
'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase							:   Optional[int]					=     logging.get_logger(__name__)

__lowercase							:   Optional[Any]					=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase							:   Union[str, Any]					=     {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__lowercase							:   List[Any]					=     {
    '''camembert-base''': 512,
}

__lowercase							:   Tuple					=     '''▁'''

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		List[Any]	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		List[str]	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		List[str]	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Dict	      =			["input_ids", "attention_mask"]

			def __init__(self			,   A			,   A="<s>"			,   A="</s>"			,   A="</s>"			,   A="<s>"			,   A="<unk>"			,   A="<pad>"			,   A="<mask>"			,   A=["<s>NOTUSED", "</s>NOTUSED"]			,   A = None			,   **A			,   ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase_					:      Union[str, Any]     =		AddedToken(A			,   lstrip=A			,   rstrip=A      ) if isinstance(A			,   A      ) else mask_token

								lowerCamelCase_					:      Any     =		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=A			,   eos_token=A			,   unk_token=A			,   sep_token=A			,   cls_token=A			,   pad_token=A			,   mask_token=A			,   additional_special_tokens=A			,   sp_model_kwargs=self.sp_model_kwargs			,   **A			,   )
								lowerCamelCase_					:      Optional[int]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(A      )      )
								lowerCamelCase_					:      Dict     =		vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								lowerCamelCase_					:      Union[str, Any]     =		{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
								lowerCamelCase_					:      Optional[int]     =		len(self.fairseq_tokens_to_ids      )
								lowerCamelCase_					:      List[str]     =		len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
								lowerCamelCase_					:      int     =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):

								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase_					:      Any     =		[self.cls_token_id]
								lowerCamelCase_					:      int     =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

			def        UpperCAmelCase__ (self			,   A			,   A = None			,   A = False      ):
								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=A			,   token_ids_a=A			,   already_has_special_tokens=A      )

								if token_ids_a is None:
													return [1] + ([0] * len(A      )) + [1]
								return [1] + ([0] * len(A      )) + [1, 1] + ([0] * len(A      )) + [1]

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      Tuple     =		[self.sep_token_id]
								lowerCamelCase_					:      Optional[Any]     =		[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]

			@property
			def        UpperCAmelCase__ (self      ):
								return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		{self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab

			def        UpperCAmelCase__ (self			,   A      ):
								return self.sp_model.encode(A			,   out_type=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(A      ) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(A      )

			def        UpperCAmelCase__ (self			,   A      ):
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[str]     =		[]
								lowerCamelCase_					:      List[Any]     =		''''''
								lowerCamelCase_					:      List[str]     =		False
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		if not prev_is_special:
																							out_string += " "
																		out_string += self.sp_model.decode(A      ) + token
																		lowerCamelCase_					:      Tuple     =		True
																		lowerCamelCase_					:      Optional[int]     =		[]
													else:
																		current_sub_tokens.append(A      )
																		lowerCamelCase_					:      Optional[Any]     =		False
								out_string += self.sp_model.decode(A      )
								return out_string.strip()

			def __getstate__(self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.__dict__.copy()
								lowerCamelCase_					:      Tuple     =		None
								return state

			def __setstate__(self			,   A      ):
								lowerCamelCase_					:      List[Any]     =		d

								# for backward compatibility
								if not hasattr(self			,   '''sp_model_kwargs'''      ):
													lowerCamelCase_					:      List[Any]     =		{}

								lowerCamelCase_					:      Union[str, Any]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )






			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								if not os.path.isdir(A      ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
													return
								lowerCamelCase_					:      Tuple     =		os.path.join(
								    A			,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file			,   A      )
								elif not os.path.isfile(self.vocab_file      ):
													with open(A			,   '''wb'''      ) as fi:
																		lowerCamelCase_					:      Union[str, Any]     =		self.sp_model.serialized_model_proto()
																		fi.write(A      )

								return (out_vocab_file,)




'''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


__lowercase							:   Union[str, Any]					=     logging.get_logger(__name__)




def lowercase_	(   )     ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		os.getenv('''SM_HP_MP_PARAMETERS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										lowerCamelCase_					:      Optional[Any]     =		json.loads(_lowercase				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					lowerCamelCase_					:      List[str]     =		os.getenv('''SM_FRAMEWORK_PARAMS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										lowerCamelCase_					:      List[str]     =		json.loads(_lowercase				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''			,       _lowercase				):
															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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		str	      =			field(
			    default=""      ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}      ,     )

			def        UpperCAmelCase__ (self      ):
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''			,   A			,   )

			@cached_property
			def        UpperCAmelCase__ (self      ):
								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:
													lowerCamelCase_					:      Optional[int]     =		torch.device('''cpu'''      )
													lowerCamelCase_					:      Dict     =		0
								elif is_sagemaker_model_parallel_available():
													lowerCamelCase_					:      int     =		smp.local_rank()
													lowerCamelCase_					:      str     =		torch.device('''cuda'''			,   A      )
													lowerCamelCase_					:      Tuple     =		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      )
													lowerCamelCase_					:      List[Any]     =		int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''      )      )
													lowerCamelCase_					:      Union[str, Any]     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		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
													lowerCamelCase_					:      List[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.
													lowerCamelCase_					:      Any     =		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      )
													lowerCamelCase_					:      Any     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1

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

								return device

			@property
			def        UpperCAmelCase__ (self      ):
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def        UpperCAmelCase__ (self      ):
								return not is_sagemaker_model_parallel_available()






			@property
			def        UpperCAmelCase__ (self      ):
								return False


