
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					:  int		,				__SCREAMING_SNAKE_CASE					:  bool = False    ):






  """simple docstring"""




  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					:  Optional[RNGType] = None		,				__SCREAMING_SNAKE_CASE					:  Optional[torch.Generator] = None    ):






  """simple docstring"""




  if rng_type == RNGType.TORCH:
    _lowerCAmelCase						=		torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    _lowerCAmelCase						=		torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    _lowerCAmelCase						=		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."
    _lowerCAmelCase						=		torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    _lowerCAmelCase						=		torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    _lowerCAmelCase						=		generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  _lowerCAmelCase						=		AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    _lowerCAmelCase						=		rng_state.to(xm.xla_device()    )
    xm.collective_broadcast([rng_state]    )
    xm.mark_step()
    _lowerCAmelCase						=		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
  ):
    _lowerCAmelCase						=		rng_state.to(state.device    )
    torch.distributed.broadcast(__SCREAMING_SNAKE_CASE		,				0    )
    _lowerCAmelCase						=		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					:  List[Union[str, RNGType]]		,				__SCREAMING_SNAKE_CASE					:  Optional[torch.Generator] = None    ):






  """simple docstring"""




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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__   :				Dict						=	{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VideoMAEForPreTraining',
           'VideoMAEModel',
           'VideoMAEPreTrainedModel',
           'VideoMAEForVideoClassification',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	['VideoMAEFeatureExtractor']
       snake_case__   :				Tuple						=	['VideoMAEImageProcessor']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

       snake_case__   :				Optional[Any]						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





