




"""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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowercase__			=		"""Create a default config file for Accelerate with only a few flags set."""







def 	_snake_case     (							lowercase__="no"				,       lowercase__ = default_json_config_file				,       lowercase__ = False						):
				_lowerCamelCase	:     Optional[Any]       =       Path(lowercase__						)
				path.parent.mkdir(parents=lowercase__				,       exist_ok=lowercase__						)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''						)
								return False
				_lowerCamelCase	:     Union[str, Any]       =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''						)
				_lowerCamelCase	:     str       =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								_lowerCamelCase	:     str       =       torch.cuda.device_count()
								_lowerCamelCase	:     Union[str, Any]       =       num_gpus
								_lowerCamelCase	:     Union[str, Any]       =       False
								if num_gpus > 1:
												_lowerCamelCase	:     Any       =       'MULTI_GPU'
								else:
												_lowerCamelCase	:     Optional[int]       =       'NO'
				elif is_xpu_available() and use_xpu:
								_lowerCamelCase	:     Any       =       torch.xpu.device_count()
								_lowerCamelCase	:     Tuple       =       num_xpus
								_lowerCamelCase	:     Optional[int]       =       False
								if num_xpus > 1:
												_lowerCamelCase	:     Dict       =       'MULTI_XPU'
								else:
												_lowerCamelCase	:     int       =       'NO'
				elif is_npu_available():
								_lowerCamelCase	:     int       =       torch.npu.device_count()
								_lowerCamelCase	:     Optional[Any]       =       num_npus
								_lowerCamelCase	:     Union[str, Any]       =       False
								if num_npus > 1:
												_lowerCamelCase	:     Tuple       =       'MULTI_NPU'
								else:
												_lowerCamelCase	:     List[Any]       =       'NO'
				else:
								_lowerCamelCase	:     List[str]       =       0
								_lowerCamelCase	:     Any       =       True
								_lowerCamelCase	:     Dict       =       1
								_lowerCamelCase	:     Dict       =       'NO'
				_lowerCamelCase	:     str       =       ClusterConfig(**lowercase__						)
				config.to_json_file(lowercase__						)
				return path







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_lowerCamelCase	:     List[Any]       =       parser.add_parser('default'				,       parents=lowercase__				,       help=lowercase__				,       formatter_class=lowercase__						)
				parser.add_argument(
				    '--config_file'				,       default=lowercase__				,       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\'.'
				    )				,       dest='save_location'				,       )

				parser.add_argument(
				    '--mixed_precision'				,       choices=['no', 'fp16', 'bf16']				,       type=lowercase__				,       help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'				,       default='no'				,       )
				parser.set_defaults(func=lowercase__						)
				return parser







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Dict       =       write_basic_config(args.mixed_precision				,       args.save_location						)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''						)





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):  # This function is recursive
				_lowerCamelCase	:     Optional[Any]       =       len(lowercase__						)
				# If the array contains only one element, we return it (it's the stop condition of
				# recursion)
				if array_length <= 1:
								return array
								# Else
				_lowerCamelCase	:     Optional[Any]       =       array[0]
				_lowerCamelCase	:     Optional[int]       =       False
				_lowerCamelCase	:     List[str]       =       1
				_lowerCamelCase	:     list[int]       =       []
				while not is_found and i < array_length:
								if array[i] < pivot:
												_lowerCamelCase	:     Union[str, Any]       =       True
												_lowerCamelCase	:     str       =       [element for element in array[i:] if element >= array[i]]
												_lowerCamelCase	:     str       =       longest_subsequence(lowercase__						)
												if len(lowercase__						) > len(lowercase__						):
																_lowerCamelCase	:     Any       =       temp_array
								else:
												i += 1

				_lowerCamelCase	:     Optional[Any]       =       [element for element in array[1:] if element >= pivot]
				_lowerCamelCase	:     Dict       =       [pivot, *longest_subsequence(lowercase__						)]
				if len(lowercase__						) > len(lowercase__						):
								return temp_array
				else:
								return longest_subseq


if __name__ == "__main__":
			import doctest

			doctest.testmod()