






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a      :       Optional[int]				  =   "src/transformers"
__a      :       List[Any]				  =   "docs/source/en/tasks"



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			str			,				__lowercase   :			List[str]    )						->      Optional[Any]:





    """simple docstring"""

    with open(__lowercase			,				"""r"""			,				encoding="""utf-8"""			,				newline="""\n"""    ) as f:
        __A         =	f.readlines()
    # Find the start prompt.
    __A         =	0
    while not lines[start_index].startswith(__lowercase    ):
        start_index += 1
    start_index += 1

    __A         =	start_index
    while not lines[end_index].startswith(__lowercase    ):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]    ) <= 1:
        start_index += 1
    while len(lines[end_index]    ) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]    ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a      :       str				  =   direct_transformers_import(TRANSFORMERS_PATH)

__a      :       Union[str, Any]				  =   {
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a      :       List[str]				  =   {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]    )						->      List[str]:





    """simple docstring"""

    __A         =	TASK_GUIDE_TO_MODELS[task_guide]
    __A         =	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowercase			,				set()    )
    __A         =	{
        code: name
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if (code in model_maping_names or code in special_model_types)
    }
    return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]    ) + "\n"



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]=False    )						->      List[Any]:





    """simple docstring"""

    __A    ,	__A    ,	__A    ,	__A         =	_find_text_in_file(
        filename=os.path.join(__lowercase			,				__lowercase    )			,				start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""			,				end_prompt="""<!--End of the generated tip-->"""			,				)

    __A         =	get_model_list_for_task(__lowercase    )

    if current_list != new_list:
        if overwrite:
            with open(os.path.join(__lowercase			,				__lowercase    )			,				"""w"""			,				encoding="""utf-8"""			,				newline="""\n"""    ) as f:
                f.writelines(lines[:start_index] + [new_list] + lines[end_index:]    )
        else:
            raise ValueError(
                f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
                """ to fix this."""    )


if __name__ == "__main__":
  __a      :       Optional[Any]				  =   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  __a      :       List[str]				  =   parser.parse_args()

  for task_guide in TASK_GUIDE_TO_MODELS.keys():
    check_model_list_for_task(task_guide, args.fix_and_overwrite)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      ImageTextPipelineOutput,
      UniDiffuserPipeline,
  )
else:
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
  from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




