




"""simple docstring"""


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_						=				"src/transformers"
A_						=				"docs/source/en/tasks"


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[Any]		,   snake_case__							:					Dict    ):
  """simple docstring"""

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

  _snake_case							:		Tuple								=  start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE__    ):
    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_						=				direct_transformers_import(TRANSFORMERS_PATH)

A_						=				{
    "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_						=				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  TASK_GUIDE_TO_MODELS[task_guide]
  _snake_case							:		Optional[Any]								=  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE__		,   set()    )
  _snake_case							:		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 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

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

  _snake_case							:		Dict								=  get_model_list_for_task(SCREAMING_SNAKE_CASE__    )

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

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









import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class     __snake_case       (							unittest.TestCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[int]   =						load_tool("""text-classification"""		)
         self.tool.setup()
         __snake_case:						Dict   =						load_tool("""text-classification"""							,				remote=A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						self.tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						self.remote_tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						self.tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						self.remote_tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






