




"""simple docstring"""

import sys
from random import randint

class     lowercase :







    def __init__(							self				:   List[Any] ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   List[str]    ):



     """simple docstring"""

     A_					:			Optional[int]						=						key
     A_					:			Optional[Any]						=						freq







    def __str__(							self				:   List[str]    ):



     """simple docstring"""

     return F"""Node(key={self.key}, freq={self.freq})"""

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




 """simple docstring"""






 if i > j or i < 0 or j > len(_UpperCAmelCase		) - 1:
  return

 A_					:			Dict						=						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(_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, node - 1					, key[node]					, _UpperCAmelCase		)
 print_binary_search_tree(_UpperCAmelCase					, _UpperCAmelCase					, node + 1					, _UpperCAmelCase					, key[node]					, _UpperCAmelCase		)

def 						lowercase_ (						_UpperCAmelCase		):




 """simple docstring"""






 nodes.sort(key=lambda _UpperCAmelCase		: node.key		)

 A_					:			Any						=						len(_UpperCAmelCase		)

 A_					:			Optional[int]						=						[nodes[i].key for i in range(_UpperCAmelCase		)]
 A_					:			Optional[int]						=						[nodes[i].freq for i in range(_UpperCAmelCase		)]

 # 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_					:			List[Any]						=						[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase		)] for i in range(_UpperCAmelCase		)]
 # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
 # array
 A_					:			str						=						[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase		)] for i in range(_UpperCAmelCase		)]
 # stores tree roots that will be used later for constructing binary search tree
 A_					:			Optional[Any]						=						[[i if i == j else 0 for j in range(_UpperCAmelCase		)] for i in range(_UpperCAmelCase		)]

 for interval_length in range(2					, n + 1		):
  for i in range(n - interval_length + 1		):
   A_					:			List[str]						=						i + interval_length - 1

   A_					:			int						=						sys.maxsize  # set the value to "infinity"
   A_					:			str						=						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_					:			List[Any]						=						dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
    A_					:			str						=						dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
    A_					:			str						=						left + total[i][j] + right

    if dp[i][j] > cost:
     A_					:			List[Any]						=						cost
     A_					:			List[Any]						=						r

 print('''Binary search tree nodes:'''		)
 for node in nodes:
  print(_UpperCAmelCase		)

 print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""		)
 print_binary_search_tree(_UpperCAmelCase					, _UpperCAmelCase					, 0					, n - 1					, -1					, _UpperCAmelCase		)

def 						lowercase_ (						):




 """simple docstring"""






 A_					:			Any						=						[Node(_UpperCAmelCase					, randint(1					, 50		)		) for i in range(10					, 0					, -1		)]
 find_optimal_binary_search_tree(_UpperCAmelCase		)


if __name__ == "__main__":
  main()











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





