


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__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Dict      =						BarthezTokenizer
 _lowercase     :					Tuple      =						BarthezTokenizerFast
 _lowercase     :					List[str]      =						True
 _lowercase     :					Optional[int]      =						True

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      super().setUp()

      _lowerCAmelCase   :					Optional[int]        =    BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"       )
      tokenizer.save_pretrained(self.tmpdirname       )
      tokenizer.save_pretrained(self.tmpdirname     ,						legacy_format=A_       )
      _lowerCAmelCase   :					str        =    tokenizer

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    "<pad>"
      _lowerCAmelCase   :					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    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    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(A_       )     ,						1_0_1_1_2_2       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size     ,						1_0_1_1_2_2       )

 @require_torch
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
      _lowerCAmelCase   :					List[str]        =    [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

      _lowerCAmelCase   :					Optional[Any]        =    self.tokenizer(
          A_     ,						max_length=len(A_       )     ,						padding=A_     ,						truncation=A_     ,						return_tensors="pt"       )
      self.assertIsInstance(A_     ,						A_       )

      self.assertEqual((2, 6)     ,						batch.input_ids.shape       )
      self.assertEqual((2, 6)     ,						batch.attention_mask.shape       )
      _lowerCAmelCase   :					Optional[Any]        =    batch.input_ids.tolist()[0]
      self.assertListEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      if not self.test_rust_tokenizer:
           return

      _lowerCAmelCase   :					List[str]        =    self.get_tokenizer()
      _lowerCAmelCase   :					Dict        =    self.get_rust_tokenizer()

      _lowerCAmelCase   :					Dict        =    "I was born in 92000, and this is falsé."

      _lowerCAmelCase   :					Optional[int]        =    tokenizer.tokenize(A_       )
      _lowerCAmelCase   :					Dict        =    rust_tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					int        =    tokenizer.encode(A_     ,						add_special_tokens=A_       )
      _lowerCAmelCase   :					Optional[int]        =    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   :					Tuple        =    rust_tokenizer.encode(A_       )
      self.assertListEqual(A_     ,						A_       )


 @slow
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    {"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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   :					Union[str, Any]        =    [
          "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="moussaKam/mbarthez"     ,						revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"     ,						sequences=A_     ,						)









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






