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


_lowerCAmelCase       =		logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase       =		{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "retribert"







							def __init__(   self      :     int ,			_A      :     int=3_0522 ,			_A      :     Dict=768 ,			_A      :     Tuple=8 ,			_A      :     int=12 ,			_A      :     Optional[Any]=3072 ,			_A      :     int="gelu" ,			_A      :     str=0.1 ,			_A      :     Union[str, Any]=0.1 ,			_A      :     int=512 ,			_A      :     str=2 ,			_A      :     str=0.02 ,			_A      :     Optional[int]=1e-12 ,			_A      :     List[Any]=True ,			_A      :     Optional[int]=128 ,			_A      :     List[str]=0 ,			**_A      :     str ,			):
												super().__init__(pad_token_id=_A ,			**_A		)

												_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			     =     share_encoders
												_UpperCamelCase			     =     projection_dim


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					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= 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			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

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

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)