


'''simple docstring'''
def     a   (    __a				,      __a				,      __a     )						->			int:



					'''simple docstring'''






					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.
					UpperCamelCase__     :Optional[Any]							      =				[p / w for p, w in zip(__a				,      __a     )]

					# Creating a copy of the list and sorting profit/weight in ascending order
					UpperCamelCase__     :Dict							      =				sorted(__a     )

					# declaring useful variables
					UpperCamelCase__     :List[Any]							      =				len(__a     )
					UpperCamelCase__     :List[Any]							      =				0
					UpperCamelCase__     :int							      =				0
					UpperCamelCase__     :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
										UpperCamelCase__     :List[str]							      =				sorted_profit_by_weight[length - i - 1]
										UpperCamelCase__     :List[Any]							      =				profit_by_weight.index(__a     )
										UpperCamelCase__     :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.'''
		)

		__snake_case							   =  [int(x) for x in input('''Input profits separated by spaces: ''').split()]
		__snake_case							   =  [int(x) for x in input('''Input weights separated by spaces: ''').split()]
		__snake_case							   =  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      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Speech2TextFeatureExtractor'
		_a									=       'Speech2TextTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.feature_extractor
							UpperCamelCase__     :Optional[Any]							      =				False




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor(*UpperCamelCase_					,     **UpperCamelCase_							)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
												UpperCamelCase__     :Optional[Any]							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :List[Any]							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Tuple							      =				args[0]
												UpperCamelCase__     :Optional[int]							      =				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:
												UpperCamelCase__     :Any							      =				self.feature_extractor(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												UpperCamelCase__     :Dict							      =				encodings['''input_ids''']
												return inputs




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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.'''							)
							UpperCamelCase__     :Optional[Any]							      =				True
							UpperCamelCase__     :Any							      =				self.tokenizer
							yield
							UpperCamelCase__     :Any							      =				self.feature_extractor
							UpperCamelCase__     :Any							      =				False