


'''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						=					{"""vocab_file""": """sentencepiece.bpe.model"""}

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

lowerCAmelCase						=					{
    """camembert-base""": 512,
}

lowerCAmelCase						=					"""▁"""






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=VOCAB_FILES_NAMES
       __UpperCAmelCase					=PRETRAINED_VOCAB_FILES_MAP
       __UpperCAmelCase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCAmelCase					=['input_ids', 'attention_mask']






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

            _A				=							{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=_UpperCamelCase			,     eos_token=_UpperCamelCase			,     unk_token=_UpperCamelCase			,     sep_token=_UpperCamelCase			,     cls_token=_UpperCamelCase			,     pad_token=_UpperCamelCase			,     mask_token=_UpperCamelCase			,     additional_special_tokens=_UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **_UpperCamelCase			,     )
            _A				=							spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(str(_UpperCamelCase   )   )
            _A				=							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>
            _A				=							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
            _A				=							len(self.fairseq_tokens_to_ids   )
            _A				=							len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
            _A				=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      List[int]:

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = False   )->      List[int]:
            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_UpperCamelCase			,     token_ids_a=_UpperCamelCase			,     already_has_special_tokens=_UpperCamelCase   )

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None   )->      List[int]:
            _A				=							[self.sep_token_id]
            _A				=							[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   )->      int:
            return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							{self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
            vocab.update(self.added_tokens_encoder   )
            return vocab






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      List[str]:
            return self.sp_model.encode(_UpperCamelCase			,     out_type=_UpperCamelCase   )






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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[int]:
            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			,     _UpperCamelCase   )->      Tuple:
            _A				=							[]
            _A				=							''
            _A				=							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(_UpperCamelCase   ) + token
                      _A				=							True
                      _A				=							[]
                 else:
                      current_sub_tokens.append(_UpperCamelCase   )
                      _A				=							False
            out_string += self.sp_model.decode(_UpperCamelCase   )
            return out_string.strip()






       def __getstate__( self   )->      Any:
            _A				=							self.__dict__.copy()
            _A				=							None
            return state






       def __setstate__( self			,     _UpperCamelCase   )->      List[str]:
            _A				=							d

            # for backward compatibility
            if not hasattr(self			,     'sp_model_kwargs'   ):
                 _A				=							{}

            _A				=							spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(self.vocab_file   )






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

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
                 copyfile(self.vocab_file			,     _UpperCamelCase   )
            elif not os.path.isfile(self.vocab_file   ):
                 with open(_UpperCamelCase			,     'wb'   ) as fi:
                      _A				=							self.sp_model.serialized_model_proto()
                      fi.write(_UpperCamelCase   )

            return (out_vocab_file,)









'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


lowerCAmelCase						=					"""Create a default config file for Accelerate with only a few flags set."""

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]="no"							,    __UpperCamelCase					:       str = default_json_config_file							,    __UpperCamelCase					:       bool = False		)					->	Tuple:







     """simple docstring"""
     _A				=							Path(__UpperCamelCase		)
     path.parent.mkdir(parents=__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
     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
     _A				=							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}'		)
     _A				=							{
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          _A				=							torch.cuda.device_count()
          _A				=							num_gpus
          _A				=							False
          if num_gpus > 1:
               _A				=							'MULTI_GPU'
          else:
               _A				=							'NO'
     elif is_xpu_available() and use_xpu:
          _A				=							torch.xpu.device_count()
          _A				=							num_xpus
          _A				=							False
          if num_xpus > 1:
               _A				=							'MULTI_XPU'
          else:
               _A				=							'NO'
     elif is_npu_available():
          _A				=							torch.npu.device_count()
          _A				=							num_npus
          _A				=							False
          if num_npus > 1:
               _A				=							'MULTI_NPU'
          else:
               _A				=							'NO'
     else:
          _A				=							0
          _A				=							True
          _A				=							1
          _A				=							'NO'
     _A				=							ClusterConfig(**__UpperCamelCase		)
     config.to_json_file(__UpperCamelCase		)
     return path

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       str		)					->	Any:







     """simple docstring"""
     _A				=							parser.add_parser('default'							,    parents=__UpperCamelCase							,    help=__UpperCamelCase							,    formatter_class=__UpperCamelCase		)
     parser.add_argument(
         '--config_file'							,    default=__UpperCamelCase							,    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=__UpperCamelCase							,    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=__UpperCamelCase		)
     return parser

def 					lowerCamelCase_ ( __UpperCamelCase					:       Any		)					->	Union[str, Any]:







     """simple docstring"""
     _A				=							write_basic_config(args.mixed_precision							,    args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)





