
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


snake_case    =		logging.get_logger(__name__)

snake_case    =		{
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Dict						  =				"rembert"


				def __init__(				self		,	lowercase__=250300		,	lowercase__=1152		,	lowercase__=32		,	lowercase__=18		,	lowercase__=256		,	lowercase__=1664		,	lowercase__=4608		,	lowercase__="gelu"		,	lowercase__=0.0		,	lowercase__=0.0		,	lowercase__=0.1		,	lowercase__=512		,	lowercase__=2		,	lowercase__=0.02		,	lowercase__=1E-12		,	lowercase__=True		,	lowercase__=0		,	lowercase__=312		,	lowercase__=313		,	**lowercase__		,	)    ->    Tuple:
									"""simple docstring"""







									super().__init__(pad_token_id=lowercase__		,	bos_token_id=lowercase__		,	eos_token_id=lowercase__		,	**lowercase__			)

									SCREAMING_SNAKE_CASE				=	vocab_size
									SCREAMING_SNAKE_CASE				=	input_embedding_size
									SCREAMING_SNAKE_CASE				=	output_embedding_size
									SCREAMING_SNAKE_CASE				=	max_position_embeddings
									SCREAMING_SNAKE_CASE				=	hidden_size
									SCREAMING_SNAKE_CASE				=	num_hidden_layers
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	intermediate_size
									SCREAMING_SNAKE_CASE				=	hidden_act
									SCREAMING_SNAKE_CASE				=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE				=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE				=	classifier_dropout_prob
									SCREAMING_SNAKE_CASE				=	initializer_range
									SCREAMING_SNAKE_CASE				=	type_vocab_size
									SCREAMING_SNAKE_CASE				=	layer_norm_eps
									SCREAMING_SNAKE_CASE				=	use_cache
									SCREAMING_SNAKE_CASE				=	False






class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				@property
				def 							A					(				self			)    ->    Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







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




				@property
				def 							A					(				self			)    ->    float:
									"""simple docstring"""







									return 1E-4


"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('k must be an integer.'			)
					if k < 0:
										raise ValueError('k integer must be greater or equal to zero.'			)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1			) + fibonacci(k - 2			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1			)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()
