





def A_	(		A__		,      A__		,      A__				)  ->		int:
       if len(A__				) != len(A__				):
              raise ValueError('The length of profit and weight must be same.'				)
       if max_weight <= 0:
              raise ValueError('max_weight must greater than zero.'				)
       if any(p < 0 for p in profit				):
              raise ValueError('Profit can not be negative.'				)
       if any(w < 0 for w in weight				):
              raise ValueError('Weight can not be negative.'				)

       # List created to store profit gained for the 1kg in case of each weight
       # respectively.  Calculate and append profit/weight for each element.
       a__						:	Tuple =				[p / w for p, w in zip(A__		,      A__				)]

       # Creating a copy of the list and sorting profit/weight in ascending order
       a__						:	Optional[int] =				sorted(A__				)

       # declaring useful variables
       a__						:	Tuple =				len(A__				)
       a__						:	List[Any] =				0
       a__						:	Dict =				0
       a__						:	List[str] =				0

       # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
       while limit <= max_weight and i < length:
              # flag value for encountered greatest element in sorted_profit_by_weight
              a__						:	str =				sorted_profit_by_weight[length - i - 1]
              a__						:	Union[str, Any] =				profit_by_weight.index(A__				)
              a__						:	str =				-1

              # check if the weight encountered is less than the total weight
              # encountered before.
              if max_weight - limit >= weight[index]:
                     limit += weight[index]
                     # Adding profit gained for the given weight 1 ===
                     # weight[index]/weight[index]
                     gain += 1 * profit[index]
              else:
                     # Since the weight encountered is greater than limit, therefore take the
                     # required number of remaining kgs and calculate profit for it.
                     # weight remaining / weight[index]
                     gain += (max_weight - limit) / weight[index] * profit[index]
                     break
              i += 1
       return gain


if __name__ == "__main__":
    print(
        """Input profits, weights, and then max_weight (all positive ints) separated by """
        """spaces."""
    )

    lowercase      :       Optional[Any]									=  [int(x) for x in input("""Input profits separated by spaces: """).split()]
    lowercase      :       Any									=  [int(x) for x in input("""Input weights separated by spaces: """).split()]
    lowercase      :       List[Any]									=  int(input("""Max weight allowed: """))

    # Function Call
    calc_profit(profit, weight, max_weight)









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



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              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.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

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



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      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 A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

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

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


