
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"""
  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=_A						)
       UpperCamelCase__				=				tokenizer







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				'''<pad>'''
       UpperCamelCase__				=				1

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







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







  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(_A						)
       UpperCamelCase__				=				rust_tokenizer.tokenize(_A						)
       self.assertListEqual(_A       ,			_A						)

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

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







  @slow
  def        UpperCAmelCase_					(self						):
       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=_A       ,			model_name="""moussaKam/mbarthez"""       ,			revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""       ,			sequences=_A       ,			)









'''simple docstring'''
import re

from filelock import FileLock


try:
  import nltk

  UpperCamelCase__    					=      True
except (ImportError, ModuleNotFoundError):
  UpperCamelCase__    					=      False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)





def 					a__       (					lowerCAmelCase__						)    ->	str:
    re.sub('''<n>'''  ,					''''''  ,					lowerCAmelCase__						)  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(lowerCAmelCase__						)						)
