
"""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    _UpperCAmelCase (					)	->   Any:
  _snake_case   	=			_ask_options(
      '''In which compute environment are you running?'''					,   ['''This machine''', '''AWS (Amazon SageMaker)''']					,   _convert_compute_environment					,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    _snake_case   	=			get_sagemaker_input()
  else:
    _snake_case   	=			get_cluster_input()
  return config
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[Any]=None						)	->   Dict:




  if subparsers is not None:
    _snake_case   	=			subparsers.add_parser('''config'''					,   description=_SCREAMING_SNAKE_CASE						)
  else:
    _snake_case   	=			argparse.ArgumentParser('''Accelerate config command'''					,   description=_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\'.'''
      )					,   )

  if subparsers is not None:
    parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)
  return parser
def    _UpperCAmelCase (					__lowerCamelCase  :			int						)	->   Optional[Any]:
  _snake_case   	=			get_user_input()
  if args.config_file is not None:
    _snake_case   	=			args.config_file
  else:
    if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
      os.makedirs(_SCREAMING_SNAKE_CASE						)
    _snake_case   	=			default_yaml_config_file

  if config_file.endswith('''.json'''						):
    config.to_json_file(_SCREAMING_SNAKE_CASE						)
  else:
    config.to_yaml_file(_SCREAMING_SNAKE_CASE						)
  print(f'''accelerate configuration saved at {config_file}'''						)
def    _UpperCAmelCase (					)	->   Union[str, Any]:
  _snake_case   	=			config_command_parser()
  _snake_case   	=			parser.parse_args()
  config_command(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
 main()










from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





