



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     ):







		@slow
		def 					a_  (    self  :   Optional[int]					)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	CamembertModel.from_pretrained('''camembert-base'''					)
						model.to(snake_case__					)

						_UpperCAmelCase						:  int     					=	torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,		device=snake_case__       ,		dtype=torch.long       ,		)  # J'aime le camembert !
						with torch.no_grad():
										_UpperCAmelCase						:  Tuple     					=	model(snake_case__					)['''last_hidden_state''']
						_UpperCAmelCase						:  Union[str, Any]     					=	torch.Size((1, 10, 768)					)
						self.assertEqual(output.shape       ,		snake_case__					)
						# compare the actual values for a slice.
						_UpperCAmelCase						:  Dict     					=	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					)					)

from hashlib import mda, shaaaa

__snake_case         =     (shaaaa, mda)







class       __snake_case :
 def __init__(	self		, snake_case__ = 8    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=0b0
       UpperCAmelCase     :			str     	=size
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.hash_(snake_case__    )
       self.bitarray |= h
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.hash_(snake_case__    )
       return (h & self.bitarray) == h
 def __contains__(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       return self.exists(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=bin(snake_case__    )[2:]
       return res.zfill(self.size    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.bitarray    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=0b0
       for func in HASH_FUNCTIONS:
             UpperCAmelCase     :			Optional[Any]     	=(
                 int.from_bytes(func(value.encode()    ).digest()		, '''little'''    ) % self.size
             )
             res |= 2**position
       return res
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.hash_(snake_case__    )    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=bin(self.bitarray    ).count('''1'''    )
       return (n_ones / self.size) ** len(snake_case__    )
