


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A :  List[str]    							=				{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  str    							=				["FunnelTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				[
      "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FunnelBaseModel",
      "FunnelForMaskedLM",
      "FunnelForMultipleChoice",
      "FunnelForPreTraining",
      "FunnelForQuestionAnswering",
      "FunnelForSequenceClassification",
      "FunnelForTokenClassification",
      "FunnelModel",
      "FunnelPreTrainedModel",
      "load_tf_weights_in_funnel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[Any]    							=				[
      "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFFunnelBaseModel",
      "TFFunnelForMaskedLM",
      "TFFunnelForMultipleChoice",
      "TFFunnelForPreTraining",
      "TFFunnelForQuestionAnswering",
      "TFFunnelForSequenceClassification",
      "TFFunnelForTokenClassification",
      "TFFunnelModel",
      "TFFunnelPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .tokenization_funnel import FunnelTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_funnel_fast import FunnelTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_funnel import (
        FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
        FunnelPreTrainedModel,
        load_tf_weights_in_funnel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_funnel import (
        TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFunnelBaseModel,
        TFFunnelForMaskedLM,
        TFFunnelForMultipleChoice,
        TFFunnelForPreTraining,
        TFFunnelForQuestionAnswering,
        TFFunnelForSequenceClassification,
        TFFunnelForTokenClassification,
        TFFunnelModel,
        TFFunnelPreTrainedModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''simple docstring'''






# 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 :  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     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[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_      :				str      =       get_sagemaker_input()
  else:
    lowerCAmelCase_      :				Optional[int]      =       get_cluster_input()
  return config







def     UpperCamelCase_      (   A__ :       Optional[Any]=None ):
  '''simple docstring'''





  if subparsers is not None:
    lowerCAmelCase_      :				List[str]      =       subparsers.add_parser("""config"""							,   description=A__ )
  else:
    lowerCAmelCase_      :				Optional[int]      =       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'''





  lowerCAmelCase_      :				Dict      =       get_user_input()
  if args.config_file is not None:
    lowerCAmelCase_      :				List[str]      =       args.config_file
  else:
    if not os.path.isdir(A__ ):
      os.makedirs(A__ )
    lowerCAmelCase_      :				List[Any]      =       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_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       config_command_parser()
  lowerCAmelCase_      :				Tuple      =       parser.parse_args()
  config_command(A__ )


if __name__ == "__main__":
  main()



