





'''simple docstring'''

def lowerCamelCase		(	__lowerCamelCase					:       Union[str, Any]   ,			__lowerCamelCase					:       Optional[Any]   ,			__lowerCamelCase					:       Tuple       )		->str:
    if len(_snake_case       ) != len(_snake_case       ):
        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.
    _SCREAMING_SNAKE_CASE   							=     [p / w for p, w in zip(_snake_case   ,			_snake_case       )]

    # Creating a copy of the list and sorting profit/weight in ascending order
    _SCREAMING_SNAKE_CASE   							=     sorted(_snake_case       )

    # declaring useful variables
    _SCREAMING_SNAKE_CASE   							=     len(_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     0
    _SCREAMING_SNAKE_CASE   							=     0
    _SCREAMING_SNAKE_CASE   							=     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
        _SCREAMING_SNAKE_CASE   							=     sorted_profit_by_weight[length - i - 1]
        _SCREAMING_SNAKE_CASE   							=     profit_by_weight.index(_snake_case       )
        _SCREAMING_SNAKE_CASE   							=     -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_      							=					[int(x) for x in input("""Input profits separated by spaces: """).split()]
      lowercase_      							=					[int(x) for x in input("""Input weights separated by spaces: """).split()]
      lowercase_      							=					int(input("""Max weight allowed: """))

      # Function Call
      calc_profit(profit, weight, max_weight)





"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[Any]     							=					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

          super().__init__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes '
                  'if you initialized the image processor with apply_ocr set to True.'      )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer(
              text=text if text is not None else features['words']     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features['boxes']     ,	word_labels=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

          if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F''' {len(UpperCamelCase_      )} and {len(UpperCamelCase_      )}'''      )

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor

