





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


_SCREAMING_SNAKE_CASE   =      '''Create a default config file for Accelerate with only a few flags set.'''




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str="no"						,    lowerCamelCase_     :					str = default_json_config_file						,    lowerCamelCase_     :					bool = False			):
			__lowercase			  =							Path(lowerCamelCase_			)
			path.parent.mkdir(parents=lowerCamelCase_						,    exist_ok=lowerCamelCase_			)
			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
			__lowercase			  =							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}"			)
			__lowercase			  =							{
			    '''compute_environment''': '''LOCAL_MACHINE''',
			    '''mixed_precision''': mixed_precision,
			}
			if torch.cuda.is_available():
						__lowercase			  =							torch.cuda.device_count()
						__lowercase			  =							num_gpus
						__lowercase			  =							False
						if num_gpus > 1:
									__lowercase			  =							'''MULTI_GPU'''
						else:
									__lowercase			  =							'''NO'''
			elif is_xpu_available() and use_xpu:
						__lowercase			  =							torch.xpu.device_count()
						__lowercase			  =							num_xpus
						__lowercase			  =							False
						if num_xpus > 1:
									__lowercase			  =							'''MULTI_XPU'''
						else:
									__lowercase			  =							'''NO'''
			elif is_npu_available():
						__lowercase			  =							torch.npu.device_count()
						__lowercase			  =							num_npus
						__lowercase			  =							False
						if num_npus > 1:
									__lowercase			  =							'''MULTI_NPU'''
						else:
									__lowercase			  =							'''NO'''
			else:
						__lowercase			  =							0
						__lowercase			  =							True
						__lowercase			  =							1
						__lowercase			  =							'''NO'''
			__lowercase			  =							ClusterConfig(**lowerCamelCase_			)
			config.to_json_file(lowerCamelCase_			)
			return path




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					Tuple			):
			__lowercase			  =							parser.add_parser('''default'''						,    parents=lowerCamelCase_						,    help=lowerCamelCase_						,    formatter_class=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\'.'''
			    )						,    dest='''save_location'''						,    )

			parser.add_argument(
			    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=lowerCamelCase_						,    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=lowerCamelCase_			)
			return parser




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]			):
			__lowercase			  =							write_basic_config(args.mixed_precision						,    args.save_location			)
			if config_file:
						print(f"accelerate configuration saved at {config_file}"			)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 6_0_0_8_5_1_4_7_5_1_4_3			):

			try:
						__lowercase			  =							int(lowerCamelCase_			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						while n % i == 0:
									__lowercase			  =							i
									n //= i
						i += 1
			if n > 1:
						__lowercase			  =							n
			return int(lowerCamelCase_			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





