


'''simple docstring'''

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   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @slow
      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
             lowercase__      			= AutoTokenizer.from_pretrained('''google/mt5-small''' )

             lowercase__      			= tokenizer('''Hello there'''   ,						return_tensors='''np''' ).input_ids
             lowercase__      			= tokenizer('''Hi I am'''   ,						return_tensors='''np''' ).input_ids

             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						model.config.pad_token_id   ,						model.config.decoder_start_token_id )

             lowercase__      			= model(UpperCamelCase   ,						decoder_input_ids=UpperCamelCase ).logits
             lowercase__      			= optax.softmax_cross_entropy(UpperCamelCase   ,						onehot(UpperCamelCase   ,						logits.shape[-1] ) ).mean()

             lowercase__      			= -(labels.shape[-1] * loss.item())

             lowercase__      			= -84.91_27
             self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )








'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




