


"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		if len(UpperCamelCase__	) != len(UpperCamelCase__	):
				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(UpperCamelCase__ ,			UpperCamelCase__	)]

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

		# declaring useful variables
		A					       = len(UpperCamelCase__	)
		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(UpperCamelCase__	)
				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."
	)

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

	# Function Call
	calc_profit(profit, weight, max_weight)









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





