
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


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


def 				__A(lowerCAmelCase="no"    ,	lowerCAmelCase = default_json_config_file    ,	lowerCAmelCase = False			)		->					Tuple:

		"""simple docstring"""






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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






		_UpperCamelCase         =						parser.add_parser("""default"""    ,	parents=_a    ,	help=_a    ,	formatter_class=_a			)
		parser.add_argument(
		    """--config_file"""    ,	default=_a    ,	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=_a    ,	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=_a			)
		return parser







def 				__A(lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






		_UpperCamelCase         =						write_basic_config(args.mixed_precision    ,	args.save_location			)
		if config_file:
				print(F'accelerate configuration saved at {config_file}'			)






"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




