
from ..utils import DummyObject, requires_backends



class 		UpperCamelCase__  (	metaclass=_SCREAMING_SNAKE_CASE   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				["note_seq"]
   def __init__(self			: Tuple ,    *snake_case_			: Optional[int] ,    **snake_case_			: Tuple       ):
    requires_backends(self ,    ['''note_seq''']       )
   @classmethod
   def 				lowerCAmelCase      (cls			: Optional[Any] ,    *snake_case_			: List[Any] ,    **snake_case_			: Dict       ):
    requires_backends(cls ,    ['''note_seq''']       )







   @classmethod
   def 				lowerCAmelCase      (cls			: Union[str, Any] ,    *snake_case_			: Union[str, Any] ,    **snake_case_			: List[Any]       ):
    requires_backends(cls ,    ['''note_seq''']       )


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 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

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

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