




'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a    :							List[str]										=    logging.get_logger(__name__)

a    :							Any										=    {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"roberta-prelayernorm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   str=5_0265			,							lowercase_       :   Optional[Any]=768			,							lowercase_       :   Dict=12			,							lowercase_       :   Dict=12			,							lowercase_       :   str=3072			,							lowercase_       :   Any="gelu"			,							lowercase_       :   Any=0.1			,							lowercase_       :   Optional[Any]=0.1			,							lowercase_       :   Union[str, Any]=512			,							lowercase_       :   Optional[int]=2			,							lowercase_       :   Any=0.02			,							lowercase_       :   Union[str, Any]=1e-12			,							lowercase_       :   Any=1			,							lowercase_       :   Dict=0			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[int]="absolute"			,							lowercase_       :   Tuple=True			,							lowercase_       :   str=None			,							**lowercase_       :   Dict			,							):
									super().__init__(pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_			,							**lowercase_   )

									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_		     =							position_embedding_type
									snake_case_		     =							use_cache
									snake_case_		     =							classifier_dropout







class 		a							(  _lowerCamelCase	):


				@property
				def     A_					(						self       :   Union[str, Any]   ):
									if self.task == "multiple-choice":
														snake_case_		     =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
														snake_case_		     =							{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]   )







'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return int((input_a, input_a).count(0      ) == 0      )


def 						__magic_name__       (      )						->   None:

					'''simple docstring'''

					assert and_gate(0, 0      ) == 0
					assert and_gate(0, 1      ) == 0
					assert and_gate(1, 0      ) == 0
					assert and_gate(1, 1      ) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))

