format_dataset.py

# Copyright 2024 PKU-Alignment Team and LlamaFactory team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================


import io
import os
import random
from typing import Any

import librosa
import requests
import torch
import torchaudio
from PIL import Image
from torchvision.io import read_video

from align_anything.utils.template_registry import register_template


ALLOWED_ATTRIBUTES = ['split_token']
DEFAULT_SPLIT_TOKEN = 'ASSISTANT:'


def load_image(image_path: str):
    try:
        if image_path.startswith('http'):
            image = Image.open(requests.get(image_path, stream=True).raw)
        else:
            image = Image.open(image_path)
        return image
    except Exception:
        raise Exception(f'Error occurred when dealing with {image_path}')


def load_image_from_base64(base64_string):
    image_stream = io.BytesIO(base64_string)
    image = Image.open(image_stream)

    return image


def insert_img_token(text, image):
    # do the same for worse
    if isinstance(image, str):
        decoded_images = [load_image(image)]
        num_images = 1
    elif isinstance(image, list):
        decoded_images = [load_image(img) for img in image]
        num_images = len(image)
    elif isinstance(image, Image.Image):
        decoded_images = [image]
        num_images = 1
    else:
        num_images = 0
        decoded_images = None

    processed_text = f"{'<image>' * num_images}{text}"
    return processed_text, decoded_images


def safe_add(list1, list2):
    if list1 is None and list2 is None:
        return []
    elif list1 is None:
        return list2.copy()
    elif list2 is None:
        return list1.copy()
    else:
        return list1 + list2


AUDIO_QUESTIONS = [
    "Summarize the audio's contents.<audio>",
    "Give an overview of what's in the audio.<audio>",
    "<audio>Detail the audio's subject matter.",
    'Explain the material covered in the audio.<audio>',
    'Outline the information in the audio.<audio>',
    "Break down the audio's key points.<audio>",
    'Describe the topics discussed in the audio.<audio>',
    '<audio>Highlight the main ideas in the audio.',
    '<audio>Recap the content of the audio.',
    "<audio>Provide a synopsis of the audio's content.",
    '<audio>Please recount what you listened to.',
    'Share the details of what reached your ears.<audio>',
    'Let me know the sounds you picked up.<audio>',
    "Could you describe the information you've heard?<audio>",
    'What did you catch from the conversation?<audio>',
    "<audio>Please inform me of the auditory information you've gathered.",
    "<audio>Relay the things you've heard, if you would.",
    '<audio>What have your ears caught wind of?',
    "I'm curious to know the reports you've heard.<audio>",
    "Let me in on the auditory details you're aware of.<audio>",
]

SPEECH_QUESTIONS = [
    '<audio>Could you please let me know the content of this speech?',
    '<audio>Can you tell me what this speech is about?',
    '<audio>Would you mind explaining the content of this speech?',
    '<audio>Please describe the content of this speech.',
    "I'd like to know the content of this speech.<audio>",
    'Can you inform me about the content of this speech?<audio>',
    'Could you summarize the content of this speech for me?<audio>',
    'What is the content of this speech, please?<audio>',
    '<audio>Could you provide details about the content of this speech?',
    "Please give me an overview of this speech's content.<audio>",
]


class BaseFormatter:
    def check_validation(self, raw_sample: dict[str, Any]) -> bool:
        return True

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        """Format the sample for supervised training.

        Args:
            raw_sample (dict[str, Any]): The raw sample from the dataset.

        Example:
            >>> self.format_supervised_sample({'instruction': 'Write a story', 'output': 'Once upon a time, there was a cat.'})
            ([{'role': 'user', 'content': 'Write a story'}, {'role': 'assistant', 'content': 'Once upon a time, there was a cat.'}], '')

        Returns:
            tuple[list[dict[str, Any]], str]: The formatted sample.
        """
        return [], ''

    def format_preference_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], str]:
        """Format the sample for preference training.

        Args:
            raw_sample (dict[str, Any]): The raw sample from the dataset.

        Returns:
            tuple[list[dict[str, Any]], list[dict[str, Any]], str]: The formatted sample.
        """
        return [], [], ''

    def format_prompt_only_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        """Format the sample for prompt-only training, e.g., PPO.

        Args:
            raw_sample (dict[str, Any]): The raw sample from the dataset.

        Returns:
            tuple[list[dict[str, Any]], str]: The formatted sample.
        """
        return [], ''

    def format_unmatched_supervised_sample(
        self, raw_sample_for_prompt: dict[str, Any], raw_sample_for_response: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        """Format the sample for unmatched supervised training, e.g., KTO.

        Args:
            raw_sample_for_prompt (dict[str, Any]): The raw sample for prompt from the dataset.
            raw_sample_for_response (dict[str, Any]): The raw sample for response from the dataset.

        Returns:
            tuple[list[dict[str, Any]], str]: The formatted sample.
        """
        return [], ''


@register_template('Alpaca')
class Alpaca(BaseFormatter):

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        prompt = ' '.join((raw_sample['instruction'], raw_sample['input']))
        response = raw_sample['output']
        return [
            {'role': 'user', 'content': prompt},
            {'role': 'assistant', 'content': response},
        ], {}


@register_template('PKUSafeRLHF')
class PKUSafeRLHF(BaseFormatter):
    system_prompt: str = ''

    def format_preference_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], str]:
        metrics = raw_sample['better_response_id']
        better_response = raw_sample[f'response_{int(metrics)}']
        worse_response = raw_sample[f'response_{1-int(metrics)}']
        prompt = raw_sample['prompt']

        better_conversation = [
            {'role': 'user', 'content': prompt},
            {'role': 'assistant', 'content': better_response},
        ]

        worse_conversation = [
            {'role': 'user', 'content': prompt},
            {'role': 'assistant', 'content': worse_response},
        ]

        return better_conversation, worse_conversation, {}

    def format_prompt_only_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        prompt = raw_sample['prompt']
        return [
            {'role': 'user', 'content': prompt},
        ], {}

    def format_unmatched_supervised_sample(
        self, raw_sample_for_prompt: dict[str, Any], raw_sample_for_response: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], str]:
        prompt = raw_sample_for_prompt['prompt']
        response = raw_sample_for_response['response_1']
        return [
            {'role': 'user', 'content': prompt},
            {'role': 'assistant', 'content': response},
        ], {}


@register_template('Aligner')
class Aligner(BaseFormatter):
    system_prompt: str = ''

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:

        prompt = '##QUESTION: ' + raw_sample['question'] + ' ##ANSWER: ' + raw_sample['answer']
        text = '##CORRECTION: ' + raw_sample['correction']

        return [
            {'role': 'system', 'content': self.system_prompt},
            {'role': 'user', 'content': prompt},
            {'role': 'assistant', 'content': text},
        ], {}


@register_template('AA_T2T')
class AA_T2T(BaseFormatter):
    system_prompt: str = ''

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = raw_sample['prompt']
        answer = raw_sample['response']

        return [
            {'role': 'user', 'content': [{'type': 'text', 'text': prompt}]},
            {'role': 'assistant', 'content': [{'type': 'text', 'text': answer}]},
        ], {}


@register_template('TLDR')
class TLDR(BaseFormatter):
    system_prompt: str = ''
    summary_prompt: list[str] = [
        'Please summarize the following text: ',
        'Please give a concise summary of the following text: ',
        'Please provide a brief summary of the following text: ',
        'Please summarize the following text in a few sentences: ',
        'I need a summary of the following text: ',
        'Could you please provide a summary of the following text? ',
        "I'm looking for a summary of the following text: ",
        'Please give me a summary of the following text: ',
        'I need a summary of the following text: ',
        'Could you summarize the following text for me? ',
        "I'm looking for a summary of the following text: ",
        'Please provide a summary of the following text: ',
        "Here's the text I need summarized: ",
        'Here is the text I need summarized: ',
    ]

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = random.choice(self.summary_prompt) + raw_sample['content']
        answer = raw_sample['summary']

        return [
            {'role': 'user', 'content': [{'type': 'text', 'text': prompt}]},
            {'role': 'assistant', 'content': [{'type': 'text', 'text': answer}]},
        ], {}


@register_template('GSM8K')
class GSM8K(BaseFormatter):
    system_prompt: str = ''

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = raw_sample['question']
        answer = raw_sample['answer']

        return [
            {'role': 'user', 'content': [{'type': 'text', 'text': prompt}]},
            {'role': 'assistant', 'content': [{'type': 'text', 'text': answer}]},
        ], {}


@register_template('AA_TI2T')
class AA_TI2T(BaseFormatter):
    system_prompt: str = ''

    def format_preference_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], dict[str, Any]]:
        better_id = int(raw_sample['overall_response'])
        worse_id = 2 if better_id == 1 else 1

        if better_id not in [1, 2] or worse_id not in [1, 2]:
            return [], [], {}

        raw_better_response = raw_sample[f'response_{better_id}']
        raw_worse_response = raw_sample[f'response_{worse_id}']
        prompt = raw_sample['question']
        image = raw_sample['image'].convert('RGBA')
        better_conversation = [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': raw_better_response}]},
        ]
        worse_conversation = [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': raw_worse_response}]},
        ]

        meta_info = {
            'image': image,
            'better_response': raw_better_response,
            'worse_response': raw_worse_response,
        }

        return better_conversation, worse_conversation, meta_info

    def format_prompt_only_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = raw_sample['question']
        image = raw_sample['image'].convert('RGBA')

        return [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
        ], {'image': image}

    def format_supervised_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = raw_sample['prompt']
        answer = raw_sample['response']
        image = raw_sample['image'].convert('RGBA')

        return [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': answer}]},
        ], {'image': image}

    def check_validation(self, raw_sample: dict[str, Any]) -> bool:
        better_id = int(raw_sample['overall_response'])
        worse_id = 2 if better_id == 1 else 1
        return better_id in [1, 2] and worse_id in [1, 2]


@register_template('AA_TA2T')
class AA_TA2T(BaseFormatter):
    system_prompt: str = 'You are a helpful assistant.'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        better_id = int(raw_sample['overall_response'])
        worse_id = 2 if better_id == 1 else 1
        better_response = raw_sample[f'response_{better_id}']
        worse_response = raw_sample[f'response_{worse_id}']
        prompt = raw_sample['prompt']
        audio_path = raw_sample['audio_path']

        better_conversation = [
            {'role': 'system', 'content': [{'type': 'text', 'text': self.system_prompt}]},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio_path},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': better_response}]},
        ]

        worse_conversation = [
            {'role': 'system', 'content': [{'type': 'text', 'text': self.system_prompt}]},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio_path},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': worse_response}]},
        ]

        meta_info = {
            'audio_path': audio_path,
            'better_response': better_response,
            'worse_response': worse_response,
        }

        return better_conversation, worse_conversation, meta_info

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['prompt']
        audio_path = raw_sample['audio_path']

        conversation = [
            {'role': 'system', 'content': [{'type': 'text', 'text': self.system_prompt}]},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio_path},
                    {'type': 'text', 'text': prompt},
                ],
            },
        ]

        return conversation, {'audio_path': audio_path}


@register_template('AA_TA2T_LLF')
class AA_TA2T_LLF(BaseFormatter):
    system_prompt: str = 'You are a helpful assistant.'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        better_response = raw_sample['refinement']
        worse_response = raw_sample['response']
        prompt = raw_sample['prompt']
        audio = raw_sample['audio']

        better_conversation = [
            {'role': 'system', 'content': self.system_prompt},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': better_response},
        ]

        worse_conversation = [
            {'role': 'system', 'content': self.system_prompt},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': worse_response},
        ]

        meta_info = {
            'audio_path': audio,
            'better_response': better_response,
            'worse_response': worse_response,
        }

        return better_conversation, worse_conversation, meta_info

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['prompt']
        audio = raw_sample['audio']

        conversation = [
            {'role': 'system', 'content': [{'type': 'text', 'text': self.system_prompt}]},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio},
                    {'type': 'text', 'text': prompt},
                ],
            },
        ]

        return conversation, {'audio_path': audio}


@register_template('AA_TI2T_LLF')
class AA_TI2T_LLF(BaseFormatter):
    system_prompt: str = ''

    def format_preference_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], dict[str, Any]]:
        raw_better_response = raw_sample['refinement']
        raw_worse_response = raw_sample['response']
        prompt = raw_sample['prompt']
        image = load_image_from_base64(raw_sample['image']).convert('RGBA')
        better_conversation = [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': raw_better_response}]},
        ]
        worse_conversation = [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': [{'type': 'text', 'text': raw_worse_response}]},
        ]

        meta_info = {
            'image': image,
            'better_response': raw_better_response,
            'worse_response': raw_worse_response,
        }

        return better_conversation, worse_conversation, meta_info

    def format_prompt_only_sample(
        self, raw_sample: dict[str, Any]
    ) -> tuple[list[dict[str, Any]], dict[str, Any]]:
        prompt = raw_sample['prompt']
        image = load_image_from_base64(raw_sample['image']).convert('RGBA')

        return [
            {
                'role': 'user',
                'content': [
                    {'type': 'image'},
                    {'type': 'text', 'text': prompt},
                ],
            },
        ], {'image': image}


@register_template('DiffusionDB')
class DiffusionDB:
    system_prompt: str = ''

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> tuple[str, Any]:
        multi_modal_info = {'image': raw_sample['image'].convert('RGB')}
        return raw_sample['prompt'], multi_modal_info


@register_template('DiffusionDBCanny')
class DiffusionDBCanny:
    system_prompt: str = ''

    def format_diffusion_supervised_sample(self, raw_sample: dict[str, Any]) -> tuple[str, Any]:
        multi_modal_info = {'image': raw_sample['image'].convert('RGB')}
        return raw_sample['text'], multi_modal_info


@register_template('Pickapic')
class Pickapic:

    def format_diffusion_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['caption']
        better_id = int(raw_sample['label_1'])
        worse_id = int(raw_sample['label_0'])

        raw_better_image = raw_sample[f'jpg_{better_id}']
        raw_worse_image = raw_sample[f'jpg_{worse_id}']

        better_image = Image.open(io.BytesIO(raw_better_image)).convert('RGB')
        worse_image = Image.open(io.BytesIO(raw_worse_image)).convert('RGB')

        multi_modal_info = {
            'better_image': better_image,
            'worse_image': worse_image,
        }

        return prompt, multi_modal_info

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        better_id = float(raw_sample['label_0'])
        if better_id == 0.5:
            return True
        else:
            return False


@register_template('WavCaps')
class WavCaps:

    def format_diffusion_supervised_sample(self, raw_sample: dict[str, Any]) -> tuple[str, Any]:
        caption = raw_sample['answer']
        audio = raw_sample['context']['array']
        sampling_rate = raw_sample['context']['sampling_rate']
        multi_modal_info = {
            'audio': audio,
            'sampling_rate': sampling_rate,
        }
        return caption, multi_modal_info


@register_template('AA_T2A')
class AA_T2A(BaseFormatter):

    def format_diffusion_preference_sample(self, raw_sample: dict[str, Any]) -> tuple[str, Any]:
        better_id = int(raw_sample['overall_audio'])
        worse_id = 2 if better_id == 1 else 1
        better_audio = raw_sample[f'response_{better_id}']
        worse_audio = raw_sample[f'response_{worse_id}']
        prompt = raw_sample['prompt']

        multi_modal_info = {
            'better_audio': better_audio,
            'worse_audio': worse_audio,
        }
        return prompt, multi_modal_info

    def check_validation(self, raw_sample: dict[str, Any]) -> bool:
        better_id = int(raw_sample['overall_audio'])
        worse_id = 2 if better_id == 1 else 1
        return better_id in [1, 2] and worse_id in [1, 2]

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        better_id = int(raw_sample['overall_audio'])
        worse_id = 2 if better_id == 1 else 1
        return better_id == worse_id


@register_template('ti2ti_preference')
class TI2TI_PREFERENCE:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        input_img = raw_sample['input_image']

        better_text = raw_sample['better_text']
        better_img = raw_sample['better_img']

        worse_text = raw_sample['worse_text']
        worse_img = raw_sample['worse_img']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        better_text_processed, better_images = insert_img_token(better_text, better_img)

        worse_text_processed, worse_images = insert_img_token(worse_text, worse_img)

        better_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=better_text_processed)}'
        )

        better_images_full = safe_add(input_images, better_images)

        worse_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=worse_text_processed)}'
        )

        worse_images_full = safe_add(input_images, worse_images)

        return {
            'better_text': better_text_full,
            'worse_text': worse_text_full,
            'better_images': better_images_full,
            'worse_images': worse_images_full,
        }

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        input_img = raw_sample['input_image']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        return {
            'text': input_text_processed,
            'image': input_images,
        }


@register_template('Chameleon_preference')
class CHAMELEON_PREFERENCE:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        input_img = raw_sample['input_image']

        better_text = raw_sample['better_text']
        better_img = raw_sample['better_img']

        worse_text = raw_sample['worse_text']
        worse_img = raw_sample['worse_img']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        better_text_processed, better_images = insert_img_token(better_text, better_img)

        worse_text_processed, worse_images = insert_img_token(worse_text, worse_img)

        better_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=better_text_processed)}'
        )

        better_images_full = safe_add(input_images, better_images)

        worse_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=worse_text_processed)}'
        )

        worse_images_full = safe_add(input_images, worse_images)

        return {
            'better_text': better_text_full,
            'worse_text': worse_text_full,
            'better_images': better_images_full,
            'worse_images': worse_images_full,
        }

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        input_img = raw_sample['input_image']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        return {
            'text': input_text_processed,
            'image': input_images,
        }


@register_template('Any2Any')
class ANY2ANY:

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        output_dict = raw_sample.copy()
        if 'input_image' in raw_sample and raw_sample['input_image'] is not None:
            output_dict['input_image'] = load_image(raw_sample['input_image'])
        if 'output_image' in raw_sample and raw_sample['output_image'] is not None:
            output_dict['output_image'] = load_image(raw_sample['output_image'])
        print(f'Get output dict: {output_dict}')
        return output_dict


@register_template('AA_textfeedback')
class AA_TF:
    system_prompt: str = 'BEGINNING OF CONVERSATION: '
    user_prompt: str = (
        'USER: Judge the following two response of the same question and give a preference: \n ##Question: {input} \n ##Response 1: {response_1} \n ##Response 2: {response_2}'
    )
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_supervised_sample(
        self, raw_sample: dict[str, Any], path: str | None = None
    ) -> dict[str, Any]:
        input_text = raw_sample['question']
        input_img = raw_sample['image_url']

        output_text_1 = raw_sample['response_1']
        output_img_1 = raw_sample['output_image_url_1']
        output_text_2 = raw_sample['response_2']
        output_img_2 = raw_sample['output_image_url_2']

        feedback = raw_sample['feedback']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        output_text_1_processed, output_images_1 = insert_img_token(output_text_1, output_img_1)
        output_text_2_processed, output_images_2 = insert_img_token(output_text_2, output_img_2)

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed, response_1=output_text_1_processed, response_2=output_text_2_processed)}'
            f'{self.assistant_prompt.format(output=feedback)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed, response_1=output_text_1_processed, response_2=output_text_2_processed)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        input_images = safe_add(safe_add(input_images, output_images_1), output_images_2)

        return {'text': text, 'prompt': prompt, 'input_image': input_images, 'image': input_images}


@register_template('spavl_ti2ti')
class TI2TI_SPAVL:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['question']
        input_img = raw_sample['image']

        better_text = raw_sample['chosen']
        better_img = None

        worse_text = raw_sample['rejected']
        worse_img = None

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        better_text_processed, better_images = insert_img_token(better_text, better_img)

        worse_text_processed, worse_images = insert_img_token(worse_text, worse_img)

        better_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=better_text_processed)}'
        )

        better_images_full = safe_add(input_images, better_images)

        worse_text_full = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text_processed)}'
            f'{self.assistant_prompt.format(output=worse_text_processed)}'
        )

        worse_images_full = safe_add(input_images, worse_images)

        return {
            'better_text': better_text_full,
            'worse_text': worse_text_full,
            'better_images': better_images_full,
            'worse_images': worse_images_full,
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return torch.equal(raw_sample['better_input_ids'], raw_sample['worse_input_ids'])

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        input_img = raw_sample['input_image']

        input_text_processed, input_images = insert_img_token(input_text, input_img)

        return {
            'text': input_text_processed,
            'image': input_images,
        }


@register_template('PICKAPIC_TI2TI')
class Pickapic_TI2TI(TI2TI_PREFERENCE):
    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['caption']
        better_id = int(raw_sample['label_1'])
        worse_id = int(raw_sample['label_0'])

        raw_better_image = raw_sample[f'jpg_{better_id}']
        raw_worse_image = raw_sample[f'jpg_{worse_id}']
        better_image = Image.open(io.BytesIO(raw_better_image)).convert('RGB')
        worse_image = Image.open(io.BytesIO(raw_worse_image)).convert('RGB')
        raw_sample_upd = {
            'input_text': prompt,
            'input_image': [],
            'better_text': '',
            'better_img': better_image,
            'worse_text': '',
            'worse_img': worse_image,
        }
        return super().format_example(raw_sample_upd)


@register_template('GQA')
class GQA:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n<image>{input}'
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        question = raw_sample['question']
        answer = raw_sample['answer']

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=answer)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        image_file = os.path.join(path, raw_sample['image_path'])
        return {
            'text': text,
            'prompt': prompt,
            'image': Image.open(image_file),
        }


@register_template('OK-VQA')
class OKVQA:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n<image>{input}'
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        question = raw_sample['question']
        answer = max(set(raw_sample['answers']), key=raw_sample['answers'].count)

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=answer)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'image': raw_sample['image'],
        }


@register_template('A-OKVQA')
class AOKVQA:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n<image>{input} give me your rationales.'
    assistant_prompt: str = '\nASSISTANT: {output}, the rationales is that {rationales}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        question = raw_sample['question']
        answer = raw_sample['choices'][raw_sample['correct_choice_idx']]
        rationales = ' '.join(raw_sample['rationales'])

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=answer, rationales=rationales)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='', rationales='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'image': raw_sample['image'],
        }


@register_template('OCRVQA')
class OCRVQA:
    system_prompt: str = ''
    user_prompt: str = (
        'USER: \n<image> According to the content of the pictures, answer the following questions in order.\n{input}'
    )
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        questions = raw_sample['questions']
        answers = raw_sample['answers']
        question = '\n'.join(questions)
        answer = '\n'.join(answers)

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=answer)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'image': raw_sample['image'],
        }


@register_template('VisualGenome')
class VisualGenome:
    system_prompt: str = ''
    user_prompt: str = (
        'USER: \n<image> According to the content of the pictures, answer the following questions in order.\n{input}'
    )
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        questions = raw_sample['questions']
        answers = raw_sample['answers']
        quetsion = '\n'.join(questions)
        answer = '\n'.join(answers)

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=quetsion)}'
            f'{self.assistant_prompt.format(output=answer)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=quetsion)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'image': raw_sample['image'],
        }


@register_template('ShareGPT-4o')
class ShareGPT4o:
    system_prompt: str = ''
    user_prompt: str = 'USER: {input}'
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        raw_conversations = raw_sample['conversations']
        text = (
            f'{self.system_prompt}'
            f"{self.user_prompt.format(input=raw_conversations[0]['value'])}"
            f"{self.assistant_prompt.format(output=raw_conversations[1]['value'])}"
        )

        prompt = (
            f'{self.system_prompt}'
            f"{self.user_prompt.format(input=raw_conversations[0]['value'])}"
            f"{self.assistant_prompt.format(output='')}"
        )

        image_file = os.path.join(
            path, 'mnt/petrelfs/wangwenhai/workspace_cef/4o/image', raw_sample['image']
        )
        return {
            'text': text,
            'prompt': prompt,
            'image': Image.open(image_file),
        }


@register_template('AudioCaps')
class AudioCaps:
    user_prompt: str = 'USER: {input}'
    assistant_prompt: str = '\nASSISTANT: {output}'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        caption = raw_sample['caption']
        audiocap_path = raw_sample['audiocap_path']
        question = random.choice(AUDIO_QUESTIONS)

        text = (
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=caption)}'
        )

        prompt = (
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )
        audio, sample_rate = torchaudio.load(os.path.join(path, f'{audiocap_path}'))
        if audio.shape[0] == 2:
            audio = audio.mean(dim=0, keepdim=True)
        return {
            'text': text,
            'prompt': prompt,
            'audio': audio.squeeze().tolist(),
            'sampling_rate': sample_rate,
        }


@register_template('LibriSpeech')
class LibriSpeech:
    user_prompt: str = 'USER: {input}'
    assistant_prompt: str = '\nASSISTANT: {output}'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        caption = raw_sample['text'].lower()
        question = random.choice(SPEECH_QUESTIONS)

        text = (
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=caption)}'
        )

        prompt = (
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )
        audio = raw_sample['audio']['array']
        sampling_rate = raw_sample['audio']['sampling_rate']
        return {'text': text, 'prompt': prompt, 'audio': audio, 'sampling_rate': sampling_rate}


@register_template('AudioSet')
class AudioSet:
    user_prompt: str = 'USER: {input}'
    assistant_prompt: str = '\nASSISTANT: {output}'

    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        caption = f"The content of audio is {', '.join(raw_sample['captions'])}."
        question = random.choice(AUDIO_QUESTIONS)

        text = (
            f'{self.user_prompt.format(input=question)}'
            f'{self.assistant_prompt.format(output=caption)}'
        )

        prompt = (
            f'{self.user_prompt.format(input=question)}'
            f"{self.assistant_prompt.format(output='')}"
        )
        audio, sample_rate = torchaudio.load(os.path.join(path, raw_sample['audio']))
        if audio.shape[0] == 2:
            audio = audio.mean(dim=0, keepdim=True)
        return {
            'text': text,
            'prompt': prompt,
            'audio': audio.squeeze().tolist(),
            'sampling_rate': sample_rate,
        }


@register_template('ti2ti')
class TI2TI:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        output_text = raw_sample['output_text']
        input_img = raw_sample['input_image']
        output_img = raw_sample['output_image']

        if isinstance(input_img, str):
            input_images = [load_image(input_img)]
            num_imput_img = 1
        elif isinstance(input_img, list):
            input_images = [load_image(img) for img in input_img]
            len(input_img)
        else:
            raise ValueError('input_image must be either a string or a list of strings')

        input_text = f"{'<image>' * num_imput_img}{input_text}"

        # do the same for output
        if isinstance(output_img, str):
            output_images = [load_image(output_img)]
            num_output_img = 1

        elif isinstance(output_img, list):
            output_images = [load_image(img) for img in output_img]
            num_output_img = len(output_img)
        else:
            raise ValueError('output_image must be either a string or a list of strings')

        output_text = f"{output_text}{'<image>' * num_output_img}"

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f'{self.assistant_prompt.format(output=output_text)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'images': input_images + output_images,
        }


@register_template('Chameleon')
class CHAMELEON:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['input_text']
        output_text = raw_sample['output_text']
        input_img = raw_sample['input_image']
        output_img = raw_sample['output_image']

        if isinstance(input_img, str):
            input_images = [load_image(input_img)]
            num_imput_img = 1
        elif isinstance(input_img, list):
            input_images = [load_image(img) for img in input_img]
        else:
            raise ValueError('input_image must be either a string or a list of strings')

        input_text = f"{'<image>' * num_imput_img}{input_text}"

        # do the same for output
        if isinstance(output_img, str):
            output_images = [load_image(output_img)]
            num_output_img = 1

        elif isinstance(output_img, list):
            output_images = [load_image(img) for img in output_img]
            num_output_img = len(output_img)
        else:
            raise ValueError('output_image must be either a string or a list of strings')

        output_text = f"{output_text}{'<image>' * num_output_img}"

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f'{self.assistant_prompt.format(output=output_text)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'images': input_images + output_images,
        }


@register_template('ANYTHING_TI2TI')
class ANYTHING_TI2TI:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'
    separator: str = '###'

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        input_text = raw_sample['question']
        output_text = raw_sample['response']
        input_img = raw_sample['image_url']
        output_img = raw_sample['output_image_url']

        if isinstance(input_img, str):
            input_images = [load_image(input_img)]
            num_imput_img = 1
        elif isinstance(input_img, list):
            input_images = [load_image(img) for img in input_img]
            len(input_img)
        else:
            raise ValueError('input_image must be either a string or a list of strings')

        input_text = f"{'<image>' * num_imput_img}{input_text}"

        # do the same for output
        if isinstance(output_img, str):
            output_images = [load_image(output_img)]
            num_output_img = 1

        elif isinstance(output_img, list):
            output_images = [load_image(img) for img in output_img]
            num_output_img = len(output_img)
        else:
            raise ValueError('output_image must be either a string or a list of strings')

        output_text = f"{output_text}{'<image>' * num_output_img}"

        text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f'{self.assistant_prompt.format(output=output_text)}'
        )

        prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=input_text)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        return {
            'text': text,
            'prompt': prompt,
            'input_image': input_images,
            'image': input_images + output_images,
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return torch.equal(raw_sample['better_input_ids'], raw_sample['worse_input_ids'])


@register_template('RLAIFV')
class RLAIFV:
    system_prompt: str = ''
    user_prompt: str = 'USER: \n<image>{input}'
    assistant_prompt: str = '\nASSISTANT:{output}'
    split_token: str = 'ASSISTANT:'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        better_response = raw_sample['chosen']
        worse_response = raw_sample['rejected']
        prompt = raw_sample['question']
        image = raw_sample['image']

        formatted_prompt = f'{self.system_prompt}' f'{self.user_prompt.format(input=prompt)}'
        formatted_better_output = f'{self.assistant_prompt.format(output=better_response)}'
        formatted_worse_output = f'{self.assistant_prompt.format(output=worse_response)}'

        return {
            'prompt': formatted_prompt,
            'better_text': formatted_better_output,
            'worse_text': formatted_worse_output,
            'image': image,
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return raw_sample['chosen'] == raw_sample['rejected']

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['question']
        image = raw_sample['image']

        formatted_prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f'{self.assistant_prompt.format(output="")}'
        )

        return {
            'text': formatted_prompt,
            'image': image,
        }


@register_template('SPA_VL')
class SPA_VL:
    system_prompt: str = (
        "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. "
    )
    user_prompt: str = 'USER: \n<image> {input}'
    assistant_prompt: str = '\nASSISTANT: {output}'
    split_token: str = 'ASSISTANT:'

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        better_response = raw_sample['chosen']
        worse_response = raw_sample['rejected']
        prompt = raw_sample['question']
        image = raw_sample['image']

        formatted_prompt = f'{self.system_prompt}' f'{self.user_prompt.format(input=prompt)}'
        formatted_better_output = f'{self.assistant_prompt.format(output=better_response)}'
        formatted_worse_output = f'{self.assistant_prompt.format(output=worse_response)}'
        image = image.convert('RGBA')

        return {
            'prompt': formatted_prompt,
            'better_text': formatted_better_output,
            'worse_text': formatted_worse_output,
            'image': image,
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return raw_sample['chosen'] == raw_sample['rejected']

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = (
            raw_sample['question']
            .replace('<image>\n', '')
            .replace('\n<image>', '')
            .replace('<image>', '')
        )
        image = raw_sample['image']

        formatted_prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f'{self.assistant_prompt.format(output="")}'
        )
        image = image.convert('RGBA')

        return {
            'text': formatted_prompt,
            'image': image,
        }


@register_template('Webvid')
class Webvid:
    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        video, _, _ = read_video(os.path.join(path, raw_sample['video_path']))
        return {
            'prompt': raw_sample['caption'],
            'video': video.squeeze(0),
        }


@register_template('SafeSora')
class SafeSora:
    def format_preference_sample(
        self, raw_sample: dict[str, Any], path: str = None
    ) -> dict[str, Any]:
        prompt = raw_sample['prompt_text']

        better_id = None
        worse_id = None
        if raw_sample['helpfulness'] == 'video_0':
            better_id = 'video_0'
            worse_id = 'video_1'
        else:
            better_id = 'video_1'
            worse_id = 'video_0'

        raw_better_video = raw_sample[better_id]['video_path']
        raw_worse_video = raw_sample[worse_id]['video_path']

        better_video, _, _ = read_video(os.path.join(path, raw_better_video))
        worse_video, _, _ = read_video(os.path.join(path, raw_worse_video))

        return {
            'prompt': prompt,
            'better_video': better_video.squeeze(0),
            'worse_video': worse_video.squeeze(0),
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return False


@register_template('SOMOS')
class SOMOS:
    def format_sample(self, raw_sample: dict[str, Any], path: str = None) -> dict[str, Any]:
        prompt = raw_sample['prompt']
        better_audio_path = os.path.join(path, raw_sample['better_data_path'])
        worse_audio_path = os.path.join(path, raw_sample['worse_data_path'])

        better_audio, _ = librosa.load(better_audio_path, sr=None)
        worse_audio, _ = librosa.load(worse_audio_path, sr=None)

        return {
            'prompt': prompt,
            'better_audio': better_audio,
            'worse_audio': worse_audio,
        }

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        return False


@register_template('Qwen2-VL')
class QWEN2VL:
    system_prompt: str = '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n'
    user_prompt: str = '<|im_start|>user\n{input}<|im_end|>\n'
    assistant_prompt: str = '<|im_start|>assistant\n{output}'
    split_token: str = '\n'
    separator: str = 'assistant\n'

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['prompt']
        output = raw_sample['output']

        return_text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f'{self.assistant_prompt.format(output=output)}'
        )

        return_prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        video_info = raw_sample['video_path']
        if isinstance(video_info, str):
            video_info = [video_info]

        return_dict = {
            'text': return_text,
            'prompt': return_prompt,
            'image': [],
            'video': video_info,
        }
        return return_dict

    def format_preference_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['prompt']
        better_output = raw_sample['better_output']
        worse_output = raw_sample['worse_output']

        return_better_text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f'{self.assistant_prompt.format(output=better_output)}'
        )

        return_worse_text = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f'{self.assistant_prompt.format(output=worse_output)}'
        )

        video_info = raw_sample['video_path']
        if isinstance(video_info, str):
            video_info = [video_info]

        return_dict = {
            'better_text': return_better_text,
            'worse_text': return_worse_text,
            'image': [],
            'video': video_info,
        }
        return return_dict

    def check_equal(self, raw_sample: dict[str, Any]) -> bool:
        if raw_sample['better_output'] == raw_sample['worse_output']:
            return True
        else:
            return False

    def format_prompt_only_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['prompt']

        return_prompt = (
            f'{self.system_prompt}'
            f'{self.user_prompt.format(input=prompt)}'
            f"{self.assistant_prompt.format(output='')}"
        )

        video_info = raw_sample['video_path']
        if isinstance(video_info, str):
            video_info = [video_info]

        return {
            'text': return_prompt,
            'image': [],
            'video': video_info,
        }


@register_template('OpenAQA')
class OpenAQA:
    system_prompt: str = 'You are a helpful assistant.'
    split_token: str = '<|im_end|>\n<|im_start|>assistant\n'
    separator: str = '<|im_end|>\n<|im_start|>assistant\n'

    def format_supervised_sample(self, raw_sample: dict[str, Any]) -> dict[str, Any]:
        prompt = raw_sample['instruction']
        audio_url = raw_sample['audio_id']
        response = raw_sample['output']

        conversation = [
            {'role': 'system', 'content': self.system_prompt},
            {
                'role': 'user',
                'content': [
                    {'type': 'audio', 'audio_url': audio_url},
                    {'type': 'text', 'text': prompt},
                ],
            },
            {'role': 'assistant', 'content': response},
        ]

        return {
            'conversation': conversation,
            'prompt': conversation[:-1],
        }