
"""simple docstring"""
import timeit




class    _snake_case  :






							def __init__(					self						:			Dict						, snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



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






							def __mul__(					self						:			Union[str, Any]						, snake_case__						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						Any		    =					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     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


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


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					# Trivial Cases
					if n == 1:
										return fa
					elif n == 2:
										return fa
					__a:						Optional[int]		    =					Matrix([[1, 1], [1, 0]] )
					__a:						Any		    =					modular_exponentiation(lowerCamelCase_	,				n - 2 )
					return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					# Trivial Cases
					if n == 1:
										return fa
					elif n == 2:
										return fa

					__a:						Union[str, Any]		    =					fa
					__a:						str		    =					fa
					n -= 2

					while n > 0:
										__a    ,				__a:						int		    =					fn_a + fn_a, fn_a
										n -= 1

					return fn_a


def     lowerCamelCase_    (	)     ->			str:


					'''simple docstring'''
					__a:						Tuple		    =					"""
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
					__a:						List[str]		    =					"""fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
					__a:						Any		    =					timeit.timeit(setup=lowerCamelCase_	,				stmt=lowerCamelCase_	,				number=1_00 )
					print("""With matrix exponentiation the average execution time is """	,				exec_time / 1_00 )
					return exec_time


def     lowerCamelCase_    (	)     ->			List[str]:


					'''simple docstring'''
					__a:						Optional[int]		    =					"""
from random import randint
from __main__ import simple_fibonacci
"""
					__a:						int		    =					"""simple_fibonacci(randint(1,70000), 1, 1)"""
					__a:						Any		    =					timeit.timeit(setup=lowerCamelCase_	,				stmt=lowerCamelCase_	,				number=1_00 )
					print(
					    """Without matrix exponentiation the average execution time is """	,				exec_time / 1_00 )
					return exec_time


def     lowerCamelCase_    (	)     ->			List[Any]:


					'''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 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, 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]						, snake_case__						, atol=1e-4    )    )
