




import timeit







class 				UpperCAmelCase:



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase    ) ->      Tuple:





								"""simple docstring"""


								if isinstance(lowerCamelCase	,				lowerCamelCase    ):  # Initializes a matrix identical to the one provided.
															lowercase__  :	int        =							arg
															lowercase__  :	str        =							len(lowerCamelCase    )
								else:  # Initializes a square matrix of the given size and set values to zero.
															lowercase__  :	str        =							arg
															lowercase__  :	Tuple        =							[[0 for _ in range(self.n    )] for _ in range(self.n    )]







	def __mul__(					self	,				lowerCamelCase    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Dict        =							Matrix(self.n    )
								for i in range(self.n    ):
															for j in range(self.n    ):
																						for k in range(self.n    ):
																													matrix.t[i][j] += self.t[i][k] * b.t[k][j]
								return matrix

def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							lowercase__  :	Dict        =							Matrix([[1, 0], [0, 1]]    )
							while b > 0:
														if b & 1:
																					matrix *= a
														a *= a
														b >>= 1
							return matrix
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Tuple:
							# Trivial Cases
							if n == 1:
														return fa
							elif n == 2:
														return fa
							lowercase__  :	Optional[int]        =							Matrix([[1, 1], [1, 0]]    )
							lowercase__  :	Any        =							modular_exponentiation(SCREAMING_SNAKE_CASE_    ,n - 2    )
							return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Optional[int]:
							# Trivial Cases
							if n == 1:
														return fa
							elif n == 2:
														return fa

							lowercase__  :	List[str]        =							fa
							lowercase__  :	Optional[Any]        =							fa
							n -= 2

							while n > 0:
														lowercase__ ,       lowercase__  :	str        =							fn_a + fn_a, fn_a
														n -= 1

							return fn_a
def  snake_case_					(		)      ->			Union[str, Any]:
							lowercase__  :	str        =							"\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
							lowercase__  :	Tuple        =							"fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
							lowercase__  :	Any        =							timeit.timeit(setup=SCREAMING_SNAKE_CASE_    ,stmt=SCREAMING_SNAKE_CASE_    ,number=1_00    )
							print("With matrix exponentiation the average execution time is "    ,exec_time / 1_00    )
							return exec_time
def  snake_case_					(		)      ->			int:
							lowercase__  :	int        =							"\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
							lowercase__  :	Optional[int]        =							"simple_fibonacci(randint(1,70000), 1, 1)"
							lowercase__  :	List[Any]        =							timeit.timeit(setup=SCREAMING_SNAKE_CASE_    ,stmt=SCREAMING_SNAKE_CASE_    ,number=1_00    )
							print(
							    "Without matrix exponentiation the average execution time is "    ,exec_time / 1_00    )
							return exec_time
def  snake_case_					(		)      ->			Union[str, Any]:
							matrix_exponentiation_time()
							simple_fibonacci_time()


if __name__ == "__main__":
						main()





from collections import OrderedDict
from typing import Mapping

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


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


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

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






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





								"""simple docstring"""


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