

'''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
lowerCAmelCase__						:       Optional[int]										=						"""src/transformers"""
lowerCAmelCase__						:       List[str]										=						"""docs/source/en/tasks"""




def 	_a       (				__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      List[Any]     ):

  """simple docstring"""

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

  snake_case__						:       Any       						=  start_index
  while not lines[end_index].startswith(__UpperCAmelCase     ):
    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__						:       List[str]										=						direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase__						:       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`).
lowerCAmelCase__						:       Optional[Any]										=						{
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}




def 	_a       (				__lowerCAmelCase		:      List[Any]     ):

  """simple docstring"""

  snake_case__						:       Tuple       						=  TASK_GUIDE_TO_MODELS[task_guide]
  snake_case__						:       Optional[int]       						=  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCAmelCase   ,							set()     )
  snake_case__						:       Optional[int]       						=  {
      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 	_a       (				__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      Optional[int]=False     ):

  """simple docstring"""

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

  snake_case__						:       Union[str, Any]       						=  get_model_list_for_task(__UpperCAmelCase     )

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

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






'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if principal <= 0:
				raise Exception('''Principal borrowed must be > 0'''     )
		if rate_per_annum < 0:
				raise Exception('''Rate of interest must be >= 0'''     )
		if years_to_repay <= 0 or not isinstance(__lowerCAmelCase   ,							__lowerCAmelCase     ):
				raise Exception('''Years to repay must be an integer > 0'''     )

		# Yearly rate is divided by 12 to get monthly rate
		snake_case__						:       int       						=  rate_per_annum / 12

		# Years to repay is multiplied by 12 to get number of payments as payment is monthly
		snake_case__						:       Tuple       						=  years_to_repay * 12

		return (
		    principal
		    * rate_per_month
		    * (1 + rate_per_month) ** number_of_payments
		    / ((1 + rate_per_month) ** number_of_payments - 1)
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



