






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






def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[int] = None  ,		__lowerCamelCase      :					Tuple = None		):
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    __UpperCAmelCase							:     int									=  torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    __UpperCAmelCase							:     List[Any]									=  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							:     List[str]									=  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[str]									=  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							:     Dict									=  torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __UpperCAmelCase							:     int									=  generator.get_state()

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

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







def     lowerCamelCase__				(   __lowerCamelCase      :					Optional[Any]  ,		__lowerCamelCase      :					Optional[Any] = None		):
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(__UpperCamelCase		)  ,		generator=__UpperCamelCase		)










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


