
# 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


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


def 							a__					(							snake_case="no"       ,			snake_case = default_json_config_file       ,			snake_case = False							):





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


def 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	int					=		parser.add_parser('''default'''       ,			parents=snake_case       ,			help=snake_case       ,			formatter_class=snake_case							)
  parser.add_argument(
      '''--config_file'''       ,			default=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=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=snake_case							)
  return parser


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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_rembert import RemBertTokenizer
else:
	lowercase    				=   None

lowercase    				=   logging.get_logger(__name__)
lowercase    				=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowercase    				=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase    				=   {
    "google/rembert": 256,
}

lowercase    				=   "▁"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					RemBertTokenizer




	def __init__(			self			,     a=None			,     a=None			,     a=True			,     a=True			,     a=False			,     a="[CLS]"			,     a="[SEP]"			,     a="<unk>"			,     a="[SEP]"			,     a="<pad>"			,     a="[CLS]"			,     a="[MASK]"			,     **a			,     )			->       Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_           =				AddedToken(a			,     lstrip=a			,     rstrip=a   ) if isinstance(a			,     a   ) else mask_token

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

								snake_case_           =				do_lower_case
								snake_case_           =				remove_space
								snake_case_           =				keep_accents
								snake_case_           =				vocab_file
								snake_case_           =				False if not self.vocab_file else True




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[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 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = False   )			->       List[int]:

								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(a   )) + [1] + ([0] * len(a   )) + [1]
								return [1] + ([0] * len(a   )) + [1]




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

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

								return (out_vocab_file,)





