



'''simple docstring'''





import numpy as np




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """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 lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A=10):



     """simple docstring"""



     _a  ,   _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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

  lowercase_			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



