

"""simple docstring"""




# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      __UpperCamelCase					:


		def __init__(self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]):
							A     =   key
							A     =   freq


		def __str__(self					:						Optional[Any]):
							return F"""Node(key={self.key}, freq={self.freq})"""

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if i > j or i < 0 or j > len(lowercase__ ) - 1:
										return

					A     =   root[i][j]
					if parent == -1:  # root does not have a parent
										print(F"""{key[node]} is the root of the binary search tree.""" )
					elif is_left:
										print(F"""{key[node]} is the left child of key {parent}.""" )
					else:
										print(F"""{key[node]} is the right child of key {parent}.""" )

					print_binary_search_tree(lowercase__     ,				lowercase__     ,				lowercase__     ,				node - 1     ,				key[node]     ,				lowercase__ )
					print_binary_search_tree(lowercase__     ,				lowercase__     ,				node + 1     ,				lowercase__     ,				key[node]     ,				lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					nodes.sort(key=lambda lowercase__ : node.key )

					A     =   len(lowercase__ )

					A     =   [nodes[i].key for i in range(lowercase__ )]
					A     =   [nodes[i].freq for i in range(lowercase__ )]

					# This 2D array stores the overall tree cost (which's as minimized as possible);
					# for a single key, cost is equal to frequency of the key.
					A     =   [[freqs[i] if i == j else 0 for j in range(lowercase__ )] for i in range(lowercase__ )]
					# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
					# array
					A     =   [[freqs[i] if i == j else 0 for j in range(lowercase__ )] for i in range(lowercase__ )]
					# stores tree roots that will be used later for constructing binary search tree
					A     =   [[i if i == j else 0 for j in range(lowercase__ )] for i in range(lowercase__ )]

					for interval_length in range(2     ,				n + 1 ):
										for i in range(n - interval_length + 1 ):
															A     =   i + interval_length - 1

															A     =   sys.maxsize  # set the value to "infinity"
															A     =   total[i][j - 1] + freqs[j]

															# Apply Knuth's optimization
															# Loop without optimization: for r in range(i, j + 1):
															for r in range(root[i][j - 1]     ,				root[i + 1][j] + 1 ):  # r is a temporal root
																				A     =   dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																				A     =   dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																				A     =   left + total[i][j] + right

																				if dp[i][j] > cost:
																									A     =   cost
																									A     =   r

					print("Binary search tree nodes:" )
					for node in nodes:
										print(lowercase__ )

					print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.""" )
					print_binary_search_tree(lowercase__     ,				lowercase__     ,				0     ,				n - 1     ,				-1     ,				lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					# A sample binary search tree
					A     =   [Node(lowercase__     ,				randint(1     ,				50 ) ) for i in range(10     ,				0     ,				-1 )]
					find_optimal_binary_search_tree(lowercase__ )


if __name__ == "__main__":
	main()








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


A__							=				logging.get_logger(__name__)

A__							=				{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: int												=	"splinter"







	def __init__( self:			str	,			__UpperCamelCase:			Any=3_05_22	,			__UpperCamelCase:			List[Any]=7_68	,			__UpperCamelCase:			str=12	,			__UpperCamelCase:			Optional[Any]=12	,			__UpperCamelCase:			Optional[int]=30_72	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Union[str, Any]=0.1	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			str=5_12	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			str=0.02	,			__UpperCamelCase:			Tuple=1E-12	,			__UpperCamelCase:			Optional[int]=True	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Optional[int]=1_04	,			**__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     initializer_range
						__magic_name__				=     type_vocab_size
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     use_cache
						__magic_name__				=     question_token_id




