


'''simple docstring'''






import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


a_   :      Dict				    =      "Run commands across TPU VMs for initial setup before running `accelerate launch`."






def 		_A			(lowerCAmelCase__					:Any=None						)				->					List[Any]:




					'''simple docstring'''



					if subparsers is not None:
										_a      =							subparsers.add_parser('tpu-config'			,  description=_description						)
					else:
										_a      =							argparse.ArgumentParser('Accelerate tpu-config command'			,  description=_description						)
					# Core arguments
					_a      =							parser.add_argument_group(
					    'Config Arguments'			,  'Arguments that can be configured through `accelerate config`.'						)
					config_args.add_argument(
					    '--config_file'			,  type=lowerCAmelCase__			,  default=lowerCAmelCase__			,  help='Path to the config file to use for accelerate.'			,  )
					config_args.add_argument(
					    '--tpu_name'			,  default=lowerCAmelCase__			,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'			,  )
					config_args.add_argument(
					    '--tpu_zone'			,  default=lowerCAmelCase__			,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'			,  )
					_a      =							parser.add_argument_group('TPU Arguments'			,  'Arguments for options ran inside the TPU.'						)
					pod_args.add_argument(
					    '--use_alpha'			,  action='store_true'			,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'			,  )
					pod_args.add_argument(
					    '--command_file'			,  default=lowerCAmelCase__			,  help='The path to the file containing the commands to run on the pod on startup.'			,  )
					pod_args.add_argument(
					    '--command'			,  action='append'			,  nargs='+'			,  help='A command to run on the pod. Can be passed multiple times.'			,  )
					pod_args.add_argument(
					    '--install_accelerate'			,  action='store_true'			,  help='Whether to install accelerate on the pod. Defaults to False.'			,  )
					pod_args.add_argument(
					    '--accelerate_version'			,  default='latest'			,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'			,  )
					pod_args.add_argument(
					    '--debug'			,  action='store_true'			,  help='If set, will print the command that would be run instead of running it.'						)

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






def 		_A			(lowerCAmelCase__					:str						)				->					str:




					'''simple docstring'''



					_a      =							None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase__						):
										_a      =							load_config_from_file(args.config_file						)
										if not args.command_file and defaults.command_file is not None and not args.command:
															_a      =							defaults.command_file
										if not args.command and defaults.commands is not None:
															_a      =							defaults.commands
										if not args.tpu_name:
															_a      =							defaults.tpu_name
										if not args.tpu_zone:
															_a      =							defaults.tpu_zone
					if args.accelerate_version == "dev":
										_a      =							'git+https://github.com/huggingface/accelerate.git'
					elif args.accelerate_version == "latest":
										_a      =							'accelerate -U'
					elif isinstance(parse(args.accelerate_version						)			,  lowerCAmelCase__						):
										_a      =							f'accelerate=={args.accelerate_version}'

					if not args.command_file and not args.command:
										raise ValueError('You must specify either a command file or a command to run on the pod.'						)

					if args.command_file:
										with open(args.command_file			,  'r'						) as f:
															_a      =							[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]			,  lowerCAmelCase__						):
										_a      =							[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					_a      =							['cd /usr/share']
					if args.install_accelerate:
										new_cmd += [f'pip install {args.accelerate_version}']
					new_cmd += args.command
					_a      =							'; '.join(lowerCAmelCase__						)

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					_a      =							['gcloud']
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f'Running {" ".join(lowerCAmelCase__						)}'						)
										return
					subprocess.run(lowerCAmelCase__						)
					print('Successfully setup pod.'						)






def 		_A			()				->					List[str]:




					'''simple docstring'''



					_a      =							tpu_command_parser()
					_a      =							parser.parse_args()

					tpu_command_launcher(lowerCAmelCase__						)





'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _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 ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




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

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

      return (out_vocab_file,)

