import warnings
from functools import wraps
from typing import Callable



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Callable     )							-> Callable:







  """simple docstring"""





  @wraps(__UpperCamelCase     )
  def _inner_fn(*__UpperCamelCase   :	str			,					**__UpperCamelCase   :	str     ):
    warnings.warn(
        (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")			,					__UpperCamelCase			,					)
    return fn(*__UpperCamelCase			,					**__UpperCamelCase     )

  return _inner_fn

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 __snake_case    (						unittest.TestCase		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

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