



from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase				:					int     					=logging.get_logger(__name__)

_lowercase				:					Optional[Any]     					={
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}
class lowerCAmelCase_       (   __lowerCamelCase			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		'prophetnet'
	A_       :     Any									=		['past_key_values']
	A_       :     Optional[Any]									=		{
	    'num_attention_heads': 'num_encoder_attention_heads',
	}

	def __init__(	self   ,       lowerCamelCase = 0.1   ,       lowerCamelCase = "gelu"   ,       lowerCamelCase = 3_0522   ,       lowerCamelCase = 1024   ,       lowerCamelCase = 4096   ,       lowerCamelCase = 12   ,       lowerCamelCase = 16   ,       lowerCamelCase = 4096   ,       lowerCamelCase = 12   ,       lowerCamelCase = 16   ,       lowerCamelCase = 0.1   ,       lowerCamelCase = 0.1   ,       lowerCamelCase = 512   ,       lowerCamelCase = 0.0_2   ,       lowerCamelCase = True   ,       lowerCamelCase = True   ,       lowerCamelCase = 0   ,       lowerCamelCase = 2   ,       lowerCamelCase = 32   ,       lowerCamelCase = 128   ,       lowerCamelCase = False   ,       lowerCamelCase = 0.0   ,       lowerCamelCase = True   ,       lowerCamelCase = 0   ,       lowerCamelCase = 1   ,       lowerCamelCase = 2   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  vocab_size
				a__					=  hidden_size
				a__					=  encoder_ffn_dim
				a__					=  num_encoder_layers
				a__					=  num_encoder_attention_heads
				a__					=  decoder_ffn_dim
				a__					=  num_decoder_layers
				a__					=  num_decoder_attention_heads
				a__					=  max_position_embeddings
				a__					=  init_std  # Normal(0, this parameter)
				a__					=  activation_function

				# parameters for prophetnet
				a__					=  ngram
				a__					=  num_buckets
				a__					=  relative_max_distance
				a__					=  disable_ngram_loss
				a__					=  eps

				# 3 Types of Dropout
				a__					=  attention_dropout
				a__					=  activation_dropout
				a__					=  dropout

				a__					=  use_cache

				super().__init__(
				    pad_token_id=__lowerCAmelCase   ,       bos_token_id=__lowerCAmelCase   ,       eos_token_id=__lowerCAmelCase   ,       is_encoder_decoder=__lowerCAmelCase   ,       add_cross_attention=__lowerCAmelCase   ,       decoder_start_token_id=__lowerCAmelCase   ,       **__lowerCAmelCase   ,       )

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				raise NotImplementedError(
				    """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
				    """ `num_decoder_layers`."""       )


'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   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 __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

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

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


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


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


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


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






