import numpy as np
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[str]:

  '''simple docstring'''




  a__  		=  n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  a__  		=  np.floor(lowerCamelCase__       )
  # 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(lowerCamelCase__       )       )[::-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(lowerCamelCase__       )			,					lowerCamelCase__       )
      a__  		=  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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=10       )					->Optional[Any]:

  '''simple docstring'''




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

    a__  		=  []
    a__  		=  []

    for i in range(lowerCamelCase__       ):
      a__  		=  rng.permutation(class_counts[i]       )
      a__  		=  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]]       )
    a__  		=  rng.permutation(lowerCamelCase__       )
    a__  		=  rng.permutation(lowerCamelCase__       )

    yield train, test







from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
