


'''simple docstring'''
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


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

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




@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     CamembertTokenizer
      lowerCAmelCase__		     =     CamembertTokenizerFast
      lowerCAmelCase__		     =     True
      lowerCAmelCase__		     =     True






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          super().setUp()

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






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				'''<pad>'''
          UpperCAmelCase__			:		int													=				1

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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				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 )						,						1_004 )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          self.assertEqual(self.get_tokenizer().vocab_size						,						1_005 )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				CamembertTokenizer(_A )
          tokenizer.save_pretrained(self.tmpdirname )
          UpperCAmelCase__			:		Optional[int]													=				CamembertTokenizerFast.from_pretrained(self.tmpdirname )

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

          UpperCAmelCase__			:		int													=				tokenizer.encode(_A )
          UpperCAmelCase__			:		Union[str, Any]													=				rust_tokenizer.encode(_A )
          self.assertListEqual(_A						,						_A )

          UpperCAmelCase__			:		List[str]													=				tokenizer.encode(_A						,						add_special_tokens=_A )
          UpperCAmelCase__			:		Dict													=				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)
          UpperCAmelCase__			:		Any													=				tokenizer.convert_ids_to_tokens(_A )
          UpperCAmelCase__			:		Optional[Any]													=				rust_tokenizer.tokenize(_A )
          self.assertListEqual(_A						,						_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__			:		Optional[Any]													=				self.get_tokenizer()
          UpperCAmelCase__			:		str													=				self.get_rust_tokenizer()

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

          UpperCAmelCase__			:		Union[str, Any]													=				tokenizer.tokenize(_A )
          UpperCAmelCase__			:		Optional[int]													=				rust_tokenizer.tokenize(_A )
          self.assertListEqual(_A						,						_A )

          UpperCAmelCase__			:		List[Any]													=				tokenizer.encode(_A						,						add_special_tokens=_A )
          UpperCAmelCase__			:		int													=				rust_tokenizer.encode(_A						,						add_special_tokens=_A )
          self.assertListEqual(_A						,						_A )

          UpperCAmelCase__			:		List[Any]													=				self.get_rust_tokenizer()
          UpperCAmelCase__			:		Optional[Any]													=				tokenizer.encode(_A )
          UpperCAmelCase__			:		int													=				rust_tokenizer.encode(_A )
          self.assertListEqual(_A						,						_A )






      @slow
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
          UpperCAmelCase__			:		Tuple													=				[
              '''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						,						)




'''simple docstring'''
import pprint

import requests

UpperCamelCase__    					=      '''https://zenquotes.io/api'''





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/today'''						).json()





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/random'''						).json()


if __name__ == "__main__":
  UpperCamelCase__    					=      random_quotes()
  pprint.pprint(response)
