






"""simple docstring"""



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	:	Optional[Any]				=      "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 	SCREAMING_SNAKE_CASE__       (  )->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							_ask_options(
         "In which compute environment are you running?"			,					["This machine", "AWS (Amazon SageMaker)"]			,					_convert_compute_environment			,					)
     if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
          UpperCAmelCase__						:    List[str]							=							get_sagemaker_input()
     else:
          UpperCAmelCase__						:    Tuple							=							get_cluster_input()
     return config


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]=None				)->		Union[str, Any]:






     '''simple docstring'''



     if subparsers is not None:
          UpperCAmelCase__						:    List[str]							=							subparsers.add_parser("config"			,					description=snake_case__				)
     else:
          UpperCAmelCase__						:    Tuple							=							argparse.ArgumentParser("Accelerate config command"			,					description=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'."
         )			,					)

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							get_user_input()
     if args.config_file is not None:
          UpperCAmelCase__						:    Tuple							=							args.config_file
     else:
          if not os.path.isdir(snake_case__				):
               os.makedirs(snake_case__				)
          UpperCAmelCase__						:    List[str]							=							default_yaml_config_file

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


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__						:    Union[str, Any]							=							config_command_parser()
     UpperCAmelCase__						:    List[Any]							=							parser.parse_args()
     config_command(snake_case__				)


if __name__ == "__main__":
 main()





"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


