


'''simple docstring'''

import timeit





class 					snake_case   :





		"""simple docstring"""




		def __init__(   self      ,  UpperCamelCase					):







			"""simple docstring"""
			if isinstance(UpperCamelCase      ,  UpperCamelCase					):  # Initializes a matrix identical to the one provided.
				lowerCamelCase_								=  arg
				lowerCamelCase_								=  len(UpperCamelCase					)
			else:  # Initializes a square matrix of the given size and set values to zero.
				lowerCamelCase_								=  arg
				lowerCamelCase_								=  [[0 for _ in range(self.n					)] for _ in range(self.n					)]







		def __mul__(   self      ,  UpperCamelCase					):







			"""simple docstring"""
			lowerCamelCase_								=  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 (				UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						List[Any]		):
	lowerCamelCase_								=  Matrix([[1, 0], [0, 1]]		)
	while b > 0:
		if b & 1:
			matrix *= a
		a *= a
		b >>= 1
	return matrix





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						Any		):
	# Trivial Cases
	if n == 1:
		return fa
	elif n == 2:
		return fa
	lowerCamelCase_								=  Matrix([[1, 1], [1, 0]]		)
	lowerCamelCase_								=  modular_exponentiation(UpperCAmelCase_ ,    n - 2		)
	return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def __snake_case (				UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						Union[str, Any]		):
	# Trivial Cases
	if n == 1:
		return fa
	elif n == 2:
		return fa

	lowerCamelCase_								=  fa
	lowerCamelCase_								=  fa
	n -= 2

	while n > 0:
		lowerCamelCase_			,lowerCamelCase_								=  fn_a + fn_a, fn_a
		n -= 1

	return fn_a





def __snake_case (				):
	lowerCamelCase_								=  "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
	lowerCamelCase_								=  "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
	lowerCamelCase_								=  timeit.timeit(setup=UpperCAmelCase_ ,    stmt=UpperCAmelCase_ ,    number=100		)
	print("With matrix exponentiation the average execution time is " ,    exec_time / 100		)
	return exec_time





def __snake_case (				):
	lowerCamelCase_								=  "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
	lowerCamelCase_								=  "simple_fibonacci(randint(1,70000), 1, 1)"
	lowerCamelCase_								=  timeit.timeit(setup=UpperCAmelCase_ ,    stmt=UpperCAmelCase_ ,    number=100		)
	print(
	    "Without matrix exponentiation the average execution time is " ,    exec_time / 100		)
	return exec_time





def __snake_case (				):
	matrix_exponentiation_time()
	simple_fibonacci_time()


if __name__ == "__main__":
					main()







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a_  :    Optional[Any]      =			logging.get_logger(__name__)

a_  :    Tuple      =			{
    """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 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "electra"






  def __init__(   self      ,  UpperCamelCase=3_0522      ,  UpperCamelCase=128      ,  UpperCamelCase=256      ,  UpperCamelCase=12      ,  UpperCamelCase=4      ,  UpperCamelCase=1024      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=512      ,  UpperCamelCase=2      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-12      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0      ,  UpperCamelCase="absolute"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase      ,  **UpperCamelCase					)

   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  embedding_size
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  layer_norm_eps

   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_last_dropout
   lowerCamelCase_								=  position_embedding_type
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  classifier_dropout





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







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



