


import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase_   :				Optional[int]						      = logging.getLogger(__name__)
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Dict       =		'''masked_bert'''
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple=3_0_5_2_2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=7_6_8  ,							SCREAMING_SNAKE_CASE__       : Dict=1_2  ,							SCREAMING_SNAKE_CASE__       : str=1_2  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=3_0_7_2  ,							SCREAMING_SNAKE_CASE__       : Optional[int]="gelu"  ,							SCREAMING_SNAKE_CASE__       : int=0.1  ,							SCREAMING_SNAKE_CASE__       : List[Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : List[Any]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=2  ,							SCREAMING_SNAKE_CASE__       : Dict=0.02  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=1E-12  ,							SCREAMING_SNAKE_CASE__       : int=0  ,							SCREAMING_SNAKE_CASE__       : List[str]="topK"  ,							SCREAMING_SNAKE_CASE__       : List[str]="constant"  ,							SCREAMING_SNAKE_CASE__       : str=0.0  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							)	->    Dict:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						vocab_size
             a_      :	int							=						hidden_size
             a_      :	str							=						num_hidden_layers
             a_      :	List[str]							=						num_attention_heads
             a_      :	Dict							=						hidden_act
             a_      :	Dict							=						intermediate_size
             a_      :	List[Any]							=						hidden_dropout_prob
             a_      :	str							=						attention_probs_dropout_prob
             a_      :	Dict							=						max_position_embeddings
             a_      :	str							=						type_vocab_size
             a_      :	List[str]							=						initializer_range
             a_      :	Any							=						layer_norm_eps
             a_      :	List[Any]							=						pruning_method
             a_      :	Union[str, Any]							=						mask_init
             a_      :	List[str]							=						mask_scale






from argparse import ArgumentParser

from .env import EnvironmentCommand







def  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	str							=						ArgumentParser('Diffusers CLI tool'						,					usage='diffusers-cli <command> [<args>]'       )
       a_      :	Any							=						parser.add_subparsers(help='diffusers-cli command helpers'       )

       # Register commands
       EnvironmentCommand.register_subcommand(__A       )

       # Let's go
       a_      :	int							=						parser.parse_args()

       if not hasattr(__A						,					'func'       ):
              parser.print_help()
              exit(1       )

       # Run
       a_      :	Union[str, Any]							=						args.func(__A       )
       service.run()


if __name__ == "__main__":
   main()


