




'''simple docstring'''





import timeit




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




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




			def __mul__(      self       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   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   UpperCamelCase_(			snake_case							: Union[str, Any]							,       snake_case							: int  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: Optional[int]							,       snake_case							: Any							,       snake_case							: List[str]  ):

							'''simple docstring'''






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


def   UpperCamelCase_(			snake_case							: Optional[int]							,       snake_case							: List[Any]							,       snake_case							: Optional[Any]  ):

							'''simple docstring'''






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

							snake_case_			      =   fa
							snake_case_			      =   fa
							n -= 2

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

							return fn_a


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
							snake_case_			      =   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
							snake_case_			      =   timeit.timeit(setup=snake_case							,       stmt=snake_case							,       number=1_0_0  )
							print("With matrix exponentiation the average execution time is "							,       exec_time / 1_0_0  )
							return exec_time


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
							snake_case_			      =   "simple_fibonacci(randint(1,70000), 1, 1)"
							snake_case_			      =   timeit.timeit(setup=snake_case							,       stmt=snake_case							,       number=1_0_0  )
							print(
							    "Without matrix exponentiation the average execution time is "							,       exec_time / 1_0_0  )
							return exec_time


def   UpperCamelCase_(			):

							'''simple docstring'''






							matrix_exponentiation_time()
							simple_fibonacci_time()


if __name__ == "__main__":
		main()










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




