



"""simple docstring"""
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 ):





     '''simple docstring'''



     @slow
     def 					__lowerCAmelCase      (   self						)				->   Union[str, Any]:
         _UpperCAmelCase				:    Tuple											=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''			,      return_dict=A_						).to(A_						)
         _UpperCAmelCase				:    str											=       AutoTokenizer.from_pretrained('''google/mt5-small'''						)

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

         _UpperCAmelCase				:    Union[str, Any]											=       model(input_ids.to(A_						)			,      labels=labels.to(A_						)						).loss
         _UpperCAmelCase				:    Any											=       -(labels.shape[-1] * loss.item())

         _UpperCAmelCase				:    Any											=       -84.9_127
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1E-4						)




"""simple docstring"""


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset