


'''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 	a       (       unittest.TestCase    ):
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'  )
      _a      =							AutoTokenizer.from_pretrained('google/mt5-small'  )

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

      _a      =							shift_tokens_right(__lowerCAmelCase ,			model.config.pad_token_id ,			model.config.decoder_start_token_id  )

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

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

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






'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

