




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 							__SCREAMING_SNAKE_CASE				(						unittest.TestCase    ):

					@slow
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				CamembertModel.from_pretrained('camembert-base'   )
						model.to(__lowercase   )

						lowerCamelCase_				:						Union[str, Any]				  =				torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]					,			device=__lowercase					,			dtype=torch.long					,			)  # J'aime le camembert !
						with torch.no_grad():
							lowerCamelCase_				:						Optional[Any]				  =				model(__lowercase   )['last_hidden_state']
						lowerCamelCase_				:						List[str]				  =				torch.Size((1, 10, 768)   )
						self.assertEqual(output.shape					,			__lowercase   )
						# compare the actual values for a slice.
						lowerCamelCase_				:						List[Any]				  =				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=__lowercase					,			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]					,			__lowercase					,			atol=1E-4   )   )




"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					int:
							snake_case_     							=		quantum_engine.allocate_qubit()
							H | qubit
							Measure | qubit
							return int(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				__SCREAMING_SNAKE_CASE				:     Dict		=     projectq.MainEngine()

				# Generate a list of 10 random numbers
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     [get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print('Random numbers', random_numbers_list)



