


import numpy as np

def 		snake_case_   (				lowercase__     :    Tuple       ,     lowercase__     :    List[Any]       ,     lowercase__     :    Any     ):






				'''simple docstring'''






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





def 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    List[str]       ,     lowercase__     :    List[str]       ,     lowercase__     :    int       ,     lowercase__     :    str=10     ):






				'''simple docstring'''






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

								_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=[]

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

								yield train, test





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWDForCTC''',
		    '''SEWDForSequenceClassification''',
		    '''SEWDModel''',
		    '''SEWDPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew_d import (
				    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWDForCTC,
				    SEWDForSequenceClassification,
				    SEWDModel,
				    SEWDPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Tuple           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

