



"""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 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					@slow
					def   lowerCAmelCase_							(			self:				int		)  ->      int:
											snake_case_					:List[Any]		=     CamembertModel.from_pretrained("""camembert-base"""		)
											model.to(snake_case		)

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






'''simple docstring'''

import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow:
						assert True
			else:
						assert False, "should have been skipped"




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow and torch_device == "cuda":
						assert True
			else:
						assert False, "should have been skipped"





@require_torch
class __magic_name__(						unittest.TestCase      ):






						@slow
						@require_torch_gpu
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




@require_torch_gpu
@slow
def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()