'''simple docstring'''
from typing import TYPE_CHECKING

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


a=	{
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	['''OneFormerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	[
      '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''OneFormerForUniversalSegmentation''',
      '''OneFormerModel''',
      '''OneFormerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
  from .processing_oneformer import OneFormerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_oneformer import OneFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_oneformer import (
        ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        OneFormerForUniversalSegmentation,
        OneFormerModel,
        OneFormerPreTrainedModel,
    )


else:
  import sys

  a=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)




'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	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
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	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			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	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)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	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]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



