






'''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     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				BarthezTokenizer
  lowercase							=				BarthezTokenizerFast
  lowercase							=				True
  lowercase							=				True

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        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   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        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   A__						(				self				)  ->				Dict:



        """simple docstring"""






        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				)      ,		101122				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        self.assertEqual(self.get_tokenizer().vocab_size      ,		101122				)

  @require_torch
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        UpperCamelCase      = [0, 57, 3018, 70307, 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   A__						(				self				)  ->				str:



        """simple docstring"""






        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   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = {"""input_ids""": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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      ,		)








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
