


from typing import TYPE_CHECKING

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


a : Optional[Any]		=				{
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Dict		=				['''ConvNextFeatureExtractor''']
	a : List[Any]		=				['''ConvNextImageProcessor''']

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

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Optional[Any]		=				[
	    '''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 : str		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)






from itertools import count


def 		lowercase_  (     _UpperCamelCase = 50  ):


			'''simple docstring'''







			__lowercase 					=   [1] * min_block_length

			for n in count(_UpperCamelCase  ):
						fill_count_functions.append(1  )

						for block_length in range(_UpperCamelCase       ,			n + 1  ):
									for block_start in range(n - block_length  ):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


if __name__ == "__main__":
	print(f'''{solution() = }''')


