



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

	__lowercase									=      [0] * (upper_limit + 1)

	# Base case: C(0) = C(1) = 1
	__lowercase									=      1
	if upper_limit > 0:
		__lowercase									=      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(snake_case__ ):
			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:
			snake_case__       :  Any     		= 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()







"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

