

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @slow
  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__					=					TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

       SCREAMING_SNAKE_CASE__					=					tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]			,     dtype=tf.intaa			,     )  # J'aime le camembert !"

       SCREAMING_SNAKE_CASE__					=					model(UpperCamelCase__      )['last_hidden_state']
       SCREAMING_SNAKE_CASE__					=					tf.TensorShape((1, 10, 768)      )
       self.assertEqual(output.shape			,     UpperCamelCase__      )
       # compare the actual values for a slice.
       SCREAMING_SNAKE_CASE__					=					tf.convert_to_tensor(
           [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]			,     dtype=tf.floataa			,     )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,     expected_slice.numpy()			,     atol=1e-4      )      )





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


