


# 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


__UpperCamelCase       :       str						       =				"Create a default config file for Accelerate with only a few flags set."

def 			_a  (				SCREAMING_SNAKE_CASE	:				Any="no"    , SCREAMING_SNAKE_CASE	:				str = default_json_config_file    , SCREAMING_SNAKE_CASE	:				bool = False     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   Path(SCREAMING_SNAKE_CASE     )
 path.parent.mkdir(parents=SCREAMING_SNAKE_CASE    , exist_ok=SCREAMING_SNAKE_CASE     )
 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__					:    Tuple          =   {
     '''compute_environment''': '''LOCAL_MACHINE''',
     '''mixed_precision''': mixed_precision,
 }
 if torch.cuda.is_available():
  UpperCamelCase__					:    List[Any]          =   torch.cuda.device_count()
  UpperCamelCase__					:    int          =   num_gpus
  UpperCamelCase__					:    Optional[Any]          =   False
  if num_gpus > 1:
   UpperCamelCase__					:    List[Any]          =   '''MULTI_GPU'''
  else:
   UpperCamelCase__					:    int          =   '''NO'''
 elif is_xpu_available() and use_xpu:
  UpperCamelCase__					:    int          =   torch.xpu.device_count()
  UpperCamelCase__					:    List[Any]          =   num_xpus
  UpperCamelCase__					:    Tuple          =   False
  if num_xpus > 1:
   UpperCamelCase__					:    str          =   '''MULTI_XPU'''
  else:
   UpperCamelCase__					:    Optional[Any]          =   '''NO'''
 elif is_npu_available():
  UpperCamelCase__					:    Union[str, Any]          =   torch.npu.device_count()
  UpperCamelCase__					:    Any          =   num_npus
  UpperCamelCase__					:    Union[str, Any]          =   False
  if num_npus > 1:
   UpperCamelCase__					:    Optional[int]          =   '''MULTI_NPU'''
  else:
   UpperCamelCase__					:    List[str]          =   '''NO'''
 else:
  UpperCamelCase__					:    str          =   0
  UpperCamelCase__					:    int          =   True
  UpperCamelCase__					:    Optional[Any]          =   1
  UpperCamelCase__					:    Dict          =   '''NO'''
 UpperCamelCase__					:    str          =   ClusterConfig(**SCREAMING_SNAKE_CASE     )
 config.to_json_file(SCREAMING_SNAKE_CASE     )
 return path

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   parser.add_parser('''default'''    , parents=SCREAMING_SNAKE_CASE    , help=SCREAMING_SNAKE_CASE    , formatter_class=SCREAMING_SNAKE_CASE     )
 parser.add_argument(
     '''--config_file'''    , default=SCREAMING_SNAKE_CASE    , 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=SCREAMING_SNAKE_CASE    , 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=SCREAMING_SNAKE_CASE     )
 return parser






def 			_a  (				SCREAMING_SNAKE_CASE	:				Any     ):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   write_basic_config(args.mixed_precision    , args.save_location     )
 if config_file:
  print(F"accelerate configuration saved at {config_file}"     )






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:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

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

__UpperCamelCase       :       Tuple						       =				{
    "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",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   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__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	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]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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							:     Optional[int]							,	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
      UpperCamelCase__					:    List[str]          =   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,)


