

"""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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							key
        lowerCAmelCase__      :							List[Any]    =							freq






       def __str__(      self      ):





        """simple docstring"""

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




def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	List[str]:
 if i > j or i < 0 or j > len(lowercase__      ) - 1:
  return

 lowerCAmelCase__      :							Any    =							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__      ) ->	Any:
 nodes.sort(key=lambda lowercase__      : node.key      )

 lowerCAmelCase__      :							Tuple    =							len(lowercase__      )

 lowerCAmelCase__      :							List[str]    =							[nodes[i].key for i in range(lowercase__      )]
 lowerCAmelCase__      :							Any    =							[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.
 lowerCAmelCase__      :							Union[str, Any]    =							[[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
 lowerCAmelCase__      :							str    =							[[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
 lowerCAmelCase__      :							Dict    =							[[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      ):
   lowerCAmelCase__      :							List[Any]    =							i + interval_length - 1

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

    if dp[i][j] > cost:
     lowerCAmelCase__      :							int    =							cost
     lowerCAmelCase__      :							Optional[Any]    =							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       (   ) ->	Union[str, Any]:
 # A sample binary search tree
 lowerCAmelCase__      :							str    =							[Node(lowercase__				,				randint(1				,				5_0      )      ) for i in range(1_0				,				0				,				-1      )]
 find_optimal_binary_search_tree(lowercase__      )


if __name__ == "__main__":
    main()









"""simple docstring"""

import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_UpperCamelCase		    =						False
_UpperCamelCase		    =						Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_UpperCamelCase		    =						datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(snake_case      ), F"""{outputfile}"""
        if subset_list is not None:
         with open(os.path.realpath(snake_case      )      ) as f:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

        lowerCAmelCase__      :							Optional[int]    =							CONFIG
        if torch.cuda.is_available():
         lowerCAmelCase__      :							Union[str, Any]    =							"cuda"
        lowerCAmelCase__      :							Dict    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Tuple    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Any    =							Preprocess(self.config      )
        lowerCAmelCase__      :							str    =							GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned" ,				config=self.config      )
        lowerCAmelCase__      :							int    =							batch_size if batch_size != 0 else 1
        lowerCAmelCase__      :							Optional[int]    =							DEFAULT_SCHEMA






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							[]
        for i, file in enumerate(os.listdir(self.inputdir      )      ):
         if self.subset_list is not None and i not in self.subset_list:
          continue
         batch.append(self._vqa_file_split(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

        for i in range(1      ):
         yield list(map(snake_case ,				zip(*snake_case      )      )      )






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=snake_case ,				padding="max_detections" ,				max_detections=self.config.MAX_DETECTIONS ,				pad_value=0 ,				return_tensors="np" ,				location="cpu" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


if __name__ == "__main__":
    _UpperCamelCase		    =						Extract(sys.argv[1:])
    extract()
    if not TEST:
        _UpperCamelCase		    =						datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)






