

def 					_lowercase     (				a_					,a_					,a_				)				->	Optional[int]:




					'''simple docstring'''







					if len(lowerCAmelCase__				) != len(lowerCAmelCase__				):
										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__							=    [p / w for p, w in zip(lowerCAmelCase__					,lowerCAmelCase__				)]

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

					# declaring useful variables
					__magic_name__							=    len(lowerCAmelCase__				)
					__magic_name__							=    0
					__magic_name__							=    0
					__magic_name__							=    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__							=    sorted_profit_by_weight[length - i - 1]
										__magic_name__							=    profit_by_weight.index(lowerCAmelCase__				)
										__magic_name__							=    -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."
						)

						A__						=							[int(x) for x in input("Input profits separated by spaces: ").split()]
						A__						=							[int(x) for x in input("Input weights separated by spaces: ").split()]
						A__						=							int(input("Max weight allowed: "))

						# Function Call
						calc_profit(profit, weight, max_weight)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




