
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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__ = False						):
					'''simple docstring'''
					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 			UpperCamelCase       (				lowerCAmelCase__ = None					,     lowerCAmelCase__ = None						):
					'''simple docstring'''
					# Get the proper rng state
					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(lowerCAmelCase__					,     0						)
										lowercase		      =		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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__ = None						):
					'''simple docstring'''
					for rng_type in rng_types:
										synchronize_rng_state(RNGType(lowerCAmelCase__						)					,     generator=lowerCAmelCase__						)






import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




