


"""simple docstring"""







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__				:							Tuple						=     ['''ConvNextFeatureExtractor''']
		a__				:							List[Any]						=     ['''ConvNextImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''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__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if upper_limit < 0:
										raise ValueError("Limit for the Catalan sequence must be ≥ 0"   )

					__SCREAMING_SNAKE_CASE						   =				[0] * (upper_limit + 1)

					# Base case: C(0) = C(1) = 1
					__SCREAMING_SNAKE_CASE						   =				1
					if upper_limit > 0:
										__SCREAMING_SNAKE_CASE						   =				1

					# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
					for i in range(2  ,						upper_limit + 1   ):
										for j in range(lowerCAmelCase_   ):
															catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

					return catalan_list


if __name__ == "__main__":
		print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
		print('''\n*** Enter -1 at any time to quit ***''')
		print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
		try:
				while True:
						a__				:							List[str]						=     int(input().strip())
						if N < 0:
								print('''\n********* Goodbye!! ************''')
								break
						else:
								print(F"The Catalan numbers from 0 through {N} are:")
								print(catalan_numbers(N))
								print('''Try another upper limit for the sequence: ''', end='''''')
		except (NameError, ValueError):
				print('''\n********* Invalid input, goodbye! ************\n''')

		import doctest

		doctest.testmod()
