






'''simple docstring'''





from functools import lru_cache



@lru_cache
def 			__UpperCAmelCase				(lowercase__				)		->				int:

						'''simple docstring'''


						if not isinstance(lowercase__	,lowercase__				):
												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				(lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						a_				     =   len(lowercase__				)
						# Find m such that F_m >= n where F_i is the i_th fibonacci number.
						a_				     =   0
						while True:
												if fibonacci(lowercase__				) >= len_list:
																		a_				     =   i
																		break
												i += 1
						a_				     =   0
						while fibb_k > 0:
												a_				     =   min(
												    offset + fibonacci(fibb_k - 1				)	,len_list - 1				)  # Prevent out of range
												a_				     =   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()











'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


a_      = logging.get_logger(__name__)

a_      = {
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		_UpperCAmelCase         ='''mobilebert'''



		def __init__(    self:    Union[str, Any]				,   a:    str=3_05_22				,   a:    List[str]=5_12				,   a:    List[str]=24				,   a:    Dict=4				,   a:    List[str]=5_12				,   a:    Optional[int]="relu"				,   a:    Dict=0.0				,   a:    Union[str, Any]=0.1				,   a:    int=5_12				,   a:    Optional[Any]=2				,   a:    List[Any]=0.02				,   a:    Optional[Any]=1e-12				,   a:    List[Any]=0				,   a:    Optional[int]=1_28				,   a:    Union[str, Any]=True				,   a:    Dict=True				,   a:    List[Any]=1_28				,   a:    Any=False				,   a:    Any=True				,   a:    Dict=4				,   a:    List[Any]="no_norm"				,   a:    Any=True				,   a:    Tuple=None				,   **a:    Any				,   )  ->Any:






								'''simple docstring'''


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

								a_				     =   vocab_size
								a_				     =   hidden_size
								a_				     =   num_hidden_layers
								a_				     =   num_attention_heads
								a_				     =   hidden_act
								a_				     =   intermediate_size
								a_				     =   hidden_dropout_prob
								a_				     =   attention_probs_dropout_prob
								a_				     =   max_position_embeddings
								a_				     =   type_vocab_size
								a_				     =   initializer_range
								a_				     =   layer_norm_eps
								a_				     =   embedding_size
								a_				     =   trigram_input
								a_				     =   use_bottleneck
								a_				     =   intra_bottleneck_size
								a_				     =   use_bottleneck_attention
								a_				     =   key_query_shared_bottleneck
								a_				     =   num_feedforward_networks
								a_				     =   normalization_type
								a_				     =   classifier_activation

								if self.use_bottleneck:
														a_				     =   intra_bottleneck_size
								else:
														a_				     =   hidden_size

								a_				     =   classifier_dropout





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		@property
		def _lowerCAmelCase     (    self:    Optional[int])  ->Mapping[str, Mapping[int, str]]:






								'''simple docstring'''


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



