






"""simple docstring"""



from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
 import accelerate


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		List[str]:






     '''simple docstring'''



     if not is_accelerate_available():
          return method
     UpperCAmelCase__						:    Union[str, Any]							=							version.parse(accelerate.__version__				).base_version
     if version.parse(snake_case				) < version.parse("0.17.0"				):
          return method

     def wrapper(self       : Tuple			,					*snake_case       : Dict			,					**snake_case       : str				):
          if hasattr(self			,					"_hf_hook"				) and hasattr(self._hf_hook			,					"pre_forward"				):
               self._hf_hook.pre_forward(self				)
          return method(self			,					*snake_case			,					**snake_case				)

     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       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

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

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


