




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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = False   )							->						List[str]:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_ = None       ,  lowercase_ = None   )							->						Any:

    """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(lowercase_       ,  0   )
        __UpperCamelCase			=    rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(lowercase_       ,  0   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = None   )							->						Optional[Any]:

    """simple docstring"""


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







"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


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



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



