



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					List[str]     		="""xglm"""
				UpperCamelCase__				:					Optional[Any]     		=["""past_key_values"""]

				UpperCamelCase__				:					int     		={
				    """num_attention_heads""": """attention_heads""",
				    """hidden_size""": """d_model""",
				    """num_hidden_layers""": """num_layers""",
				}




				def __init__(						self					:						str,					__lowercase					:						Any=25_6008,					__lowercase					:						List[str]=2048,					__lowercase					:						List[Any]=1024,					__lowercase					:						Union[str, Any]=4096,					__lowercase					:						Any=24,					__lowercase					:						List[Any]=16,					__lowercase					:						Tuple="gelu",					__lowercase					:						Tuple=0.1,					__lowercase					:						List[Any]=0.1,					__lowercase					:						List[Any]=0.0,					__lowercase					:						Optional[Any]=0.0,					__lowercase					:						List[Any]=0.02,					__lowercase					:						Optional[int]=True,					__lowercase					:						Tuple=True,					__lowercase					:						int=2,					__lowercase					:						Dict=1,					__lowercase					:						Any=0,					__lowercase					:						List[Any]=2,					**__lowercase					:						Optional[Any],					):
							lowercase__					  =				vocab_size
							lowercase__					  =				max_position_embeddings
							lowercase__					  =				d_model
							lowercase__					  =				ffn_dim
							lowercase__					  =				num_layers
							lowercase__					  =				attention_heads
							lowercase__					  =				activation_function
							lowercase__					  =				dropout
							lowercase__					  =				attention_dropout
							lowercase__					  =				activation_dropout
							lowercase__					  =				layerdrop
							lowercase__					  =				init_std
							lowercase__					  =				scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__					  =				use_cache

							super().__init__(
							    pad_token_id=__lowercase,					bos_token_id=__lowercase,					eos_token_id=__lowercase,					decoder_start_token_id=__lowercase,					**__lowercase,					)









def __lowerCAmelCase (     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(SCREAMING_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:
																					lowercase_								=     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()




