"""simple docstring"""




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


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

# TODO: upload to AWS
_a   :	Dict 					=     {
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    int	      =	"retribert"






     def __init__(	self       ,  a__=30522       ,  a__=768       ,  a__=8       ,  a__=12       ,  a__=3072       ,  a__="gelu"       ,  a__=0.1       ,  a__=0.1       ,  a__=512       ,  a__=2       ,  a__=0.0_2       ,  a__=1e-12       ,  a__=True       ,  a__=128       ,  a__=0       ,  **a__       ,  ):
       super().__init__(pad_token_id=a__       ,  **a__						)

       _lowerCAmelCase		: Dict						    =  vocab_size
       _lowerCAmelCase		: Dict						    =  hidden_size
       _lowerCAmelCase		: Tuple						    =  num_hidden_layers
       _lowerCAmelCase		: str						    =  num_attention_heads
       _lowerCAmelCase		: Tuple						    =  hidden_act
       _lowerCAmelCase		: int						    =  intermediate_size
       _lowerCAmelCase		: List[Any]						    =  hidden_dropout_prob
       _lowerCAmelCase		: str						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: Optional[int]						    =  max_position_embeddings
       _lowerCAmelCase		: str						    =  type_vocab_size
       _lowerCAmelCase		: Optional[int]						    =  initializer_range
       _lowerCAmelCase		: List[str]						    =  layer_norm_eps
       _lowerCAmelCase		: str						    =  share_encoders
       _lowerCAmelCase		: int						    =  projection_dim





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




