
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(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      BarthezTokenizer
  SCREAMING_SNAKE_CASE__         =      BarthezTokenizerFast
  SCREAMING_SNAKE_CASE__         =      True
  SCREAMING_SNAKE_CASE__         =      True







  def 	UpperCAmelCase_ (self						):
       super().setUp()

       UpperCamelCase__				=    BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""						)
       tokenizer.save_pretrained(self.tmpdirname						)
       tokenizer.save_pretrained(self.tmpdirname	,      legacy_format=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """<pad>"""
       UpperCamelCase__				=    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						):
       UpperCamelCase__				=    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_						)	,      10_11_22						)







  def 	UpperCAmelCase_ (self						):
       self.assertEqual(self.get_tokenizer().vocab_size	,      10_11_22						)







  @require_torch
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       UpperCamelCase__				=    [0, 57, 30_18, 7_03_07, 91, 2]

       UpperCamelCase__				=    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						)
       UpperCamelCase__				=    batch.input_ids.tolist()[0]
       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       if not self.test_rust_tokenizer:
            return

       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_rust_tokenizer()

       UpperCamelCase__				=    """I was born in 92000, and this is falsé."""

       UpperCamelCase__				=    tokenizer.tokenize(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_						)
       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tokenizer.encode(SCREAMING_SNAKE_CASE_	,      add_special_tokens=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    rust_tokenizer.encode(SCREAMING_SNAKE_CASE_	,      add_special_tokens=SCREAMING_SNAKE_CASE_						)
       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.get_rust_tokenizer()
       UpperCamelCase__				=    tokenizer.encode(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    rust_tokenizer.encode(SCREAMING_SNAKE_CASE_						)
       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       # fmt: off
       UpperCamelCase__				=    {"""input_ids""": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 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.
       UpperCamelCase__				=    [
           """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_	,      )







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa





