





'''simple docstring'''

from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
   import accelerate




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if not is_accelerate_available():
            return method
      _snake_case		:	Any					     =     version.parse(accelerate.__version__	).base_version
      if version.parse(lowerCAmelCase_	) < version.parse('''0.17.0'''	):
            return method

      def wrapper(self			,      *lowerCAmelCase_			,      **lowerCAmelCase_	):
            if hasattr(self			,      '''_hf_hook'''	) and hasattr(self._hf_hook			,      '''pre_forward'''	):
                  self._hf_hook.pre_forward(self	)
            return method(self			,      *lowerCAmelCase_			,      **lowerCAmelCase_	)

      return wrapper









'''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					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


