

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 					A		(				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    @register_to_config
    def __init__( self					:			Union[str, Any],lowercase_					:			int,lowercase_					:			int,lowercase_					:			int,lowercase_					:			float,lowercase_					:			int,lowercase_					:			int,lowercase_					:			int,lowercase_					:			int,lowercase_					:			str,lowercase_					:			bool = False,)->       Union[str, Any]:





        '''simple docstring'''







        super().__init__()

        A__        =  nn.Linear(lowercase_,lowercase_,bias=lowercase_						)

        A__        =  nn.Embedding(lowercase_,lowercase_						)
        A__        =  False

        A__        =  nn.Dropout(p=lowercase_						)

        A__        =  TaConfig(
            d_model=lowercase_,num_heads=lowercase_,d_kv=lowercase_,d_ff=lowercase_,feed_forward_proj=lowercase_,dropout_rate=lowercase_,is_decoder=lowercase_,is_encoder_decoder=lowercase_,)
        A__        =  nn.ModuleList()
        for lyr_num in range(lowercase_						):
            A__        =  TaBlock(lowercase_						)
            self.encoders.append(lowercase_						)

        A__        =  TaLayerNorm(lowercase_						)
        A__        =  nn.Dropout(p=lowercase_						)


    def 		snake_case__	( self					:			List[str],lowercase_					:			List[str],lowercase_					:			List[Any]						)->       int:





        '''simple docstring'''







        A__        =  self.input_proj(lowercase_						)

        # terminal relative positional encodings
        A__        =  encoder_inputs.shape[1]
        A__        =  torch.arange(lowercase_,device=encoder_inputs.device						)

        A__        =  encoder_inputs_mask.sum(-1						)
        A__        =  torch.roll(input_positions.unsqueeze(0						),tuple(seq_lens.tolist()						),dims=0						)
        x += self.position_encoding(lowercase_						)

        A__        =  self.dropout_pre(lowercase_						)

        # inverted the attention mask
        A__        =  encoder_inputs.size()
        A__        =  self.get_extended_attention_mask(lowercase_,lowercase_						)

        for lyr in self.encoders:
            A__        =  lyr(lowercase_,lowercase_						)[0]
        A__        =  self.layer_norm(lowercase_						)

        return self.dropout_post(lowercase_						), encoder_inputs_mask




import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







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


    def __mul__( self					:			Union[str, Any],lowercase_					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  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__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  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__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






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

    A__        =  fa
    A__        =  fa
    n -= 2

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

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

