
"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
		import tensorflow as tf


_lowercase				 =					logging.get_logger(__name__)







@dataclass
class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     int   =     [
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]


						def __init__(			self  :   Optional[int]	,**A_  :   List[Any]   )  -> str:
							for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
									A     = deprecated_arg[3:]
									A     = not kwargs.pop(A_   )
									logger.warning(
									    F'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
									    F' {positive_arg}={kwargs[positive_arg]}'   )
							A     = kwargs.pop('tpu_name'	,self.tpu_name   )
							A     = kwargs.pop('device_idx'	,self.device_idx   )
							A     = kwargs.pop('eager_mode'	,self.eager_mode   )
							A     = kwargs.pop('use_xla'	,self.use_xla   )
							super().__init__(**A_   )

						_lowerCamelCase:     str   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Name of TPU'''}		,       )
						_lowerCamelCase:     int   =     field(
						    default=0		,       metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}		,       )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Benchmark models in eager model.'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
						    }		,       )


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							requires_backends(self	,['tf']   )
							A     = None
							if self.tpu:
								try:
									if self.tpu_name:
										A     = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name   )
									else:
										A     = tf.distribute.cluster_resolver.TPUClusterResolver()
								except ValueError:
									A     = None
							return tpu


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
							requires_backends(self	,['tf']   )
							if self.is_tpu:
								tf.config.experimental_connect_to_cluster(self._setup_tpu   )
								tf.tpu.experimental.initialize_tpu_system(self._setup_tpu   )

								A     = tf.distribute.TPUStrategy(self._setup_tpu   )
							else:
								# currently no multi gpu is allowed
								if self.is_gpu:
									# TODO: Currently only single GPU is supported
									tf.config.set_visible_devices(self.gpu_list[self.device_idx]	,'GPU'   )
									A     = tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}'   )
								else:
									tf.config.set_visible_devices([]	,'GPU'   )  # disable GPU
									A     = tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}'   )

							return strategy


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> bool:
							requires_backends(self	,['tf']   )
							return self._setup_tpu is not None


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> "tf.distribute.Strategy":
							requires_backends(self	,['tf']   )
							return self._setup_strategy


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							requires_backends(self	,['tf']   )
							return tf.config.list_physical_devices('GPU'   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							requires_backends(self	,['tf']   )
							if self.cuda:
								return len(self.gpu_list   )
							return 0




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> bool:
							return self.n_gpu > 0

"""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


_lowercase				 =					'''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					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

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






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

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






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()