



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 			_snake_case     (   __UpperCamelCase):
				UpperCamelCase__			:   List[str]       						="""data2vec-text"""




				def __init__(  self   :  Dict,   __lowercase   :  Dict=3_0522,   __lowercase   :  Any=768,   __lowercase   :  Dict=12,   __lowercase   :  int=12,   __lowercase   :  int=3072,   __lowercase   :  Optional[int]="gelu",   __lowercase   :  int=0.1,   __lowercase   :  int=0.1,   __lowercase   :  List[Any]=512,   __lowercase   :  Any=2,   __lowercase   :  str=0.02,   __lowercase   :  Any=1e-1_2,   __lowercase   :  Optional[int]=1,   __lowercase   :  List[Any]=0,   __lowercase   :  Union[str, Any]=2,   __lowercase   :  Optional[int]="absolute",   __lowercase   :  List[str]=True,   __lowercase   :  str=None,   **__lowercase   :  Dict,   ):
							super().__init__(pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   **__lowercase  )

							lowercase__   					=			vocab_size
							lowercase__   					=			hidden_size
							lowercase__   					=			num_hidden_layers
							lowercase__   					=			num_attention_heads
							lowercase__   					=			hidden_act
							lowercase__   					=			intermediate_size
							lowercase__   					=			hidden_dropout_prob
							lowercase__   					=			attention_probs_dropout_prob
							lowercase__   					=			max_position_embeddings
							lowercase__   					=			type_vocab_size
							lowercase__   					=			initializer_range
							lowercase__   					=			layer_norm_eps
							lowercase__   					=			position_embedding_type
							lowercase__   					=			use_cache
							lowercase__   					=			classifier_dropout
class 			_snake_case     (   __UpperCamelCase):




				@property
				def 							A__ (  self   :  Tuple  ):
							if self.task == "multiple-choice":
										lowercase__   					=			{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
										lowercase__   					=			{0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						lowercase__   					=			n - k
			# Calculate C(n,k)
			for i in range(SCREAMING_SNAKE_CASE_					):
						result *= n - i
						result //= i + 1
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return binomial_coefficient(2 * node_count  ,					SCREAMING_SNAKE_CASE_					) // (node_count + 1)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if n < 0:
						raise ValueError("factorial() not defined for negative values"					)
			lowercase__   					=			1
			for i in range(1  ,					n + 1					):
						result *= i
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return catalan_number(SCREAMING_SNAKE_CASE_					) * factorial(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
							    F'binary trees and {catalan_number(node_count)} binary search trees.'
							)




