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



def       _lowercase					(      __SCREAMING_SNAKE_CASE = None				,    __SCREAMING_SNAKE_CASE = None )	->     Union[str, Any]:
			# Get the proper rng state
			if rng_type == RNGType.TORCH:
						UpperCamelCase__    :		List[str]     =				torch.get_rng_state()
			elif rng_type == RNGType.CUDA:
						UpperCamelCase__    :		Optional[int]     =				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__    :		Any     =				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__    :		List[Any]     =				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__    :		Any     =				torch.xpu.get_rng_state()
			elif rng_type == RNGType.GENERATOR:
						assert generator is not None, "Need a generator to synchronize its seed."
						UpperCamelCase__    :		Optional[Any]     =				generator.get_state()

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

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



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE = None )	->     Dict:
			for rng_type in rng_types:
						synchronize_rng_state(RNGType(__SCREAMING_SNAKE_CASE )				,    generator=__SCREAMING_SNAKE_CASE )





from __future__ import annotations

import re



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     list[str]:

			def alphanum_key(__SCREAMING_SNAKE_CASE ):
						return [int(__SCREAMING_SNAKE_CASE ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'				,    __SCREAMING_SNAKE_CASE )]

			return sorted(__SCREAMING_SNAKE_CASE				,    key=__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



