





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase			       =      get_tests_dir('fixtures/test_sentencepiece.model')
_lowerCamelCase			       =      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_lowerCamelCase			       =      'pt' if is_torch_available() else 'tf'







@require_sentencepiece
@require_tokenizers
class 			__A       (      lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      CamembertTokenizer
      UpperCAmelCase__						=      CamembertTokenizerFast
      UpperCAmelCase__						=      True
      UpperCAmelCase__						=      True


      def 				__snake_case			(		self):



             """simple docstring"""





             super().setUp()

             # We have a SentencePiece fixture for testing
             _lowerCamelCase							:     Optional[Any]	=		CamembertTokenizer(a__)
             tokenizer.save_pretrained(self.tmpdirname)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		'''<pad>'''
             _lowerCamelCase							:     Tuple	=		1

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		list(self.get_tokenizer().get_vocab().keys())

             self.assertEqual(vocab_keys[0]      ,							'''<s>NOTUSED''')
             self.assertEqual(vocab_keys[1]      ,							'''<pad>''')
             self.assertEqual(vocab_keys[-1]      ,							'''<mask>''')
             self.assertEqual(len(a__)      ,							1004)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.assertEqual(self.get_tokenizer().vocab_size      ,							1005)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		CamembertTokenizer(a__)
             tokenizer.save_pretrained(self.tmpdirname)
             _lowerCamelCase							:     Optional[Any]	=		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

             _lowerCamelCase							:     Optional[int]	=		'''I was born in 92000, and this is falsé.'''

             _lowerCamelCase							:     Optional[int]	=		tokenizer.encode(a__)
             _lowerCamelCase							:     List[Any]	=		rust_tokenizer.encode(a__)
             self.assertListEqual(a__      ,							a__)

             _lowerCamelCase							:     Optional[Any]	=		tokenizer.encode(a__      ,							add_special_tokens=a__)
             _lowerCamelCase							:     Optional[Any]	=		rust_tokenizer.encode(a__      ,							add_special_tokens=a__)
             self.assertListEqual(a__      ,							a__)

             # <unk> tokens are not the same for `rust` than for `slow`.
             # Because spm gives back raw token instead of `unk` in EncodeAsPieces
             # tokens = tokenizer.tokenize(sequence)
             _lowerCamelCase							:     Tuple	=		tokenizer.convert_ids_to_tokens(a__)
             _lowerCamelCase							:     List[str]	=		rust_tokenizer.tokenize(a__)
             self.assertListEqual(a__      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase							:     List[str]	=		self.get_tokenizer()
             _lowerCamelCase							:     int	=		self.get_rust_tokenizer()

             _lowerCamelCase							:     Tuple	=		'''I was born in 92000, and this is falsé.'''

             _lowerCamelCase							:     List[Any]	=		tokenizer.tokenize(a__)
             _lowerCamelCase							:     Optional[Any]	=		rust_tokenizer.tokenize(a__)
             self.assertListEqual(a__      ,							a__)

             _lowerCamelCase							:     Dict	=		tokenizer.encode(a__      ,							add_special_tokens=a__)
             _lowerCamelCase							:     List[str]	=		rust_tokenizer.encode(a__      ,							add_special_tokens=a__)
             self.assertListEqual(a__      ,							a__)

             _lowerCamelCase							:     Tuple	=		self.get_rust_tokenizer()
             _lowerCamelCase							:     Union[str, Any]	=		tokenizer.encode(a__)
             _lowerCamelCase							:     Optional[Any]	=		rust_tokenizer.encode(a__)
             self.assertListEqual(a__      ,							a__)



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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]]}  # noqa: E501
             # fmt: on

             # camembert is a french model. So we also use french texts.
             _lowerCamelCase							:     str	=		[
                 '''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=a__      ,							model_name='''camembert-base'''      ,							revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''      ,							sequences=a__      ,							)








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

