import logging
import os

from .state import PartialState



class  SCREAMING_SNAKE_CASE_ (					logging.LoggerAdapter			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Optional[int]:
         A						:     Optional[int]         =PartialState()
         return not main_process_only or (main_process_only and state.is_main_process)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int      ,	*SCREAMING_SNAKE_CASE__    :					Tuple      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Union[str, Any]:
         if PartialState._shared_state == {}:
                raise RuntimeError(
                    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'     )
         A						:     Union[str, Any]         =kwargs.pop('main_process_only'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =kwargs.pop('in_order'      ,	SCREAMING_SNAKE_CASE__     )

         if self.isEnabledFor(SCREAMING_SNAKE_CASE__     ):
                if self._should_log(SCREAMING_SNAKE_CASE__     ):
                       A						:     int         =self.process(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                       self.logger.log(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

                elif in_order:
                       A						:     Dict         =PartialState()
                       for i in range(state.num_processes     ):
                              if i == state.process_index:
                                     A						:     Tuple         =self.process(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                                     self.logger.log(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                              state.wait_for_everyone()







def A__  (			lowercase:     str,		lowercase:     str = None					)				->		Dict:
       if log_level is None:
              A						:     Union[str, Any]         =os.environ.get('ACCELERATE_LOG_LEVEL',		lowercase					)
       A						:     List[Any]         =logging.getLogger(lowercase					)
       if log_level is not None:
              logger.setLevel(log_level.upper()					)
              logger.root.setLevel(log_level.upper()					)
       return MultiProcessAdapter(lowercase,		{}					)







# 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


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



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		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=lowercase					)
       return parser



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

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



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






