





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	int =				'hf-internal-testing/tiny-random-t5'
          a__						:	List[str] =				AutoTokenizer.from_pretrained(lowercase)
          a__						:	Union[str, Any] =				AutoModelForSeqaSeqLM.from_pretrained(lowercase)

          a__						:	List[str] =				tokenizer('This is me'					,  return_tensors='pt')

          a__						:	List[Any] =				model.to_bettertransformer()

          self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()))

          a__						:	List[str] =				model.generate(**lowercase)

          a__						:	Tuple =				model.reverse_bettertransformer()

          self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()))

          with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(lowercase)

                 a__						:	Tuple =				AutoModelForSeqaSeqLM.from_pretrained(lowercase)

                 self.assertFalse(
                     any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()))

                 a__						:	Tuple =				model_reloaded.generate(**lowercase)
                 self.assertTrue(torch.allclose(lowercase					,  lowercase))

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Any =				'hf-internal-testing/tiny-random-t5'
          a__						:	str =				AutoModelForSeqaSeqLM.from_pretrained(lowercase)

          a__						:	Optional[Any] =				model.to_bettertransformer()

          with tempfile.TemporaryDirectory() as tmpdirname:
                 with self.assertRaises(lowercase):
                        model.save_pretrained(lowercase)

                 a__						:	str =				model.reverse_bettertransformer()
                 model.save_pretrained(lowercase)




"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""masked_bert"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any=30522    ,      _lowerCamelCase			:			Union[str, Any]=768    ,      _lowerCamelCase			:			Tuple=12    ,      _lowerCamelCase			:			Any=12    ,      _lowerCamelCase			:			str=3072    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=512    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			int=0.0_2    ,      _lowerCamelCase			:			Union[str, Any]=1e-12    ,      _lowerCamelCase			:			Union[str, Any]=0    ,      _lowerCamelCase			:			List[str]="topK"    ,      _lowerCamelCase			:			Optional[Any]="constant"    ,      _lowerCamelCase			:			Optional[Any]=0.0    ,      **_lowerCamelCase			:			str    ,      ):
       super().__init__(pad_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)

       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       type_vocab_size
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       pruning_method
       _snake_case					     =       mask_init
       _snake_case					     =       mask_scale



