



# 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


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






def  UpperCAmelCase__    (    lowercase__=None   ) ->      Dict:
						if subparsers is not None:
												__lowercase					=				subparsers.add_parser("""config"""						,       description=__UpperCamelCase   )
						else:
												__lowercase					=				argparse.ArgumentParser("""Accelerate config command"""						,       description=__UpperCamelCase   )

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






def  UpperCAmelCase__    (    lowercase__   ) ->      str:
						__lowercase					=				get_user_input()
						if args.config_file is not None:
												__lowercase					=				args.config_file
						else:
												if not os.path.isdir(__UpperCamelCase   ):
																		os.makedirs(__UpperCamelCase   )
												__lowercase					=				default_yaml_config_file

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






def  UpperCAmelCase__    (    ) ->      List[str]:
						__lowercase					=				config_command_parser()
						__lowercase					=				parser.parse_args()
						config_command(__UpperCamelCase   )


if __name__ == "__main__":
			main()








from typing import TYPE_CHECKING

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


UpperCamelCase__ =						{
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "CTRLForSequenceClassification",
			    "CTRLLMHeadModel",
			    "CTRLModel",
			    "CTRLPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFCTRLForSequenceClassification",
			    "TFCTRLLMHeadModel",
			    "TFCTRLModel",
			    "TFCTRLPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
			from .tokenization_ctrl import CTRLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ctrl import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						    CTRLPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						    TFCTRLPreTrainedModel,
						)

else:
			import sys

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





