





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class 	__lowerCAmelCase      (       unittest.TestCase	):





   @slow
   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Any   		=	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,      return_dict=snake_case   ).to(snake_case   )
      a__						:    Optional[int]   		=	AutoTokenizer.from_pretrained("google/mt5-small"   )

      a__						:    Dict   		=	tokenizer("Hello there"       ,      return_tensors="pt"   ).input_ids
      a__						:    str   		=	tokenizer("Hi I am"       ,      return_tensors="pt"   ).input_ids

      a__						:    Any   		=	model(input_ids.to(snake_case   )       ,      labels=labels.to(snake_case   )   ).loss
      a__						:    Optional[Any]   		=	-(labels.shape[-1] * loss.item())

      a__						:    List[Any]   		=	-84.9_127
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )








from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
  import accelerate






def    _A	(   lowerCamelCase ):
   if not is_accelerate_available():
      return method
   a__						:    List[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

