

'''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
_snake_case     						=						False
_snake_case     						=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_snake_case     						=						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_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class      a__						:


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





      """simple docstring"""





      _lowercase    :   Optional[int]      =     None
      _lowercase    :   List[Any]      =     None
      _lowercase    :   Any      =     None
      _lowercase    :   int      =     1
      _lowercase    :   Optional[Any]      =     getopt.getopt(lowerCamelCase__    ,       "i:o:b:s"    ,       ["inputdir=", "outfile=", "batch_size=", "subset_list="]						)
      for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
            _lowercase    :   Union[str, Any]      =     arg
         elif opt in ("-o", "--outfile"):
            _lowercase    :   Tuple      =     arg
         elif opt in ("-b", "--batch_size"):
            _lowercase    :   Any      =     int(lowerCamelCase__						)
         elif opt in ("-s", "--subset_list"):
            _lowercase    :   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(lowerCamelCase__						), f'''{outputfile}'''
      if subset_list is not None:
         with open(os.path.realpath(lowerCamelCase__						)						) as f:
            _lowercase    :   Optional[int]      =     {self._vqa_file_split()[0] for x in tryload(lowerCamelCase__						)}
      else:
         _lowercase    :   Optional[Any]      =     None

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     int(file.split("."						)[0].split("_"						)[-1]						)
      _lowercase    :   Tuple      =     os.path.join(self.inputdir    ,       lowerCamelCase__						)
      return (img_id, filepath)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Tuple      =     []
      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(lowerCamelCase__						)						)
         if len(lowerCamelCase__						) == self.batch:
            _lowercase    :   str      =     batch
            _lowercase    :   Tuple      =     []
            yield list(map(lowerCamelCase__    ,       zip(*lowerCamelCase__						)						)						)

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






   def __call__(				self						):





      """simple docstring"""





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







def       _A	(			snake_case  )    ->			Any:
   try:
      _lowercase    :   Dict      =     json.load(snake_case  )
      try:
         _lowercase    :   List[Any]      =     list(data.keys()  )
      except Exception:
         _lowercase    :   List[Any]      =     [d["img_id"] for d in data]
   except Exception:
      try:
         _lowercase    :   Optional[Any]      =     eval(stream.read()  )
      except Exception:
         _lowercase    :   Any      =     stream.read().split("\n"  )
   return data


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








'''simple docstring'''
import requests

try:
    from django.contrib.auth import authenticate, login
    from django.shortcuts import redirect, render
except ImportError:
    _snake_case     						=						_snake_case     						=						_snake_case     						=						_snake_case     						=						print







def       _A	(			snake_case  )    ->			Any:
   # Enter your recaptcha secret key here
   _lowercase    :   Optional[Any]      =     "secretKey"
   _lowercase    :   Union[str, Any]      =     "https://www.google.com/recaptcha/api/siteverify"

   # when method is not POST, direct user to login page
   if request.method != "POST":
      return render(snake_case	,							"login.html"  )

   # from the frontend, get username, password, and client_key
   _lowercase    :   Optional[int]      =     request.POST.get("username"  )
   _lowercase    :   Tuple      =     request.POST.get("password"  )
   _lowercase    :   Tuple      =     request.POST.get("g-recaptcha-response"  )

   # post recaptcha response to Google's recaptcha api
   _lowercase    :   str      =     requests.post(snake_case	,							data={"secret": secret_key, "response": client_key}  )
   # if the recaptcha api verified our keys
   if response.json().get("success"	,							snake_case  ):
      # authenticate the user
      _lowercase    :   Tuple      =     authenticate(snake_case	,							username=snake_case	,							password=snake_case  )
      if user_in_database:
         login(snake_case	,							snake_case  )
         return redirect("/your-webpage"  )
   return render(snake_case	,							"login.html"  )






