




'''simple docstring'''


import numpy as np
def 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  str ,						UpperCamelCase       :  Any      ):





							'''simple docstring'''


							_a	     =				n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							_a	     =				np.floor(UpperCamelCase      )
							# 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(UpperCamelCase      )      )[::-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(UpperCamelCase      ) ,						UpperCamelCase      )
																					_a	     =				rng.choice(UpperCamelCase ,						size=UpperCamelCase ,						replace=UpperCamelCase      )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa      )

def 		snake_case_    (UpperCamelCase       :  Tuple ,						UpperCamelCase       :  int ,						UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  str=10      ):





							'''simple docstring'''


							_a     ,							_a	     =				np.unique(UpperCamelCase ,						return_inverse=UpperCamelCase      )
							_a	     =				classes.shape[0]
							_a	     =				np.bincount(UpperCamelCase      )
							if np.min(UpperCamelCase      ) < 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(UpperCamelCase ,						kind='''mergesort'''      ) ,						np.cumsum(UpperCamelCase      )[:-1]      )
							for _ in range(UpperCamelCase      ):
														_a	     =				approximate_mode(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
														_a	     =				class_counts - n_i
														_a	     =				approximate_mode(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )

														_a	     =				[]
														_a	     =				[]

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

														yield train, test







'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

