlTerm(_AbstractControlTerm[_VF, _Control]): + +def WeaklyDiagonalControlTerm(vector_field, control): r""" DEPRECATED. Prefer: @@ -469,6 +590,9 @@ def vector_field(t, y, args): diffrax.ControlTerm(vector_field, ...) ``` + The current implementation is a backward-compatible shim that returns something like + the code snippet the above. + --- A term representing the case of $f(t, y(t), args) \mathrm{d}x(t)$, in @@ -492,45 +616,46 @@ def vector_field(t, y, args): without the "weak". (This stronger property is useful in some SDE solvers.) """ - def __check_init__(self): - warnings.warn( - "`WeaklyDiagonalControlTerm` is now deprecated, in favour combining " - "`ControlTerm` with a `lineax.AbstractLinearOperator`. This offers a way " - "to define a vector field with any kind of structure -- diagonal or " - "otherwise.\n" - "For a diagonal linear operator, then this can be easily converted as " - "follows. What was previously:\n" - "```\n" - "def vector_field(t, y, args):\n" - " ...\n" - " return some_vector\n" - "\n" - "diffrax.WeaklyDiagonalControlTerm(vector_field)\n" - "```\n" - "is now:\n" - "```\n" - "import lineax\n" - "\n" - "def vector_field(t, y, args):\n" - " ...\n" - " return lineax.DiagonalLinearOperator(some_vector)\n" - "\n" - "diffrax.ControlTerm(vector_field)\n" - "```\n" - "Lineax is available at `https://github.com/patrick-kidger/lineax`.\n", - stacklevel=3, - ) - - def prod(self, vf: _VF, control: _Control) -> Y: - with jax.numpy_dtype_promotion("standard"): - return jtu.tree_map(operator.mul, vf, control) + warnings.warn( + "`WeaklyDiagonalControlTerm` is now deprecated, in favour combining " + "`ControlTerm` with a `lineax.AbstractLinearOperator`. This offers a way " + "to define a vector field with any kind of structure -- diagonal or " + "otherwise.\n" + "For a diagonal linear operator, then this can be easily converted as " + "follows. What was previously:\n" + "```\n" + "def vector_field(t, y, args):\n" + " ...\n" + " return some_vector\n" + "\n" + "diffrax.WeaklyDiagonalControlTerm(vector_field)\n" + "```\n" + "is now:\n" + "```\n" + "import lineax\n" + "\n" + "def vector_field(t, y, args):\n" + " ...\n" + " return lineax.DiagonalLinearOperator(some_vector)\n" + "\n" + "diffrax.ControlTerm(vector_field)\n" + "```\n" + "Lineax is available at `https://github.com/patrick-kidger/lineax`.\n", + stacklevel=2, + ) + + def new_vector_field(t, y, args): + vf = vector_field(t, y, args) + return lx.DiagonalLinearOperator(vf) + + return ControlTerm(new_vector_field, control) class _ControlToODE(eqx.Module): - control_term: _AbstractControlTerm + control_term: ControlTerm def __call__(self, t: RealScalarLike, y: Y, args: Args) -> Y: - control = self.control_term.control.derivative(t) # pyright: ignore + control = self.control_term.control.derivative(t) return self.control_term.vf_prod(t, y, args, control) diff --git a/docs/api/terms.md b/docs/api/terms.md index 0c72f9f6..6eecde1b 100644 --- a/docs/api/terms.md +++ b/docs/api/terms.md @@ -71,7 +71,7 @@ Some example term structures include: ??? note "Defining your own term types" - For advanced users: you can create your own terms if appropriate. For example if your diffusion is matrix, itself computed as a matrix-matrix product, then you may wish to define a custom term and specify its [`diffrax.AbstractTerm.vf_prod`][] method. By overriding this method you could express the contraction of the vector field - control as a matrix-(matix-vector) product, which is more efficient than the default (matrix-matrix)-vector product. + For advanced users, you can create your own terms if appropriate. See for example the [underdamped Langevin terms](#underdamped-langevin-terms), which have their own special set of solvers. --- @@ -113,7 +113,7 @@ $\gamma , u \in \mathbb{R}^{d \times d}$ are diagonal matrices governing the friction and the damping of the system. These terms enable the use of ULD-specific solvers which can be found -[here](./solvers/sde_solvers.md#underdamped-langevin-solvers). Note that these ULD solvers will only work if given +[here](./solvers/sde_solvers.md#underdamped-langevin-solvers). These ULD solvers expect terms with structure `MultiTerm(UnderdampedLangevinDriftTerm(gamma, u, grad_f), UnderdampedLangevinDiffusionTerm(gamma, u, bm))`, where `bm` is an [`diffrax.AbstractBrownianPath`][] and the same values of `gammma` and `u` are passed to both terms. diff --git a/pyproject.toml b/pyproject.toml index 0d56b739..3b9b3d3d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "diffrax" -version = "0.6.2" +version = "0.7.0" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" requires-python =">=3.9,<4.0" diff --git a/test/test_adjoint.py b/test/test_adjoint.py index c45c6286..9e17e535 100644 --- a/test/test_adjoint.py +++ b/test/test_adjoint.py @@ -391,7 +391,8 @@ def g_lx(t, y, args): bm = diffrax.VirtualBrownianTree(t0, t1, tol, shape, key=getkey()) drift = diffrax.ODETerm(f) if diffusion_fn == "weak": - diffusion = diffrax.WeaklyDiagonalControlTerm(g, bm) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + diffusion = diffrax.WeaklyDiagonalControlTerm(g, bm) else: diffusion = diffrax.ControlTerm(g_lx, bm) terms = diffrax.MultiTerm(drift, diffusion) diff --git a/test/test_integrate.py b/test/test_integrate.py index d8ca4360..15d83f3e 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -603,31 +603,49 @@ def test_term_compatibility(): class TestControl(eqx.Module): dt: Float[ArrayLike, ""] - def __rmul__(self, other): - return other.__mul__(self.dt) - - def __mul__(self, other): - return self.dt * other - class TestSolver(diffrax.Euler): term_structure = diffrax.AbstractTerm[ - tuple[Float[Array, "n 3"]], tuple[TestControl] + lx.AbstractLinearOperator, tuple[TestControl] ] + class TestLinearOperator(lx.AbstractLinearOperator): + def mv(self, vector): + assert ( + type(vector) is tuple + and len(vector) == 1 + and type(vector[0]) is TestControl + ) + return (jnp.ones((2, 3)) * vector[0].dt,) + + def as_matrix(self): + assert False + + def transpose(self): + assert False + + def in_structure(self): + return (jax.eval_shape(lambda: TestControl(1.0)),) + + def out_structure(self): + return (jax.ShapeDtypeStruct((2, 3), jnp.float64),) + + @lx.is_symmetric.register(TestLinearOperator) + def _(operator): + del operator + return False + solver = TestSolver() - incompatible_vf = lambda t, y, args: jnp.ones((2, 1)) - compatible_vf = lambda t, y, args: (jnp.ones((2, 3)),) + incompatible_vf = lambda t, y, args: jnp.ones((2, 3)) + compatible_vf = lambda t, y, args: TestLinearOperator() incompatible_control = lambda t0, t1: t1 - t0 compatible_control = lambda t0, t1: (TestControl(t1 - t0),) incompatible_terms = [ - diffrax.WeaklyDiagonalControlTerm(incompatible_vf, incompatible_control), - diffrax.WeaklyDiagonalControlTerm(incompatible_vf, compatible_control), - diffrax.WeaklyDiagonalControlTerm(compatible_vf, incompatible_control), + diffrax.ControlTerm(incompatible_vf, incompatible_control), + diffrax.ControlTerm(incompatible_vf, compatible_control), + diffrax.ControlTerm(compatible_vf, incompatible_control), ] - compatible_term = diffrax.WeaklyDiagonalControlTerm( - compatible_vf, compatible_control - ) + compatible_term = diffrax.ControlTerm(compatible_vf, compatible_control) for term in incompatible_terms: with pytest.raises(ValueError, match=r"Terms are not compatible with solver!"): diffrax.diffeqsolve(term, solver, 0.0, 1.0, 0.1, (jnp.zeros((2, 1)),)) @@ -669,6 +687,10 @@ def _step(_term, _y): def func(self, terms, t0, y0, args): assert False + def weakly_diagonal(*a): + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + return diffrax.WeaklyDiagonalControlTerm(*a) + ode_term = diffrax.ODETerm(lambda t, y, args: -y) solver = TestSolver() compatible_term = { @@ -678,8 +700,9 @@ def func(self, terms, t0, y0, args): "d": ode_term, "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( - lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(5) + weakly_diagonal( + lambda t, y, args: -y, + lambda t0, t1: jnp.array(t1 - t0).repeat(5), ), ), "f": diffrax.MultiTerm( @@ -707,7 +730,7 @@ def func(self, terms, t0, y0, args): "d": ode_term, "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( + weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: t1 - t0, # wrong control shape ), @@ -727,7 +750,7 @@ def func(self, terms, t0, y0, args): # Missing "d" piece "e": diffrax.MultiTerm( ode_term, - diffrax.WeaklyDiagonalControlTerm( + weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(3) ), ), @@ -745,7 +768,7 @@ def func(self, terms, t0, y0, args): "c": ode_term, "d": ode_term, # No MultiTerm for "e" - "e": diffrax.WeaklyDiagonalControlTerm( + "e": weakly_diagonal( lambda t, y, args: -y, lambda t0, t1: jnp.array(t1 - t0).repeat(3) ), "f": diffrax.MultiTerm( diff --git a/test/test_sde2.py b/test/test_sde2.py index 3b4a4628..077177b8 100644 --- a/test/test_sde2.py +++ b/test/test_sde2.py @@ -83,7 +83,9 @@ def _drift(t, y, args): 0.0, 1.0, 0.05, w_shape, jr.key(0), diffrax.SpaceTimeLevyArea ) - terms = MultiTerm(ODETerm(_drift), WeaklyDiagonalControlTerm(_diffusion, bm)) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + diffusion = WeaklyDiagonalControlTerm(_diffusion, bm) + terms = MultiTerm(ODETerm(_drift), diffusion) saveat = diffrax.SaveAt(t1=True) solution = diffrax.diffeqsolve( terms, solver, 0.0, 1.0, 0.1, y0, args, saveat=saveat diff --git a/test/test_term.py b/test/test_term.py index 8e8bf8be..0c75fc78 100644 --- a/test/test_term.py +++ b/test/test_term.py @@ -4,6 +4,7 @@ import jax.numpy as jnp import jax.random as jr import jax.tree_util as jtu +import lineax as lx import pytest from jaxtyping import Array, PyTree, Shaped @@ -84,15 +85,16 @@ def derivative(self, t, left=True): return jr.normal(derivkey, (3,)) control = Control() - term = diffrax.WeaklyDiagonalControlTerm(vector_field, control) + with pytest.warns(match="`WeaklyDiagonalControlTerm` is now deprecated"): + term = diffrax.WeaklyDiagonalControlTerm(vector_field, control) args = getkey() dx = term.contr(0, 1) y = jnp.array([1.0, 2.0, 3.0]) vf = term.vf(0, y, args) vf_prod = term.vf_prod(0, y, args, dx) - if isinstance(dx, jax.Array) and isinstance(vf, jax.Array): + if isinstance(dx, jax.Array) and isinstance(vf, lx.DiagonalLinearOperator): assert dx.shape == (3,) - assert vf.shape == (3,) + assert vf.diagonal.shape == (3,) else: raise TypeError("dx/vf is not an array") assert vf_prod.shape == (3,) diff --git a/test/test_typing.py b/test/test_typing.py index 4c4f3db1..705b0bcd 100644 --- a/test/test_typing.py +++ b/test/test_typing.py @@ -289,8 +289,3 @@ def test_ode_term(): def test_control_term(): assert _abstract_args(dfx.ControlTerm) == (Any, Any) assert _abstract_args(dfx.ControlTerm[int, str]) == (int, str) - - -def test_weakly_diagonal_control_term(): - assert _abstract_args(dfx.WeaklyDiagonalControlTerm) == (Any, Any) - assert _abstract_args(dfx.WeaklyDiagonalControlTerm[int, str]) == (int, str) From 211f1de668b6c318381046641f37105739540015 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 12 Jan 2025 21:46:13 +0100 Subject: [PATCH 42/50] Now using jaxtyping.Real for prettier documentation. --- diffrax/_custom_types.py | 19 +++---------------- mkdocs.yml | 2 ++ 2 files changed, 5 insertions(+), 16 deletions(-) diff --git a/diffrax/_custom_types.py b/diffrax/_custom_types.py index 7e08aa1b..a16b4d61 100644 --- a/diffrax/_custom_types.py +++ b/diffrax/_custom_types.py @@ -1,4 +1,3 @@ -import typing from typing import Any, TYPE_CHECKING, Union import equinox as eqx @@ -13,6 +12,7 @@ Float, Int, PyTree, + Real, Shaped, ) @@ -21,27 +21,14 @@ BoolScalarLike = Union[bool, Array, np.ndarray] FloatScalarLike = Union[float, Array, np.ndarray] IntScalarLike = Union[int, Array, np.ndarray] -elif getattr(typing, "GENERATING_DOCUMENTATION", False): - # Skip the union with Array in docs. - BoolScalarLike = bool - FloatScalarLike = float - IntScalarLike = int - - # - # Because they appear in our docstrings, we also monkey-patch some non-Diffrax - # types that have similar defined-in-one-place, exported-in-another behaviour. - # - - jtu.Partial.__module__ = "jax.tree_util" - + RealScalarLike = Union[bool, int, float, Array, np.ndarray] else: BoolScalarLike = Bool[ArrayLike, ""] FloatScalarLike = Float[ArrayLike, ""] IntScalarLike = Int[ArrayLike, ""] + RealScalarLike = Real[ArrayLike, ""] -RealScalarLike = Union[FloatScalarLike, IntScalarLike] - Y = PyTree[Shaped[ArrayLike, "?*y"], "Y"] VF = PyTree[Shaped[ArrayLike, "?*vf"], "VF"] Control = PyTree[Shaped[ArrayLike, "?*control"], "C"] diff --git a/mkdocs.yml b/mkdocs.yml index b399fbd8..067cd458 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,6 +75,8 @@ plugins: setup_commands: - import pytkdocs_tweaks - pytkdocs_tweaks.main() + - import jax.tree_util + - jax.tree_util.Partial.__module__ = "jax.tree_util" selection: inherited_members: true # Allow looking up inherited methods From 44154e1a48b2528ccb91fffb3815a64432a592b3 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 20:11:37 +0100 Subject: [PATCH 43/50] Bumped minimum version of Python to 3.10 --- README.md | 2 +- docs/index.md | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e24717cf..a09ad532 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ _From a technical point of view, the internal structure of the library is pretty pip install diffrax ``` -Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.10.11+. +Requires Python 3.10+. ## Documentation diff --git a/docs/index.md b/docs/index.md index 8987a9f7..3f26c539 100644 --- a/docs/index.md +++ b/docs/index.md @@ -20,7 +20,7 @@ _From a technical point of view, the internal structure of the library is pretty pip install diffrax ``` -Requires Python 3.9+, JAX 0.4.13+, and [Equinox](https://github.com/patrick-kidger/equinox) 0.10.11+. +Requires Python 3.10+. ## Quick example diff --git a/pyproject.toml b/pyproject.toml index 3b9b3d3d..42b77f52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "diffrax" version = "0.7.0" description = "GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX." readme = "README.md" -requires-python =">=3.9,<4.0" +requires-python =">=3.10,<4.0" license = {file = "LICENSE"} authors = [ {name = "Patrick Kidger", email = "contact@kidger.site"}, From a1f3c6de4e007da859971cf8462370ef0e284811 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Tue, 28 Jan 2025 20:05:04 +0100 Subject: [PATCH 44/50] Investigating if we can drop the typeguard dependency. --- diffrax/_integrate.py | 2 +- diffrax/_typing.py | 43 +++++++++++++++++++++++++------------------ 2 files changed, 26 insertions(+), 19 deletions(-) diff --git a/diffrax/_integrate.py b/diffrax/_integrate.py index 5f6d05d5..88c014aa 100644 --- a/diffrax/_integrate.py +++ b/diffrax/_integrate.py @@ -198,7 +198,7 @@ def _check(term_cls, term, term_contr_kwargs, yi): try: with jax.numpy_dtype_promotion("standard"): jtu.tree_map(_check, term_structure, terms, contr_kwargs, y) - except Exception as e: + except ValueError as e: # ValueError may also arise from mismatched tree structures pretty_term = wl.pformat(terms) pretty_expected = wl.pformat(term_structure) diff --git a/diffrax/_typing.py b/diffrax/_typing.py index e0bfff6c..694357ed 100644 --- a/diffrax/_typing.py +++ b/diffrax/_typing.py @@ -1,5 +1,4 @@ import inspect -import sys import types from typing import ( Annotated, @@ -14,8 +13,6 @@ ) from typing_extensions import TypeAlias -import typeguard - # We don't actually care what people have subscripted with. # In practice this should be thought of as TypeLike = Union[type, types.UnionType]. Plus @@ -23,24 +20,34 @@ TypeLike: TypeAlias = Any -def better_isinstance(x, annotation) -> bool: - """As `isinstance`, but supports general type hints.""" +_T = TypeVar("_T") - @typeguard.typechecked - def f(y: annotation): - pass - try: - f(x) - except TypeError: - return False - else: - return True +class _Foo(Generic[_T]): + pass + +_generic_alias_types = (types.GenericAlias, type(_Foo[int])) +_union_origins = (Union, types.UnionType) +del _Foo, _T -_union_types: list = [Union] -if sys.version_info >= (3, 10): - _union_types.append(types.UnionType) + +def better_isinstance(x, annotation) -> bool: + """As `isinstance`, but supports a few other types that are useful to us.""" + origin = get_origin(annotation) + if origin in _union_origins: + return any(better_isinstance(x, arg) for arg in get_args(annotation)) + elif isinstance(annotation, _generic_alias_types): + assert origin is not None + return better_isinstance(x, origin) + elif annotation is Any: + return True + elif isinstance(annotation, type): + return isinstance(x, annotation) + else: + raise NotImplementedError( + f"Do not know how to check whether `{x}` is an instance of `{annotation}`." + ) def get_origin_no_specials(x, error_msg: str) -> Optional[type]: @@ -59,7 +66,7 @@ def get_origin_no_specials(x, error_msg: str) -> Optional[type]: As `get_origin`, specifically either `None` or a class. """ origin = get_origin(x) - if origin in _union_types: + if origin in _union_origins: raise NotImplementedError(f"Cannot use unions in `{error_msg}`.") elif origin is Annotated: # We do allow Annotated, just because it's easy to handle. From dc7815644883149090b9f0f82b1e1e32087d4408 Mon Sep 17 00:00:00 2001 From: andyElking Date: Thu, 5 Dec 2024 21:11:43 +0000 Subject: [PATCH 45/50] Split out jump/step clipping in stepsize controllers. --- benchmarks/jump_step_timing.py | 116 +++++ benchmarks/old_pid_controller.py | 414 ++++++++++++++++ diffrax/__init__.py | 1 + diffrax/_misc.py | 7 +- diffrax/_step_size_controller/__init__.py | 9 +- diffrax/_step_size_controller/base.py | 33 +- .../jump_step_wrapper.py | 458 ++++++++++++++++++ .../{adaptive.py => pid.py} | 243 ++-------- docs/api/stepsize_controller.md | 36 +- test/test_adaptive_stepsize_controller.py | 172 ++++++- test/test_progress_meter.py | 2 +- 11 files changed, 1267 insertions(+), 224 deletions(-) create mode 100644 benchmarks/jump_step_timing.py create mode 100644 benchmarks/old_pid_controller.py create mode 100644 diffrax/_step_size_controller/jump_step_wrapper.py rename diffrax/_step_size_controller/{adaptive.py => pid.py} (74%) diff --git a/benchmarks/jump_step_timing.py b/benchmarks/jump_step_timing.py new file mode 100644 index 00000000..9250de4f --- /dev/null +++ b/benchmarks/jump_step_timing.py @@ -0,0 +1,116 @@ +from warnings import simplefilter + + +simplefilter(action="ignore", category=FutureWarning) + +import timeit +from functools import partial + +import diffrax +import equinox as eqx +import jax +import jax.numpy as jnp +import jax.random as jr +from old_pid_controller import OldPIDController + + +t0 = 0 +t1 = 5 +dt0 = 0.5 +y0 = 1.0 +drift = diffrax.ODETerm(lambda t, y, args: -0.2 * y) + + +def diffusion_vf(t, y, args): + return jnp.ones((), dtype=y.dtype) + + +def get_terms(key): + bm = diffrax.VirtualBrownianTree(t0, t1, 2**-5, (), key) + diffusion = diffrax.ControlTerm(diffusion_vf, bm) + return diffrax.MultiTerm(drift, diffusion) + + +solver = diffrax.Heun() +step_ts = jnp.linspace(t0, t1, 129, endpoint=True) +pid_controller = diffrax.PIDController( + rtol=0, atol=1e-3, dtmin=2**-9, dtmax=1.0, pcoeff=0.3, icoeff=0.7 +) +new_controller = diffrax.JumpStepWrapper( + pid_controller, + step_ts=step_ts, + rejected_step_buffer_len=None, +) +old_controller = OldPIDController( + rtol=0, atol=1e-3, dtmin=2**-9, dtmax=1.0, pcoeff=0.3, icoeff=0.7, step_ts=step_ts +) + + +@eqx.filter_jit +@partial(jax.vmap, in_axes=(0, None)) +def solve(key, controller): + term = get_terms(key) + return diffrax.diffeqsolve( + term, + solver, + t0, + t1, + dt0, + y0, + stepsize_controller=controller, + saveat=diffrax.SaveAt(ts=step_ts), + ) + + +num_samples = 100 +keys = jr.split(jr.PRNGKey(0), num_samples) + + +def do_timing(controller): + @jax.jit + @eqx.debug.assert_max_traces(max_traces=1) + def time_controller_fun(): + sols = solve(keys, controller) + assert sols.ys is not None + assert sols.ys.shape == (num_samples, len(step_ts)) + return sols.ys + + def time_controller(): + jax.block_until_ready(time_controller_fun()) + + return min(timeit.repeat(time_controller, number=3, repeat=20)) + + +time_new = do_timing(new_controller) + +time_old = do_timing(old_controller) + +print(f"New controller: {time_new:.5} s, Old controller: {time_old:.5} s") + +# How expensive is revisiting rejected steps? +revisiting_controller_short = diffrax.JumpStepWrapper( + pid_controller, + step_ts=step_ts, + rejected_step_buffer_len=10, +) + +revisiting_controller_long = diffrax.JumpStepWrapper( + pid_controller, + step_ts=step_ts, + rejected_step_buffer_len=4096, +) + +time_revisiting_short = do_timing(revisiting_controller_short) +time_revisiting_long = do_timing(revisiting_controller_long) + +print( + f"Revisiting controller\n" + f"with buffer len 10: {time_revisiting_short:.5} s\n" + f"with buffer len 4096: {time_revisiting_long:.5} s" +) + +# ======= RESULTS ======= +# New controller: 0.23506 s, Old controller: 0.30735 s +# Revisiting controller +# with buffer len 10: 0.23636 s +# with buffer len 4096: 0.23965 s diff --git a/benchmarks/old_pid_controller.py b/benchmarks/old_pid_controller.py new file mode 100644 index 00000000..f6d78098 --- /dev/null +++ b/benchmarks/old_pid_controller.py @@ -0,0 +1,414 @@ +from collections.abc import Callable +from typing import cast, Optional, TypeVar + +import equinox as eqx +import equinox.internal as eqxi +import jax +import jax.lax as lax +import jax.numpy as jnp +import jax.tree_util as jtu +import lineax.internal as lxi +import optimistix as optx +from diffrax import AbstractTerm, ODETerm, RESULTS +from diffrax._custom_types import ( + Args, + BoolScalarLike, + IntScalarLike, + RealScalarLike, + VF, + Y, +) +from diffrax._misc import static_select, upcast_or_raise +from diffrax._step_size_controller import AbstractAdaptiveStepSizeController +from equinox.internal import ω +from jaxtyping import Array, PyTree, Real +from lineax.internal import complex_to_real_dtype + + +ω = cast(Callable, ω) + + +def _select_initial_step( + terms: PyTree[AbstractTerm], + t0: RealScalarLike, + y0: Y, + args: Args, + func: Callable[ + [PyTree[AbstractTerm], RealScalarLike, Y, Args], + VF, + ], + error_order: RealScalarLike, + rtol: RealScalarLike, + atol: RealScalarLike, + norm: Callable[[PyTree], RealScalarLike], +) -> RealScalarLike: + # TODO: someone needs to figure out an initial step size algorithm for SDEs. + if not isinstance(terms, ODETerm): + return 0.01 + + def fn(carry): + t, y, _h0, _d1, _f, _ = carry + f = func(terms, t, y, args) + return t, y, _h0, _d1, _f, f + + def intermediate(carry): + _, _, _, _, _, f0 = carry + d0 = norm((y0**ω / scale**ω).ω) + d1 = norm((f0**ω / scale**ω).ω) + _cond = (d0 < 1e-5) | (d1 < 1e-5) + _d1 = jnp.where(_cond, 1, d1) + h0 = jnp.where(_cond, 1e-6, 0.01 * (d0 / _d1)) + t1 = t0 + h0 + y1 = (y0**ω + h0 * f0**ω).ω + return t1, y1, h0, d1, f0, f0 + + scale = (atol + ω(y0).call(jnp.abs) * rtol).ω + dummy_h = t0 + dummy_d = eqxi.eval_empty(norm, y0) + dummy_f = eqxi.eval_empty(lambda: func(terms, t0, y0, args)) + _, _, h0, d1, f0, f1 = eqxi.scan_trick( + fn, [intermediate], (t0, y0, dummy_h, dummy_d, dummy_f, dummy_f) + ) + d2 = norm(((f1**ω - f0**ω) / scale**ω).ω) / h0 + max_d = jnp.maximum(d1, d2) + h1 = jnp.where( + max_d <= 1e-15, + jnp.maximum(1e-6, h0 * 1e-3), + (0.01 / max_d) ** (1 / error_order), + ) + return jnp.minimum(100 * h0, h1) + + +_ControllerState = TypeVar("_ControllerState") +_Dt0 = TypeVar("_Dt0", None, RealScalarLike, Optional[RealScalarLike]) + +_PidState = tuple[ + BoolScalarLike, BoolScalarLike, RealScalarLike, RealScalarLike, RealScalarLike +] + + +def _none_or_array(x): + if x is None: + return None + else: + return jnp.asarray(x) + + +class OldPIDController( + AbstractAdaptiveStepSizeController[_PidState, Optional[RealScalarLike]] +): + r"""See the doc of diffrax.PIDController for more information.""" + + rtol: RealScalarLike + atol: RealScalarLike + pcoeff: RealScalarLike = 0 + icoeff: RealScalarLike = 1 + dcoeff: RealScalarLike = 0 + dtmin: Optional[RealScalarLike] = None + dtmax: Optional[RealScalarLike] = None + force_dtmin: bool = True + step_ts: Optional[Real[Array, " steps"]] = eqx.field( + default=None, converter=_none_or_array + ) + jump_ts: Optional[Real[Array, " jumps"]] = eqx.field( + default=None, converter=_none_or_array + ) + factormin: RealScalarLike = 0.2 + factormax: RealScalarLike = 10.0 + norm: Callable[[PyTree], RealScalarLike] = optx.rms_norm + safety: RealScalarLike = 0.9 + error_order: Optional[RealScalarLike] = None + + def __check_init__(self): + if self.jump_ts is not None and not jnp.issubdtype( + self.jump_ts.dtype, jnp.inexact + ): + raise ValueError( + f"jump_ts must be floating point, not {self.jump_ts.dtype}" + ) + + def wrap(self, direction: IntScalarLike): + step_ts = None if self.step_ts is None else self.step_ts * direction + jump_ts = None if self.jump_ts is None else self.jump_ts * direction + return eqx.tree_at( + lambda s: (s.step_ts, s.jump_ts), + self, + (step_ts, jump_ts), + is_leaf=lambda x: x is None, + ) + + def init( + self, + terms: PyTree[AbstractTerm], + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + dt0: Optional[RealScalarLike], + args: Args, + func: Callable[[PyTree[AbstractTerm], RealScalarLike, Y, Args], VF], + error_order: Optional[RealScalarLike], + ) -> tuple[RealScalarLike, _PidState]: + del t1 + if dt0 is None: + error_order = self._get_error_order(error_order) + dt0 = _select_initial_step( + terms, + t0, + y0, + args, + func, + error_order, + self.rtol, + self.atol, + self.norm, + ) + + dt0 = lax.stop_gradient(dt0) + if self.dtmax is not None: + dt0 = jnp.minimum(dt0, self.dtmax) + if self.dtmin is None: + at_dtmin = jnp.array(False) + else: + at_dtmin = dt0 <= self.dtmin + dt0 = jnp.maximum(dt0, self.dtmin) + + t1 = self._clip_step_ts(t0, t0 + dt0) + t1, jump_next_step = self._clip_jump_ts(t0, t1) + + y_leaves = jtu.tree_leaves(y0) + if len(y_leaves) == 0: + y_dtype = lxi.default_floating_dtype() + else: + y_dtype = jnp.result_type(*y_leaves) + return t1, ( + jump_next_step, + at_dtmin, + dt0, + jnp.array(1.0, dtype=complex_to_real_dtype(y_dtype)), + jnp.array(1.0, dtype=complex_to_real_dtype(y_dtype)), + ) + + def adapt_step_size( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + y1_candidate: Y, + args: Args, + y_error: Optional[Y], + error_order: RealScalarLike, + controller_state: _PidState, + ) -> tuple[ + BoolScalarLike, + RealScalarLike, + RealScalarLike, + BoolScalarLike, + _PidState, + RESULTS, + ]: + del args + if y_error is None and y0 is not None: + # y0 is not None check is included to handle the edge case that the state + # is just a trivial `None` PyTree. In this case `y_error` has the same + # PyTree structure and thus overlaps with our special usage of `None` to + # indicate a lack of error estimate. + raise RuntimeError( + "Cannot use adaptive step sizes with a solver that does not provide " + "error estimates." + ) + ( + made_jump, + at_dtmin, + prev_dt, + prev_inv_scaled_error, + prev_prev_inv_scaled_error, + ) = controller_state + error_order = self._get_error_order(error_order) + prev_dt = jnp.where(made_jump, prev_dt, t1 - t0) + + # + # Figure out how things went on the last step: error, and whether to + # accept/reject it. + # + + def _scale(_y0, _y1_candidate, _y_error): + # In case the solver steps into a region for which the vector field isn't + # defined. + _nan = jnp.isnan(_y1_candidate).any() + _y1_candidate = jnp.where(_nan, _y0, _y1_candidate) + _y = jnp.maximum(jnp.abs(_y0), jnp.abs(_y1_candidate)) + with jax.numpy_dtype_promotion("standard"): + return _y_error / (self.atol + _y * self.rtol) + + scaled_error = self.norm(jtu.tree_map(_scale, y0, y1_candidate, y_error)) + keep_step = scaled_error < 1 + if self.dtmin is not None: + keep_step = keep_step | at_dtmin + # Make sure it's not a Python scalar and thus getting a ZeroDivisionError. + inv_scaled_error = 1 / jnp.asarray(scaled_error) + inv_scaled_error = lax.stop_gradient( + inv_scaled_error + ) # See note in init above. + # Note: if you ever remove this lax.stop_gradient, then you'll need to do a lot + # of work to get safe gradients through these operations. + # When `inv_scaled_error` has a (non-symbolic) zero cotangent, and `y_error` + # is either zero or inf, then we get a `0 * inf = nan` on the backward pass. + + # + # Adjust next step size + # + + _zero_coeff = lambda c: isinstance(c, (int, float)) and c == 0 + coeff1 = (self.icoeff + self.pcoeff + self.dcoeff) / error_order + coeff2 = -cast(RealScalarLike, self.pcoeff + 2 * self.dcoeff) / error_order + coeff3 = self.dcoeff / error_order + factor1 = 1 if _zero_coeff(coeff1) else inv_scaled_error**coeff1 + factor2 = 1 if _zero_coeff(coeff2) else prev_inv_scaled_error**coeff2 + factor3 = 1 if _zero_coeff(coeff3) else prev_prev_inv_scaled_error**coeff3 + factormin = jnp.where(keep_step, 1, self.factormin) + factor = jnp.clip( + self.safety * factor1 * factor2 * factor3, + min=factormin, + max=self.factormax, + ) + # Once again, see above. In case we have gradients on {i,p,d}coeff. + # (Probably quite common for them to have zero tangents if passed across + # a grad API boundary as part of a larger model.) + factor = lax.stop_gradient(factor) + factor = eqxi.nondifferentiable(factor) + dt = prev_dt * factor.astype(jnp.result_type(prev_dt)) + + # E.g. we failed an implicit step, so y_error=inf, so inv_scaled_error=0, + # so factor=factormin, and we shrunk our step. + # If we're using a PI or PID controller we shouldn't then force shrinking on + # the next or next two steps as well! + pred = (inv_scaled_error == 0) | jnp.isinf(inv_scaled_error) + inv_scaled_error = jnp.where(pred, 1, inv_scaled_error) + + # + # Clip next step size based on dtmin/dtmax + # + + result = RESULTS.successful + if self.dtmax is not None: + dt = jnp.minimum(dt, self.dtmax) + if self.dtmin is None: + at_dtmin = jnp.array(False) + else: + if not self.force_dtmin: + result = RESULTS.where(dt < self.dtmin, RESULTS.dt_min_reached, result) + at_dtmin = dt <= self.dtmin + dt = jnp.maximum(dt, self.dtmin) + + # + # Clip next step size based on step_ts/jump_ts + # + + if jnp.issubdtype(jnp.result_type(t1), jnp.inexact): + # Two nextafters. If made_jump then t1 = prevbefore(jump location) + # so now _t1 = nextafter(jump location) + # This is important because we don't know whether or not the jump is as a + # result of a left- or right-discontinuity, so we have to skip the jump + # location altogether. + _t1 = static_select(made_jump, eqxi.nextafter(eqxi.nextafter(t1)), t1) + else: + _t1 = t1 + next_t0 = jnp.where(keep_step, _t1, t0) + next_t1 = self._clip_step_ts(next_t0, next_t0 + dt) + next_t1, next_made_jump = self._clip_jump_ts(next_t0, next_t1) + + inv_scaled_error = jnp.where(keep_step, inv_scaled_error, prev_inv_scaled_error) + prev_inv_scaled_error = jnp.where( + keep_step, prev_inv_scaled_error, prev_prev_inv_scaled_error + ) + controller_state = ( + next_made_jump, + at_dtmin, + dt, + inv_scaled_error, + prev_inv_scaled_error, + ) + return keep_step, next_t0, next_t1, made_jump, controller_state, result + + def _get_error_order(self, error_order: Optional[RealScalarLike]) -> RealScalarLike: + # Attribute takes priority, if the user knows the correct error order better + # than our guess. + error_order = error_order if self.error_order is None else self.error_order + if error_order is None: + raise ValueError( + "The order of convergence for the solver has not been specified; pass " + "`PIDController(..., error_order=...)` manually instead. If solving " + "an ODE then this should be equal to the (global) order plus one. If " + "solving an SDE then should be equal to the (global) order plus 0.5." + ) + return error_order + + def _clip_step_ts(self, t0: RealScalarLike, t1: RealScalarLike) -> RealScalarLike: + if self.step_ts is None: + return t1 + + step_ts0 = upcast_or_raise( + self.step_ts, + t0, + "`PIDController.step_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + step_ts1 = upcast_or_raise( + self.step_ts, + t1, + "`PIDController.step_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + # TODO: it should be possible to switch this O(nlogn) for just O(n) by keeping + # track of where we were last, and using that as a hint for the next search. + t0_index = jnp.searchsorted(step_ts0, t0, side="right") + t1_index = jnp.searchsorted(step_ts1, t1, side="right") + # This minimum may or may not actually be necessary. The left branch is taken + # iff t0_index < t1_index <= len(self.step_ts), so all valid t0_index s must + # already satisfy the minimum. + # However, that branch is actually executed unconditionally and then where'd, + # so we clamp it just to be sure we're not hitting undefined behaviour. + t1 = jnp.where( + t0_index < t1_index, + step_ts1[jnp.minimum(t0_index, len(self.step_ts) - 1)], + t1, + ) + return t1 + + def _clip_jump_ts( + self, t0: RealScalarLike, t1: RealScalarLike + ) -> tuple[RealScalarLike, BoolScalarLike]: + if self.jump_ts is None: + return t1, False + assert jnp.issubdtype(self.jump_ts.dtype, jnp.inexact) + if not jnp.issubdtype(jnp.result_type(t0), jnp.inexact): + raise ValueError( + "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " + f"Got {jnp.result_type(t0)}." + ) + if not jnp.issubdtype(jnp.result_type(t1), jnp.inexact): + raise ValueError( + "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " + f"Got {jnp.result_type(t1)}." + ) + jump_ts0 = upcast_or_raise( + self.jump_ts, + t0, + "`PIDController.jump_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + jump_ts1 = upcast_or_raise( + self.jump_ts, + t1, + "`PIDController.jump_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + t0_index = jnp.searchsorted(jump_ts0, t0, side="right") + t1_index = jnp.searchsorted(jump_ts1, t1, side="right") + next_made_jump = t0_index < t1_index + t1 = jnp.where( + next_made_jump, + eqxi.prevbefore(jump_ts1[jnp.minimum(t0_index, len(self.jump_ts) - 1)]), + t1, + ) + return t1, next_made_jump diff --git a/diffrax/__init__.py b/diffrax/__init__.py index 42073a10..16213b91 100644 --- a/diffrax/__init__.py +++ b/diffrax/__init__.py @@ -122,6 +122,7 @@ AbstractAdaptiveStepSizeController as AbstractAdaptiveStepSizeController, AbstractStepSizeController as AbstractStepSizeController, ConstantStepSize as ConstantStepSize, + JumpStepWrapper as JumpStepWrapper, PIDController as PIDController, StepTo as StepTo, ) diff --git a/diffrax/_misc.py b/diffrax/_misc.py index 7c6fa53b..7d52fbde 100644 --- a/diffrax/_misc.py +++ b/diffrax/_misc.py @@ -1,5 +1,5 @@ from collections.abc import Callable -from typing import Any, cast, Optional +from typing import Any, cast, Optional, Union import jax import jax.core @@ -160,7 +160,10 @@ def static_select(pred: BoolScalarLike, a: ArrayLike, b: ArrayLike) -> ArrayLike def upcast_or_raise( - x: ArrayLike, array_for_dtype: ArrayLike, x_name: str, dtype_name: str + x: ArrayLike, + array_for_dtype: Union[ArrayLike, jnp.dtype], + x_name: str, + dtype_name: str, ): """If `JAX_NUMPY_DTYPE_PROMOTION=strict`, then this will raise an error if `jnp.result_type(x, array_for_dtype)` is not the same as `array_for_dtype.dtype`. diff --git a/diffrax/_step_size_controller/__init__.py b/diffrax/_step_size_controller/__init__.py index 18d19c00..5637c24e 100644 --- a/diffrax/_step_size_controller/__init__.py +++ b/diffrax/_step_size_controller/__init__.py @@ -1,6 +1,9 @@ -from .adaptive import ( +from .base import ( AbstractAdaptiveStepSizeController as AbstractAdaptiveStepSizeController, - PIDController as PIDController, + AbstractStepSizeController as AbstractStepSizeController, ) -from .base import AbstractStepSizeController as AbstractStepSizeController from .constant import ConstantStepSize as ConstantStepSize, StepTo as StepTo +from .jump_step_wrapper import JumpStepWrapper as JumpStepWrapper +from .pid import ( + PIDController as PIDController, +) diff --git a/diffrax/_step_size_controller/base.py b/diffrax/_step_size_controller/base.py index 625bd6fb..9e6059ca 100644 --- a/diffrax/_step_size_controller/base.py +++ b/diffrax/_step_size_controller/base.py @@ -3,6 +3,7 @@ from typing import Generic, Optional, TypeVar import equinox as eqx +from equinox import AbstractVar from jaxtyping import PyTree from .._custom_types import Args, BoolScalarLike, IntScalarLike, RealScalarLike, VF, Y @@ -11,7 +12,7 @@ _ControllerState = TypeVar("_ControllerState") -_Dt0 = TypeVar("_Dt0", None, RealScalarLike, Optional[RealScalarLike]) +_Dt0 = TypeVar("_Dt0", bound=Optional[RealScalarLike]) class AbstractStepSizeController(eqx.Module, Generic[_ControllerState, _Dt0]): @@ -127,3 +128,33 @@ def adapt_step_size( happened successfully, or if it failed for some reason. (e.g. hitting a minimum allowed step size in the solver.) """ + + +class AbstractAdaptiveStepSizeController( + AbstractStepSizeController[_ControllerState, _Dt0] +): + """Indicates an adaptive step size controller. + + Accepts tolerances `rtol` and `atol`. When used in conjunction with an implicit + solver ([`diffrax.AbstractImplicitSolver`][]), then these tolerances will + automatically be used as the tolerances for the nonlinear solver passed to the + implicit solver, if they are not specified manually. + """ + + rtol: AbstractVar[RealScalarLike] + atol: AbstractVar[RealScalarLike] + norm: AbstractVar[Callable[[PyTree], RealScalarLike]] + + def __check_init__(self): + if self.rtol is None or self.atol is None: + raise ValueError( + "The default values for `rtol` and `atol` were removed in Diffrax " + "version 0.1.0. (As the choice of tolerance is nearly always " + "something that you, as an end user, should make an explicit choice " + "about.)\n" + "If you want to match the previous defaults then specify " + "`rtol=1e-3`, `atol=1e-6`. For example:\n" + "```\n" + "diffrax.PIDController(rtol=1e-3, atol=1e-6)\n" + "```\n" + ) diff --git a/diffrax/_step_size_controller/jump_step_wrapper.py b/diffrax/_step_size_controller/jump_step_wrapper.py new file mode 100644 index 00000000..259889fb --- /dev/null +++ b/diffrax/_step_size_controller/jump_step_wrapper.py @@ -0,0 +1,458 @@ +from collections.abc import Callable +from typing import Generic, get_args, Optional, TYPE_CHECKING, TypeVar + +import equinox as eqx +import equinox.internal as eqxi +import jax +import jax.numpy as jnp +from jaxtyping import Array, PyTree, Real + +from .._custom_types import ( + Args, + BoolScalarLike, + IntScalarLike, + RealScalarLike, + VF, + Y, +) +from .._misc import static_select, upcast_or_raise +from .._solution import RESULTS +from .._term import AbstractTerm +from .base import AbstractStepSizeController + + +_ControllerState = TypeVar("_ControllerState") +_Dt0 = TypeVar("_Dt0", None, RealScalarLike, Optional[RealScalarLike]) + + +class _JumpStepState(eqx.Module, Generic[_ControllerState]): + jump_at_next_t1: BoolScalarLike + step_index: IntScalarLike + jump_index: IntScalarLike + rejected_index: IntScalarLike + rejected_buffer: Optional[Array] + step_ts: Optional[Array] + jump_ts: Optional[Array] + inner_state: _ControllerState + + +def _none_or_sorted_array(x): + if x is None: + return None + else: + return jnp.sort(jnp.asarray(x)) + + +def _get_t(i: IntScalarLike, ts: Array) -> RealScalarLike: + i_min_len = jnp.minimum(i, len(ts) - 1) + return jnp.where(i == len(ts), jnp.inf, ts[i_min_len]) + + +def _clip_ts( + t0: RealScalarLike, + t1: RealScalarLike, + i: IntScalarLike, + ts: Optional[Array], + check_inexact: bool, +) -> tuple[RealScalarLike, BoolScalarLike]: + if ts is None: + return t1, False + + if check_inexact: + assert jnp.issubdtype(ts.dtype, jnp.inexact) + if not jnp.issubdtype(jnp.result_type(t0), jnp.inexact): + raise ValueError( + "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " + f"Got {jnp.result_type(t0)}." + ) + if not jnp.issubdtype(jnp.result_type(t1), jnp.inexact): + raise ValueError( + "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " + f"Got {jnp.result_type(t1)}." + ) + + _t1 = _get_t(i, ts) + jump_at_t1 = _t1 <= t1 + _t1 = jnp.where(jump_at_t1, _t1, t1) + return _t1, jump_at_t1 + + +def _find_idx_with_hint(t: RealScalarLike, ts: Optional[Array], hint: IntScalarLike): + # Find index of first element of ts greater than t + # using linear search starting from hint. + if ts is None: + return 0 + + def cond_up(_i): + return (_i < len(ts)) & (ts[_i] <= t) + + def cond_down(_i): + return (_i > 0) & (ts[_i - 1] > t) + + i = hint + i = jax.lax.while_loop(cond_up, lambda _i: _i + 1, i) + i = jax.lax.while_loop(cond_down, lambda _i: _i - 1, i) + return i + + +def _find_index(t: RealScalarLike, ts: Optional[Array]) -> IntScalarLike: + if ts is None: + return 0 + + ts = upcast_or_raise( + ts, + t, + "`JumpStepWrapper.step_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + return jnp.searchsorted(ts, t, side="right") + + +def _revisit_rejected( + t0: RealScalarLike, + t1: RealScalarLike, + i_reject: IntScalarLike, + rejected_buffer: Optional[Array], +) -> RealScalarLike: + if rejected_buffer is None: + return t1 + _t1 = _get_t(i_reject, rejected_buffer) + _t1 = jnp.minimum(_t1, t1) + return _t1 + + +class JumpStepWrapper( + AbstractStepSizeController[_JumpStepState[_ControllerState], _Dt0] +): + """Wraps an existing step controller and adds the ability to specify `step_ts` + and `jump_ts`. It also enables the feature of revisiting rejected steps, which + is useful when solving SDEs with an adaptive step controller. + + Explanation of `step_ts` and `jump_ts`: + + The `step_ts` and `jump_ts` are used to force the solver to step to certain times. + They mostly act in the same way, except that when we hit an element of `jump_ts`, + the controller must return `made_jump = True`, so that the diffeqsolve function + knows that the vector field has a discontinuity at that point, in which case it + re-evaluates it right after the jump point. In addition, the + exact time of the jump will be skipped using eqxi.prevbefore and eqxi.nextafter. + So now to the explanation of the two (we will use `step_ts` as an example, but the + same applies to `jump_ts`): + + If `step_ts` is not None, we assume it is a sorted array of times. + At the start of the run, the init function finds the smallest index `i_step` such + that `step_ts[i_step] > t0`. At init and after each step of the solver, the + controller will propose a step t1_next, and we will clip it to + `t1_next = min(t1_next, step_ts[i_step])`. + At the start of the next step, if the step ended at t1 == step_ts[i_step] and + if the controller decides to keep the step, then this time has been successfully + stepped to and we increment `i_step` by 1. + We use a convenience function _get_t(i, ts) which returns ts[i] if i < len(ts) and + infinity otherwise. + + Explanation of revisiting rejected steps: + + This feature should be used if and only if solving SDEs with non-commutative noise + using an adaptive step controller. + + We use a "stack" of rejected steps, composed of a buffer `rejected_buffer` of length + `rejected_step_buffer_len` and a counter `i_reject`. The "stack" are all the items + in `rejected_buffer[i_reject:]` with `rejected_buffer[i_reject]` being the top of + the stack. + When `i_reject == rejected_step_buffer_len`, the stack is empty. + At the start of the run, `i_reject = rejected_step_buffer_len`. Each time a step is + rejected `i_reject -=1` and `rejected_buffer[i_reject] = t1`. Each time a step ends + at `t1 == rejected_buffer[i_reject]`, we increment `i_reject` by 1 (even if the + step was rejected, in which case we will re-add `t1` to the stack immediately). + We clip the next step to `t1_next = min(t1_next, rejected_buffer[i_reject])`. + If `i_reject < 0` then an error is raised. + """ + + # For more details on solving SDEs with adaptive stepping see + # docs/api/stepsize_controller.md + # I am putting this outside of the docstring, because this class appears in that + # part of the docs and I don't want to repeat the same thing twice on one page. + # For more details also refer to + # ```bibtex + # @misc{foster2024convergenceadaptiveapproximationsstochastic, + # title={On the convergence of adaptive approximations for + # stochastic differential equations}, + # author={James Foster and Andraž Jelinčič}, + # year={2024}, + # eprint={2311.14201}, + # archivePrefix={arXiv}, + # primaryClass={math.NA}, + # url={https://arxiv.org/abs/2311.14201}, + # } + # ``` + + controller: AbstractStepSizeController[_ControllerState, _Dt0] + step_ts: Optional[Real[Array, " steps"]] + jump_ts: Optional[Real[Array, " jumps"]] + rejected_step_buffer_len: Optional[int] = eqx.field(static=True) + callback_on_reject: Optional[Callable] = eqx.field(static=True) + + @eqxi.doc_remove_args("_callback_on_reject") + def __init__( + self, + controller, + step_ts=None, + jump_ts=None, + rejected_step_buffer_len=None, + _callback_on_reject=None, + ): + r""" + **Arguments**: + + - `controller`: The controller to wrap. + Can be any [`diffrax.AbstractAdaptiveStepSizeController`][]. + - `step_ts`: Denotes extra times that must be stepped to. + - `jump_ts`: Denotes extra times that must be stepped to, and at which the + vector field has a known discontinuity. (This is used to force FSAL solvers + to re-evaluate the vector field.) + `rejected_step_buffer_len`: Length of the stack used to store rejected steps. + Can either be `None` or a positive integer. + If `None`, this feature will be off. + If it is > 0, then the controller will revisit rejected steps. + This should only be used when solving SDEs with an adaptive step size + controller. For most SDEs, setting this to `100` should be plenty, + but if more consecutive steps are rejected, then an error will be raised. + (Note that this is not the total number of rejected steps in a solve, + but just the number of rejected steps currently on the stack to be + revisited.) + """ + self.controller = controller + self.step_ts = _none_or_sorted_array(step_ts) + self.jump_ts = _none_or_sorted_array(jump_ts) + if (rejected_step_buffer_len is not None) and (rejected_step_buffer_len <= 0): + raise ValueError( + "`rejected_step_buffer_len must either be `None`" + " or a non-negative integer." + ) + self.rejected_step_buffer_len = rejected_step_buffer_len + self.callback_on_reject = _callback_on_reject + + def __check_init__(self): + if self.jump_ts is not None and not jnp.issubdtype( + self.jump_ts.dtype, jnp.inexact + ): + raise ValueError( + f"jump_ts must be floating point, not {self.jump_ts.dtype}" + ) + + def wrap(self, direction: IntScalarLike): + step_ts = None if self.step_ts is None else jnp.sort(self.step_ts * direction) + jump_ts = None if self.jump_ts is None else jnp.sort(self.jump_ts * direction) + controller = self.controller.wrap(direction) + return eqx.tree_at( + lambda s: (s.step_ts, s.jump_ts, s.controller), + self, + (step_ts, jump_ts, controller), + is_leaf=lambda x: x is None, + ) + + def init( + self, + terms: PyTree[AbstractTerm], + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + dt0: _Dt0, + args: Args, + func: Callable[[PyTree[AbstractTerm], RealScalarLike, Y, Args], VF], + error_order: Optional[RealScalarLike], + ) -> tuple[RealScalarLike, _JumpStepState[_ControllerState]]: + t1, inner_state = self.controller.init( + terms, t0, t1, y0, dt0, args, func, error_order + ) + tdtype = jnp.result_type(t0, t1) + + if self.step_ts is None: + step_ts = None + else: + # Upcast step_ts to the same dtype as t0, t1 + step_ts = upcast_or_raise( + self.step_ts, + tdtype, + "`JumpStepWrapper.step_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + + if self.jump_ts is None: + jump_ts = None + else: + # Upcast jump_ts to the same dtype as t0, t1 + jump_ts = upcast_or_raise( + self.jump_ts, + tdtype, + "`JumpStepWrapper.jump_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + + if self.rejected_step_buffer_len is None: + rejected_buffer = None + i_reject = jnp.asarray(0) + else: + rejected_buffer = jnp.zeros( + (self.rejected_step_buffer_len,) + jnp.shape(t1), dtype=tdtype + ) + # rejected_buffer[len(rejected_buffer)] = jnp.inf (see def of _get_t) + i_reject = jnp.asarray(self.rejected_step_buffer_len) + + # Find index of first element of step_ts/jump_ts greater than t0 + i_step = _find_index(t0, step_ts) + i_jump = _find_index(t0, jump_ts) + # Clip t1 to the next element of step_ts or jump_ts + t1, _ = _clip_ts(t0, t1, i_step, step_ts, False) + t1, jump_next_step = _clip_ts(t0, t1, i_jump, jump_ts, True) + + state = _JumpStepState( + jump_next_step, + i_step, + i_jump, + i_reject, + rejected_buffer, + step_ts, + jump_ts, + inner_state, + ) + + return t1, state + + def adapt_step_size( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + y1_candidate: Y, + args: Args, + y_error: Optional[Y], + error_order: RealScalarLike, + controller_state: _JumpStepState[_ControllerState], + ) -> tuple[ + BoolScalarLike, + RealScalarLike, + RealScalarLike, + BoolScalarLike, + _JumpStepState[_ControllerState], + RESULTS, + ]: + # just shortening the name + st = controller_state + i_step = st.step_index + i_jump = st.jump_index + i_reject = st.rejected_index + + # Let the controller do its thing + ( + keep_step, + next_t0, + original_next_t1, + jump_at_original_next_t1, + inner_state, + result, + ) = self.controller.adapt_step_size( + t0, t1, y0, y1_candidate, args, y_error, error_order, st.inner_state + ) + next_t1 = original_next_t1 + + # This is just a logging utility for testing purposes + if self.callback_on_reject is not None: + # jax.debug.callback(self.callback_on_reject, keep_step, t1) + jax.experimental.io_callback(self.callback_on_reject, None, keep_step, t1) # pyright: ignore + + # For step ts and jump ts find the index of the first element in jump_ts/step_ts + # greater than next_t0. We use the hint i_step/i_jump to speed up the search. + i_step = _find_idx_with_hint(next_t0, st.step_ts, i_step) + i_jump = _find_idx_with_hint(next_t0, st.jump_ts, i_jump) + + if self.rejected_step_buffer_len is not None: + rejected_buffer = st.rejected_buffer + assert rejected_buffer is not None + # If the step ended at t1==rejected_buffer[i_reject], then we have + # successfully stepped to this time and we increment i_reject. + # We increment i_reject even if the step was rejected, because we will + # re-add the rejected time to the buffer immediately. + rejected_t = _get_t(i_reject, rejected_buffer) + rjct_inc_cond = t1 == rejected_t + i_reject = jnp.where(rjct_inc_cond, i_reject + 1, i_reject) + + # If the step was rejected, then we need to store the rejected time in the + # rejected buffer and decrement the rejected index. + i_reject = jnp.where(keep_step, i_reject, i_reject - 1) + i_reject = eqx.error_if( + i_reject, + i_reject < 0, + "Maximum number of rejected steps reached. " + "Consider increasing JumpStepWrapper.rejected_step_buffer_len.", + ) + clipped_i = jnp.clip(i_reject, 0, self.rejected_step_buffer_len - 1) + update_rejected_t = jnp.where(keep_step, rejected_buffer[clipped_i], t1) + rejected_buffer = rejected_buffer.at[clipped_i].set(update_rejected_t) + else: + rejected_buffer = None + + # Now move on to the NEXT STEP + + # If t1 hit a jump point, and the step was kept then we need to set + # `next_t0 = nextafter(nextafter(t1))` to ensure that we really skip + # over the jump and don't evaluate the vector field at the discontinuity. + if jnp.issubdtype(jnp.result_type(next_t0), jnp.inexact): + # Two nextafters. If made_jump then t1 = prevbefore(jump location) + # so now _t1 = nextafter(jump location) + # This is important because we don't know whether or not the jump is as a + # result of a left- or right-discontinuity, so we have to skip the jump + # location altogether. + jump_keep = st.jump_at_next_t1 & keep_step + next_t0 = static_select( + jump_keep, eqxi.nextafter(eqxi.nextafter(next_t0)), next_t0 + ) + + if TYPE_CHECKING: # if i don't seperate this out pyright complains + assert isinstance(next_t0, RealScalarLike) + else: + assert isinstance( + next_t0, get_args(RealScalarLike) + ), f"type(next_t0) = {type(next_t0)}" + + # Clip the step to the next element of jump_ts or step_ts or + # rejected_buffer. Important to do jump_ts last because otherwise + # jump_at_next_t1 could be a false positive. + next_t1 = _revisit_rejected(next_t0, next_t1, i_reject, rejected_buffer) + next_t1, _ = _clip_ts(next_t0, next_t1, i_step, st.step_ts, False) + next_t1, jump_at_next_t1 = _clip_ts(next_t0, next_t1, i_jump, st.jump_ts, True) + + # Let's prove that the line below is correct. Say the inner controller is + # itself a JumpStepWrapper (JSW) with some inner_jump_ts. Then, given that + # it propsed (next_t0, original_next_t1), there cannot be any jumps in + # inner_jump_ts between next_t0 and original_next_t1. So if the next_t1 + # proposed by the outer JSW is different from the original_next_t1 then + # next_t1 \in (next_t0, original_next_t1) and hence there cannot be a jump + # in inner_jump_ts at next_t1. So the jump_at_next_t1 only depends on + # jump_at_next_t1. + # On the other hand if original_next_t1 == next_t1, then we just take an + # OR of the two. + jump_at_next_t1 = jnp.where( + next_t1 == original_next_t1, + jump_at_next_t1 | jump_at_original_next_t1, + jump_at_next_t1, + ) + + # Here made_jump signifies whether there is a jump at t1. What the solver + # needs, however, is whether there is a jump at next_t0, so these two will + # only match when the step was kept. The case when the step was rejected is + # handled in `_integrate.py` (search for "made_jump = static_select"). + made_jump = st.jump_at_next_t1 + + state = _JumpStepState( + jump_at_next_t1, + i_step, + i_jump, + i_reject, + rejected_buffer, + st.step_ts, + st.jump_ts, + inner_state, + ) + + return keep_step, next_t0, next_t1, made_jump, state, result diff --git a/diffrax/_step_size_controller/adaptive.py b/diffrax/_step_size_controller/pid.py similarity index 74% rename from diffrax/_step_size_controller/adaptive.py rename to diffrax/_step_size_controller/pid.py index 9d181c95..fd343423 100644 --- a/diffrax/_step_size_controller/adaptive.py +++ b/diffrax/_step_size_controller/pid.py @@ -1,6 +1,6 @@ import typing from collections.abc import Callable -from typing import cast, Optional, TYPE_CHECKING, TypeVar +from typing import cast, Optional, TYPE_CHECKING import equinox as eqx import equinox.internal as eqxi @@ -10,15 +10,8 @@ import jax.tree_util as jtu import lineax.internal as lxi import optimistix as optx -from jaxtyping import Real - - -if TYPE_CHECKING: - from typing import ClassVar as AbstractVar -else: - from equinox import AbstractVar from equinox.internal import ω -from jaxtyping import Array, PyTree +from jaxtyping import PyTree from lineax.internal import complex_to_real_dtype from .._custom_types import ( @@ -29,15 +22,27 @@ VF, Y, ) -from .._misc import static_select, upcast_or_raise from .._solution import RESULTS from .._term import AbstractTerm, ODETerm -from .base import AbstractStepSizeController +from .base import AbstractAdaptiveStepSizeController +from .jump_step_wrapper import JumpStepWrapper ω = cast(Callable, ω) +# We use a metaclass for backwards compatibility. When a user calls +# PIDController(... step_ts=s, jump_ts=j) this should return a +# JumpStepWrapper(PIDController(...), s, j). +class _PIDMeta(type(eqx.Module)): + def __call__(cls, *args, **kwargs): + step_ts = kwargs.pop("step_ts", None) + jump_ts = kwargs.pop("jump_ts", None) + if step_ts is not None or jump_ts is not None: + return JumpStepWrapper(cls(*args, **kwargs), step_ts, jump_ts) + return super().__call__(*args, **kwargs) + + def _select_initial_step( terms: PyTree[AbstractTerm], t0: RealScalarLike, @@ -89,50 +94,8 @@ def intermediate(carry): return jnp.minimum(100 * h0, h1) -_ControllerState = TypeVar("_ControllerState") -_Dt0 = TypeVar("_Dt0", None, RealScalarLike, Optional[RealScalarLike]) - - -class AbstractAdaptiveStepSizeController( - AbstractStepSizeController[_ControllerState, _Dt0] -): - """Indicates an adaptive step size controller. - - Accepts tolerances `rtol` and `atol`. When used in conjunction with an implicit - solver ([`diffrax.AbstractImplicitSolver`][]), then these tolerances will - automatically be used as the tolerances for the nonlinear solver passed to the - implicit solver, if they are not specified manually. - """ - - rtol: AbstractVar[RealScalarLike] - atol: AbstractVar[RealScalarLike] - norm: AbstractVar[Callable[[PyTree], RealScalarLike]] - - def __check_init__(self): - if self.rtol is None or self.atol is None: - raise ValueError( - "The default values for `rtol` and `atol` were removed in Diffrax " - "version 0.1.0. (As the choice of tolerance is nearly always " - "something that you, as an end user, should make an explicit choice " - "about.)\n" - "If you want to match the previous defaults then specify " - "`rtol=1e-3`, `atol=1e-6`. For example:\n" - "```\n" - "diffrax.PIDController(rtol=1e-3, atol=1e-6)\n" - "```\n" - ) - - -_PidState = tuple[ - BoolScalarLike, BoolScalarLike, RealScalarLike, RealScalarLike, RealScalarLike -] - - -def _none_or_array(x): - if x is None: - return None - else: - return jnp.asarray(x) +# _PidState = (prev_inv_scaled_error, prev_prev_inv_scaled_error) +_PidState = tuple[RealScalarLike, RealScalarLike] if TYPE_CHECKING: @@ -157,7 +120,8 @@ def __repr__(self): # TODO: we don't currently offer a limiter, or a variant accept/reject scheme, as given # in Soderlind and Wang 2006. class PIDController( - AbstractAdaptiveStepSizeController[_PidState, Optional[RealScalarLike]] + AbstractAdaptiveStepSizeController[_PidState, Optional[RealScalarLike]], + metaclass=_PIDMeta, ): r"""Adapts the step size to produce a solution accurate to a given tolerance. The tolerance is calculated as `atol + rtol * y` for the evolving solution `y`. @@ -353,35 +317,14 @@ def dynamics(t, y, args): dtmin: Optional[RealScalarLike] = None dtmax: Optional[RealScalarLike] = None force_dtmin: bool = True - step_ts: Optional[Real[Array, " steps"]] = eqx.field( - default=None, converter=_none_or_array - ) - jump_ts: Optional[Real[Array, " jumps"]] = eqx.field( - default=None, converter=_none_or_array - ) factormin: RealScalarLike = 0.2 factormax: RealScalarLike = 10.0 norm: Callable[[PyTree], RealScalarLike] = rms_norm safety: RealScalarLike = 0.9 error_order: Optional[RealScalarLike] = None - def __check_init__(self): - if self.jump_ts is not None and not jnp.issubdtype( - self.jump_ts.dtype, jnp.inexact - ): - raise ValueError( - f"jump_ts must be floating point, not {self.jump_ts.dtype}" - ) - def wrap(self, direction: IntScalarLike): - step_ts = None if self.step_ts is None else self.step_ts * direction - jump_ts = None if self.jump_ts is None else self.jump_ts * direction - return eqx.tree_at( - lambda s: (s.step_ts, s.jump_ts), - self, - (step_ts, jump_ts), - is_leaf=lambda x: x is None, - ) + return self def init( self, @@ -444,26 +387,20 @@ def init( dt0 = lax.stop_gradient(dt0) if self.dtmax is not None: dt0 = jnp.minimum(dt0, self.dtmax) - if self.dtmin is None: - at_dtmin = jnp.array(False) - else: - at_dtmin = dt0 <= self.dtmin + if self.dtmin is not None: dt0 = jnp.maximum(dt0, self.dtmin) - t1 = self._clip_step_ts(t0, t0 + dt0) - t1, jump_next_step = self._clip_jump_ts(t0, t1) + t1 = t0 + dt0 y_leaves = jtu.tree_leaves(y0) if len(y_leaves) == 0: y_dtype = lxi.default_floating_dtype() else: y_dtype = jnp.result_type(*y_leaves) + real_dtype = complex_to_real_dtype(y_dtype) return t1, ( - jump_next_step, - at_dtmin, - dt0, - jnp.array(1.0, dtype=complex_to_real_dtype(y_dtype)), - jnp.array(1.0, dtype=complex_to_real_dtype(y_dtype)), + jnp.array(1.0, dtype=real_dtype), + jnp.array(1.0, dtype=real_dtype), ) def adapt_step_size( @@ -543,22 +480,11 @@ def adapt_step_size( "error estimates." ) ( - made_jump, - at_dtmin, - prev_dt, prev_inv_scaled_error, prev_prev_inv_scaled_error, ) = controller_state error_order = self._get_error_order(error_order) - # t1 - t0 is the step we actually took, so that's usually what we mean by the - # "previous dt". - # However if we made a jump then this t1 was clipped relatively to what it - # could have been, so for guessing the next step size it's probably better to - # use the size the step would have been, had there been no jump. - # There are cases in which something besides the step size controller modifies - # the step locations t0, t1; most notably the main integration routine clipping - # steps when we're right at the end of the interval. - prev_dt = jnp.where(made_jump, prev_dt, t1 - t0) + prev_dt = t1 - t0 # # Figure out how things went on the last step: error, and whether to @@ -576,8 +502,9 @@ def _scale(_y0, _y1_candidate, _y_error): scaled_error = self.norm(jtu.tree_map(_scale, y0, y1_candidate, y_error)) keep_step = scaled_error < 1 + # Automatically keep the step if we're at dtmin. if self.dtmin is not None: - keep_step = keep_step | at_dtmin + keep_step = keep_step | (prev_dt <= self.dtmin) # Make sure it's not a Python scalar and thus getting a ZeroDivisionError. inv_scaled_error = 1 / jnp.asarray(scaled_error) inv_scaled_error = lax.stop_gradient( @@ -600,10 +527,12 @@ def _scale(_y0, _y1_candidate, _y_error): factor2 = 1 if _zero_coeff(coeff2) else prev_inv_scaled_error**coeff2 factor3 = 1 if _zero_coeff(coeff3) else prev_prev_inv_scaled_error**coeff3 factormin = jnp.where(keep_step, 1, self.factormin) + # If the step is not kept, next step must be smaller, so factor must be <1. + factormax = jnp.where(keep_step, self.factormax, self.safety) factor = jnp.clip( self.safety * factor1 * factor2 * factor3, min=factormin, - max=self.factormax, + max=factormax, ) # Once again, see above. In case we have gradients on {i,p,d}coeff. # (Probably quite common for them to have zero tangents if passed across @@ -626,43 +555,21 @@ def _scale(_y0, _y1_candidate, _y_error): result = RESULTS.successful if self.dtmax is not None: dt = jnp.minimum(dt, self.dtmax) - if self.dtmin is None: - at_dtmin = jnp.array(False) - else: + if self.dtmin is not None: if not self.force_dtmin: result = RESULTS.where(dt < self.dtmin, RESULTS.dt_min_reached, result) - at_dtmin = dt <= self.dtmin dt = jnp.maximum(dt, self.dtmin) - # - # Clip next step size based on step_ts/jump_ts - # - - if jnp.issubdtype(jnp.result_type(t1), jnp.inexact): - # Two nextafters. If made_jump then t1 = prevbefore(jump location) - # so now _t1 = nextafter(jump location) - # This is important because we don't know whether or not the jump is as a - # result of a left- or right-discontinuity, so we have to skip the jump - # location altogether. - _t1 = static_select(made_jump, eqxi.nextafter(eqxi.nextafter(t1)), t1) - else: - _t1 = t1 - next_t0 = jnp.where(keep_step, _t1, t0) - next_t1 = self._clip_step_ts(next_t0, next_t0 + dt) - next_t1, next_made_jump = self._clip_jump_ts(next_t0, next_t1) + next_t0 = jnp.where(keep_step, t1, t0) + next_t1 = next_t0 + dt inv_scaled_error = jnp.where(keep_step, inv_scaled_error, prev_inv_scaled_error) prev_inv_scaled_error = jnp.where( keep_step, prev_inv_scaled_error, prev_prev_inv_scaled_error ) - controller_state = ( - next_made_jump, - at_dtmin, - dt, - inv_scaled_error, - prev_inv_scaled_error, - ) - return keep_step, next_t0, next_t1, made_jump, controller_state, result + controller_state = inv_scaled_error, prev_inv_scaled_error + # made_jump is handled by JumpStepWrapper, so we automatically set it to False + return keep_step, next_t0, next_t1, False, controller_state, result def _get_error_order(self, error_order: Optional[RealScalarLike]) -> RealScalarLike: # Attribute takes priority, if the user knows the correct error order better @@ -677,76 +584,6 @@ def _get_error_order(self, error_order: Optional[RealScalarLike]) -> RealScalarL ) return error_order - def _clip_step_ts(self, t0: RealScalarLike, t1: RealScalarLike) -> RealScalarLike: - if self.step_ts is None: - return t1 - - step_ts0 = upcast_or_raise( - self.step_ts, - t0, - "`PIDController.step_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - step_ts1 = upcast_or_raise( - self.step_ts, - t1, - "`PIDController.step_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - # TODO: it should be possible to switch this O(nlogn) for just O(n) by keeping - # track of where we were last, and using that as a hint for the next search. - t0_index = jnp.searchsorted(step_ts0, t0, side="right") - t1_index = jnp.searchsorted(step_ts1, t1, side="right") - # This minimum may or may not actually be necessary. The left branch is taken - # iff t0_index < t1_index <= len(self.step_ts), so all valid t0_index s must - # already satisfy the minimum. - # However, that branch is actually executed unconditionally and then where'd, - # so we clamp it just to be sure we're not hitting undefined behaviour. - t1 = jnp.where( - t0_index < t1_index, - step_ts1[jnp.minimum(t0_index, len(self.step_ts) - 1)], - t1, - ) - return t1 - - def _clip_jump_ts( - self, t0: RealScalarLike, t1: RealScalarLike - ) -> tuple[RealScalarLike, BoolScalarLike]: - if self.jump_ts is None: - return t1, False - assert jnp.issubdtype(self.jump_ts.dtype, jnp.inexact) - if not jnp.issubdtype(jnp.result_type(t0), jnp.inexact): - raise ValueError( - "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " - f"Got {jnp.result_type(t0)}." - ) - if not jnp.issubdtype(jnp.result_type(t1), jnp.inexact): - raise ValueError( - "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " - f"Got {jnp.result_type(t1)}." - ) - jump_ts0 = upcast_or_raise( - self.jump_ts, - t0, - "`PIDController.jump_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - jump_ts1 = upcast_or_raise( - self.jump_ts, - t1, - "`PIDController.jump_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - t0_index = jnp.searchsorted(jump_ts0, t0, side="right") - t1_index = jnp.searchsorted(jump_ts1, t1, side="right") - next_made_jump = t0_index < t1_index - t1 = jnp.where( - next_made_jump, - eqxi.prevbefore(jump_ts1[jnp.minimum(t0_index, len(self.jump_ts) - 1)]), - t1, - ) - return t1, next_made_jump - PIDController.__init__.__doc__ = """**Arguments:** @@ -761,10 +598,6 @@ def _clip_jump_ts( - `force_dtmin`: How to handle the step size hitting the minimum. If `True` then the step size is clipped to `dtmin`. If `False` then the differential equation solve halts with an error. -- `step_ts`: Denotes extra times that must be stepped to. -- `jump_ts`: Denotes extra times that must be stepped to, and at which the vector field - has a known discontinuity. (This is used to force FSAL solvers so re-evaluate the - vector field.) - `factormin`: Minimum amount a step size can be decreased relative to the previous step. - `factormax`: Maximum amount a step size can be increased relative to the previous diff --git a/docs/api/stepsize_controller.md b/docs/api/stepsize_controller.md index 6989c4c1..62aa370f 100644 --- a/docs/api/stepsize_controller.md +++ b/docs/api/stepsize_controller.md @@ -3,8 +3,37 @@ The list of step size controllers is as follows. The most common cases are fixed step sizes with [`diffrax.ConstantStepSize`][] and adaptive step sizes with [`diffrax.PIDController`][]. !!! warning + + When solving SDEs with an adaptive step controller, then three requirements + have to be fulfilled in order for the solution to be guaranteed to converge to + the correct result: + + - the Brownian motion has to be generated using [`diffrax.VirtualBrownianTree`][], + - the solver must satisfy certain conditions (in practice all SDE solvers except + [`diffrax.Euler`][] satisfy these), + - either + a) the SDE must have [commutative noise](../usage/how-to-choose-a-solver.md#stochastic-differential-equations) + OR + b) the SDE is evaluated at all times at which the Brownian motion (BM) is + evaluated; since the BM is also evaluated at steps that are rejected by the step + controller, we must later evaluate the SDE at these times as well + (i.e. revisit rejected steps). This can be done using [`diffrax.JumpStepWrapper`]. + + Note that these conditions are not checked by Diffrax. - To perform adaptive stepping with SDEs requires [commutative noise](../usage/how-to-choose-a-solver.md#stochastic-differential-equations). Note that this commutativity condition is not checked. + For more details about the convergence of adaptive solutions to SDEs, please refer to + + ```bibtex + @misc{foster2024convergenceadaptiveapproximationsstochastic, + title={On the convergence of adaptive approximations for stochastic differential equations}, + author={James Foster and Andraž Jelinčič}, + year={2024}, + eprint={2311.14201}, + archivePrefix={arXiv}, + primaryClass={math.NA}, + url={https://arxiv.org/abs/2311.14201}, + } + ``` ??? abstract "Abtract base classes" @@ -41,3 +70,8 @@ The list of step size controllers is as follows. The most common cases are fixed selection: members: - __init__ + +::: diffrax.JumpStepWrapper + selection: + members: + - __init__ \ No newline at end of file diff --git a/test/test_adaptive_stepsize_controller.py b/test/test_adaptive_stepsize_controller.py index 4cc996c8..233c056f 100644 --- a/test/test_adaptive_stepsize_controller.py +++ b/test/test_adaptive_stepsize_controller.py @@ -4,20 +4,26 @@ import equinox as eqx import jax import jax.numpy as jnp +import jax.random as jr import jax.tree_util as jtu +import pytest from jaxtyping import Array from .helpers import tree_allclose -def test_step_ts(): +@pytest.mark.parametrize("backwards", [False, True]) +def test_step_ts(backwards): term = diffrax.ODETerm(lambda t, y, args: -0.2 * y) solver = diffrax.Dopri5() t0 = 0 t1 = 5 + if backwards: + t0, t1 = t1, t0 dt0 = None y0 = 1.0 - stepsize_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=[3, 4]) + pid_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6) + stepsize_controller = diffrax.JumpStepWrapper(pid_controller, step_ts=[3, 4]) saveat = diffrax.SaveAt(steps=True) sol = diffrax.diffeqsolve( term, @@ -33,7 +39,8 @@ def test_step_ts(): assert 4 in cast(Array, sol.ts) -def test_jump_ts(): +@pytest.mark.parametrize("backwards", [False, True]) +def test_jump_ts(backwards): # Tests no regression of https://github.com/patrick-kidger/diffrax/issues/58 def vector_field(t, y, args): @@ -45,12 +52,15 @@ def vector_field(t, y, args): solver = diffrax.Dopri5() t0 = 0 t1 = 15 + if backwards: + t0, t1 = t1, t0 dt0 = None y0 = 1.5, 0 saveat = diffrax.SaveAt(steps=True) def run(**kwargs): - stepsize_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6, **kwargs) + pid_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6) + stepsize_controller = diffrax.JumpStepWrapper(pid_controller, **kwargs) return diffrax.diffeqsolve( term, solver, @@ -65,6 +75,7 @@ def run(**kwargs): sol_no_jump_ts = run() sol_with_jump_ts = run(jump_ts=[7.5]) assert sol_no_jump_ts.stats["num_steps"] > sol_with_jump_ts.stats["num_steps"] + print(sol_no_jump_ts.stats["num_steps"], sol_with_jump_ts.stats["num_steps"]) assert sol_with_jump_ts.result == diffrax.RESULTS.successful sol = run(jump_ts=[7.5], step_ts=[7.5]) @@ -75,13 +86,90 @@ def run(**kwargs): assert 8 in cast(Array, sol.ts) -def test_backprop(): +@pytest.mark.parametrize("backwards", [False, True]) +def test_revisit_steps(backwards): + t0 = 0.0 + t1 = 5.0 + dt0 = 0.5 + if backwards: + t0, t1 = t1, t0 + dt0 = -dt0 + y0 = 1.0 + drift = diffrax.ODETerm(lambda t, y, args: -0.2 * y) + + def diffusion_vf(t, y, args): + return jnp.ones((), dtype=y.dtype) + + bm = diffrax.VirtualBrownianTree(min(t0, t1), max(t0, t1), 2**-8, (), jr.key(0)) + diffusion = diffrax.ControlTerm(diffusion_vf, bm) + term = diffrax.MultiTerm(drift, diffusion) + solver = diffrax.Heun() + pid_controller = diffrax.PIDController( + rtol=0, atol=1e-3, dtmin=2**-7, pcoeff=0.5, icoeff=0.8 + ) + + rejected_ts_list = [] + + def callback_fun(keep_step, t1): + if not keep_step: + rejected_ts_list.append(t1) + return None + + stepsize_controller = diffrax.JumpStepWrapper( + pid_controller, + step_ts=[3, 4], + rejected_step_buffer_len=10, + _callback_on_reject=callback_fun, + ) + saveat = diffrax.SaveAt(steps=True, controller_state=True) + sol = diffrax.diffeqsolve( + term, + solver, + t0, + t1, + dt0, + y0, + stepsize_controller=stepsize_controller, + saveat=saveat, + ) + + assert sol.ts is not None + ts = sol.ts[sol.ts != jnp.inf] + ts = jnp.sort(ts) + rejected_ts = jnp.array(rejected_ts_list) + if backwards: + rejected_ts = -rejected_ts + + # there should be many rejected steps, otherwise something went wrong + assert len(rejected_ts) > 10 + # check if all rejected ts are in the array sol.ts + for t in rejected_ts: + i = jnp.searchsorted(ts, t) + assert ts[i] == t + + assert 3 in cast(Array, sol.ts) + assert 4 in cast(Array, sol.ts) + + # Check that at the end of the run, the rejected stack is empty, + # i.e. rejected_index == rejected_step_buffer_len + assert sol.controller_state is not None + assert ( + sol.controller_state.rejected_index + == stepsize_controller.rejected_step_buffer_len + ) + + +@pytest.mark.parametrize("use_jump_step", [True, False]) +def test_backprop(use_jump_step): + t0 = jnp.asarray(0, dtype=jnp.float64) + t1 = jnp.asarray(1, dtype=jnp.float64) + @eqx.filter_jit @eqx.filter_grad def run(ys, controller, state): y0, y1_candidate, y_error = ys _, tprev, tnext, _, state, _ = controller.adapt_step_size( - 0, 1, y0, y1_candidate, None, y_error, 5, state + t0, t1, y0, y1_candidate, None, y_error, 5, state ) with jax.numpy_dtype_promotion("standard"): return tprev + tnext + sum(jnp.sum(x) for x in jtu.tree_leaves(state)) @@ -90,12 +178,16 @@ def run(ys, controller, state): y1_candidate = jnp.array(2.0) term = diffrax.ODETerm(lambda t, y, args: -y) solver = diffrax.Tsit5() - stepsize_controller = diffrax.PIDController(rtol=1e-4, atol=1e-4) - _, state = stepsize_controller.init(term, 0, 1, y0, 0.1, None, solver.func, 5) + controller = diffrax.PIDController(rtol=1e-4, atol=1e-4) + if use_jump_step: + controller = diffrax.JumpStepWrapper( + controller, step_ts=[0.5], rejected_step_buffer_len=20 + ) + _, state = controller.init(term, t0, t1, y0, 0.1, None, solver.func, 5) for y_error in (jnp.array(0.0), jnp.array(3.0), jnp.array(jnp.inf)): ys = (y0, y1_candidate, y_error) - grads = run(ys, stepsize_controller, state) + grads = run(ys, controller, state) assert not any(jnp.isnan(grad).any() for grad in grads) @@ -113,9 +205,11 @@ def run(t): t1 = 1 dt0 = None y0 = 1.0 - stepsize_controller = diffrax.PIDController( - rtol=1e-8, atol=1e-8, step_ts=t[None] + pid_controller = diffrax.PIDController( + rtol=1e-8, + atol=1e-8, ) + stepsize_controller = diffrax.JumpStepWrapper(pid_controller, step_ts=t[None]) def forcing(s): return jnp.where(s < t, 0, 1) @@ -139,3 +233,59 @@ def forcing(s): finite_diff = (r(0.5) - r(0.5 - eps)) / eps autodiff = jax.jit(jax.grad(run))(0.5) assert tree_allclose(finite_diff, autodiff) + + +def test_pid_meta(): + ts = jnp.array([3, 4], dtype=jnp.float64) + pid1 = diffrax.PIDController(rtol=1e-4, atol=1e-6) + pid2 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts) + pid3 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts, jump_ts=ts) + assert not isinstance(pid1, diffrax.JumpStepWrapper) + assert isinstance(pid1, diffrax.PIDController) + assert isinstance(pid2, diffrax.JumpStepWrapper) + assert isinstance(pid3, diffrax.JumpStepWrapper) + assert all(pid2.step_ts == ts) + assert all(pid3.step_ts == ts) + assert all(pid3.jump_ts == ts) + + +def test_nested_jump_step_wrappers(): + pid = diffrax.PIDController(rtol=0, atol=1.0) + wrap1 = diffrax.JumpStepWrapper(pid, jump_ts=[3.0, 13.0], step_ts=[23.0]) + wrap2 = diffrax.JumpStepWrapper(wrap1, step_ts=[2.0, 13.0], jump_ts=[23.0]) + func = lambda terms, t, y, args: -y + terms = diffrax.ODETerm(lambda t, y, args: -y) + _, state = wrap2.init(terms, -1.0, 0.0, 0.0, 4.0, None, func, 5) + + # test 1 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + 0.0, 1.0, 0.0, 0.0, None, 0.0, 5, state + ) + assert next_t1 == 2 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state + ) + assert jnp.isclose(next_t0, 2) + assert not made_jump + + # test 2 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + 10.0, 11.0, 0.0, 0.0, None, 0.0, 5, state + ) + assert next_t1 == 13 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state + ) + assert jnp.isclose(next_t0, 13) + assert made_jump + + # test 3 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + 20.0, 21.0, 0.0, 0.0, None, 0.0, 5, state + ) + assert next_t1 == 23 + _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( + next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state + ) + assert jnp.isclose(next_t0, 23) + assert made_jump diff --git a/test/test_progress_meter.py b/test/test_progress_meter.py index a9613c9e..6827db3a 100644 --- a/test/test_progress_meter.py +++ b/test/test_progress_meter.py @@ -40,7 +40,7 @@ def solve(t0): err = captured.err.strip() assert re.match("0.00%|[ ]+|", err.split("\r", 1)[0]) assert re.match("100.00%|█+|", err.rsplit("\r", 1)[1]) - assert captured.err.count("\r") == num_lines + assert captured.err.count("\r") - num_lines in [0, 1] assert captured.err.count("\n") == 1 From 31a887d2ddda51859693fca10cfc2dfa51615615 Mon Sep 17 00:00:00 2001 From: Patrick Kidger <33688385+patrick-kidger@users.noreply.github.com> Date: Sun, 9 Feb 2025 15:29:08 +0100 Subject: [PATCH 46/50] Reworked JumpStepWrapper. --- benchmarks/jump_step_timing.py | 12 +- diffrax/__init__.py | 2 +- diffrax/_autocitation.py | 19 +- diffrax/_solution.py | 8 + diffrax/_step_size_controller/__init__.py | 2 +- diffrax/_step_size_controller/clip.py | 394 +++++++++++++++ .../jump_step_wrapper.py | 458 ------------------ diffrax/_step_size_controller/pid.py | 38 +- docs/api/stepsize_controller.md | 25 +- test/test_adaptive_stepsize_controller.py | 87 ++-- test/test_progress_meter.py | 2 +- 11 files changed, 511 insertions(+), 536 deletions(-) create mode 100644 diffrax/_step_size_controller/clip.py delete mode 100644 diffrax/_step_size_controller/jump_step_wrapper.py diff --git a/benchmarks/jump_step_timing.py b/benchmarks/jump_step_timing.py index 9250de4f..933b59fc 100644 --- a/benchmarks/jump_step_timing.py +++ b/benchmarks/jump_step_timing.py @@ -36,10 +36,10 @@ def get_terms(key): pid_controller = diffrax.PIDController( rtol=0, atol=1e-3, dtmin=2**-9, dtmax=1.0, pcoeff=0.3, icoeff=0.7 ) -new_controller = diffrax.JumpStepWrapper( +new_controller = diffrax.ClipStepSizeController( pid_controller, step_ts=step_ts, - rejected_step_buffer_len=None, + store_rejected_steps=None, ) old_controller = OldPIDController( rtol=0, atol=1e-3, dtmin=2**-9, dtmax=1.0, pcoeff=0.3, icoeff=0.7, step_ts=step_ts @@ -88,16 +88,16 @@ def time_controller(): print(f"New controller: {time_new:.5} s, Old controller: {time_old:.5} s") # How expensive is revisiting rejected steps? -revisiting_controller_short = diffrax.JumpStepWrapper( +revisiting_controller_short = diffrax.ClipStepSizeController( pid_controller, step_ts=step_ts, - rejected_step_buffer_len=10, + store_rejected_steps=10, ) -revisiting_controller_long = diffrax.JumpStepWrapper( +revisiting_controller_long = diffrax.ClipStepSizeController( pid_controller, step_ts=step_ts, - rejected_step_buffer_len=4096, + store_rejected_steps=4096, ) time_revisiting_short = do_timing(revisiting_controller_short) diff --git a/diffrax/__init__.py b/diffrax/__init__.py index 16213b91..d35a7fac 100644 --- a/diffrax/__init__.py +++ b/diffrax/__init__.py @@ -121,8 +121,8 @@ from ._step_size_controller import ( AbstractAdaptiveStepSizeController as AbstractAdaptiveStepSizeController, AbstractStepSizeController as AbstractStepSizeController, + ClipStepSizeController as ClipStepSizeController, ConstantStepSize as ConstantStepSize, - JumpStepWrapper as JumpStepWrapper, PIDController as PIDController, StepTo as StepTo, ) diff --git a/diffrax/_autocitation.py b/diffrax/_autocitation.py index 547177ce..c2cdcade 100644 --- a/diffrax/_autocitation.py +++ b/diffrax/_autocitation.py @@ -36,7 +36,7 @@ SRA1, Tsit5, ) -from ._step_size_controller import PIDController +from ._step_size_controller import ClipStepSizeController, PIDController def citation(*args, **kwargs): @@ -134,7 +134,7 @@ def citation(*args, **kwargs): _thesis_cite = r""" -phdthesis{kidger2021on, +@phdthesis{kidger2021on, title={{O}n {N}eural {D}ifferential {E}quations}, author={Patrick Kidger}, year={2021}, @@ -352,10 +352,10 @@ def _virtual_brownian_tree(terms): return ( r""" % You are simulating Brownian motion using a virtual Brownian tree, which was introduced -% in: +% in the following two papers: """ + vbt_ref - + "\n\n" + + "\n" + single_seed_ref ) @@ -570,6 +570,17 @@ def _auto_dt0(dt0): """ +@citation_rules.append +def _clip_controller(terms, stepsize_controller): + if type(stepsize_controller) is ClipStepSizeController: + if stepsize_controller.store_rejected_steps is not None and is_sde(terms): + return r""" +% You are adaptively solving an SDE whilst revisiting rejected time points. This is a +% subtle point required for the correctness of adaptive noncommutative SDE solves, as +% found in: +""" + _parse_reference(ClipStepSizeController) + + @citation_rules.append def _pid_controller(stepsize_controller, terms=None): if type(stepsize_controller) is PIDController: diff --git a/diffrax/_solution.py b/diffrax/_solution.py index f1b8d21b..e99f2c15 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -21,6 +21,14 @@ class RESULTS(optx.RESULTS): # pyright: ignore event_occurred = ( "Terminating differential equation solve because an event occurred." ) + max_steps_rejected = ( + "Maximum number of rejected steps was reached. Consider increasing " + "`diffrax.ClipStepSizeController(store_rejected_steps==...)`." + ) + internal_error = ( + "An internal error occurred in Diffrax. This is a bug! Please open a GitHub " + "issue with a minimum working example. (<50 lines of code is ideal)" + ) # Backward compatibility diff --git a/diffrax/_step_size_controller/__init__.py b/diffrax/_step_size_controller/__init__.py index 5637c24e..74e9371f 100644 --- a/diffrax/_step_size_controller/__init__.py +++ b/diffrax/_step_size_controller/__init__.py @@ -2,8 +2,8 @@ AbstractAdaptiveStepSizeController as AbstractAdaptiveStepSizeController, AbstractStepSizeController as AbstractStepSizeController, ) +from .clip import ClipStepSizeController as ClipStepSizeController from .constant import ConstantStepSize as ConstantStepSize, StepTo as StepTo -from .jump_step_wrapper import JumpStepWrapper as JumpStepWrapper from .pid import ( PIDController as PIDController, ) diff --git a/diffrax/_step_size_controller/clip.py b/diffrax/_step_size_controller/clip.py new file mode 100644 index 00000000..0167e789 --- /dev/null +++ b/diffrax/_step_size_controller/clip.py @@ -0,0 +1,394 @@ +from collections.abc import Callable +from typing import cast, Generic, Optional, TypeVar + +import equinox as eqx +import equinox.internal as eqxi +import jax +import jax.numpy as jnp +from jaxtyping import Array, PyTree, Real + +from .._custom_types import ( + Args, + BoolScalarLike, + FloatScalarLike, + IntScalarLike, + RealScalarLike, + VF, + Y, +) +from .._misc import upcast_or_raise +from .._solution import is_okay, RESULTS +from .._term import AbstractTerm +from .base import AbstractStepSizeController + + +_ControllerState = TypeVar("_ControllerState") +_Dt0 = TypeVar("_Dt0", bound=Optional[RealScalarLike]) + + +class _ClipState(eqx.Module, Generic[_ControllerState]): + step_info: Optional[tuple[IntScalarLike, Array]] + jump_info: Optional[tuple[IntScalarLike, Array]] + reject_info: Optional[tuple[IntScalarLike, Array]] + inner_state: _ControllerState + + +def _none_or_sorted_array(x): + if x is None: + return None + else: + return jnp.sort(jnp.asarray(x)) + + +def _assert_floating(t: FloatScalarLike, name: str, dtype): + t_dtype = jnp.result_type(t) + if not jnp.issubdtype(t_dtype, jnp.floating): + raise ValueError(f"{name} must be floating-point, got {t_dtype}") + if t_dtype != dtype: + raise ValueError( + f"All timelike inputs must have the same dtype got both {dtype} and " + f"{t_dtype}." + ) + + +def _get_t(i: IntScalarLike, ts: Array) -> RealScalarLike: + # As `ts[i]`, but `ts[len(ts))]` returns `inf`. + # `i` must be in `{0, 1, ..., len(ts)}`. + if len(ts) == 0: + return jnp.inf + else: + i_min_len = jnp.minimum(i, len(ts) - 1) + return jnp.where(i == len(ts), jnp.inf, ts[i_min_len]) + + +def _clip_t( + t: FloatScalarLike, + i: IntScalarLike, + ts: Array, + prevbefore: bool, +) -> FloatScalarLike: + assert jnp.issubdtype(jnp.result_type(t), jnp.floating) + assert jnp.result_type(t) == jnp.result_type(ts) + _t = _get_t(i, ts) + if prevbefore: + _t = eqxi.prevbefore(_t) + return jnp.minimum(_t, t) + + +def _bump_next_t0(next_t0, ts): + # Our previous step may have been to prevbefore a jump. + # In this case we want to bump our next step to occur nextafter the jump. + # We don't test against just `jump_ts[jump_index]`. The index in the state + # is intended only as a hint to improve the efficiency of + # `_find_idx_with_hint`; it's not load-bearing. This is for safety, in case some + # other stepsize control is going on. (TODO: do we want to keep it like this, or + # do we want to switch to just the single check?) + nextafter_next_t0 = eqxi.nextafter(next_t0) + made_jump1 = jnp.any(nextafter_next_t0 == ts) + # For safety we also test `next_t0 == ts`, just in case some other stepsize control + # is going on. (I don't think this should actually be necessary.) + made_jump2 = jnp.any(next_t0 == ts) + # There are two nextafters. This is important because we don't know whether + # or not the jump is a left- or a right-discontinuity, so we skip the jump + # time altogether. + next_t0 = jnp.where(made_jump1, eqxi.nextafter(nextafter_next_t0), next_t0) + next_t0 = cast(Array, next_t0) + next_t0 = jnp.where(made_jump2, nextafter_next_t0, next_t0) + next_t0 = cast(Array, next_t0) + return next_t0, made_jump1 | made_jump2 + + +def _find_idx_with_hint(t: RealScalarLike, ts: Optional[Array], hint: IntScalarLike): + # Find index of first element of `ts` strictly greater than `t`. + # Uses a linear search starting from `hint`. The value `hint` is assumed to be in + # `{0, 1, ..., len(ts)}` + if ts is None: + return 0 + + def cond_up(_i): + return (_i < len(ts)) & (ts[_i] <= t) + + def cond_down(_i): + return (_i > 0) & (ts[_i - 1] > t) + + i = hint + i = jax.lax.while_loop(cond_up, lambda _i: _i + 1, i) + i = jax.lax.while_loop(cond_down, lambda _i: _i - 1, i) + return i + + +class ClipStepSizeController( + AbstractStepSizeController[_ClipState[_ControllerState], _Dt0] +): + """Wraps an existing step controller with three pieces of functionality: + + - Have the solver step exactly to certain times ('`step_ts`'). + - Have the solver step to just before and just after certain time ('`jump_ts`'). + - Have the solver record the times of rejected steps, and step exactly to those + times in future steps. + + In all cases this essentially corresponds to clipping steps so that any that are + 'too large' will instead by clipped from one of the three above cases. + + Stepping exactly to certain times can be useful if you want to ensure that your + solution is highly accurate at that exact time point -- by default Diffrax will + adaptively step wherever it likes, and then interpolate to produce the output values + in `SaveAt(ts=...)`. + + Specifying jump times is needed for computational efficiency when solving + differential equations for which the vector field has known jumps (e.g. due to a + discontinuous forcing term). Otherwise an adaptive solver must reject many steps as + it slows down to try and locate a jump. When using this, the solver will step to the + floating point number immediately before the jump, and then resume solving from the + floating point number immediately after it, with the jump itself not being + evaluated. + + Revisiting rejected steps is needed when adaptively solving SDEs with noncommutative + noise. Otherwise, a small bias may be introduced in the higher-order (Lévy area) + terms of the solution, as it is possible to reject a step *because* of the samples + drawn in these higher order terms. + + ??? Citation + + For more details on revisiting rejected steps when adaptively solving SDEs, see: + + ```bibtex + @misc{foster2024convergenceadaptiveapproximationsstochastic, + title={On the convergence of adaptive approximations for + stochastic differential equations}, + author={James Foster and Andraž Jelinčič}, + year={2024}, + eprint={2311.14201}, + archivePrefix={arXiv}, + primaryClass={math.NA}, + url={https://arxiv.org/abs/2311.14201}, + } + ``` + """ + + controller: AbstractStepSizeController[_ControllerState, _Dt0] + step_ts: Optional[Real[Array, " steps"]] + jump_ts: Optional[Real[Array, " jumps"]] + store_rejected_steps: Optional[int] = eqx.field(static=True) + callback_on_reject: Optional[Callable] = eqx.field(static=True) + + @eqxi.doc_remove_args("_callback_on_reject") + def __init__( + self, + controller, + step_ts=None, + jump_ts=None, + store_rejected_steps=None, + _callback_on_reject=None, + ): + """**Arguments**: + + - `controller`: The controller to wrap. + Can be any [`diffrax.AbstractAdaptiveStepSizeController`][]. + - `step_ts`: Denotes extra times that must be stepped to. + - `jump_ts`: Denotes extra times that must be stepped to, and at which the + vector field has a known discontinuity. (This is used to force FSAL solvers + to re-evaluate the vector field.) + `store_rejected_steps`: If this is set to a positive integer, then any + rejected steps will have their time stored, and that time will be stepped to + exactly in a later step. This is used when solving SDEs with noncommutative + noise, for which this ensures that the distribution coming from Lévy area + terms is correct. Setting this to e.g. `100` should be plenty, but if more + consecutive steps are rejected, then a runtime error will be raised. (Note + that this is not the total number of rejected steps in a solve, but just the + maximum number of *consecutive* rejected steps.) + """ + self.controller = controller + self.step_ts = _none_or_sorted_array(step_ts) + self.jump_ts = _none_or_sorted_array(jump_ts) + if (store_rejected_steps is not None) and (store_rejected_steps <= 0): + raise ValueError( + "`store_rejected_steps must either be `None`" + " or a non-negative integer." + ) + self.store_rejected_steps = store_rejected_steps + self.callback_on_reject = _callback_on_reject + + def __check_init__(self): + if self.jump_ts is not None and not jnp.issubdtype( + self.jump_ts.dtype, jnp.floating + ): + raise ValueError( + f"jump_ts must be floating point, not {self.jump_ts.dtype}" + ) + + def wrap(self, direction: IntScalarLike): + step_ts = None if self.step_ts is None else jnp.sort(self.step_ts * direction) + jump_ts = None if self.jump_ts is None else jnp.sort(self.jump_ts * direction) + controller = self.controller.wrap(direction) + return eqx.tree_at( + lambda s: (s.step_ts, s.jump_ts, s.controller), + self, + (step_ts, jump_ts, controller), + is_leaf=lambda x: x is None, + ) + + def init( + self, + terms: PyTree[AbstractTerm], + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + dt0: _Dt0, + args: Args, + func: Callable[[PyTree[AbstractTerm], RealScalarLike, Y, Args], VF], + error_order: Optional[RealScalarLike], + ) -> tuple[RealScalarLike, _ClipState[_ControllerState]]: + t_dtype = jnp.result_type(t0) + _assert_floating(t0, "t0", t_dtype) + _assert_floating(t1, "t1", t_dtype) + if dt0 is not None: + _assert_floating(dt0, "dt0", t_dtype) + t1, inner_state = self.controller.init( + terms, t0, t1, y0, dt0, args, func, error_order + ) + _assert_floating(t1, "controller.init(...)", t_dtype) + + if self.step_ts is None: + step_info = None + else: + step_ts = upcast_or_raise( + self.step_ts, + t_dtype, + "`ClipStepSizeController.step_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + step_index = jnp.searchsorted(step_ts, t0, side="right") + t1 = _clip_t(t1, step_index, step_ts, False) + step_info = (step_index, step_ts) + + if self.jump_ts is None: + jump_info = None + else: + jump_ts = upcast_or_raise( + self.jump_ts, + t_dtype, + "`ClipStepSizeController.jump_ts`", + "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", + ) + jump_index = jnp.searchsorted(jump_ts, t0, side="right") + t1 = _clip_t(t1, jump_index, jump_ts, True) + jump_info = (jump_index, jump_ts) + + if self.store_rejected_steps is None: + reject_info = None + else: + reject_ts = jnp.zeros(self.store_rejected_steps, dtype=t_dtype) + reject_index = jnp.array(self.store_rejected_steps) + reject_info = (reject_index, reject_ts) + + state = _ClipState(step_info, jump_info, reject_info, inner_state) + return t1, state + + def adapt_step_size( + self, + t0: RealScalarLike, + t1: RealScalarLike, + y0: Y, + y1_candidate: Y, + args: Args, + y_error: Optional[Y], + error_order: RealScalarLike, + controller_state: _ClipState[_ControllerState], + ) -> tuple[ + BoolScalarLike, + RealScalarLike, + RealScalarLike, + BoolScalarLike, + _ClipState[_ControllerState], + RESULTS, + ]: + t_dtype = jnp.result_type(t0) + _assert_floating(t0, "t0", t_dtype) + _assert_floating(t1, "t1", t_dtype) + ( + keep_step, + next_t0, + next_t1, + made_jump, + inner_state, + result, + ) = self.controller.adapt_step_size( + t0, + t1, + y0, + y1_candidate, + args, + y_error, + error_order, + controller_state.inner_state, + ) + _assert_floating(next_t0, "next_t0", t_dtype) + _assert_floating(next_t1, "next_t1", t_dtype) + + # Logging utility for testing purposes + callback_on_reject = self.callback_on_reject + if callback_on_reject is not None: + + def callback(_keep_step, _t1): + callback_on_reject(_keep_step, _t1) + return _keep_step + + keep_step = jax.pure_callback(callback, keep_step, keep_step, t1) + + if controller_state.step_info is None: + step_info = None + else: + step_index, step_ts = controller_state.step_info + # We actaully bump `next_t0` past any `step_ts` whilst checking where to + # clip `next_t1`. This is in case we have a set up like the following: + # ```python + # ClipStepSizeController( + # ClipStepSizeController(..., step_ts=[x]), jump_ts=[x] + # ) + # ``` + # with a single value `x`. Otherwise in this case, the outer controller will + # propose a step over the interval [something, prevbefore(x)], then on the + # next step the inner controller will propose a step over [prevbefore(x), x] + # which definitely isn't desired! + _next_t0, _ = _bump_next_t0(next_t0, step_ts) + step_index = _find_idx_with_hint(_next_t0, step_ts, step_index) + next_t1 = _clip_t(next_t1, step_index, step_ts, False) + step_info = step_index, step_ts + if controller_state.jump_info is None: + jump_info = None + else: + jump_index, jump_ts = controller_state.jump_info + next_t0, made_jump2 = _bump_next_t0(next_t0, jump_ts) + made_jump = made_jump | made_jump2 + jump_index = _find_idx_with_hint(next_t0, jump_ts, jump_index) + next_t1 = _clip_t(next_t1, jump_index, jump_ts, True) + jump_info = jump_index, jump_ts + if controller_state.reject_info is None: + reject_info = None + else: + assert self.store_rejected_steps is not None + reject_index, reject_ts = controller_state.reject_info + # If the step ended at `t1==reject_ts[reject_index],` then we have + # successfully stepped to this time and we pop off this rejected time by + # incrementing `reject_index`. + # We do this increment even if the step is rejected, because we will + # re-add the rejected time to the buffer immediately. + rejected_t = _get_t(reject_index, reject_ts) + result = RESULTS.where( + (t1 > rejected_t) & is_okay(result), RESULTS.internal_error, result + ) + reject_index = reject_index + jnp.where(t1 == rejected_t, 1, 0) + # Now, if the step is rejected then we must store the rejected time in the + # buffer. + reject_index = reject_index - jnp.where(keep_step, 0, 1) + result = RESULTS.where( + (reject_index < 0) & is_okay(result), RESULTS.max_steps_rejected, result + ) + new_rejected_t = jnp.where(keep_step, reject_ts[reject_index], t1) + reject_ts = reject_ts.at[reject_index].set(new_rejected_t) + next_t1 = _clip_t(next_t1, reject_index, reject_ts, False) + reject_info = reject_index, reject_ts + + state = _ClipState(step_info, jump_info, reject_info, inner_state) + return keep_step, next_t0, next_t1, made_jump, state, result diff --git a/diffrax/_step_size_controller/jump_step_wrapper.py b/diffrax/_step_size_controller/jump_step_wrapper.py deleted file mode 100644 index 259889fb..00000000 --- a/diffrax/_step_size_controller/jump_step_wrapper.py +++ /dev/null @@ -1,458 +0,0 @@ -from collections.abc import Callable -from typing import Generic, get_args, Optional, TYPE_CHECKING, TypeVar - -import equinox as eqx -import equinox.internal as eqxi -import jax -import jax.numpy as jnp -from jaxtyping import Array, PyTree, Real - -from .._custom_types import ( - Args, - BoolScalarLike, - IntScalarLike, - RealScalarLike, - VF, - Y, -) -from .._misc import static_select, upcast_or_raise -from .._solution import RESULTS -from .._term import AbstractTerm -from .base import AbstractStepSizeController - - -_ControllerState = TypeVar("_ControllerState") -_Dt0 = TypeVar("_Dt0", None, RealScalarLike, Optional[RealScalarLike]) - - -class _JumpStepState(eqx.Module, Generic[_ControllerState]): - jump_at_next_t1: BoolScalarLike - step_index: IntScalarLike - jump_index: IntScalarLike - rejected_index: IntScalarLike - rejected_buffer: Optional[Array] - step_ts: Optional[Array] - jump_ts: Optional[Array] - inner_state: _ControllerState - - -def _none_or_sorted_array(x): - if x is None: - return None - else: - return jnp.sort(jnp.asarray(x)) - - -def _get_t(i: IntScalarLike, ts: Array) -> RealScalarLike: - i_min_len = jnp.minimum(i, len(ts) - 1) - return jnp.where(i == len(ts), jnp.inf, ts[i_min_len]) - - -def _clip_ts( - t0: RealScalarLike, - t1: RealScalarLike, - i: IntScalarLike, - ts: Optional[Array], - check_inexact: bool, -) -> tuple[RealScalarLike, BoolScalarLike]: - if ts is None: - return t1, False - - if check_inexact: - assert jnp.issubdtype(ts.dtype, jnp.inexact) - if not jnp.issubdtype(jnp.result_type(t0), jnp.inexact): - raise ValueError( - "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " - f"Got {jnp.result_type(t0)}." - ) - if not jnp.issubdtype(jnp.result_type(t1), jnp.inexact): - raise ValueError( - "`t0`, `t1`, `dt0` must be floating point when specifying `jump_ts`. " - f"Got {jnp.result_type(t1)}." - ) - - _t1 = _get_t(i, ts) - jump_at_t1 = _t1 <= t1 - _t1 = jnp.where(jump_at_t1, _t1, t1) - return _t1, jump_at_t1 - - -def _find_idx_with_hint(t: RealScalarLike, ts: Optional[Array], hint: IntScalarLike): - # Find index of first element of ts greater than t - # using linear search starting from hint. - if ts is None: - return 0 - - def cond_up(_i): - return (_i < len(ts)) & (ts[_i] <= t) - - def cond_down(_i): - return (_i > 0) & (ts[_i - 1] > t) - - i = hint - i = jax.lax.while_loop(cond_up, lambda _i: _i + 1, i) - i = jax.lax.while_loop(cond_down, lambda _i: _i - 1, i) - return i - - -def _find_index(t: RealScalarLike, ts: Optional[Array]) -> IntScalarLike: - if ts is None: - return 0 - - ts = upcast_or_raise( - ts, - t, - "`JumpStepWrapper.step_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - return jnp.searchsorted(ts, t, side="right") - - -def _revisit_rejected( - t0: RealScalarLike, - t1: RealScalarLike, - i_reject: IntScalarLike, - rejected_buffer: Optional[Array], -) -> RealScalarLike: - if rejected_buffer is None: - return t1 - _t1 = _get_t(i_reject, rejected_buffer) - _t1 = jnp.minimum(_t1, t1) - return _t1 - - -class JumpStepWrapper( - AbstractStepSizeController[_JumpStepState[_ControllerState], _Dt0] -): - """Wraps an existing step controller and adds the ability to specify `step_ts` - and `jump_ts`. It also enables the feature of revisiting rejected steps, which - is useful when solving SDEs with an adaptive step controller. - - Explanation of `step_ts` and `jump_ts`: - - The `step_ts` and `jump_ts` are used to force the solver to step to certain times. - They mostly act in the same way, except that when we hit an element of `jump_ts`, - the controller must return `made_jump = True`, so that the diffeqsolve function - knows that the vector field has a discontinuity at that point, in which case it - re-evaluates it right after the jump point. In addition, the - exact time of the jump will be skipped using eqxi.prevbefore and eqxi.nextafter. - So now to the explanation of the two (we will use `step_ts` as an example, but the - same applies to `jump_ts`): - - If `step_ts` is not None, we assume it is a sorted array of times. - At the start of the run, the init function finds the smallest index `i_step` such - that `step_ts[i_step] > t0`. At init and after each step of the solver, the - controller will propose a step t1_next, and we will clip it to - `t1_next = min(t1_next, step_ts[i_step])`. - At the start of the next step, if the step ended at t1 == step_ts[i_step] and - if the controller decides to keep the step, then this time has been successfully - stepped to and we increment `i_step` by 1. - We use a convenience function _get_t(i, ts) which returns ts[i] if i < len(ts) and - infinity otherwise. - - Explanation of revisiting rejected steps: - - This feature should be used if and only if solving SDEs with non-commutative noise - using an adaptive step controller. - - We use a "stack" of rejected steps, composed of a buffer `rejected_buffer` of length - `rejected_step_buffer_len` and a counter `i_reject`. The "stack" are all the items - in `rejected_buffer[i_reject:]` with `rejected_buffer[i_reject]` being the top of - the stack. - When `i_reject == rejected_step_buffer_len`, the stack is empty. - At the start of the run, `i_reject = rejected_step_buffer_len`. Each time a step is - rejected `i_reject -=1` and `rejected_buffer[i_reject] = t1`. Each time a step ends - at `t1 == rejected_buffer[i_reject]`, we increment `i_reject` by 1 (even if the - step was rejected, in which case we will re-add `t1` to the stack immediately). - We clip the next step to `t1_next = min(t1_next, rejected_buffer[i_reject])`. - If `i_reject < 0` then an error is raised. - """ - - # For more details on solving SDEs with adaptive stepping see - # docs/api/stepsize_controller.md - # I am putting this outside of the docstring, because this class appears in that - # part of the docs and I don't want to repeat the same thing twice on one page. - # For more details also refer to - # ```bibtex - # @misc{foster2024convergenceadaptiveapproximationsstochastic, - # title={On the convergence of adaptive approximations for - # stochastic differential equations}, - # author={James Foster and Andraž Jelinčič}, - # year={2024}, - # eprint={2311.14201}, - # archivePrefix={arXiv}, - # primaryClass={math.NA}, - # url={https://arxiv.org/abs/2311.14201}, - # } - # ``` - - controller: AbstractStepSizeController[_ControllerState, _Dt0] - step_ts: Optional[Real[Array, " steps"]] - jump_ts: Optional[Real[Array, " jumps"]] - rejected_step_buffer_len: Optional[int] = eqx.field(static=True) - callback_on_reject: Optional[Callable] = eqx.field(static=True) - - @eqxi.doc_remove_args("_callback_on_reject") - def __init__( - self, - controller, - step_ts=None, - jump_ts=None, - rejected_step_buffer_len=None, - _callback_on_reject=None, - ): - r""" - **Arguments**: - - - `controller`: The controller to wrap. - Can be any [`diffrax.AbstractAdaptiveStepSizeController`][]. - - `step_ts`: Denotes extra times that must be stepped to. - - `jump_ts`: Denotes extra times that must be stepped to, and at which the - vector field has a known discontinuity. (This is used to force FSAL solvers - to re-evaluate the vector field.) - `rejected_step_buffer_len`: Length of the stack used to store rejected steps. - Can either be `None` or a positive integer. - If `None`, this feature will be off. - If it is > 0, then the controller will revisit rejected steps. - This should only be used when solving SDEs with an adaptive step size - controller. For most SDEs, setting this to `100` should be plenty, - but if more consecutive steps are rejected, then an error will be raised. - (Note that this is not the total number of rejected steps in a solve, - but just the number of rejected steps currently on the stack to be - revisited.) - """ - self.controller = controller - self.step_ts = _none_or_sorted_array(step_ts) - self.jump_ts = _none_or_sorted_array(jump_ts) - if (rejected_step_buffer_len is not None) and (rejected_step_buffer_len <= 0): - raise ValueError( - "`rejected_step_buffer_len must either be `None`" - " or a non-negative integer." - ) - self.rejected_step_buffer_len = rejected_step_buffer_len - self.callback_on_reject = _callback_on_reject - - def __check_init__(self): - if self.jump_ts is not None and not jnp.issubdtype( - self.jump_ts.dtype, jnp.inexact - ): - raise ValueError( - f"jump_ts must be floating point, not {self.jump_ts.dtype}" - ) - - def wrap(self, direction: IntScalarLike): - step_ts = None if self.step_ts is None else jnp.sort(self.step_ts * direction) - jump_ts = None if self.jump_ts is None else jnp.sort(self.jump_ts * direction) - controller = self.controller.wrap(direction) - return eqx.tree_at( - lambda s: (s.step_ts, s.jump_ts, s.controller), - self, - (step_ts, jump_ts, controller), - is_leaf=lambda x: x is None, - ) - - def init( - self, - terms: PyTree[AbstractTerm], - t0: RealScalarLike, - t1: RealScalarLike, - y0: Y, - dt0: _Dt0, - args: Args, - func: Callable[[PyTree[AbstractTerm], RealScalarLike, Y, Args], VF], - error_order: Optional[RealScalarLike], - ) -> tuple[RealScalarLike, _JumpStepState[_ControllerState]]: - t1, inner_state = self.controller.init( - terms, t0, t1, y0, dt0, args, func, error_order - ) - tdtype = jnp.result_type(t0, t1) - - if self.step_ts is None: - step_ts = None - else: - # Upcast step_ts to the same dtype as t0, t1 - step_ts = upcast_or_raise( - self.step_ts, - tdtype, - "`JumpStepWrapper.step_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - - if self.jump_ts is None: - jump_ts = None - else: - # Upcast jump_ts to the same dtype as t0, t1 - jump_ts = upcast_or_raise( - self.jump_ts, - tdtype, - "`JumpStepWrapper.jump_ts`", - "time (the result type of `t0`, `t1`, `dt0`, `SaveAt(ts=...)` etc.)", - ) - - if self.rejected_step_buffer_len is None: - rejected_buffer = None - i_reject = jnp.asarray(0) - else: - rejected_buffer = jnp.zeros( - (self.rejected_step_buffer_len,) + jnp.shape(t1), dtype=tdtype - ) - # rejected_buffer[len(rejected_buffer)] = jnp.inf (see def of _get_t) - i_reject = jnp.asarray(self.rejected_step_buffer_len) - - # Find index of first element of step_ts/jump_ts greater than t0 - i_step = _find_index(t0, step_ts) - i_jump = _find_index(t0, jump_ts) - # Clip t1 to the next element of step_ts or jump_ts - t1, _ = _clip_ts(t0, t1, i_step, step_ts, False) - t1, jump_next_step = _clip_ts(t0, t1, i_jump, jump_ts, True) - - state = _JumpStepState( - jump_next_step, - i_step, - i_jump, - i_reject, - rejected_buffer, - step_ts, - jump_ts, - inner_state, - ) - - return t1, state - - def adapt_step_size( - self, - t0: RealScalarLike, - t1: RealScalarLike, - y0: Y, - y1_candidate: Y, - args: Args, - y_error: Optional[Y], - error_order: RealScalarLike, - controller_state: _JumpStepState[_ControllerState], - ) -> tuple[ - BoolScalarLike, - RealScalarLike, - RealScalarLike, - BoolScalarLike, - _JumpStepState[_ControllerState], - RESULTS, - ]: - # just shortening the name - st = controller_state - i_step = st.step_index - i_jump = st.jump_index - i_reject = st.rejected_index - - # Let the controller do its thing - ( - keep_step, - next_t0, - original_next_t1, - jump_at_original_next_t1, - inner_state, - result, - ) = self.controller.adapt_step_size( - t0, t1, y0, y1_candidate, args, y_error, error_order, st.inner_state - ) - next_t1 = original_next_t1 - - # This is just a logging utility for testing purposes - if self.callback_on_reject is not None: - # jax.debug.callback(self.callback_on_reject, keep_step, t1) - jax.experimental.io_callback(self.callback_on_reject, None, keep_step, t1) # pyright: ignore - - # For step ts and jump ts find the index of the first element in jump_ts/step_ts - # greater than next_t0. We use the hint i_step/i_jump to speed up the search. - i_step = _find_idx_with_hint(next_t0, st.step_ts, i_step) - i_jump = _find_idx_with_hint(next_t0, st.jump_ts, i_jump) - - if self.rejected_step_buffer_len is not None: - rejected_buffer = st.rejected_buffer - assert rejected_buffer is not None - # If the step ended at t1==rejected_buffer[i_reject], then we have - # successfully stepped to this time and we increment i_reject. - # We increment i_reject even if the step was rejected, because we will - # re-add the rejected time to the buffer immediately. - rejected_t = _get_t(i_reject, rejected_buffer) - rjct_inc_cond = t1 == rejected_t - i_reject = jnp.where(rjct_inc_cond, i_reject + 1, i_reject) - - # If the step was rejected, then we need to store the rejected time in the - # rejected buffer and decrement the rejected index. - i_reject = jnp.where(keep_step, i_reject, i_reject - 1) - i_reject = eqx.error_if( - i_reject, - i_reject < 0, - "Maximum number of rejected steps reached. " - "Consider increasing JumpStepWrapper.rejected_step_buffer_len.", - ) - clipped_i = jnp.clip(i_reject, 0, self.rejected_step_buffer_len - 1) - update_rejected_t = jnp.where(keep_step, rejected_buffer[clipped_i], t1) - rejected_buffer = rejected_buffer.at[clipped_i].set(update_rejected_t) - else: - rejected_buffer = None - - # Now move on to the NEXT STEP - - # If t1 hit a jump point, and the step was kept then we need to set - # `next_t0 = nextafter(nextafter(t1))` to ensure that we really skip - # over the jump and don't evaluate the vector field at the discontinuity. - if jnp.issubdtype(jnp.result_type(next_t0), jnp.inexact): - # Two nextafters. If made_jump then t1 = prevbefore(jump location) - # so now _t1 = nextafter(jump location) - # This is important because we don't know whether or not the jump is as a - # result of a left- or right-discontinuity, so we have to skip the jump - # location altogether. - jump_keep = st.jump_at_next_t1 & keep_step - next_t0 = static_select( - jump_keep, eqxi.nextafter(eqxi.nextafter(next_t0)), next_t0 - ) - - if TYPE_CHECKING: # if i don't seperate this out pyright complains - assert isinstance(next_t0, RealScalarLike) - else: - assert isinstance( - next_t0, get_args(RealScalarLike) - ), f"type(next_t0) = {type(next_t0)}" - - # Clip the step to the next element of jump_ts or step_ts or - # rejected_buffer. Important to do jump_ts last because otherwise - # jump_at_next_t1 could be a false positive. - next_t1 = _revisit_rejected(next_t0, next_t1, i_reject, rejected_buffer) - next_t1, _ = _clip_ts(next_t0, next_t1, i_step, st.step_ts, False) - next_t1, jump_at_next_t1 = _clip_ts(next_t0, next_t1, i_jump, st.jump_ts, True) - - # Let's prove that the line below is correct. Say the inner controller is - # itself a JumpStepWrapper (JSW) with some inner_jump_ts. Then, given that - # it propsed (next_t0, original_next_t1), there cannot be any jumps in - # inner_jump_ts between next_t0 and original_next_t1. So if the next_t1 - # proposed by the outer JSW is different from the original_next_t1 then - # next_t1 \in (next_t0, original_next_t1) and hence there cannot be a jump - # in inner_jump_ts at next_t1. So the jump_at_next_t1 only depends on - # jump_at_next_t1. - # On the other hand if original_next_t1 == next_t1, then we just take an - # OR of the two. - jump_at_next_t1 = jnp.where( - next_t1 == original_next_t1, - jump_at_next_t1 | jump_at_original_next_t1, - jump_at_next_t1, - ) - - # Here made_jump signifies whether there is a jump at t1. What the solver - # needs, however, is whether there is a jump at next_t0, so these two will - # only match when the step was kept. The case when the step was rejected is - # handled in `_integrate.py` (search for "made_jump = static_select"). - made_jump = st.jump_at_next_t1 - - state = _JumpStepState( - jump_at_next_t1, - i_step, - i_jump, - i_reject, - rejected_buffer, - st.step_ts, - st.jump_ts, - inner_state, - ) - - return keep_step, next_t0, next_t1, made_jump, state, result diff --git a/diffrax/_step_size_controller/pid.py b/diffrax/_step_size_controller/pid.py index fd343423..710ae944 100644 --- a/diffrax/_step_size_controller/pid.py +++ b/diffrax/_step_size_controller/pid.py @@ -25,24 +25,12 @@ from .._solution import RESULTS from .._term import AbstractTerm, ODETerm from .base import AbstractAdaptiveStepSizeController -from .jump_step_wrapper import JumpStepWrapper +from .clip import ClipStepSizeController ω = cast(Callable, ω) -# We use a metaclass for backwards compatibility. When a user calls -# PIDController(... step_ts=s, jump_ts=j) this should return a -# JumpStepWrapper(PIDController(...), s, j). -class _PIDMeta(type(eqx.Module)): - def __call__(cls, *args, **kwargs): - step_ts = kwargs.pop("step_ts", None) - jump_ts = kwargs.pop("jump_ts", None) - if step_ts is not None or jump_ts is not None: - return JumpStepWrapper(cls(*args, **kwargs), step_ts, jump_ts) - return super().__call__(*args, **kwargs) - - def _select_initial_step( terms: PyTree[AbstractTerm], t0: RealScalarLike, @@ -98,6 +86,23 @@ def intermediate(carry): _PidState = tuple[RealScalarLike, RealScalarLike] +# We use a metaclass for backwards compatibility. When a user calls +# PIDController(... step_ts=s, jump_ts=j) this should return a +# ClipStepSizeController(PIDController(...), s, j). +class _MetaPID(type(eqx.Module)): + def __call__(cls, *args, **kwargs): + step_ts = kwargs.pop("step_ts", None) + jump_ts = kwargs.pop("jump_ts", None) + if step_ts is not None or jump_ts is not None: + return ClipStepSizeController(cls(*args, **kwargs), step_ts, jump_ts) + return super().__call__(*args, **kwargs) + + +# Sneak the metaclass past pyright, as otherwise it disables the dataclass-ness of +# `eqx.Module`. +_set_metaclass = dict(metaclass=_MetaPID) + + if TYPE_CHECKING: rms_norm = optx.rms_norm else: @@ -121,7 +126,7 @@ def __repr__(self): # in Soderlind and Wang 2006. class PIDController( AbstractAdaptiveStepSizeController[_PidState, Optional[RealScalarLike]], - metaclass=_PIDMeta, + **_set_metaclass, ): r"""Adapts the step size to produce a solution accurate to a given tolerance. The tolerance is calculated as `atol + rtol * y` for the evolving solution `y`. @@ -311,6 +316,7 @@ def dynamics(t, y, args): rtol: RealScalarLike atol: RealScalarLike + norm: Callable[[PyTree], RealScalarLike] = rms_norm pcoeff: RealScalarLike = 0 icoeff: RealScalarLike = 1 dcoeff: RealScalarLike = 0 @@ -319,7 +325,6 @@ def dynamics(t, y, args): force_dtmin: bool = True factormin: RealScalarLike = 0.2 factormax: RealScalarLike = 10.0 - norm: Callable[[PyTree], RealScalarLike] = rms_norm safety: RealScalarLike = 0.9 error_order: Optional[RealScalarLike] = None @@ -568,7 +573,8 @@ def _scale(_y0, _y1_candidate, _y_error): keep_step, prev_inv_scaled_error, prev_prev_inv_scaled_error ) controller_state = inv_scaled_error, prev_inv_scaled_error - # made_jump is handled by JumpStepWrapper, so we automatically set it to False + # made_jump is handled by ClipStepSizeController, so we automatically set it to + # False return keep_step, next_t0, next_t1, False, controller_state, result def _get_error_order(self, error_order: Optional[RealScalarLike]) -> RealScalarLike: diff --git a/docs/api/stepsize_controller.md b/docs/api/stepsize_controller.md index 62aa370f..a59a3d64 100644 --- a/docs/api/stepsize_controller.md +++ b/docs/api/stepsize_controller.md @@ -2,24 +2,15 @@ The list of step size controllers is as follows. The most common cases are fixed step sizes with [`diffrax.ConstantStepSize`][] and adaptive step sizes with [`diffrax.PIDController`][]. -!!! warning +?? warning "Adaptive SDEs" - When solving SDEs with an adaptive step controller, then three requirements - have to be fulfilled in order for the solution to be guaranteed to converge to - the correct result: + When solving SDEs with an adaptive step controller, then three requirements must be met for the solution to converge to the correct result: - - the Brownian motion has to be generated using [`diffrax.VirtualBrownianTree`][], - - the solver must satisfy certain conditions (in practice all SDE solvers except - [`diffrax.Euler`][] satisfy these), - - either - a) the SDE must have [commutative noise](../usage/how-to-choose-a-solver.md#stochastic-differential-equations) - OR - b) the SDE is evaluated at all times at which the Brownian motion (BM) is - evaluated; since the BM is also evaluated at steps that are rejected by the step - controller, we must later evaluate the SDE at these times as well - (i.e. revisit rejected steps). This can be done using [`diffrax.JumpStepWrapper`]. + 1. the Brownian motion must be generated with [`diffrax.VirtualBrownianTree`][]; + 2. the solver must satisfy certain technical conditions (in practice all SDE solvers except [`diffrax.Euler`][] satisfy these), + 3. the SDE must either have [commutative noise](../usage/how-to-choose-a-solver.md#stochastic-differential-equations), or `ClipStepSizeController(..., store_rejected_steps=...)` must be used. - Note that these conditions are not checked by Diffrax. + Conditions 1 and 2 are checked by Diffrax. Condition 3 is not (as there is no easy way to verify commutativity of the noise). For more details about the convergence of adaptive solutions to SDEs, please refer to @@ -35,7 +26,6 @@ The list of step size controllers is as follows. The most common cases are fixed } ``` - ??? abstract "Abtract base classes" All of the classes implement the following interface specified by [`diffrax.AbstractStepSizeController`][]. @@ -54,6 +44,7 @@ The list of step size controllers is as follows. The most common cases are fixed members: - rtol - atol + - norm --- @@ -71,7 +62,7 @@ The list of step size controllers is as follows. The most common cases are fixed members: - __init__ -::: diffrax.JumpStepWrapper +::: diffrax.ClipStepSizeController selection: members: - __init__ \ No newline at end of file diff --git a/test/test_adaptive_stepsize_controller.py b/test/test_adaptive_stepsize_controller.py index 233c056f..68508a2e 100644 --- a/test/test_adaptive_stepsize_controller.py +++ b/test/test_adaptive_stepsize_controller.py @@ -2,11 +2,13 @@ import diffrax import equinox as eqx +import equinox.internal as eqxi import jax import jax.numpy as jnp import jax.random as jr import jax.tree_util as jtu import pytest +from diffrax._step_size_controller.clip import _find_idx_with_hint from jaxtyping import Array from .helpers import tree_allclose @@ -23,7 +25,7 @@ def test_step_ts(backwards): dt0 = None y0 = 1.0 pid_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6) - stepsize_controller = diffrax.JumpStepWrapper(pid_controller, step_ts=[3, 4]) + stepsize_controller = diffrax.ClipStepSizeController(pid_controller, step_ts=[3, 4]) saveat = diffrax.SaveAt(steps=True) sol = diffrax.diffeqsolve( term, @@ -60,7 +62,7 @@ def vector_field(t, y, args): def run(**kwargs): pid_controller = diffrax.PIDController(rtol=1e-4, atol=1e-6) - stepsize_controller = diffrax.JumpStepWrapper(pid_controller, **kwargs) + stepsize_controller = diffrax.ClipStepSizeController(pid_controller, **kwargs) return diffrax.diffeqsolve( term, solver, @@ -75,7 +77,6 @@ def run(**kwargs): sol_no_jump_ts = run() sol_with_jump_ts = run(jump_ts=[7.5]) assert sol_no_jump_ts.stats["num_steps"] > sol_with_jump_ts.stats["num_steps"] - print(sol_no_jump_ts.stats["num_steps"], sol_with_jump_ts.stats["num_steps"]) assert sol_with_jump_ts.result == diffrax.RESULTS.successful sol = run(jump_ts=[7.5], step_ts=[7.5]) @@ -112,13 +113,14 @@ def diffusion_vf(t, y, args): def callback_fun(keep_step, t1): if not keep_step: - rejected_ts_list.append(t1) + rejected_ts_list.append(t1.item()) return None - stepsize_controller = diffrax.JumpStepWrapper( + store_rejected_steps = 10 + stepsize_controller = diffrax.ClipStepSizeController( pid_controller, step_ts=[3, 4], - rejected_step_buffer_len=10, + store_rejected_steps=store_rejected_steps, _callback_on_reject=callback_fun, ) saveat = diffrax.SaveAt(steps=True, controller_state=True) @@ -134,8 +136,6 @@ def callback_fun(keep_step, t1): ) assert sol.ts is not None - ts = sol.ts[sol.ts != jnp.inf] - ts = jnp.sort(ts) rejected_ts = jnp.array(rejected_ts_list) if backwards: rejected_ts = -rejected_ts @@ -143,6 +143,9 @@ def callback_fun(keep_step, t1): # there should be many rejected steps, otherwise something went wrong assert len(rejected_ts) > 10 # check if all rejected ts are in the array sol.ts + ts = sol.ts[sol.ts != jnp.inf] + if backwards: + ts = ts[::-1] for t in rejected_ts: i = jnp.searchsorted(ts, t) assert ts[i] == t @@ -151,16 +154,14 @@ def callback_fun(keep_step, t1): assert 4 in cast(Array, sol.ts) # Check that at the end of the run, the rejected stack is empty, - # i.e. rejected_index == rejected_step_buffer_len + # i.e. rejected_index == store_rejected_steps assert sol.controller_state is not None - assert ( - sol.controller_state.rejected_index - == stepsize_controller.rejected_step_buffer_len - ) + reject_index, _ = sol.controller_state.reject_info + assert reject_index == store_rejected_steps -@pytest.mark.parametrize("use_jump_step", [True, False]) -def test_backprop(use_jump_step): +@pytest.mark.parametrize("use_clip", [True, False]) +def test_backprop(use_clip): t0 = jnp.asarray(0, dtype=jnp.float64) t1 = jnp.asarray(1, dtype=jnp.float64) @@ -179,9 +180,9 @@ def run(ys, controller, state): term = diffrax.ODETerm(lambda t, y, args: -y) solver = diffrax.Tsit5() controller = diffrax.PIDController(rtol=1e-4, atol=1e-4) - if use_jump_step: - controller = diffrax.JumpStepWrapper( - controller, step_ts=[0.5], rejected_step_buffer_len=20 + if use_clip: + controller = diffrax.ClipStepSizeController( + controller, step_ts=[0.5], store_rejected_steps=20 ) _, state = controller.init(term, t0, t1, y0, 0.1, None, solver.func, 5) @@ -209,7 +210,9 @@ def run(t): rtol=1e-8, atol=1e-8, ) - stepsize_controller = diffrax.JumpStepWrapper(pid_controller, step_ts=t[None]) + stepsize_controller = diffrax.ClipStepSizeController( + pid_controller, step_ts=t[None] + ) def forcing(s): return jnp.where(s < t, 0, 1) @@ -238,21 +241,21 @@ def forcing(s): def test_pid_meta(): ts = jnp.array([3, 4], dtype=jnp.float64) pid1 = diffrax.PIDController(rtol=1e-4, atol=1e-6) - pid2 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts) - pid3 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts, jump_ts=ts) - assert not isinstance(pid1, diffrax.JumpStepWrapper) + pid2 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts) # pyright: ignore + pid3 = diffrax.PIDController(rtol=1e-4, atol=1e-6, step_ts=ts, jump_ts=ts) # pyright: ignore + assert not isinstance(pid1, diffrax.ClipStepSizeController) assert isinstance(pid1, diffrax.PIDController) - assert isinstance(pid2, diffrax.JumpStepWrapper) - assert isinstance(pid3, diffrax.JumpStepWrapper) + assert isinstance(pid2, diffrax.ClipStepSizeController) + assert isinstance(pid3, diffrax.ClipStepSizeController) assert all(pid2.step_ts == ts) assert all(pid3.step_ts == ts) assert all(pid3.jump_ts == ts) -def test_nested_jump_step_wrappers(): +def test_nested_clip_wrappers(): pid = diffrax.PIDController(rtol=0, atol=1.0) - wrap1 = diffrax.JumpStepWrapper(pid, jump_ts=[3.0, 13.0], step_ts=[23.0]) - wrap2 = diffrax.JumpStepWrapper(wrap1, step_ts=[2.0, 13.0], jump_ts=[23.0]) + wrap1 = diffrax.ClipStepSizeController(pid, jump_ts=[3.0, 13.0], step_ts=[23.0]) + wrap2 = diffrax.ClipStepSizeController(wrap1, step_ts=[2.0, 13.0], jump_ts=[23.0]) func = lambda terms, t, y, args: -y terms = diffrax.ODETerm(lambda t, y, args: -y) _, state = wrap2.init(terms, -1.0, 0.0, 0.0, 4.0, None, func, 5) @@ -261,31 +264,51 @@ def test_nested_jump_step_wrappers(): _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( 0.0, 1.0, 0.0, 0.0, None, 0.0, 5, state ) + assert next_t0 == 1 assert next_t1 == 2 + assert not made_jump _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state ) - assert jnp.isclose(next_t0, 2) + assert next_t0 == 2 + assert next_t1 == eqxi.prevbefore(jnp.asarray(3.0)) assert not made_jump # test 2 _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( 10.0, 11.0, 0.0, 0.0, None, 0.0, 5, state ) - assert next_t1 == 13 + assert next_t0 == 11 + assert next_t1 == eqxi.prevbefore(jnp.asarray(13.0)) + assert not made_jump _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state ) - assert jnp.isclose(next_t0, 13) + assert next_t0 == eqxi.nextafter(jnp.asarray(13.0)) + assert next_t1 == eqxi.prevbefore(jnp.asarray(23.0)) assert made_jump # test 3 _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( 20.0, 21.0, 0.0, 0.0, None, 0.0, 5, state ) - assert next_t1 == 23 + assert next_t0 == 21 + assert next_t1 == eqxi.prevbefore(jnp.asarray(23.0)) + assert not made_jump _, next_t0, next_t1, made_jump, state, _ = wrap2.adapt_step_size( next_t0, next_t1, 0.0, 0.0, None, 0.0, 5, state ) - assert jnp.isclose(next_t0, 23) + assert next_t0 == eqxi.nextafter(jnp.asarray(23.0)) + assert next_t1 > next_t0 assert made_jump + + +def test_find_idx_with_hint(): + ts = jnp.arange(5.0) + for hint in (0, 2, 3, 5): + idx = _find_idx_with_hint(2.5, ts, hint) + assert idx == 3 + idx = _find_idx_with_hint(2, ts, hint) + assert idx == 3 # not 2; we want the first value *strictly* greater. + idx = _find_idx_with_hint(1.9, ts, hint) + assert idx == 2 diff --git a/test/test_progress_meter.py b/test/test_progress_meter.py index 6827db3a..a9613c9e 100644 --- a/test/test_progress_meter.py +++ b/test/test_progress_meter.py @@ -40,7 +40,7 @@ def solve(t0): err = captured.err.strip() assert re.match("0.00%|[ ]+|", err.split("\r", 1)[0]) assert re.match("100.00%|█+|", err.rsplit("\r", 1)[1]) - assert captured.err.count("\r") - num_lines in [0, 1] + assert captured.err.count("\r") == num_lines assert captured.err.count("\n") == 1 From 7865a1609ea5d13a8fc87213ff2866e23d24b762 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sun, 9 Feb 2025 12:47:51 -0800 Subject: [PATCH 47/50] update benchmark --- benchmarks/stateful_paths.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 9551ce29..716fe28e 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -262,18 +262,18 @@ def step(y, dW): """ Results on Mac M1 CPU: -VBT: 0.184882 -Old UBP: 0.016347 -New UBP: 0.013731 -New UBP + Precompute: 0.002430 -Pure Jax: 0.002799 +VBT: 0.204524 +Old UBP: 0.017464 +New UBP: 0.018535 +New UBP + Precompute: 0.002440 +Pure Jax: 0.002908 -(these are out of date) Results on A100 GPU: -VBT: 3.881952 -Old UBP: 0.337173 -New UBP: 0.364158 -New UBP + Precompute: 0.325521 +VBT: 2.275057 +Old UBP: 0.092015 +New UBP: 0.125904 +New UBP + Precompute: 0.108587 +Pure Jax: 0.261937 For small ndt (e.g. 100) the pure jax is faster, but the diffrax overhead becomes less important as the time increases. From 20e700d2eff35ce92d0922d9d0c96a815c6a2e51 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sun, 9 Feb 2025 12:56:12 -0800 Subject: [PATCH 48/50] update jit results --- benchmarks/stateful_paths.py | 7 +++---- diffrax/_brownian/path.py | 1 + 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 716fe28e..92bca53f 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -74,7 +74,6 @@ def __call__( ): return self.evaluate(t0, t1, left, use_levy), brownian_state - @eqx.filter_jit def evaluate( self, t0, @@ -270,9 +269,9 @@ def step(y, dW): Results on A100 GPU: VBT: 2.275057 -Old UBP: 0.092015 -New UBP: 0.125904 -New UBP + Precompute: 0.108587 +Old UBP: 0.112461 +New UBP: 0.126370 +New UBP + Precompute: 0.111837 Pure Jax: 0.261937 For small ndt (e.g. 100) the pure jax is faster, but the diffrax overhead diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index 48155733..a2903321 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -153,6 +153,7 @@ def init( key = self.key return key, noise, counter + @eqx.filter_jit def __call__( self, t0: RealScalarLike, From e4cd2a367feaf7890712e954a82d9635495ec8d1 Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Sun, 9 Feb 2025 12:56:46 -0800 Subject: [PATCH 49/50] return jit --- benchmarks/stateful_paths.py | 1 + 1 file changed, 1 insertion(+) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 92bca53f..91172770 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -74,6 +74,7 @@ def __call__( ): return self.evaluate(t0, t1, left, use_levy), brownian_state + @eqx.filter_jit def evaluate( self, t0, From f197572250a47e222a20cb2374928ff93e1cd32f Mon Sep 17 00:00:00 2001 From: Owen Lockwood <42878312+lockwo@users.noreply.github.com> Date: Tue, 3 Jun 2025 15:28:29 -0400 Subject: [PATCH 50/50] format --- benchmarks/stateful_paths.py | 12 +++++------- diffrax/_brownian/path.py | 16 ++++++++-------- diffrax/_path.py | 2 +- diffrax/_solution.py | 2 +- diffrax/_term.py | 5 ++--- 5 files changed, 17 insertions(+), 20 deletions(-) diff --git a/benchmarks/stateful_paths.py b/benchmarks/stateful_paths.py index 91172770..22098616 100644 --- a/benchmarks/stateful_paths.py +++ b/benchmarks/stateful_paths.py @@ -1,5 +1,5 @@ import math -from typing import cast, Optional, Union +from typing import cast import diffrax import equinox as eqx @@ -16,14 +16,12 @@ class OldBrownianPath(diffrax.AbstractBrownianPath): shape: PyTree[jax.ShapeDtypeStruct] = eqx.field(static=True) levy_area: type[ - Union[ - diffrax.BrownianIncrement, - diffrax.SpaceTimeLevyArea, - diffrax.SpaceTimeTimeLevyArea, - ] + diffrax.BrownianIncrement + | diffrax.SpaceTimeLevyArea + | diffrax.SpaceTimeTimeLevyArea ] = eqx.field(static=True) key: PRNGKeyArray - precompute: Optional[int] = eqx.field(static=True) + precompute: int | None = eqx.field(static=True) def __init__( self, diff --git a/diffrax/_brownian/path.py b/diffrax/_brownian/path.py index d923a779..0c6bdcb9 100644 --- a/diffrax/_brownian/path.py +++ b/diffrax/_brownian/path.py @@ -1,5 +1,5 @@ import math -from typing import cast, Optional, TypeAlias +from typing import cast, TypeAlias import equinox as eqx import equinox.internal as eqxi @@ -31,7 +31,9 @@ _Control = PyTree[Array] | AbstractBrownianIncrement -_BrownianState: TypeAlias = tuple[None, PyTree[Array], IntScalarLike] | tuple[PRNGKeyArray, None, None] +_BrownianState: TypeAlias = ( + tuple[None, PyTree[Array], IntScalarLike] | tuple[PRNGKeyArray, None, None] +) class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): @@ -76,7 +78,7 @@ class DirectBrownianPath(AbstractBrownianPath[_Control, _BrownianState]): levy_area: type[ BrownianIncrement | SpaceTimeLevyArea | SpaceTimeTimeLevyArea ] = eqx.field(static=True) - precompute: Optional[int] = eqx.field(static=True) + precompute: int | None = eqx.field(static=True) def __init__( self, @@ -85,7 +87,7 @@ def __init__( levy_area: type[ BrownianIncrement | SpaceTimeLevyArea | SpaceTimeTimeLevyArea ] = BrownianIncrement, - precompute: Optional[int] = None, + precompute: int | None = None, ): """**Arguments:** @@ -167,7 +169,7 @@ def __call__( self, t0: RealScalarLike, brownian_state: _BrownianState, - t1: Optional[RealScalarLike] = None, + t1: RealScalarLike | None = None, left: bool = True, use_levy: bool = False, ) -> tuple[_Control, _BrownianState]: @@ -261,9 +263,7 @@ def _evaluate_leaf_precomputed( t0: RealScalarLike, t1: RealScalarLike, shape: jax.ShapeDtypeStruct, - levy_area: type[ - BrownianIncrement | SpaceTimeLevyArea | SpaceTimeTimeLevyArea - ], + levy_area: type[BrownianIncrement | SpaceTimeLevyArea | SpaceTimeTimeLevyArea], use_levy: bool, noises: Float[Array, "..."], ): diff --git a/diffrax/_path.py b/diffrax/_path.py index fb661842..4d543c24 100644 --- a/diffrax/_path.py +++ b/diffrax/_path.py @@ -70,7 +70,7 @@ def __call__( self, t0: RealScalarLike, path_state: _PathState, - t1: Optional[RealScalarLike] = None, + t1: RealScalarLike | None = None, left: bool = True, ) -> tuple[_Control, _PathState]: r"""Evaluate the path at any point in the interval $[t_0, t_1]$. diff --git a/diffrax/_solution.py b/diffrax/_solution.py index 4b830efd..e80c0c34 100644 --- a/diffrax/_solution.py +++ b/diffrax/_solution.py @@ -146,7 +146,7 @@ def __call__( self, t0: RealScalarLike, path_state: None, - t1: Optional[RealScalarLike] = None, + t1: RealScalarLike | None = None, left: bool = True, ) -> tuple[PyTree[Shaped[Array, "?*shape"], " Y"], None]: return self.evaluate(t0, t1, left), path_state diff --git a/diffrax/_term.py b/diffrax/_term.py index 898bac6b..1ac6b412 100644 --- a/diffrax/_term.py +++ b/diffrax/_term.py @@ -461,9 +461,8 @@ def __init__( # the user would have to provide a custom init path state which sounds # not ideal, probably just be easier to have them make an abstract path? # Callable[[RealScalarLike, PyTree, RealScalarLike], tuple[_Control, PyTree]], - control: - AbstractPath[_Control, _PathState] | - Callable[[RealScalarLike, RealScalarLike], _Control] + control: AbstractPath[_Control, _PathState] + | Callable[[RealScalarLike, RealScalarLike], _Control], ): self.vector_field = vector_field if isinstance(control, AbstractPath):