


# 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


A      = "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       (		)						->					int:



       '''simple docstring'''





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





def __UpperCAmelCase       (		__A=None			)						->					List[Any]:



       '''simple docstring'''





       if subparsers is not None:
              UpperCAmelCase__										=     subparsers.add_parser("config" ,						description=__A			)
       else:
              UpperCAmelCase__										=     argparse.ArgumentParser("Accelerate config command" ,						description=__A			)

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





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     get_user_input()
       if args.config_file is not None:
              UpperCAmelCase__										=     args.config_file
       else:
              if not os.path.isdir(__A			):
                     os.makedirs(__A			)
              UpperCAmelCase__										=     default_yaml_config_file

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





def __UpperCAmelCase       (		)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     config_command_parser()
       UpperCAmelCase__										=     parser.parse_args()
       config_command(__A			)


if __name__ == "__main__":
    main()









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





