"""simple docstring"""



# Copyright 2022 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available



def 					_lowerCamelCase(					a=None							):
       if subparsers is not None:
              __a     				=  subparsers.add_parser("env"							)
       else:
              __a     				=  argparse.ArgumentParser("Accelerate env command"							)

       parser.add_argument(
           "--config_file" ,  default=a ,  help="The config file to use for the default values in the launching script."							)

       if subparsers is not None:
              parser.set_defaults(func=a							)
       return parser



def 					_lowerCamelCase(					a							):
       __a     				=  torch.__version__
       __a     				=  torch.cuda.is_available()
       __a     				=  is_xpu_available()
       __a     				=  is_npu_available()

       __a     				=  "Not found"
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(a							):
              __a     				=  load_config_from_file(args.config_file							).to_dict()

       __a     				=  {
           "`Accelerate` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "Numpy version": np.__version__,
           "PyTorch version (GPU?)": F"{pt_version} ({pt_cuda_available})",
           "PyTorch XPU available": str(a							),
           "PyTorch NPU available": str(a							),
           "System RAM": F"{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB",
       }
       if pt_cuda_available:
              __a     				=  torch.cuda.get_device_name()

       print("\nCopy-and-paste the text below in your GitHub issue\n"							)
       print("\n".join([F"- {prop}: {val}" for prop, val in info.items()]							)							)

       print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"							)
       __a     				=  (
           "\n".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]							)
           if isinstance(a ,  a							)
           else F"\t{accelerate_config}"
       )
       print(a							)

       __a     				=  accelerate_config

       return info



def 					_lowerCamelCase(					):
       __a     				=  env_command_parser()
       __a     				=  parser.parse_args()
       env_command(a							)
       return 0


if __name__ == "__main__":
   raise SystemExit(main())

"""simple docstring"""



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:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


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

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

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """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""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  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  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __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]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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
        __a     				=  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,)
