"""simple docstring"""
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  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = False  )      ->Any:
  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 		__a  (      _SCREAMING_SNAKE_CASE = None						,	_SCREAMING_SNAKE_CASE = None  )      ->Tuple:
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    a__:	int     				=      torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    a__:	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."
    a__:	Optional[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."
    a__:	int     				=      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__:	List[Any]     				=      torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    a__:	Tuple     				=      generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  a__:	int     				=      AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    a__:	List[Any]     				=      rng_state.to(xm.xla_device()  )
    xm.collective_broadcast([rng_state]  )
    xm.mark_step()
    a__:	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
  ):
    a__:	Optional[Any]     				=      rng_state.to(state.device  )
    torch.distributed.broadcast(_SCREAMING_SNAKE_CASE						,	0  )
    a__:	List[str]     				=      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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = None  )      ->Optional[Any]:
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE  )						,	generator=_SCREAMING_SNAKE_CASE  )









from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()