



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


a__    =	logging.get_logger(__name__)



def       lowercase       (   )  ->     Any:
 # Get the sagemaker specific mp parameters from smp_options variable.
 _snake_case    :  List[str]					=					os.getenv("""SM_HP_MP_PARAMETERS"""				,       """{}"""				)
 try:
  # Parse it and check the field "partitions" is included, it is required for model parallel.
  _snake_case    :  Optional[int]					=					json.loads(SCREAMING_SNAKE_CASE__				)
  if "partitions" not in smp_options:
   return False
 except json.JSONDecodeError:
  return False

 # Get the sagemaker specific framework parameters from mpi_options variable.
 _snake_case    :  Union[str, Any]					=					os.getenv("""SM_FRAMEWORK_PARAMS"""				,       """{}"""				)
 try:
  # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
  _snake_case    :  Any					=					json.loads(SCREAMING_SNAKE_CASE__				)
  if not mpi_options.get("""sagemaker_mpi_enabled"""				,       SCREAMING_SNAKE_CASE__				):
   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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: str          =		field(
       default=""""""					,metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}					,)

   def      UpperCamelCase_  (					self							:			Tuple)				->				Optional[Any]:

    """simple docstring"""







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

   @cached_property
   def      UpperCamelCase_  (					self							:			List[str])				->				"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:
     _snake_case    :  Union[str, Any]					=					torch.device("""cpu""")
     _snake_case    :  Union[str, Any]					=					0
    elif is_sagemaker_model_parallel_available():
     _snake_case    :  Tuple					=					smp.local_rank()
     _snake_case    :  List[str]					=					torch.device("""cuda"""				,		lowerCAmelCase)
     _snake_case    :  List[Any]					=					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)
     _snake_case    :  Tuple					=					int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""))
     _snake_case    :  Dict					=					torch.device("""cuda"""				,		self.local_rank)
     _snake_case    :  Dict					=					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
     _snake_case    :  str					=					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.
     _snake_case    :  Optional[int]					=					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)
     _snake_case    :  Any					=					torch.device("""cuda"""				,		self.local_rank)
     _snake_case    :  int					=					1

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

    return device

   @property
   def      UpperCamelCase_  (					self							:			Dict)				->				Any:

    """simple docstring"""







    if is_sagemaker_model_parallel_available():
     return smp.dp_size()

    return super().world_size

   @property
   def      UpperCamelCase_  (					self							:			List[Any])				->				Union[str, Any]:

    """simple docstring"""







    return not is_sagemaker_model_parallel_available()


   @property
   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[str]:

    """simple docstring"""







    return False





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  a__    =	None


a__    =	logging.get_logger(__name__)

a__    =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a__    =	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]="<s>"				,		lowerCAmelCase							:			List[str]="</s>"				,		lowerCAmelCase							:			Union[str, Any]="</s>"				,		lowerCAmelCase							:			Dict="<s>"				,		lowerCAmelCase							:			Dict="<unk>"				,		lowerCAmelCase							:			Any="<pad>"				,		lowerCAmelCase							:			List[str]="<mask>"				,		lowerCAmelCase							:			List[str]=["<s>NOTUSED", "</s>NOTUSED"]				,		**lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					AddedToken(lowerCAmelCase				,		lstrip=lowerCAmelCase				,		rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase				,		lowerCAmelCase) else mask_token

    super().__init__(
        lowerCAmelCase				,		tokenizer_file=lowerCAmelCase				,		bos_token=lowerCAmelCase				,		eos_token=lowerCAmelCase				,		sep_token=lowerCAmelCase				,		cls_token=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		pad_token=lowerCAmelCase				,		mask_token=lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase				,		)

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Optional[Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[str]					=					[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







    if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

    if not os.path.isdir(lowerCAmelCase):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
     return
    _snake_case    :  Optional[int]					=					os.path.join(
        lowerCAmelCase				,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

    if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase):
     copyfile(self.vocab_file				,		lowerCAmelCase)

    return (out_vocab_file,)
