code: 
  
  
  
  
  
  """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
  __A :		str      							=    False
  __A :		Dict      							=    Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
  __A :		List[str]      							=    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, 2048), dtype='''float32'''),
              '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
              '''preds_per_image''': datasets.Value(dtype='''int32'''),
          }
      )
  )
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=sys.argv[1:] ):
         UpperCamelCase				:   List[Any]          =							None
         UpperCamelCase				:   Tuple          =							None
         UpperCamelCase				:   List[Any]          =							None
         UpperCamelCase				:   List[str]          =							1
         UpperCamelCase				:   Any          =							getopt.getopt(SCREAMING_SNAKE_CASE_    ,      """i:o:b:s"""    ,      ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""] )
         for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                 UpperCamelCase				:   Any          =							arg
             elif opt in ("-o", "--outfile"):
                 UpperCamelCase				:   List[str]          =							arg
             elif opt in ("-b", "--batch_size"):
                 UpperCamelCase				:   int          =							int(SCREAMING_SNAKE_CASE_ )
             elif opt in ("-s", "--subset_list"):
                 UpperCamelCase				:   Tuple          =							arg
  
         assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
         assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_ ), f'{outputfile}'
         if subset_list is not None:
             with open(os.path.realpath(SCREAMING_SNAKE_CASE_ ) ) as f:
                 UpperCamelCase				:   Optional[Any]          =							{self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_ )}
         else:
             UpperCamelCase				:   Tuple          =							None
  
         UpperCamelCase				:   List[str]          =							CONFIG
         if torch.cuda.is_available():
             UpperCamelCase				:   Tuple          =							"""cuda"""
         UpperCamelCase				:   int          =							os.path.realpath(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							os.path.realpath(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Any          =							Preprocess(self.config )
         UpperCamelCase				:   int          =							GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""    ,      config=self.config )
         UpperCamelCase				:   List[Any]          =							batch_size if batch_size != 0 else 1
         UpperCamelCase				:   Union[str, Any]          =							DEFAULT_SCHEMA
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							int(file.split(""".""" )[0].split("""_""" )[-1] )
         UpperCamelCase				:   int          =							os.path.join(self.inputdir    ,      SCREAMING_SNAKE_CASE_ )
         return (img_id, filepath)
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   List[str]          =							[]
         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(SCREAMING_SNAKE_CASE_ ) )
             if len(SCREAMING_SNAKE_CASE_ ) == self.batch:
                 UpperCamelCase				:   Dict          =							batch
                 UpperCamelCase				:   List[str]          =							[]
                 yield list(map(SCREAMING_SNAKE_CASE_    ,      zip(*SCREAMING_SNAKE_CASE_ ) ) )
  
         for i in range(1 ):
             yield list(map(SCREAMING_SNAKE_CASE_    ,      zip(*SCREAMING_SNAKE_CASE_ ) ) )
  
  
  
  
  
  
  
     def __call__(							self ):
         # make writer
         if not TEST:
             UpperCamelCase				:   int          =							datasets.ArrowWriter(features=self.schema    ,      path=self.outputfile )
         # do file generator
         for i, (img_ids, filepaths) in enumerate(self.file_generator ):
             UpperCamelCase				:   Any          =							self.preprocess(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   int          =							self.model(
                 SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      scales_yx=SCREAMING_SNAKE_CASE_    ,      padding="""max_detections"""    ,      max_detections=self.config.MAX_DETECTIONS    ,      pad_value=0    ,      return_tensors="""np"""    ,      location="""cpu"""    ,      )
             UpperCamelCase				:   str          =							output_dict.pop("""normalized_boxes""" )
             if not TEST:
                 UpperCamelCase				:   Dict          =							np.array(SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   Optional[Any]          =							self.schema.encode_batch(SCREAMING_SNAKE_CASE_ )
                 writer.write_batch(SCREAMING_SNAKE_CASE_ )
             if TEST:
                 break
             # finalizer the writer
         if not TEST:
             UpperCamelCase				:   Union[str, Any]          =							writer.finalize()
             print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb' )
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       str    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      try:
          UpperCamelCase				:   List[Any]          =							json.load(snake_case_    )
          try:
              UpperCamelCase				:   Tuple          =							list(data.keys()    )
          except Exception:
              UpperCamelCase				:   str          =							[d["""img_id"""] for d in data]
      except Exception:
          try:
              UpperCamelCase				:   Dict          =							eval(stream.read()    )
          except Exception:
              UpperCamelCase				:   str          =							stream.read().split("""\n"""    )
      return data
  
  
  if __name__ == "__main__":
    __A :		Optional[int]      							=    Extract(sys.argv[1:])
    extract()
    if not TEST:
      __A :		Tuple      							=    datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import math
  import sys
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self ):
         UpperCamelCase				:   Tuple          =							0
         UpperCamelCase				:   Optional[int]          =							[]
         UpperCamelCase				:   Optional[int]          =							{}  # To store the pos of node in array
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.cur_size == 0
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[str]          =							self.left(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[int]          =							self.right(SCREAMING_SNAKE_CASE_ )
         if lc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
             UpperCamelCase				:   str          =							lc
         else:
             UpperCamelCase				:   Union[str, Any]          =							idx
         if rc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
             UpperCamelCase				:   Optional[Any]          =							rc
         if smallest != idx:
             self.swap(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
             self.min_heapify(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         # Inserts a node into the Priority Queue
         UpperCamelCase				:   List[Any]          =							self.cur_size
         self.cur_size += 1
         self.array.append((sys.maxsize, tup[1]) )
         self.decrease_key((sys.maxsize, tup[1])    ,      tup[0] )
  
  
  
  
  
  
  
     def       a_							(							self ):
         # Removes and returns the min element at top of priority queue
         UpperCamelCase				:   str          =							self.array[0][1]
         UpperCamelCase				:   Optional[int]          =							self.array[self.cur_size - 1]
         self.cur_size -= 1
         self.min_heapify(1 )
         del self.pos[min_node]
         return min_node
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         # returns the index of left child
         return 2 * i + 1
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         # returns the index of right child
         return 2 * i + 2
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         # returns the index of parent
         return math.floor(i / 2 )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         # swaps array elements at indices i and j
         # update the pos{}
         UpperCamelCase				:   Dict          =							j
         UpperCamelCase				:   List[str]          =							i
         UpperCamelCase				:   Optional[int]          =							self.array[i]
         UpperCamelCase				:   Union[str, Any]          =							self.array[j]
         UpperCamelCase				:   Optional[int]          =							temp
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Tuple          =							self.pos[tup[1]]
         # assuming the new_d is atmost old_d
         UpperCamelCase				:   Union[str, Any]          =							(new_d, tup[1])
         while idx > 0 and self.array[self.par(SCREAMING_SNAKE_CASE_ )][0] > self.array[idx][0]:
             self.swap(SCREAMING_SNAKE_CASE_    ,      self.par(SCREAMING_SNAKE_CASE_ ) )
             UpperCamelCase				:   Union[str, Any]          =							self.par(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							{}  # To store graph: u -> (v,w)
         UpperCamelCase				:   int          =							num  # Number of nodes in graph
         # To store the distance from source vertex
         UpperCamelCase				:   Any          =							[0] * self.num_nodes
         UpperCamelCase				:   Union[str, Any]          =							[-1] * self.num_nodes  # To store the path
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         #  Edge going from node u to v and v to u with weight w
         # u (w)-> v, v (w) -> u
         # Check if u already in graph
         if u in self.adjList:
             self.adjList[u].append((v, w) )
         else:
             UpperCamelCase				:   Optional[int]          =							[(v, w)]
  
         # Assuming undirected graph
         if v in self.adjList:
             self.adjList[v].append((u, w) )
         else:
             UpperCamelCase				:   List[Any]          =							[(u, w)]
  
  
  
  
  
  
  
     def       a_							(							self ):
         # u -> v(w)
         for u in self.adjList:
             print(SCREAMING_SNAKE_CASE_    ,      """->"""    ,      """ -> """.join(str(f'{v}({w})' ) for v, w in self.adjList[u] ) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         # Flush old junk values in par[]
         UpperCamelCase				:   Tuple          =							[-1] * self.num_nodes
         # src is the source node
         UpperCamelCase				:   Dict          =							0
         UpperCamelCase				:   int          =							PriorityQueue()
         q.insert((0, src) )  # (dist from src, node)
         for u in self.adjList:
             if u != src:
                 UpperCamelCase				:   Any          =							sys.maxsize  # Infinity
                 UpperCamelCase				:   int          =							-1
  
         while not q.is_empty():
             UpperCamelCase				:   int          =							q.extract_min()  # Returns node with the min dist from source
             # Update the distance of all the neighbours of u and
             # if their prev dist was INFINITY then push them in Q
             for v, w in self.adjList[u]:
                 UpperCamelCase				:   List[Any]          =							self.dist[u] + w
                 if self.dist[v] > new_dist:
                     if self.dist[v] == sys.maxsize:
                         q.insert((new_dist, v) )
                     else:
                         q.decrease_key((self.dist[v], v)    ,      SCREAMING_SNAKE_CASE_ )
                     UpperCamelCase				:   Any          =							new_dist
                     UpperCamelCase				:   Tuple          =							u
  
          # Show the shortest distances from src
         self.show_distances(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         print(f'Distance from node: {src}' )
         for u in range(self.num_nodes ):
             print(f'Node {u} has distance: {self.dist[u]}' )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         # To show the shortest path from src to dest
         # WARNING: Use it *after* calling dijkstra
         UpperCamelCase				:   Tuple          =							[]
         UpperCamelCase				:   Optional[Any]          =							0
         UpperCamelCase				:   int          =							dest
         # Backtracking from dest to src
         while self.par[temp] != -1:
             path.append(SCREAMING_SNAKE_CASE_ )
             if temp != src:
                 for v, w in self.adjList[temp]:
                     if v == self.par[temp]:
                         cost += w
                         break
             UpperCamelCase				:   int          =							self.par[temp]
         path.append(SCREAMING_SNAKE_CASE_ )
         path.reverse()
  
         print(f'----Path to reach {dest} from {src}----' )
         for u in path:
             print(f'{u}'    ,      end=""" """ )
             if u != dest:
                 print("""-> """    ,      end="""""" )
  
         print("""\nTotal cost of path: """    ,      SCREAMING_SNAKE_CASE_ )
  
  
  if __name__ == "__main__":
    __A :		int      							=    Graph(9)
    graph.add_edge(0, 1, 4)
    graph.add_edge(0, 7, 8)
    graph.add_edge(1, 2, 8)
    graph.add_edge(1, 7, 11)
    graph.add_edge(2, 3, 7)
    graph.add_edge(2, 8, 2)
    graph.add_edge(2, 5, 4)
    graph.add_edge(3, 4, 9)
    graph.add_edge(3, 5, 14)
    graph.add_edge(4, 5, 10)
    graph.add_edge(5, 6, 2)
    graph.add_edge(6, 7, 1)
    graph.add_edge(6, 8, 6)
    graph.add_edge(7, 8, 7)
    graph.show_graph()
    graph.dijkstra(0)
    graph.show_path(0, 4)
  
  # OUTPUT
  # 0 -> 1(4) -> 7(8)
  # 1 -> 0(4) -> 2(8) -> 7(11)
  # 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
  # 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
  # 3 -> 2(7) -> 4(9) -> 5(14)
  # 8 -> 2(2) -> 6(6) -> 7(7)
  # 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
  # 4 -> 3(9) -> 5(10)
  # 6 -> 5(2) -> 7(1) -> 8(6)
  # Distance from node: 0
  # Node 0 has distance: 0
  # Node 1 has distance: 4
  # Node 2 has distance: 12
  # Node 3 has distance: 19
  # Node 4 has distance: 21
  # Node 5 has distance: 11
  # Node 6 has distance: 9
  # Node 7 has distance: 8
  # Node 8 has distance: 14
  # ----Path to reach 4 from 0----
  # 0 -> 7 -> 6 -> 5 -> 4
  # Total cost of path:  21
  
  
  
  
  