


def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 if not isinstance(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Dict							=						(
      'operation can not be conducted on a object of type '
      f'{type(SCREAMING_SNAKE_CASE__						).__name__}'
  )
  raise TypeError(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[int]							=						''
 for _ in range(0			,       32						):
  bit_string += str(number % 2						)
  __lowerCamelCase		:							Tuple							=						number >> 1
 return bit_string
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 if number < 0:
  raise ValueError('the value of input must be positive'						)
 elif isinstance(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
  raise TypeError('Input value must be a \'int\' type'						)
 elif isinstance(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
  raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''						)
 __lowerCamelCase		:							Optional[int]							=						0
 # iterator over [1 to 32],since we are dealing with 32 bit integer
 for _ in range(1			,       33						):
  # left shift the bits by unity
  __lowerCamelCase		:							List[Any]							=						result << 1
  # get the end bit
  __lowerCamelCase		:							List[Any]							=						number % 2
  # right shift the bits by unity
  __lowerCamelCase		:							List[str]							=						number >> 1
  # add that bit to our ans
  __lowerCamelCase		:							List[Any]							=						result | end_bit
 return get_reverse_bit_string(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





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        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						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=a						,      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]						,      a						,      atol=1e-4				)				)

