
"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"electra"






			def __init__(   self			,  __A=3_0522			,  __A=128			,  __A=256			,  __A=12			,  __A=4			,  __A=1024			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A="first"			,  __A=True			,  __A="gelu"			,  __A=0.1			,  __A=0			,  __A="absolute"			,  __A=True			,  __A=None			,  **__A			,  )     ->	Any:
						super().__init__(pad_token_id=__A			,  **__A			)

						lowerCAmelCase_       :List[str]     	=    vocab_size
						lowerCAmelCase_       :int     	=    embedding_size
						lowerCAmelCase_       :int     	=    hidden_size
						lowerCAmelCase_       :Tuple     	=    num_hidden_layers
						lowerCAmelCase_       :str     	=    num_attention_heads
						lowerCAmelCase_       :List[str]     	=    intermediate_size
						lowerCAmelCase_       :Union[str, Any]     	=    hidden_act
						lowerCAmelCase_       :Union[str, Any]     	=    hidden_dropout_prob
						lowerCAmelCase_       :str     	=    attention_probs_dropout_prob
						lowerCAmelCase_       :Union[str, Any]     	=    max_position_embeddings
						lowerCAmelCase_       :Dict     	=    type_vocab_size
						lowerCAmelCase_       :Optional[Any]     	=    initializer_range
						lowerCAmelCase_       :Dict     	=    layer_norm_eps

						lowerCAmelCase_       :int     	=    summary_type
						lowerCAmelCase_       :Optional[Any]     	=    summary_use_proj
						lowerCAmelCase_       :Tuple     	=    summary_activation
						lowerCAmelCase_       :Tuple     	=    summary_last_dropout
						lowerCAmelCase_       :List[Any]     	=    position_embedding_type
						lowerCAmelCase_       :Dict     	=    use_cache
						lowerCAmelCase_       :Optional[Any]     	=    classifier_dropout




class      _SCREAMING_SNAKE_CASE   (     A__				):






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									lowerCAmelCase_       :Optional[int]     	=    {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									lowerCAmelCase_       :Union[str, Any]     	=    {0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						        ("""token_type_ids""", dynamic_axis),
						    ]			)








"""simple docstring"""






from __future__ import annotations






def 		_snake_case			(      lowercase__						:    list[int | float]						)    ->							int | float:


			'''simple docstring'''





			if len(lowercase__						) == 0:
						raise ValueError("""find_min() arg is an empty sequence"""						)
			lowerCAmelCase_       :Optional[int]     	=    nums[0]
			for num in nums:
						lowerCAmelCase_       :Optional[Any]     	=    min(lowercase__ ,	lowercase__						)
			return min_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






