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embedding_test_utils.py
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from typing import Any, Optional
import pytest
import torch
from flair.data import Sentence
from flair.embeddings import Embeddings
from flair.embeddings.base import load_embeddings
class BaseEmbeddingsTest:
embedding_cls: type[Embeddings[Sentence]]
is_token_embedding: bool
is_document_embedding: bool
default_args: dict[str, Any]
valid_args: list[dict[str, Any]] = []
invalid_args: list[dict[str, Any]] = []
invalid_names: list[str] = []
name_field: Optional[str] = None
weired_texts: list[str] = [
"Hybrid mesons , qq ̄ states with an admixture",
"typical proportionalities of \u223C 1nmV \u2212 1 [ 3,4 ] .",
"🤟 🤟 🤟 hüllo",
"🤟hallo 🤟 🤟 🤟 🤟",
"🤟",
"\uF8F9",
]
def create_embedding_from_name(self, name: str):
"""Overwrite this method if it is more complex to load an embedding by name."""
assert self.name_field is not None
kwargs = dict(self.default_args)
kwargs.pop(self.name_field)
return self.embedding_cls(name, **kwargs) # type: ignore[call-arg]
def create_embedding_with_args(self, args: dict[str, Any]):
kwargs = dict(self.default_args)
for k, v in args.items():
kwargs[k] = v
return self.embedding_cls(**kwargs)
@pytest.mark.parametrize("text", weired_texts)
def test_embedding_works_with_weird_text(self, text):
embeddings = self.create_embedding_with_args(self.default_args)
embedding_names = embeddings.get_names()
sentence = Sentence(text)
embeddings.embed(sentence)
if self.is_token_embedding:
for token in sentence:
assert len(token.get_embedding(embedding_names)) == embeddings.embedding_length
if self.is_document_embedding:
assert len(sentence.get_embedding(embedding_names)) == embeddings.embedding_length
@pytest.mark.parametrize("args", valid_args)
def test_embedding_also_sets_trailing_whitespaces(self, args):
if not self.is_token_embedding:
pytest.skip("The test is only valid for token embeddings")
embeddings = self.create_embedding_with_args(args)
sentence: Sentence = Sentence(["hello", " ", "hm", " "])
embeddings.embed(sentence)
names = embeddings.get_names()
for token in sentence:
assert len(token.get_embedding(names)) == embeddings.embedding_length
@pytest.mark.parametrize("args", valid_args)
def test_generic_sentence(self, args):
embeddings = self.create_embedding_with_args(args)
sentence: Sentence = Sentence("I love Berlin")
embeddings.embed(sentence)
names = embeddings.get_names()
if self.is_token_embedding:
for token in sentence:
assert len(token.get_embedding(names)) == embeddings.embedding_length
if self.is_document_embedding:
assert len(sentence.get_embedding(names)) == embeddings.embedding_length
@pytest.mark.parametrize("name", invalid_names)
def test_load_non_existing_embedding(self, name):
with pytest.raises(ValueError):
self.create_embedding_from_name(name)
def test_keep_batch_order(self):
embeddings = self.create_embedding_with_args(self.default_args)
embedding_names = embeddings.get_names()
sentences_1 = [Sentence("First sentence"), Sentence("This is second sentence")]
sentences_2 = [Sentence("This is second sentence"), Sentence("First sentence")]
embeddings.embed(sentences_1)
embeddings.embed(sentences_2)
assert sentences_1[0].to_original_text() == "First sentence"
assert sentences_1[1].to_original_text() == "This is second sentence"
if self.is_document_embedding:
assert (
torch.norm(
sentences_1[0].get_embedding(embedding_names) - sentences_2[1].get_embedding(embedding_names)
)
== 0.0
)
assert (
torch.norm(
sentences_1[1].get_embedding(embedding_names) - sentences_2[0].get_embedding(embedding_names)
)
== 0.0
)
if self.is_token_embedding:
for i in range(len(sentences_1[0])):
assert (
torch.norm(
sentences_1[0][i].get_embedding(embedding_names)
- sentences_2[1][i].get_embedding(embedding_names)
)
== 0.0
)
for i in range(len(sentences_1[1])):
assert (
torch.norm(
sentences_1[1][i].get_embedding(embedding_names)
- sentences_2[0][i].get_embedding(embedding_names)
)
== 0.0
)
del embeddings
@pytest.mark.parametrize("args", valid_args)
def test_embeddings_stay_the_same_after_saving_and_loading(self, args):
embeddings = self.create_embedding_with_args(args)
sentence_old: Sentence = Sentence("I love Berlin")
embeddings.embed(sentence_old)
names_old = embeddings.get_names()
embedding_length_old = embeddings.embedding_length
save_data = embeddings.save_embeddings(use_state_dict=True)
del embeddings
new_embeddings = load_embeddings(save_data)
sentence_new: Sentence = Sentence("I love Berlin")
new_embeddings.embed(sentence_new)
names_new = new_embeddings.get_names()
embedding_length_new = new_embeddings.embedding_length
assert names_old == names_new
assert embedding_length_old == embedding_length_new
if self.is_token_embedding:
for token_old, token_new in zip(sentence_old, sentence_new):
assert (token_old.get_embedding(names_old) == token_new.get_embedding(names_new)).all()
if self.is_document_embedding:
assert (sentence_old.get_embedding(names_old) == sentence_new.get_embedding(names_new)).all()
def test_default_embeddings_stay_the_same_after_saving_and_loading(self):
embeddings = self.create_embedding_with_args(self.default_args)
sentence_old: Sentence = Sentence("I love Berlin")
embeddings.embed(sentence_old)
names_old = embeddings.get_names()
embedding_length_old = embeddings.embedding_length
save_data = embeddings.save_embeddings(use_state_dict=True)
new_embeddings = load_embeddings(save_data)
sentence_new: Sentence = Sentence("I love Berlin")
new_embeddings.embed(sentence_new)
names_new = new_embeddings.get_names()
embedding_length_new = new_embeddings.embedding_length
assert not new_embeddings.training
assert names_old == names_new
assert embedding_length_old == embedding_length_new
if self.is_token_embedding:
for token_old, token_new in zip(sentence_old, sentence_new):
assert (token_old.get_embedding(names_old) == token_new.get_embedding(names_new)).all()
if self.is_document_embedding:
assert (sentence_old.get_embedding(names_old) == sentence_new.get_embedding(names_new)).all()
def test_embeddings_load_in_eval_mode(self):
embeddings = self.create_embedding_with_args(self.default_args)
assert not embeddings.training