
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__							:							List[Any]													=	logging.get_logger(__name__)

lowercase__							:							Dict													=	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}






class 				SCREAMING_SNAKE_CASE__			(							SCREAMING_SNAKE_CASE_			):

							"""simple docstring"""






							_snake_case   						=     """lilt"""



							def __init__(     self   ,		SCREAMING_SNAKE_CASE_=30522   ,		SCREAMING_SNAKE_CASE_=768   ,		SCREAMING_SNAKE_CASE_=12   ,		SCREAMING_SNAKE_CASE_=12   ,		SCREAMING_SNAKE_CASE_=3072   ,		SCREAMING_SNAKE_CASE_="gelu"   ,		SCREAMING_SNAKE_CASE_=0.1   ,		SCREAMING_SNAKE_CASE_=0.1   ,		SCREAMING_SNAKE_CASE_=512   ,		SCREAMING_SNAKE_CASE_=2   ,		SCREAMING_SNAKE_CASE_=0.0_2   ,		SCREAMING_SNAKE_CASE_=1E-12   ,		SCREAMING_SNAKE_CASE_=0   ,		SCREAMING_SNAKE_CASE_="absolute"   ,		SCREAMING_SNAKE_CASE_=None   ,		SCREAMING_SNAKE_CASE_=4   ,		SCREAMING_SNAKE_CASE_=1024   ,		**SCREAMING_SNAKE_CASE_   ,		)->      str:





													'''simple docstring'''






													super().__init__(pad_token_id=__UpperCAmelCase   ,		**__UpperCAmelCase	)

													__UpperCamelCase        =	vocab_size
													__UpperCamelCase        =	hidden_size
													__UpperCamelCase        =	num_hidden_layers
													__UpperCamelCase        =	num_attention_heads
													__UpperCamelCase        =	hidden_act
													__UpperCamelCase        =	intermediate_size
													__UpperCamelCase        =	hidden_dropout_prob
													__UpperCamelCase        =	attention_probs_dropout_prob
													__UpperCamelCase        =	max_position_embeddings
													__UpperCamelCase        =	type_vocab_size
													__UpperCamelCase        =	initializer_range
													__UpperCamelCase        =	layer_norm_eps
													__UpperCamelCase        =	position_embedding_type
													__UpperCamelCase        =	classifier_dropout
													__UpperCamelCase        =	channel_shrink_ratio
													__UpperCamelCase        =	max_ad_position_embeddings











"""simple docstring"""





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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

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

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							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(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

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

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

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

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
