




from collections import OrderedDict
from typing import Mapping

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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    """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 		A__				(   SCREAMING_SNAKE_CASE__ ):
	lowerCamelCase__		: Optional[Any]				    ="electra"






	def __init__(	self				,     lowerCamelCase=30522				,     lowerCamelCase=128				,     lowerCamelCase=256				,     lowerCamelCase=12				,     lowerCamelCase=4				,     lowerCamelCase=1024				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=512				,     lowerCamelCase=2				,     lowerCamelCase=0.0_2				,     lowerCamelCase=1e-12				,     lowerCamelCase="first"				,     lowerCamelCase=True				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0				,     lowerCamelCase="absolute"				,     lowerCamelCase=True				,     lowerCamelCase=None				,     **lowerCamelCase				,     )      ->   str:




						"""simple docstring"""






						super().__init__(pad_token_id=snake_case__				,     **snake_case__						)

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

						__magic_name__					:							Optional[int]		   =							summary_type
						__magic_name__					:							Union[str, Any]		   =							summary_use_proj
						__magic_name__					:							str		   =							summary_activation
						__magic_name__					:							List[str]		   =							summary_last_dropout
						__magic_name__					:							List[str]		   =							position_embedding_type
						__magic_name__					:							Tuple		   =							use_cache
						__magic_name__					:							Tuple		   =							classifier_dropout





class 		A__				(   SCREAMING_SNAKE_CASE__ ):






	@property
	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						if self.task == "multiple-choice":
											__magic_name__					:							int		   =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
											__magic_name__					:							int		   =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						        ('''token_type_ids''', dynamic_axis),
						    ]						)











"""simple docstring"""







import math
from decimal import Decimal, getcontext





def 						lowercase__(A = 4_000_000						)				->int:




						"""simple docstring"""



						try:
												lowercase__				:      int=       int(A						)
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."						)
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."						)
						lowercase__				:      Dict=       100
						lowercase__				:      Optional[Any]=       (Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

						lowercase__				:      str=       (math.floor(math.log(n * (phi + 2)					,		A						) - 1						) // 3) * 3 + 2
						lowercase__				:      Optional[Any]=       Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
						lowercase__				:      Tuple=       num // 2
						return int(A						)


if __name__ == "__main__":
			print(F"""{solution() = }""")






