
import numpy as np




def     a						(							snake_case__: List[str]						,       snake_case__: Optional[Any]						,       snake_case__: Optional[int] ):


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




def     a						(							snake_case__: Any						,       snake_case__: Dict						,       snake_case__: Optional[Any]						,       snake_case__: Tuple						,       snake_case__: int=10 ):


					'''simple docstring'''
					lowercase_  ,     lowercase_					  = np.unique(snake_case__						,       return_inverse=snake_case__ )
					lowercase_					  = classes.shape[0]
					lowercase_					  = np.bincount(snake_case__ )
					if np.min(snake_case__ ) < 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) )
					lowercase_					  = np.split(np.argsort(snake_case__						,       kind='''mergesort''' )						,       np.cumsum(snake_case__ )[:-1] )
					for _ in range(snake_case__ ):
										lowercase_					  = approximate_mode(snake_case__						,       snake_case__						,       snake_case__ )
										lowercase_					  = class_counts - n_i
										lowercase_					  = approximate_mode(snake_case__						,       snake_case__						,       snake_case__ )

										lowercase_					  = []
										lowercase_					  = []

										for i in range(snake_case__ ):
															lowercase_					  = rng.permutation(class_counts[i] )
															lowercase_					  = class_indices[i].take(snake_case__						,       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]] )
										lowercase_					  = rng.permutation(snake_case__ )
										lowercase_					  = rng.permutation(snake_case__ )

										yield train, test






from typing import TYPE_CHECKING

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


__a        = {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DonutSwinModel',
						    'DonutSwinPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DonutFeatureExtractor']
						__a        = ['DonutImageProcessor']


if TYPE_CHECKING:
						from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
						from .processing_donut import DonutProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_donut_swin import (
												    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DonutSwinModel,
												    DonutSwinPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_donut import DonutFeatureExtractor
												from .image_processing_donut import DonutImageProcessor

else:
						import sys

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




