





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
_lowerCAmelCase 				=     """src/transformers"""
_lowerCAmelCase 				=     """docs/source/en/tasks"""



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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

   A_     :				Tuple			      =	start_index
   while not lines[end_index].startswith(__lowerCAmelCase     ):
      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.
_lowerCAmelCase 				=     direct_transformers_import(TRANSFORMERS_PATH)

_lowerCAmelCase 				=     {
    """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`).
_lowerCAmelCase 				=     {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	TASK_GUIDE_TO_MODELS[task_guide]
   A_     :				Optional[Any]			      =	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase  ,set()     )
   A_     :				Optional[Any]			      =	{
       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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	_find_text_in_file(
       filename=os.path.join(__lowerCAmelCase  ,__lowerCAmelCase     )  ,start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""  ,end_prompt="""<!--End of the generated tip-->"""  ,)

   A_     :				List[str]			      =	get_model_list_for_task(__lowerCAmelCase     )

   if current_list != new_list:
      if overwrite:
         with open(os.path.join(__lowerCAmelCase  ,__lowerCAmelCase     )  ,"""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__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      _lowerCAmelCase 				=     parser.parse_args()

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













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






