






import numpy as np






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  Union[str, Any]	):
  __UpperCAmelCase							:			Optional[Any]     =						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  __UpperCAmelCase							:			int     =						np.floor(__lowerCamelCase	)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  __UpperCAmelCase							:			Optional[Any]     =						int(n_draws - floored.sum()	)
  if need_to_add > 0:
    __UpperCAmelCase							:			Tuple     =						continuous - floored
    __UpperCAmelCase							:			Optional[Any]     =						np.sort(np.unique(__lowerCamelCase	)	)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((__UpperCAmelCase)   ,				)							:			List[str]     =						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							:			Optional[int]     =						min(len(__lowerCamelCase	)						,	__lowerCamelCase	)
      __UpperCAmelCase							:			Dict     =						rng.choice(__lowerCamelCase						,	size=__lowerCamelCase						,	replace=__lowerCamelCase	)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa	)







def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  List[Any]						,	__lowerCamelCase    :  List[Any]						,	__lowerCamelCase    :  Optional[int]=10	):
  __UpperCAmelCase   ,				__UpperCAmelCase							:			List[str]     =						np.unique(__lowerCamelCase						,	return_inverse=__lowerCamelCase	)
  __UpperCAmelCase							:			List[Any]     =						classes.shape[0]
  __UpperCAmelCase							:			List[str]     =						np.bincount(__lowerCamelCase	)
  if np.min(__lowerCamelCase	) < 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(__lowerCamelCase						,	kind="""mergesort"""	)						,	np.cumsum(__lowerCamelCase	)[:-1]	)
  for _ in range(__lowerCamelCase	):
    __UpperCAmelCase							:			Optional[Any]     =						approximate_mode(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)
    __UpperCAmelCase							:			List[str]     =						class_counts - n_i
    __UpperCAmelCase							:			List[Any]     =						approximate_mode(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)

    __UpperCAmelCase							:			int     =						[]
    __UpperCAmelCase							:			Tuple     =						[]

    for i in range(__lowerCamelCase	):
      __UpperCAmelCase							:			Dict     =						rng.permutation(class_counts[i]	)
      __UpperCAmelCase							:			Dict     =						class_indices[i].take(__lowerCamelCase						,	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							:			Optional[int]     =						rng.permutation(__lowerCamelCase	)
    __UpperCAmelCase							:			Optional[Any]     =						rng.permutation(__lowerCamelCase	)

    yield train, test










from typing import TYPE_CHECKING

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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


