



from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


a	:   int       			=       'Create a default config file for Accelerate with only a few flags set.'







def       lowerCAmelCase_     (lowerCAmelCase__:						Tuple="no"						,		lowerCAmelCase__:						str = default_json_config_file						,		lowerCAmelCase__:						bool = False   ):





			"""simple docstring"""


			UpperCAmelCase_:						List[Any]					=			Path(__a   )
			path.parent.mkdir(parents=__a						,		exist_ok=__a   )
			if path.exists():
						print(
						    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'   )
						return False
			UpperCAmelCase_:						int					=			mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'   )
			UpperCAmelCase_:						List[Any]					=			{
			    'compute_environment': 'LOCAL_MACHINE',
			    'mixed_precision': mixed_precision,
			}
			if torch.cuda.is_available():
						UpperCAmelCase_:						Optional[int]					=			torch.cuda.device_count()
						UpperCAmelCase_:						int					=			num_gpus
						UpperCAmelCase_:						Any					=			False
						if num_gpus > 1:
									UpperCAmelCase_:						Union[str, Any]					=			'MULTI_GPU'
						else:
									UpperCAmelCase_:						List[str]					=			'NO'
			elif is_xpu_available() and use_xpu:
						UpperCAmelCase_:						Dict					=			torch.xpu.device_count()
						UpperCAmelCase_:						Tuple					=			num_xpus
						UpperCAmelCase_:						List[Any]					=			False
						if num_xpus > 1:
									UpperCAmelCase_:						Union[str, Any]					=			'MULTI_XPU'
						else:
									UpperCAmelCase_:						Any					=			'NO'
			elif is_npu_available():
						UpperCAmelCase_:						int					=			torch.npu.device_count()
						UpperCAmelCase_:						Optional[Any]					=			num_npus
						UpperCAmelCase_:						List[Any]					=			False
						if num_npus > 1:
									UpperCAmelCase_:						List[Any]					=			'MULTI_NPU'
						else:
									UpperCAmelCase_:						List[str]					=			'NO'
			else:
						UpperCAmelCase_:						Dict					=			0
						UpperCAmelCase_:						int					=			True
						UpperCAmelCase_:						Optional[int]					=			1
						UpperCAmelCase_:						Dict					=			'NO'
			UpperCAmelCase_:						Optional[int]					=			ClusterConfig(**__a   )
			config.to_json_file(__a   )
			return path







def       lowerCAmelCase_     (lowerCAmelCase__:						str						,		lowerCAmelCase__:						Optional[Any]   ):





			"""simple docstring"""


			UpperCAmelCase_:						Any					=			parser.add_parser("""default"""						,		parents=__a						,		help=__a						,		formatter_class=__a   )
			parser.add_argument(
			    """--config_file"""						,		default=__a						,		help=(
			        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
			        """location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
			        """such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
			        """with \'huggingface\'."""
			    )						,		dest="""save_location"""						,		)

			parser.add_argument(
			    """--mixed_precision"""						,		choices=["""no""", """fp16""", """bf16"""]						,		type=__a						,		help="""Whether or not to use mixed precision training. """
			    """Choose between FP16 and BF16 (bfloat16) training. """
			    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""						,		default="""no"""						,		)
			parser.set_defaults(func=__a   )
			return parser







def       lowerCAmelCase_     (lowerCAmelCase__:						List[str]   ):





			"""simple docstring"""


			UpperCAmelCase_:						Union[str, Any]					=			write_basic_config(args.mixed_precision						,		args.save_location   )
			if config_file:
						print(F'accelerate configuration saved at {config_file}'   )








'''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


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    """▁"""

__lowerCAmelCase         =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__lowerCAmelCase         =    {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__lowerCAmelCase         =    {"""vinai/bartpho-syllable""": 1_0_2_4}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Dict   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Optional[int]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				Dict   =					['''input_ids''', '''attention_mask''']


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


     '''simple docstring'''






     _a				:	Any			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

     _a				:	Optional[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							,sp_model_kwargs=self.sp_model_kwargs							,**_a							,)

     _a				:	Optional[int]			=   vocab_file
     _a				:	Union[str, Any]			=   monolingual_vocab_file
     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(_a       )       )

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     _a				:	Union[str, Any]			=   {}
     _a				:	int			=   0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(_a       ) not in self.fairseq_tokens_to_ids:
           _a				:	int			=   cnt
           cnt += 1
     with open(_a							,'r'							,encoding='utf-8'       ) as f:
        for line in f.readlines():
           _a				:	str			=   line.strip().split()[0]
           _a				:	Tuple			=   len(self.fairseq_tokens_to_ids       )
     if str(_a       ) not in self.fairseq_tokens_to_ids:
        _a				:	List[str]			=   len(self.fairseq_tokens_to_ids       )

     _a				:	Tuple			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


  def __getstate__(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   self.__dict__.copy()
     _a				:	str			=   None
     _a				:	Optional[Any]			=   self.sp_model.serialized_model_proto()
     return state


  def __setstate__(		self      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Tuple			=   d

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

     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


  def   __lowercase		(		self      :     Dict							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     int							,_a      :     List[int]							,_a      :     Optional[List[int]] = None							,_a      :     bool = False       ):


     '''simple docstring'''






     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   __lowercase		(		self      :     Tuple							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     _a				:	List[str]			=   [self.sep_token_id]
     _a				:	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]


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return len(self.fairseq_ids_to_tokens       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	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   __lowercase		(		self      :     Tuple							,_a      :     str       ):


     '''simple docstring'''






     return self.sp_model.encode(_a							,out_type=_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     else:
        return self.unk_token_id


  def   __lowercase		(		self      :     Any							,_a      :     int       ):


     '''simple docstring'''






     return self.fairseq_ids_to_tokens[index]


  def   __lowercase		(		self      :     Tuple							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	str			=   ''.join(_a       ).replace(_a							,' '       ).strip()
     return out_string


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






     if not os.path.isdir(_a       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
        return
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
           _a				:	List[Any]			=   self.sp_model.serialized_model_proto()
           fi.write(_a       )

     if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
         _a       ) and os.path.isfile(self.monolingual_vocab_file       ):
        copyfile(self.monolingual_vocab_file							,_a       )
     elif not os.path.isfile(self.monolingual_vocab_file       ):
        with open(_a							,'w'							,encoding='utf-8'       ) as fp:
           for token in self.fairseq_tokens_to_ids:
              if token not in self.all_special_tokens:
                 fp.write(F"""{str(_a       )} \n"""       )

     return out_vocab_file, out_monolingual_vocab_file



