

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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)



class     lowercase						:



 '''simple docstring'''


 def __init__(self       ,       __a=sys.argv[1:] ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				None
  UpperCAmelCase__							=				1
  UpperCAmelCase__				,    UpperCAmelCase__							=				getopt.getopt(__a       ,       'i:o:b:s'       ,       ['inputdir=', 'outfile=', 'batch_size=', 'subset_list='] )
  for opt, arg in opts:
   if opt in ("-i", "--inputdir"):
    UpperCAmelCase__							=				arg
   elif opt in ("-o", "--outfile"):
    UpperCAmelCase__							=				arg
   elif opt in ("-b", "--batch_size"):
    UpperCAmelCase__							=				int(__a )
   elif opt in ("-s", "--subset_list"):
    UpperCAmelCase__							=				arg

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

  UpperCAmelCase__							=				CONFIG
  if torch.cuda.is_available():
   UpperCAmelCase__							=				'cuda'
  UpperCAmelCase__							=				os.path.realpath(__a )
  UpperCAmelCase__							=				os.path.realpath(__a )
  UpperCAmelCase__							=				Preprocess(self.config )
  UpperCAmelCase__							=				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'       ,       config=self.config )
  UpperCAmelCase__							=				batch_size if batch_size != 0 else 1
  UpperCAmelCase__							=				DEFAULT_SCHEMA




 def   UpperCamelCase__							(self       ,       __a ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				int(file.split('.' )[0].split('_' )[-1] )
  UpperCAmelCase__							=				os.path.join(self.inputdir       ,       __a )
  return (img_id, filepath)




 @property
 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				[]
  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(__a ) )
   if len(__a ) == self.batch:
    UpperCAmelCase__							=				batch
    UpperCAmelCase__							=				[]
    yield list(map(__a       ,       zip(*__a ) ) )

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




 def __call__(self ) ->					Optional[int]:






  """simple docstring"""





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







def 						UpperCamelCase_(			snake_case__:  Optional[Any]   )       -> Union[str, Any]:
 try:
  UpperCAmelCase__							=				json.load(snake_case__   )
  try:
   UpperCAmelCase__							=				list(data.keys()   )
  except Exception:
   UpperCAmelCase__							=				[d['img_id'] for d in data]
 except Exception:
  try:
   UpperCAmelCase__							=				eval(stream.read()   )
  except Exception:
   UpperCAmelCase__							=				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)






from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase			       =			{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 						UpperCamelCase_(			snake_case__:  dict[str, list[str]]   )       -> set[tuple[str, str]]:
 UpperCAmelCase__							=				{node: {node} for node in graph}

 UpperCAmelCase__							=				{node: graph[node][:] for node in graph}

 while len(snake_case__   ) > 2:
  # Choose a random edge.
  UpperCAmelCase__							=				random.choice(list(graph_copy.keys()   )   )
  UpperCAmelCase__							=				random.choice(graph_copy[u]   )

  # Contract edge (u, v) to new node uv
  UpperCAmelCase__							=				u + v
  UpperCAmelCase__							=				list(set(graph_copy[u] + graph_copy[v]   )   )
  uv_neighbors.remove(snake_case__   )
  uv_neighbors.remove(snake_case__   )
  UpperCAmelCase__							=				uv_neighbors
  for neighbor in uv_neighbors:
   graph_copy[neighbor].append(snake_case__   )

  UpperCAmelCase__							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

  # Remove nodes u and v.
  del graph_copy[u]
  del graph_copy[v]
  for neighbor in uv_neighbors:
   if u in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )
   if v in graph_copy[neighbor]:
    graph_copy[neighbor].remove(snake_case__   )

    # Find cutset.
 UpperCAmelCase__							=				[contracted_nodes[node] for node in graph_copy]
 return {
     (node, neighbor)
     for node in groups[0]
     for neighbor in graph[node]
     if neighbor in groups[1]
 }


if __name__ == "__main__":
 print(partition_graph(TEST_GRAPH))



