'''simple docstring'''




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       0
			UpperCamelCase__				=       False
			UpperCamelCase__				=       3.0





class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										# If no defaults are changed, `to_kwargs` returns an empty dict.
										self.assertDictEqual(MockClass().to_kwargs()				,			{}    )
										self.assertDictEqual(MockClass(a=2    ).to_kwargs()				,			{"""a""": 2}    )
										self.assertDictEqual(MockClass(a=2				,			b=lowercase_    ).to_kwargs()				,			{"""a""": 2, """b""": True}    )
										self.assertDictEqual(MockClass(a=2				,			c=2.25    ).to_kwargs()				,			{"""a""": 2, """c""": 2.25}    )







			@require_cuda
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										# If no defaults are changed, `to_kwargs` returns an empty dict.
										lowercase_					:			Any										=       GradScalerKwargs(init_scale=1024				,			growth_factor=2    )
										AcceleratorState._reset_state()
										lowercase_					:			Tuple										=       Accelerator(mixed_precision="""fp16"""				,			kwargs_handlers=[scaler_handler]    )
										print(accelerator.use_fpaa    )
										lowercase_					:			Dict										=       accelerator.scaler

										# Check the kwargs have been applied
										self.assertEqual(scaler._init_scale				,			10_24.0    )
										self.assertEqual(scaler._growth_factor				,			2.0    )

										# Check the other values are at the default
										self.assertEqual(scaler._backoff_factor				,			0.5    )
										self.assertEqual(scaler._growth_interval				,			2000    )
										self.assertEqual(scaler._enabled				,			lowercase_    )







			@require_multi_gpu
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			List[str]										=       ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
										execute_subprocess_async(lowercase_				,			env=os.environ.copy()    )


if __name__ == "__main__":
						_lowercase						: Any						=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						_lowercase						: str						=						Accelerator(kwargs_handlers=[ddp_scaler])
						_lowercase						: Tuple						=						torch.nn.Linear(100, 200)
						_lowercase						: Dict						=						accelerator.prepare(model)

						# Check the values changed in kwargs
						_lowercase						: str						=						""
						_lowercase						: List[str]						=						model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)






'''simple docstring'''




_lowercase						: str						=						tuple[float, float, float]
_lowercase						: List[Any]						=						tuple[float, float, float]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad )			->      Vectorad:
							lowercase_					:			List[str]										=       end_pointa[0] - end_pointa[0]
							lowercase_					:			Union[str, Any]										=       end_pointa[1] - end_pointa[1]
							lowercase_					:			List[Any]										=       end_pointa[2] - end_pointa[2]
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					Vectorad )			->      Vectorad:
							lowercase_					:			List[Any]										=       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							lowercase_					:			Union[str, Any]										=       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							lowercase_					:			List[str]										=       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Vectorad      ,      UpperCAmelCase__       :					int )			->      bool:
							return tuple(round(UpperCAmelCase__      ,      UpperCAmelCase__ ) for x in vector ) == (0, 0, 0)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					Pointad      ,      UpperCAmelCase__       :					int = 10 )			->      bool:
							lowercase_					:			Dict										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_vector(UpperCAmelCase__      ,      UpperCAmelCase__ )
							return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      UpperCAmelCase__ )





