






def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				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.
				__magic_name__		:List[Any]   =   [p / w for p, w in zip(snake_case,	snake_case			)]

				# Creating a copy of the list and sorting profit/weight in ascending order
				__magic_name__		:Tuple   =   sorted(snake_case			)

				# declaring useful variables
				__magic_name__		:str   =   len(snake_case			)
				__magic_name__		:List[str]   =   0
				__magic_name__		:List[str]   =   0
				__magic_name__		:Union[str, Any]   =   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
								__magic_name__		:List[Any]   =   sorted_profit_by_weight[length - i - 1]
								__magic_name__		:int   =   profit_by_weight.index(snake_case			)
								__magic_name__		:Any   =   -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."""
					)

					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		[int(x) for x in input("""Input profits separated by spaces: """).split()]
					SCREAMING_SNAKE_CASE__   :      Tuple				=		[int(x) for x in input("""Input weights separated by spaces: """).split()]
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		int(input("""Max weight allowed: """))

					# Function Call
					calc_profit(profit, weight, max_weight)










"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False

							def __call__(	self       ,  *__a       ,  **__a      ):
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*__a       ,  **__a      )

										if "raw_speech" in kwargs:
													warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     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:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

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

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	self      ):
										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."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




