
'''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
SCREAMING_SNAKE_CASE     =       """src/transformers"""
SCREAMING_SNAKE_CASE     =       """docs/source/en/tasks"""







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  with open(lowercase__   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"		) as f:
    UpperCAmelCase__      :					Dict				       =							f.readlines()
  # Find the start prompt.
  UpperCAmelCase__      :					List[str]				       =							0
  while not lines[start_index].startswith(lowercase__		):
    start_index += 1
  start_index += 1

  UpperCAmelCase__      :					str				       =							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.
SCREAMING_SNAKE_CASE     =       direct_transformers_import(TRANSFORMERS_PATH)

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







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					List[str]				       =							TASK_GUIDE_TO_MODELS[task_guide]
  UpperCAmelCase__      :					str				       =							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__   ,       set()		)
  UpperCAmelCase__      :					Tuple				       =							{
      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 snake_case_     (					lowercase__   ,       lowercase__=False		):
  UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							_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-->"   ,       )

  UpperCAmelCase__      :					Dict				       =							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__":
      SCREAMING_SNAKE_CASE     =       argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      SCREAMING_SNAKE_CASE     =       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'''





import operator as op







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							[]
  UpperCAmelCase__      :					Any				       =							lambda lowercase__   ,       lowercase__		: int(x / y		)  # noqa: E731 integer division operation
  UpperCAmelCase__      :					Any				       =							{
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8		)   ,       "Action".center(1_2		)   ,       "Stack"   ,       sep=" | "		)
  print("-" * (3_0 + len(lowercase__		))		)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowercase__		)  # append x to stack
      # output in tabular format
      print(x.rjust(8		)   ,       ("push(" + x + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)
    else:
      UpperCAmelCase__      :					str				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      UpperCAmelCase__      :					List[Any]				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + a + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      stack.append(
          str(opr[x](int(lowercase__		)   ,       int(lowercase__		)		)		)		)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8		)   ,       ("push(" + a + x + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "   ,       )

  return int(stack[0]		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
      print("""\n\tResult = """, solve(Postfix))



