






"""simple docstring"""





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= BarthezTokenizer
    SCREAMING_SNAKE_CASE						:     Tuple						= BarthezTokenizerFast
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= True
    SCREAMING_SNAKE_CASE						:     Tuple						= True






    def    UpperCamelCase       (       self     :							Any					)     ->		Tuple:
      super().setUp()

      lowerCamelCase_         =						BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'					)
      tokenizer.save_pretrained(self.tmpdirname					)
      tokenizer.save_pretrained(self.tmpdirname       ,						legacy_format=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer






    def    UpperCamelCase       (       self     :							str					)     ->		Any:
      lowerCamelCase_         =						'<pad>'
      lowerCamelCase_         =						1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						list(self.get_tokenizer().get_vocab().keys()					)

      self.assertEqual(vocab_keys[0]       ,						'<s>'					)
      self.assertEqual(vocab_keys[1]       ,						'<pad>'					)
      self.assertEqual(vocab_keys[-1]       ,						'<mask>'					)
      self.assertEqual(len(__SCREAMING_SNAKE_CASE					)       ,						101122					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		List[str]:
      self.assertEqual(self.get_tokenizer().vocab_size       ,						101122					)






    @require_torch
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      lowerCamelCase_         =						['A long paragraph for summarization.', 'Another paragraph for summarization.']
      lowerCamelCase_         =						[0, 57, 3018, 70307, 91, 2]

      lowerCamelCase_         =						self.tokenizer(
          __SCREAMING_SNAKE_CASE       ,						max_length=len(__SCREAMING_SNAKE_CASE					)       ,						padding=__SCREAMING_SNAKE_CASE       ,						truncation=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)
      self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      self.assertEqual((2, 6)       ,						batch.input_ids.shape					)
      self.assertEqual((2, 6)       ,						batch.attention_mask.shape					)
      lowerCamelCase_         =						batch.input_ids.tolist()[0]
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							int					)     ->		List[Any]:
      if not self.test_rust_tokenizer:
        return

      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      lowerCamelCase_         =						'I was born in 92000, and this is falsé.'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    @slow
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Union[str, Any]:
      # fmt: off
      lowerCamelCase_         =						{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      lowerCamelCase_         =						[
          'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
          'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
          'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
          'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
          'telles que la traduction et la synthèse de texte.',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=__SCREAMING_SNAKE_CASE       ,						model_name='moussaKam/mbarthez'       ,						revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,						sequences=__SCREAMING_SNAKE_CASE       ,						)












"""simple docstring"""





def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		int )      ->				int:
  lowerCamelCase_         =						1  # To kept the Calculated Value
  # Since C(n, k) = C(n, n-k)
  if k > (n - k):
    lowerCamelCase_         =						n - k
  # Calculate C(n,k)
  for i in range(_lowerCamelCase ):
    result *= n - i
    result //= i + 1
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  return binomial_coefficient(2 * node_count		,				_lowerCamelCase ) // (node_count + 1)







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  if n < 0:
    raise ValueError('factorial() not defined for negative values' )
  lowerCamelCase_         =						1
  for i in range(1		,				n + 1 ):
    result *= i
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				int:
  return catalan_number(_lowerCamelCase ) * factorial(_lowerCamelCase )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				int					    =						int(input('''Enter the number of nodes: ''').strip() or 0)
  if node_count <= 0:
    raise ValueError('''We need some nodes to work with.''')
  print(
      F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
      F'''binary trees and {catalan_number(node_count)} binary search trees.'''
  )




