

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			a					(		unittest.TestCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"		)
										_UpperCAmelCase    :						Dict							=      AutoTokenizer.from_pretrained("google/mt5-small"		)

										_UpperCAmelCase    :						List[Any]							=      tokenizer("Hello there"  ,   return_tensors="np"		).input_ids
										_UpperCAmelCase    :						int							=      tokenizer("Hi I am"  ,   return_tensors="np"		).input_ids

										_UpperCAmelCase    :						List[Any]							=      shift_tokens_right(A_  ,   model.config.pad_token_id  ,   model.config.decoder_start_token_id		)

										_UpperCAmelCase    :						List[str]							=      model(A_  ,   decoder_input_ids=A_		).logits
										_UpperCAmelCase    :						Optional[Any]							=      optax.softmax_cross_entropy(A_  ,   onehot(A_  ,   logits.shape[-1]		)		).mean()

										_UpperCAmelCase    :						str							=      -(labels.shape[-1] * loss.item())

										_UpperCAmelCase    :						str							=      -84.91_27
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


