

from bisect import bisect
from itertools import accumulate




def 					_lowercase     (				a_				:					str					,a_				:					Union[str, Any]					,a_				:					Tuple					,a_				:					str				)				->	str:




					'''simple docstring'''







					__magic_name__							=    sorted(zip(a_					,a_				)					,key=lambda a_				: x[0] / x[1]					,reverse=a_				)
					__magic_name__,	__magic_name__							=    [i[0] for i in r], [i[1] for i in r]
					__magic_name__							=    list(accumulate(a_				)				)
					__magic_name__							=    bisect(a_					,a_				)
					return (
					    0
					    if k == 0
					    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
					    if k != n
					    else sum(vl[:k]				)
					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()







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      )




