






'''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 						__magic_name__(			_A    ,   _A = False       ):
						'''simple docstring'''





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







def 						__magic_name__(			_A = None    ,   _A = None       ):
						'''simple docstring'''





						if rng_type == RNGType.TORCH:
												UpperCamelCase__       =			torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												UpperCamelCase__       =			torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												UpperCamelCase__       =			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."
												UpperCamelCase__       =			torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												UpperCamelCase__       =			torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												UpperCamelCase__       =			generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						UpperCamelCase__       =			AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												UpperCamelCase__       =			rng_state.to(xm.xla_device()       )
												xm.collective_broadcast([rng_state]       )
												xm.mark_step()
												UpperCamelCase__       =			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
						):
												UpperCamelCase__       =			rng_state.to(state.device       )
												torch.distributed.broadcast(_A    ,   0       )
												UpperCamelCase__       =			rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(_A    ,   0       )

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







def 						__magic_name__(			_A    ,   _A = None       ):
						'''simple docstring'''





						for rng_type in rng_types:
												synchronize_rng_state(RNGType(_A       )    ,   generator=_A       )








'''simple docstring'''


from itertools import combinations_with_replacement







def 						__magic_name__(			_A = 100       ):
						'''simple docstring'''





						UpperCamelCase__       =			[*list(range(1    ,   21       )       ), 25]
						UpperCamelCase__       =			[2 * x for x in range(1    ,   21       )] + [50]
						UpperCamelCase__       =			[3 * x for x in range(1    ,   21       )]
						UpperCamelCase__       =			singles + doubles + triples + [0]

						UpperCamelCase__       =			0
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42
						UpperCamelCase__       =			42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_A    ,   2       ):
																		UpperCamelCase__       =			double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


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