






"""simple docstring"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_ = False):
					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(lowerCamelCase_)
					np.random.seed(lowerCamelCase_)
					torch.manual_seed(lowerCamelCase_)
					if is_xpu_available():
										torch.xpu.manual_seed_all(lowerCamelCase_)
					elif is_npu_available():
										torch.npu.manual_seed_all(lowerCamelCase_)
					else:
										torch.cuda.manual_seed_all(lowerCamelCase_)
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_ = None			,  lowerCamelCase_ = None):
					# Get the proper rng state
					if rng_type == RNGType.TORCH:
										a__ 							=	torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a__ 							=	torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a__ 							=	torch.tensor(xm.get_rng_state())
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a__ 							=	torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a__ 							=	torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a__ 							=	generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a__ 							=	AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a__ 							=	rng_state.to(xm.xla_device())
										xm.collective_broadcast([rng_state])
										xm.mark_step()
										a__ 							=	rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a__ 							=	rng_state.to(state.device)
										torch.distributed.broadcast(lowerCamelCase_			,  0)
										a__ 							=	rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(lowerCamelCase_			,  0)

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(lowerCamelCase_)
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(lowerCamelCase_)
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(lowerCamelCase_)
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(lowerCamelCase_)
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item())
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_ = None):
					for rng_type in rng_types:
										synchronize_rng_state(RNGType(lowerCamelCase_)			,  generator=lowerCamelCase_)










"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


