


# 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		       =							'''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    snake_case_     (  )  ->				Optional[int]:
       lowercase__:   int  			=   _ask_options(
           'In which compute environment are you running?'			,							['This machine', 'AWS (Amazon SageMaker)']			,							_convert_compute_environment			,							)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              lowercase__:   Tuple  			=   get_sagemaker_input()
       else:
              lowercase__:   Union[str, Any]  			=   get_cluster_input()
       return config




def    snake_case_     (  snake_case=None     )  ->				Tuple:



       if subparsers is not None:
              lowercase__:   List[str]  			=   subparsers.add_parser('config'			,							description=A__     )
       else:
              lowercase__:   Tuple  			=   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    snake_case_     (  snake_case     )  ->				Tuple:
       lowercase__:   Optional[int]  			=   get_user_input()
       if args.config_file is not None:
              lowercase__:   Union[str, Any]  			=   args.config_file
       else:
              if not os.path.isdir(A__     ):
                     os.makedirs(A__     )
              lowercase__:   Tuple  			=   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    snake_case_     (  )  ->				int:
       lowercase__:   List[Any]  			=   config_command_parser()
       lowercase__:   List[str]  			=   parser.parse_args()
       config_command(A__     )


if __name__ == "__main__":
 main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


