





'''simple docstring'''

import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class     a_						(      lowerCamelCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = "▁"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = "<unk>"      ,		_SCREAMING_SNAKE_CASE = "</s>"      ,		_SCREAMING_SNAKE_CASE = "<pad>"      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = {
            """pad""": {"""id""": 0, """token""": pad_token},
            """eos""": {"""id""": 1, """token""": eos_token},
            """unk""": {"""id""": 2, """token""": unk_token},
        }

        UpperCamelCase      = [None] * len(self.special_tokens				)
        for token_dict in self.special_tokens.values():
              UpperCamelCase      = token_dict["""token"""]

        UpperCamelCase      = Tokenizer(Unigram()				)

        UpperCamelCase      = normalizers.Sequence(
            [
                normalizers.Nmt(),
                normalizers.NFKC(),
                normalizers.Replace(Regex(""" {2,}"""				)      ,		""" """				),
                normalizers.Lowercase(),
            ]				)
        UpperCamelCase      = pre_tokenizers.Sequence(
            [
                pre_tokenizers.Metaspace(replacement=_SCREAMING_SNAKE_CASE      ,		add_prefix_space=_SCREAMING_SNAKE_CASE				),
                pre_tokenizers.Digits(individual_digits=_SCREAMING_SNAKE_CASE				),
                pre_tokenizers.Punctuation(),
            ]				)
        UpperCamelCase      = decoders.Metaspace(replacement=_SCREAMING_SNAKE_CASE      ,		add_prefix_space=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = TemplateProcessing(
            single=F"$A {self.special_tokens['eos']['token']}"      ,		special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]      ,		)

        UpperCamelCase      = {
            """model""": """SentencePieceUnigram""",
            """replacement""": replacement,
            """add_prefix_space""": add_prefix_space,
        }

        super().__init__(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 8000      ,		_SCREAMING_SNAKE_CASE = True      ,		)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = trainers.UnigramTrainer(
            vocab_size=_SCREAMING_SNAKE_CASE      ,		special_tokens=self.special_tokens_list      ,		show_progress=_SCREAMING_SNAKE_CASE      ,		)

        if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              UpperCamelCase      = [files]
        self._tokenizer.train(_SCREAMING_SNAKE_CASE      ,		trainer=_SCREAMING_SNAKE_CASE				)

        self.add_unk_id()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 8000      ,		_SCREAMING_SNAKE_CASE = True      ,		)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = trainers.UnigramTrainer(
            vocab_size=_SCREAMING_SNAKE_CASE      ,		special_tokens=self.special_tokens_list      ,		show_progress=_SCREAMING_SNAKE_CASE      ,		)

        self._tokenizer.train_from_iterator(_SCREAMING_SNAKE_CASE      ,		trainer=_SCREAMING_SNAKE_CASE				)

        self.add_unk_id()


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



        """simple docstring"""






        UpperCamelCase      = json.loads(self._tokenizer.to_str()				)

        UpperCamelCase      = self.special_tokens["""unk"""]["""id"""]

        UpperCamelCase      = Tokenizer.from_str(json.dumps(_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
