"""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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_UpperCamelCase								=		"Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"

def  _A(		):
							A__       :     int								=			_ask_options(
							    """In which compute environment are you running?"""			,  ["""This machine""", """AWS (Amazon SageMaker)"""]			,  _convert_compute_environment			,  )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														A__       :     Optional[int]								=			get_sagemaker_input()
							else:
														A__       :     int								=			get_cluster_input()
							return config

def  _A(		lowerCAmelCase=None							):
							if subparsers is not None:
														A__       :     List[str]								=			subparsers.add_parser("""config"""			,  description=lowerCAmelCase							)
							else:
														A__       :     int								=			argparse.ArgumentParser("""Accelerate config command"""			,  description=lowerCAmelCase							)

							parser.add_argument(
							    """--config_file"""			,  default=lowerCAmelCase			,  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'."""
							    )			,  )

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

def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			get_user_input()
							if args.config_file is not None:
														A__       :     int								=			args.config_file
							else:
														if not os.path.isdir(lowerCAmelCase							):
																					os.makedirs(lowerCAmelCase							)
														A__       :     List[Any]								=			default_yaml_config_file

							if config_file.endswith(""".json"""							):
														config.to_json_file(lowerCAmelCase							)
							else:
														config.to_yaml_file(lowerCAmelCase							)
							print(F'''accelerate configuration saved at {config_file}'''							)

def  _A(		):
							A__       :     Optional[int]								=			config_command_parser()
							A__       :     List[str]								=			parser.parse_args()
							config_command(lowerCAmelCase							)


if __name__ == "__main__":
				main()






"""simple docstring"""



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

import sentencepiece as spm

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{"vocab_file": "sentencepiece.model"}

_UpperCamelCase								=		{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCamelCase								=		{
    "google/rembert": 2_56,
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						Any					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(			self   ,      snake_case_   ,      snake_case_=False   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="[CLS]"   ,      snake_case_="[SEP]"   ,      snake_case_="[UNK]"   ,      snake_case_="[SEP]"   ,      snake_case_="[PAD]"   ,      snake_case_="[CLS]"   ,      snake_case_="[MASK]"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    do_lower_case=snake_case_   ,      remove_space=snake_case_   ,      keep_accents=snake_case_   ,      bos_token=snake_case_   ,      eos_token=snake_case_   ,      unk_token=snake_case_   ,      sep_token=snake_case_   ,      pad_token=snake_case_   ,      cls_token=snake_case_   ,      mask_token=snake_case_   ,      **snake_case_   ,      )

										A__       :     List[Any]								=			do_lower_case
										A__       :     Dict								=			remove_space
										A__       :     Optional[int]								=			keep_accents
										A__       :     Tuple								=			vocab_file

										A__       :     List[str]								=			spm.SentencePieceProcessor()
										self.sp_model.Load(snake_case_							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return len(self.sp_model							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab





			def __getstate__(			self							):







										'''simple docstring'''


										A__       :     str								=			self.__dict__.copy()
										A__       :     Tuple								=			None
										return state





			def __setstate__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			d
										A__       :     int								=			spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										A__       :     Tuple								=			self.sp_model.EncodeAsPieces(snake_case_							)
										return pieces





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.PieceToId(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.IdToPiece(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.sp_model.decode_pieces(snake_case_							)
										return out_string





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







										'''simple docstring'''


										A__       :     List[Any]								=			[self.sep_token_id]
										A__       :     str								=			[self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = False							):







										'''simple docstring'''


										if already_has_special_tokens:
																	if token_ids_a is not None:
																								raise ValueError(
																								    """You should not supply a second sequence if the provided sequence of """
																								    """ids is already formatted with special tokens for the model."""							)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is not None:
																	return [1] + ([0] * len(snake_case_							)) + [1] + ([0] * len(snake_case_							)) + [1]
										return [1] + ([0] * len(snake_case_							)) + [1]





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







										'''simple docstring'''


										A__       :     int								=			[self.sep_token_id]
										A__       :     Any								=			[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							) * [0] + len(token_ids_a + sep							) * [1]





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







										'''simple docstring'''


										if not os.path.isdir(snake_case_							):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(snake_case_							)							)
																	return
										A__       :     Any								=			os.path.join(
										    snake_case_   ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																	copyfile(self.vocab_file   ,      snake_case_							)

										return (out_vocab_file,)





