




'''simple docstring'''
import numpy as np
def     snake_case_  (       a__		:     Dict   ,a__		:     List[Any]   ,a__		:     Union[str, Any]      ):



    """simple docstring"""




    __lowercase          =						n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __lowercase          =						np.floor(a__      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __lowercase          =						int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __lowercase          =						continuous - floored
        __lowercase          =						np.sort(np.unique(a__      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__lowercase)    ,)          =						np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __lowercase          =						min(len(a__      )   ,a__      )
            __lowercase          =						rng.choice(a__   ,size=a__   ,replace=a__      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )



def     snake_case_  (       a__		:     Optional[Any]   ,a__		:     int   ,a__		:     str   ,a__		:     Optional[Any]   ,a__		:     Tuple=10      ):



    """simple docstring"""




    __lowercase    ,__lowercase          =						np.unique(a__   ,return_inverse=a__      )
    __lowercase          =						classes.shape[0]
    __lowercase          =						np.bincount(a__      )
    if np.min(a__      ) < 2:
        raise ValueError("""Minimum class count error"""      )
    if n_train < n_classes:
        raise ValueError(
            """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
    __lowercase          =						np.split(np.argsort(a__   ,kind="""mergesort"""      )   ,np.cumsum(a__      )[:-1]      )
    for _ in range(a__      ):
        __lowercase          =						approximate_mode(a__   ,a__   ,a__      )
        __lowercase          =						class_counts - n_i
        __lowercase          =						approximate_mode(a__   ,a__   ,a__      )

        __lowercase          =						[]
        __lowercase          =						[]

        for i in range(a__      ):
            __lowercase          =						rng.permutation(class_counts[i]      )
            __lowercase          =						class_indices[i].take(a__   ,mode="""clip"""      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __lowercase          =						rng.permutation(a__      )
        __lowercase          =						rng.permutation(a__      )

        yield train, test










'''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     snake_case_  (       a__		:     int   ,a__		:     bool = False      ):



    """simple docstring"""




    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(a__      )
    np.random.seed(a__      )
    torch.manual_seed(a__      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(a__      )
    elif is_npu_available():
        torch.npu.manual_seed_all(a__      )
    else:
        torch.cuda.manual_seed_all(a__      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(a__      )
def     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




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

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

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



def     snake_case_  (       a__		:     List[Union[str, RNGType]]   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




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




