
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       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				Dict = False      )					->						Optional[int]:




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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int = None   ,							SCREAMING_SNAKE_CASE	:				str = None      )					->						Dict:




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

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

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Any = None      )					->						Optional[int]:




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








from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





