





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class       lowerCAmelCase__(			UpperCamelCase__     ):






       '''simple docstring'''


       __snake_case			      =     'Speech2TextFeatureExtractor'
       __snake_case			      =     'Speech2TextTokenizer'




       def __init__(   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Union[str, Any]:
            super().__init__(__lowerCamelCase     ,    __lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				self.feature_extractor
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				False




       def __call__(   self     ,    *__lowerCamelCase     ,    **__lowerCamelCase				)      ->  int:
            # For backward compatibility
            if self._in_target_context_manager:
                 return self.current_processor(*__lowerCamelCase     ,    **__lowerCamelCase				)

            if "raw_speech" in kwargs:
                 warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
                 _SCREAMING_SNAKE_CASE							:						List[str]         =				kwargs.pop("raw_speech"				)
            else:
                 _SCREAMING_SNAKE_CASE							:						Dict         =				kwargs.pop("audio"     ,    __lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						int         =				kwargs.pop("sampling_rate"     ,    __lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						Tuple         =				kwargs.pop("text"     ,    __lowerCamelCase				)
            if len(__lowerCamelCase				) > 0:
                 _SCREAMING_SNAKE_CASE							:						List[Any]         =				args[0]
                 _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				args[1:]

            if audio is None and text is None:
                 raise ValueError("You need to specify either an `audio` or `text` input to process."				)

            if audio is not None:
                 _SCREAMING_SNAKE_CASE							:						List[Any]         =				self.feature_extractor(__lowerCamelCase     ,    *__lowerCamelCase     ,    sampling_rate=__lowerCamelCase     ,    **__lowerCamelCase				)
            if text is not None:
                 _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.tokenizer(__lowerCamelCase     ,    **__lowerCamelCase				)

            if text is None:
                 return inputs
            elif audio is None:
                 return encodings
            else:
                 _SCREAMING_SNAKE_CASE							:						str         =				encodings["input_ids"]
                 return inputs




       def      UpperCamelCase_      (   self     ,    *__lowerCamelCase     ,    **__lowerCamelCase				)      ->  int:
            return self.tokenizer.batch_decode(*__lowerCamelCase     ,    **__lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    *__lowerCamelCase     ,    **__lowerCamelCase				)      ->  str:
            return self.tokenizer.decode(*__lowerCamelCase     ,    **__lowerCamelCase				)







       @contextmanager
       def      UpperCamelCase_      (   self				)      ->  Optional[int]:
            warnings.warn(
                "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                "your audio inputs, or in a separate call."				)
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				True
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				self.tokenizer
            yield
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.feature_extractor
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				False

'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	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_      				=						[p / w for p, w in zip(A__  ,      A__						)]

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

	# declaring useful variables
	a_      				=						len(A__						)
	a_      				=						0
	a_      				=						0
	a_      				=						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_      				=						sorted_profit_by_weight[length - i - 1]
		a_      				=						profit_by_weight.index(A__						)
		a_      				=						-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)
