



"""simple docstring"""


def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					1
						UpperCAmelCase			 =					0
						UpperCAmelCase			 =					1
						UpperCAmelCase			 =					1
						UpperCAmelCase			 =					3
						UpperCAmelCase			 =					3
						UpperCAmelCase			 =					limit
						UpperCAmelCase			 =					0

						UpperCAmelCase			 =					''
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(lowercase_		)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		UpperCAmelCase			 =					10 * (r - n * t)
																		UpperCAmelCase			 =					((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		UpperCAmelCase			 =					nr
												else:
																		UpperCAmelCase			 =					(2 * q + r) * l
																		UpperCAmelCase			 =					(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		UpperCAmelCase			 =					nn
																		UpperCAmelCase			 =					nr
						return result

def 					_lowerCAmelCase (  ):
						print(F"""{calculate_pi(50		) = }"""		)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
				main()







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    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						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]	,						_lowerCamelCase	,						atol=1E-4			)			)




