


'''simple docstring'''

import numpy as np






def __UpperCamelCase (      __lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			Any   ,	__lowerCamelCase		:			Dict					)   ->  Union[str, Any]:







     '''simple docstring'''






     _a            =		n_draws * class_counts / class_counts.sum()
     # floored means we don't overshoot n_samples, but probably undershoot
     _a            =		np.floor(_a					)
     # we add samples according to how much "left over" probability
     # they had, until we arrive at n_samples
     _a            =		int(n_draws - floored.sum()					)
     if need_to_add > 0:
          _a            =		continuous - floored
          _a            =		np.sort(np.unique(_a					)					)[::-1]
          # add according to remainder, but break ties
          # randomly to avoid biases
          for value in values:
               (_a       )            =		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
               _a            =		min(len(_a					)   ,	_a					)
               _a            =		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 __UpperCamelCase (      __lowerCamelCase		:			Optional[int]   ,	__lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			str=10					)   ->  str:







     '''simple docstring'''






     _a            =		np.unique(_a   ,	return_inverse=_a					)
     _a            =		classes.shape[0]
     _a            =		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)					)
     _a            =		np.split(np.argsort(_a   ,	kind="mergesort"					)   ,	np.cumsum(_a					)[:-1]					)
     for _ in range(_a					):
          _a            =		approximate_mode(_a   ,	_a   ,	_a					)
          _a            =		class_counts - n_i
          _a            =		approximate_mode(_a   ,	_a   ,	_a					)

          _a            =		[]
          _a            =		[]

          for i in range(_a					):
               _a            =		rng.permutation(class_counts[i]					)
               _a            =		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]]					)
          _a            =		rng.permutation(_a					)
          _a            =		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 			_UpperCamelCase			(  _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 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    if rng_type == RNGType.TORCH:
        __UpperCamelCase						:						Union[str, Any]		     =	torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __UpperCamelCase						:						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."
        __UpperCamelCase						:						Dict		     =	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						:						Optional[Any]		     =	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						:						str		     =	torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __UpperCamelCase						:						List[Any]		     =	generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __UpperCamelCase						:						List[Any]		     =	AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __UpperCamelCase						:						Union[str, Any]		     =	rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __UpperCamelCase						:						str		     =	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						:						List[Any]		     =	rng_state.to(state.device      )
        torch.distributed.broadcast(_a					,  0      )
        __UpperCamelCase						:						Optional[Any]		     =	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 			_UpperCamelCase			(  _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      )



