

import math
import sys






class 							A_			:


     def __init__(							self      :		Optional[Any] )							->     Dict:
      __lowerCAmelCase:       Union[str, Any]            =				0
      __lowerCAmelCase:       List[Any]            =				[]
      __lowerCAmelCase:       List[str]            =				{}  # To store the pos of node in array


     def   UpperCAmelCase  (							self      :		Any )							->     str:
      return self.cur_size == 0


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Tuple )							->     Union[str, Any]:
      __lowerCAmelCase:       int            =				self.left(UpperCAmelCase )
      __lowerCAmelCase:       List[str]            =				self.right(UpperCAmelCase )
      if lc < self.cur_size and self.array(UpperCAmelCase )[0] < self.array(UpperCAmelCase )[0]:
       __lowerCAmelCase:       Any            =				lc
      else:
       __lowerCAmelCase:       Any            =				idx
      if rc < self.cur_size and self.array(UpperCAmelCase )[0] < self.array(UpperCAmelCase )[0]:
       __lowerCAmelCase:       List[str]            =				rc
      if smallest != idx:
       self.swap(UpperCAmelCase      ,  UpperCAmelCase )
       self.min_heapify(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		str )							->     Optional[Any]:
      # Inserts a node into the Priority Queue
      __lowerCAmelCase:       Tuple            =				self.cur_size
      self.cur_size += 1
      self.array.append((sys.maxsize, tup[1]) )
      self.decrease_key((sys.maxsize, tup[1])      ,  tup[0] )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     str:
      # Removes and returns the min element at top of priority queue
      __lowerCAmelCase:       List[Any]            =				self.array[0][1]
      __lowerCAmelCase:       List[str]            =				self.array[self.cur_size - 1]
      self.cur_size -= 1
      self.min_heapify(1 )
      del self.pos[min_node]
      return min_node


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		List[Any] )							->     Union[str, Any]:
      # returns the index of left child
      return 2 * i + 1


     def   UpperCAmelCase  (							self      :		int      ,  UpperCAmelCase      :		List[Any] )							->     int:
      # returns the index of right child
      return 2 * i + 2


     def   UpperCAmelCase  (							self      :		int      ,  UpperCAmelCase      :		List[Any] )							->     Any:
      # returns the index of parent
      return math.floor(i / 2 )


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Dict      ,  UpperCAmelCase      :		Optional[Any] )							->     Optional[int]:
      # swaps array elements at indices i and j
      # update the pos{}
      __lowerCAmelCase:       int            =				j
      __lowerCAmelCase:       str            =				i
      __lowerCAmelCase:       Tuple            =				self.array[i]
      __lowerCAmelCase:       List[str]            =				self.array[j]
      __lowerCAmelCase:       Dict            =				temp


     def   UpperCAmelCase  (							self      :		int      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     Tuple:
      __lowerCAmelCase:       Optional[int]            =				self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      __lowerCAmelCase:       str            =				(new_d, tup[1])
      while idx > 0 and self.array[self.par(UpperCAmelCase )][0] > self.array[idx][0]:
       self.swap(UpperCAmelCase      ,  self.par(UpperCAmelCase ) )
       __lowerCAmelCase:       int            =				self.par(UpperCAmelCase )







class 							A_			:


     def __init__(							self      :		Any      ,  UpperCAmelCase      :		Any )							->     int:
      __lowerCAmelCase:       Optional[int]            =				{}  # To store graph: u -> (v,w)
      __lowerCAmelCase:       Optional[int]            =				num  # Number of nodes in graph
      # To store the distance from source vertex
      __lowerCAmelCase:       List[str]            =				[0] * self.num_nodes
      __lowerCAmelCase:       Any            =				[-1] * self.num_nodes  # To store the path


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[int] )							->     Tuple:
      #  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:
       __lowerCAmelCase:       List[Any]            =				[(v, w)]

      # Assuming undirected graph
      if v in self.adjList:
       self.adjList[v].append((u, w) )
      else:
       __lowerCAmelCase:       str            =				[(u, w)]


     def   UpperCAmelCase  (							self      :		str )							->     Union[str, Any]:
      # u -> v(w)
      for u in self.adjList:
       print(UpperCAmelCase      ,  '->'      ,  ' -> '.join(str(F'''{v}({w})''' ) for v, w in self.adjList[u] ) )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Optional[Any] )							->     List[Any]:
      # Flush old junk values in par[]
      __lowerCAmelCase:       List[Any]            =				[-1] * self.num_nodes
      # src is the source node
      __lowerCAmelCase:       str            =				0
      __lowerCAmelCase:       Tuple            =				PriorityQueue()
      q.insert((0, src) )  # (dist from src, node)
      for u in self.adjList:
       if u != src:
        __lowerCAmelCase:       Optional[Any]            =				sys.maxsize  # Infinity
        __lowerCAmelCase:       List[Any]            =				-1

      while not q.is_empty():
       __lowerCAmelCase:       Dict            =				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]:
        __lowerCAmelCase:       Dict            =				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)      ,  UpperCAmelCase )
         __lowerCAmelCase:       Tuple            =				new_dist
         __lowerCAmelCase:       Any            =				u

        # Show the shortest distances from src
      self.show_distances(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		int )							->     Tuple:
      print(F'''Distance from node: {src}''' )
      for u in range(self.num_nodes ):
       print(F'''Node {u} has distance: {self.dist[u]}''' )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Optional[Any] )							->     Optional[int]:
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      __lowerCAmelCase:       Optional[int]            =				[]
      __lowerCAmelCase:       Optional[int]            =				0
      __lowerCAmelCase:       Tuple            =				dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
       path.append(UpperCAmelCase )
       if temp != src:
        for v, w in self.adjList[temp]:
         if v == self.par[temp]:
          cost += w
          break
       __lowerCAmelCase:       Optional[Any]            =				self.par[temp]
      path.append(UpperCAmelCase )
      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: '      ,  UpperCAmelCase )


if __name__ == "__main__":
  _a			=       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, 1_1)
  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, 1_4)
  graph.add_edge(4, 5, 1_0)
  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








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			=       False
_a			=       Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_a			=       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_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		str=sys.argv[1:] )							->     Optional[int]:
      __lowerCAmelCase:       List[Any]            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       str            =				None
      __lowerCAmelCase:       Any            =				1
      __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				getopt.getopt(UpperCAmelCase      ,  'i:o:b:s'      ,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
      for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
        __lowerCAmelCase:       Any            =				arg
       elif opt in ("-o", "--outfile"):
        __lowerCAmelCase:       List[Any]            =				arg
       elif opt in ("-b", "--batch_size"):
        __lowerCAmelCase:       List[Any]            =				int(UpperCAmelCase )
       elif opt in ("-s", "--subset_list"):
        __lowerCAmelCase:       Union[str, Any]            =				arg

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

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


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Dict )							->     Dict:
      __lowerCAmelCase:       str            =				int(file.split('.' )[0].split('_' )[-1] )
      __lowerCAmelCase:       Union[str, Any]            =				os.path.join(self.inputdir      ,  UpperCAmelCase )
      return (img_id, filepath)


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __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(UpperCAmelCase ) )
       if len(UpperCAmelCase ) == self.batch:
        __lowerCAmelCase:       Optional[int]            =				batch
        __lowerCAmelCase:       Tuple            =				[]
        yield list(map(UpperCAmelCase      ,  zip(*UpperCAmelCase ) ) )

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


     def __call__(							self      :		Tuple )							->     Dict:
      # make writer
      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:       List[Any]            =				self.preprocess(UpperCAmelCase )
       __lowerCAmelCase:       str            =				self.model(
           UpperCAmelCase      ,  UpperCAmelCase      ,  scales_yx=UpperCAmelCase      ,  padding='max_detections'      ,  max_detections=self.config.MAX_DETECTIONS      ,  pad_value=0      ,  return_tensors='np'      ,  location='cpu'      ,  )
       __lowerCAmelCase:       str            =				output_dict.pop('normalized_boxes' )
       if not TEST:
        __lowerCAmelCase:       Dict            =				np.array(UpperCAmelCase )
        __lowerCAmelCase:       Optional[int]            =				self.schema.encode_batch(UpperCAmelCase )
        writer.write_batch(UpperCAmelCase )
       if TEST:
        break
       # finalizer the writer
      if not TEST:
       __lowerCAmelCase     ,							__lowerCAmelCase:       Optional[int]            =				writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb''' )







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Any:

 """simple docstring"""






 try:
  __lowerCAmelCase:       Optional[Any]            =				json.load(SCREAMING_SNAKE_CASE		)
  try:
   __lowerCAmelCase:       Tuple            =				list(data.keys()		)
  except Exception:
   __lowerCAmelCase:       int            =				[d['img_id'] for d in data]
 except Exception:
  try:
   __lowerCAmelCase:       Dict            =				eval(stream.read()		)
  except Exception:
   __lowerCAmelCase:       Dict            =				stream.read().split('\n'		)
 return data


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





