






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 		_a					(   a				:int			,							a				:bool = False				)							->    Tuple:
	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 		_a					(   a				:Optional[RNGType] = None			,							a				:Optional[torch.Generator] = None				)							->    Dict:
	# 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(a			,							0				)
		a							=   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 		_a					(   a				:List[Union[str, RNGType]]			,							a				:Optional[torch.Generator] = None				)							->    Optional[Any]:
	for rng_type in rng_types:
		synchronize_rng_state(RNGType(a				)			,							generator=a				)










def 		_a					(   a				:float				)							->    float:
 return -num if num < 0 else num
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_min() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs_val(a				) < abs_val(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs(a				) > abs(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max_sort() arg is an empty sequence'''				)
 return sorted(a			,							key=a				)[-1]
def 		_a					(   )							->    int:
 assert abs_val(0				) == 0
 assert abs_val(34				) == 34
 assert abs_val(-100_000_000_000				) == 100_000_000_000

 a							=   [-3, -1, 2, -11]
 assert abs_max(a				) == -11
 assert abs_max_sort(a				) == -11
 assert abs_min(a				) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34


