



# 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 (       ):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__=None						):

				"""simple docstring"""


				if subparsers is not None:
								lowercase__					:   Tuple													=					subparsers.add_parser("config"					,			description=lowerCamelCase__						)
				else:
								lowercase__					:   List[str]													=					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__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					get_user_input()
				if args.config_file is not None:
								lowercase__					:   Any													=					args.config_file
				else:
								if not os.path.isdir(lowerCamelCase__						):
												os.makedirs(lowerCamelCase__						)
								lowercase__					:   Optional[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 (       ):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					config_command_parser()
				lowercase__					:   str													=					parser.parse_args()
				config_command(lowerCamelCase__						)


if __name__ == "__main__":
				main()





from typing import TYPE_CHECKING

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


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

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




