




# 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


_lowerCamelCase      :  Dict      						=						"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  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      _ask_options(
				    """In which compute environment are you running?"""      ,    ["""This machine""", """AWS (Amazon SageMaker)"""]      ,    _convert_compute_environment      ,    )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								_lowerCAmelCase    :    List[Any]										=      get_sagemaker_input()
				else:
								_lowerCAmelCase    :    Optional[int]										=      get_cluster_input()
				return config




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple=None       ):
				'''simple docstring'''
				if subparsers is not None:
								_lowerCAmelCase    :    List[str]										=      subparsers.add_parser("""config"""      ,    description=UpperCamelCase_       )
				else:
								_lowerCAmelCase    :    List[str]										=      argparse.ArgumentParser("""Accelerate config command"""      ,    description=UpperCamelCase_       )

				parser.add_argument(
				    """--config_file"""      ,    default=UpperCamelCase_      ,    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=UpperCamelCase_       )
				return parser




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      get_user_input()
				if args.config_file is not None:
								_lowerCAmelCase    :    int										=      args.config_file
				else:
								if not os.path.isdir(UpperCamelCase_       ):
												os.makedirs(UpperCamelCase_       )
								_lowerCAmelCase    :    List[str]										=      default_yaml_config_file

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




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      config_command_parser()
				_lowerCAmelCase    :    Optional[int]										=      parser.parse_args()
				config_command(UpperCamelCase_       )


if __name__ == "__main__":
	main()







import logging

from transformers.configuration_utils import PretrainedConfig


_lowerCamelCase      :  Union[str, Any]      						=						logging.getLogger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     "masked_bert"



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]=3_0522		,	_UpperCAmelCase      :		Optional[Any]=768		,	_UpperCAmelCase      :		List[Any]=12		,	_UpperCAmelCase      :		Dict=12		,	_UpperCAmelCase      :		List[str]=3072		,	_UpperCAmelCase      :		List[Any]="gelu"		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		Optional[Any]=0.1		,	_UpperCAmelCase      :		List[Any]=512		,	_UpperCAmelCase      :		int=2		,	_UpperCAmelCase      :		Optional[int]=0.02		,	_UpperCAmelCase      :		str=1E-12		,	_UpperCAmelCase      :		Tuple=0		,	_UpperCAmelCase      :		Dict="topK"		,	_UpperCAmelCase      :		List[str]="constant"		,	_UpperCAmelCase      :		Optional[Any]=0.0		,	**_UpperCAmelCase      :		str		,	)  ->    Tuple:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase		,	**_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      vocab_size
								_lowerCAmelCase    :    List[str]										=      hidden_size
								_lowerCAmelCase    :    Any										=      num_hidden_layers
								_lowerCAmelCase    :    Optional[Any]										=      num_attention_heads
								_lowerCAmelCase    :    Optional[Any]										=      hidden_act
								_lowerCAmelCase    :    Union[str, Any]										=      intermediate_size
								_lowerCAmelCase    :    Tuple										=      hidden_dropout_prob
								_lowerCAmelCase    :    Optional[int]										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    str										=      max_position_embeddings
								_lowerCAmelCase    :    int										=      type_vocab_size
								_lowerCAmelCase    :    List[str]										=      initializer_range
								_lowerCAmelCase    :    Optional[Any]										=      layer_norm_eps
								_lowerCAmelCase    :    Tuple										=      pruning_method
								_lowerCAmelCase    :    str										=      mask_init
								_lowerCAmelCase    :    List[str]										=      mask_scale

