


'''simple docstring'''

import logging
import os

from .state import PartialState
class      _lowerCAmelCase				(			logging.LoggerAdapter			):





  """simple docstring"""



  @staticmethod
  def __A				(      SCREAMING_SNAKE_CASE      :     Dict				)	->						Optional[int]:
    """simple docstring"""

    lowerCAmelCase											=	PartialState()
    return not main_process_only or (main_process_only and state.is_main_process)


  def __A				(      self      :     List[str]      , SCREAMING_SNAKE_CASE      :     Dict      , SCREAMING_SNAKE_CASE      :     Any      , *SCREAMING_SNAKE_CASE      :     Any      , **SCREAMING_SNAKE_CASE      :     Tuple				)	->						List[Any]:
    """simple docstring"""

    if PartialState._shared_state == {}:
      raise RuntimeError(
          "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."				)
    lowerCAmelCase											=	kwargs.pop("main_process_only"      , A_				)
    lowerCAmelCase											=	kwargs.pop("in_order"      , A_				)

    if self.isEnabledFor(A_				):
      if self._should_log(A_				):
        lowerCAmelCase				,       lowerCAmelCase											=	self.process(A_      , A_				)
        self.logger.log(A_      , A_      , *A_      , **A_				)

      elif in_order:
        lowerCAmelCase											=	PartialState()
        for i in range(state.num_processes				):
          if i == state.process_index:
            lowerCAmelCase				,       lowerCAmelCase											=	self.process(A_      , A_				)
            self.logger.log(A_      , A_      , *A_      , **A_				)
          state.wait_for_everyone()






def      __a	(							A__						,       A__ = None       )						->			Dict:







  if log_level is None:
    lowerCAmelCase											=	os.environ.get("ACCELERATE_LOG_LEVEL"						,       UpperCamelCase__       )
  lowerCAmelCase											=	logging.getLogger(UpperCamelCase__       )
  if log_level is not None:
    logger.setLevel(log_level.upper()       )
    logger.root.setLevel(log_level.upper()       )
  return MultiProcessAdapter(UpperCamelCase__						,       {}       )






"""simple docstring"""

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  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						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				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

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

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""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.
       __lowercase						=						[
           """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_   ,							)
