


def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   "list[int]":





	"""simple docstring"""







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

	A								=				[0] * (upper_limit + 1)

	# Base case: C(0) = C(1) = 1
	A								=				1
	if upper_limit > 0:
		A								=				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(UpperCamelCase__						):
			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:
												_lowercase       :      Optional[int]	=	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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



