

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







class        __lowercase     :

  def __init__(					self						:     Any   ,A						:     List[Any]=sys.argv[1:]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   None
   UpperCAmelCase__   :					Optional[Any]   				=   None
   UpperCAmelCase__   :					Optional[int]   				=   None
   UpperCAmelCase__   :					int   				=   1
   UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   getopt.getopt(A   ,"""i:o:b:s"""   ,["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]      )
   for opt, arg in opts:
    if opt in ("-i", "--inputdir"):
     UpperCAmelCase__   :					Union[str, Any]   				=   arg
    elif opt in ("-o", "--outfile"):
     UpperCAmelCase__   :					str   				=   arg
    elif opt in ("-b", "--batch_size"):
     UpperCAmelCase__   :					Union[str, Any]   				=   int(A      )
    elif opt in ("-s", "--subset_list"):
     UpperCAmelCase__   :					List[str]   				=   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__   :					Any   				=   {self._vqa_file_split()[0] for x in tryload(A      )}
   else:
    UpperCAmelCase__   :					Tuple   				=   None

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

  def 				__lowercase    (					self						:     str   ,A						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   int(file.split("""."""      )[0].split("""_"""      )[-1]      )
   UpperCAmelCase__   :					Optional[Any]   				=   os.path.join(self.inputdir   ,A      )
   return (img_id, filepath)

  @property
  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   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__   :					Optional[Any]   				=   batch
     UpperCAmelCase__   :					str   				=   []
     yield list(map(A   ,zip(*A      )      )      )

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




  def __call__(					self						:     Optional[Any]      ):






   '''simple docstring'''
   # 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__				, UpperCAmelCase__				, UpperCAmelCase__   :					int   				=   self.preprocess(A      )
    UpperCAmelCase__   :					str   				=   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__   :					Optional[Any]   				=   output_dict.pop("""normalized_boxes"""      )
    if not TEST:
     UpperCAmelCase__   :					Tuple   				=   np.array(A      )
     UpperCAmelCase__   :					Union[str, Any]   				=   self.schema.encode_batch(A      )
     writer.write_batch(A      )
    if TEST:
     break
    # finalizer the writer
   if not TEST:
    UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   writer.finalize()
    print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"      )







def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 try:
  UpperCAmelCase__   :					Tuple   				=   json.load(__UpperCamelCase )
  try:
   UpperCAmelCase__   :					List[str]   				=   list(data.keys() )
  except Exception:
   UpperCAmelCase__   :					List[str]   				=   [d["""img_id"""] for d in data]
 except Exception:
  try:
   UpperCAmelCase__   :					Any   				=   eval(stream.read() )
  except Exception:
   UpperCAmelCase__   :					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)








"""simple docstring"""




from __future__ import annotations
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   m - 1, 0
 while i >= 0 and j < n:
  if key == mat[i][j]:
   print(F"Key {key} found at row- {i + 1} column- {j + 1}" )
   return
  if key < mat[i][j]:
   i -= 1
  else:
   j += 1
 print(F"Key {key} not found" )
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
 UpperCAmelCase__   :					int   				=   int(input("""Enter the element to be searched:""" ) )
 print(__UpperCamelCase )
 search_in_a_sorted_matrix(__UpperCamelCase							,      len(__UpperCamelCase )							,      len(mat[0] )							,      __UpperCamelCase )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





