




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     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = 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     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = 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(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    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     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







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











from math import cos, sin



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__							=    float(_lowerCAmelCase						)
 return [[scaling_factor * int(x == y						) for x in range(2						)] for y in range(2						)]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 return [[c, -s], [s, c]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[c * c, cs], [cs, s * s]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'''    {scaling(5) = }''')
print(F'''  {rotation(4_5) = }''')
print(F'''{projection(4_5) = }''')


print(F'''{reflection(4_5) = }''')





