






"""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     UpperCamelCase_ (__A  ):
	__magic_name__			   =       0
	__magic_name__			   =       False
	__magic_name__			   =       3.0







class     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Optional[int]:
				self.assertDictEqual(MockClass().to_kwargs()			,			{}		)
				self.assertDictEqual(MockClass(a=2		).to_kwargs()			,			{"a": 2}		)
				self.assertDictEqual(MockClass(a=2			,			b=__lowerCAmelCase		).to_kwargs()			,			{"a": 2, "b": True}		)
				self.assertDictEqual(MockClass(a=2			,			c=2.2_5		).to_kwargs()			,			{"a": 2, "c": 2.2_5}		)





	@require_cuda
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
				UpperCAmelCase_  :				Union[str, Any]      =      GradScalerKwargs(init_scale=1_024			,			growth_factor=2		)
				AcceleratorState._reset_state()
				UpperCAmelCase_  :				Tuple      =      Accelerator(mixed_precision="fp16"			,			kwargs_handlers=[scaler_handler]		)
				print(accelerator.use_fpaa		)
				UpperCAmelCase_  :				List[Any]      =      accelerator.scaler

				# Check the kwargs have been applied
				self.assertEqual(scaler._init_scale			,			1_0_2_4.0		)
				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			,			2_000		)
				self.assertEqual(scaler._enabled			,			__lowerCAmelCase		)





	@require_multi_gpu
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				str      =      ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
				execute_subprocess_async(__lowerCAmelCase			,			env=os.environ.copy()		)


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

						# Check the values changed in kwargs
						lowerCamelCase_       			=   ''''''
						lowerCamelCase_       			=   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"""
from __future__ import annotations

import math

def        snake_case   (     A__		):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5			,int(math.sqrt(A__		) + 1		)			,6		):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True


lowerCamelCase_       			=   [num for num in range(3, 10_0001, 2) if not is_prime(num)]

def        snake_case   (     A__		):
			if not isinstance(A__			,A__		):
						raise ValueError("n must be an integer"		)
			if n <= 0:
						raise ValueError("n must be >= 0"		)

			UpperCAmelCase_  :				List[str]      =      []
			for num in range(len(A__		)		):
						UpperCAmelCase_  :				Dict      =      0
						while 2 * i * i <= odd_composites[num]:
									UpperCAmelCase_  :				List[str]      =      odd_composites[num] - 2 * i * i
									if is_prime(A__		):
												break
									i += 1
						else:
									list_nums.append(odd_composites[num]		)
									if len(A__		) == n:
												return list_nums

			return []

def        snake_case   (     ):
			return compute_nums(1		)[0]


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



