






from typing import TYPE_CHECKING

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


_a      	=  {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''ConvNextFeatureExtractor''']
		_a      	=  ['''ConvNextImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvNextForImageClassification''',
		    '''ConvNextModel''',
		    '''ConvNextPreTrainedModel''',
		    '''ConvNextBackbone''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''TFConvNextForImageClassification''',
		    '''TFConvNextModel''',
		    '''TFConvNextPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_convnext import ConvNextFeatureExtractor
				from .image_processing_convnext import ConvNextImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convnext import (
				    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvNextBackbone,
				    ConvNextForImageClassification,
				    ConvNextModel,
				    ConvNextPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)





"""simple docstring"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
