





from typing import TYPE_CHECKING

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


lowerCAmelCase_						   =    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_						   =    ["""ElectraTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_						   =    [
          """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ElectraForCausalLM""",
          """ElectraForMaskedLM""",
          """ElectraForMultipleChoice""",
          """ElectraForPreTraining""",
          """ElectraForQuestionAnswering""",
          """ElectraForSequenceClassification""",
          """ElectraForTokenClassification""",
          """ElectraModel""",
          """ElectraPreTrainedModel""",
          """load_tf_weights_in_electra""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_						   =    [
          """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFElectraForMaskedLM""",
          """TFElectraForMultipleChoice""",
          """TFElectraForPreTraining""",
          """TFElectraForQuestionAnswering""",
          """TFElectraForSequenceClassification""",
          """TFElectraForTokenClassification""",
          """TFElectraModel""",
          """TFElectraPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_						   =    [
          """FlaxElectraForCausalLM""",
          """FlaxElectraForMaskedLM""",
          """FlaxElectraForMultipleChoice""",
          """FlaxElectraForPreTraining""",
          """FlaxElectraForQuestionAnswering""",
          """FlaxElectraForSequenceClassification""",
          """FlaxElectraForTokenClassification""",
          """FlaxElectraModel""",
          """FlaxElectraPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
      from .tokenization_electra import ElectraTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_electra_fast import ElectraTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_electra import (
                ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                ElectraForCausalLM,
                ElectraForMaskedLM,
                ElectraForMultipleChoice,
                ElectraForPreTraining,
                ElectraForQuestionAnswering,
                ElectraForSequenceClassification,
                ElectraForTokenClassification,
                ElectraModel,
                ElectraPreTrainedModel,
                load_tf_weights_in_electra,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_electra import (
                TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFElectraForMaskedLM,
                TFElectraForMultipleChoice,
                TFElectraForPreTraining,
                TFElectraForQuestionAnswering,
                TFElectraForSequenceClassification,
                TFElectraForTokenClassification,
                TFElectraModel,
                TFElectraPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_electra import (
                FlaxElectraForCausalLM,
                FlaxElectraForMaskedLM,
                FlaxElectraForMultipleChoice,
                FlaxElectraForPreTraining,
                FlaxElectraForQuestionAnswering,
                FlaxElectraForSequenceClassification,
                FlaxElectraForTokenClassification,
                FlaxElectraModel,
                FlaxElectraPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase_						   =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











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


def     lowerCamelCase_	(      lowerCAmelCase:	str=None				)->		Any:
  if subparsers is not None:
    _snake_case   :				List[Any]														=	subparsers.add_parser('config'    ,							description=lowerCAmelCase				)
  else:
    _snake_case   :				Dict														=	argparse.ArgumentParser('Accelerate config command'    ,							description=lowerCAmelCase				)

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


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Any:
  _snake_case   :				Dict														=	get_user_input()
  if args.config_file is not None:
    _snake_case   :				List[str]														=	args.config_file
  else:
    if not os.path.isdir(lowerCAmelCase				):
      os.makedirs(lowerCAmelCase				)
    _snake_case   :				Union[str, Any]														=	default_yaml_config_file

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


def     lowerCamelCase_	(      )->		Dict:
  _snake_case   :				List[str]														=	config_command_parser()
  _snake_case   :				str														=	parser.parse_args()
  config_command(lowerCAmelCase				)


if __name__ == "__main__":
      main()




