
"""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   _UpperCAmelCase					(       unittest.TestCase):
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = CamembertModel.from_pretrained("camembert-base"  )
      model.to(snake_case_  )

      _snake_case  :    Union[str, Any]            = torch.tensor(
          [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,     device=snake_case_    ,     dtype=torch.long    ,     )  # J'aime le camembert !
      with torch.no_grad():
        _snake_case  :    Optional[Any]            = model(snake_case_  )["last_hidden_state"]
      _snake_case  :    Union[str, Any]            = torch.Size((1, 10, 7_68)  )
      self.assertEqual(output.shape    ,     snake_case_  )
      # compare the actual values for a slice.
      _snake_case  :    Optional[Any]            = torch.tensor(
          [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     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"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

