
"""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      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       Dict			)			->   Dict:






    """simple docstring"""




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

    UpperCAmelCase          =       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.
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      lowercase__    (       lowerCAmelCase					:       int			)			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       TASK_GUIDE_TO_MODELS[task_guide]
    UpperCAmelCase          =       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase			,					set()			)
    UpperCAmelCase          =       {
        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      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Dict=False			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       _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-->'			,					)

    UpperCAmelCase          =       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__":
   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 math




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Optional[Any]			)			->   List[Any]:






    """simple docstring"""




    if 0 not in (x, y):
        # We use the relation x^y = y*log10(x), where 10 is the base.
        return y * math.logaa(lowerCAmelCase			)
    else:
        if x == 0:  # 0 raised to any number is 0
            return 0
        elif y == 0:
            return 1  # any number raised to 0 is 1
    raise AssertionError('This should never happen'			)


if __name__ == "__main__":  # Main function
   # Read two numbers from input and typecast them to int using map function.
   # Here x is the base and y is the power.
   SCREAMING_SNAKE_CASE_            =    '''Enter the base and the power separated by a comma: '''
   SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    map(int, input(prompt).split(''','''))
   SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    map(int, input(prompt).split(''','''))

   # We find the log of each number, using the function res(), which takes two
   # arguments.
   SCREAMING_SNAKE_CASE_            =    res(xa, ya)
   SCREAMING_SNAKE_CASE_            =    res(xa, ya)

   # We check for the largest number
   if resa > resa:
      print('''Largest number is''', xa, '''^''', ya)
   elif resa > resa:
      print('''Largest number is''', xa, '''^''', ya)
   else:
      print('''Both are equal''')



