




'''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
lowerCamelCase__				:     Any			       =							False
lowerCamelCase__				:     str			       =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
lowerCamelCase__				:     int			       =							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  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	Dict       ,       _lowerCAmelCase						:	Dict=sys.argv[1:]		):
       SCREAMING_SNAKE_CASE_					=    None
       SCREAMING_SNAKE_CASE_					=    None
       SCREAMING_SNAKE_CASE_					=    None
       SCREAMING_SNAKE_CASE_					=    1
       SCREAMING_SNAKE_CASE_					=    getopt.getopt(_lowerCAmelCase       ,       'i:o:b:s'       ,       ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']		)
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   SCREAMING_SNAKE_CASE_					=    arg
             elif opt in ("-o", "--outfile"):
                   SCREAMING_SNAKE_CASE_					=    arg
             elif opt in ("-b", "--batch_size"):
                   SCREAMING_SNAKE_CASE_					=    int(_lowerCAmelCase		)
             elif opt in ("-s", "--subset_list"):
                   SCREAMING_SNAKE_CASE_					=    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:
                   SCREAMING_SNAKE_CASE_					=    {self._vqa_file_split()[0] for x in tryload(_lowerCAmelCase		)}
       else:
             SCREAMING_SNAKE_CASE_					=    None

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



 def    lowerCAmelCase_						(							self						:	Any       ,       _lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    int(file.split('.'		)[0].split('_'		)[-1]		)
       SCREAMING_SNAKE_CASE_					=    os.path.join(self.inputdir       ,       _lowerCAmelCase		)
       return (img_id, filepath)



 @property
 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    []
       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:
                   SCREAMING_SNAKE_CASE_					=    batch
                   SCREAMING_SNAKE_CASE_					=    []
                   yield list(map(_lowerCAmelCase       ,       zip(*_lowerCAmelCase		)		)		)

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




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


def       UpperCAmelCase_							(   __UpperCAmelCase		:			Any							)      ->				Dict:
      try:
            SCREAMING_SNAKE_CASE_					=    json.load(__lowerCamelCase							)
            try:
                  SCREAMING_SNAKE_CASE_					=    list(data.keys()							)
            except Exception:
                  SCREAMING_SNAKE_CASE_					=    [d["""img_id"""] for d in data]
      except Exception:
            try:
                  SCREAMING_SNAKE_CASE_					=    eval(stream.read()							)
            except Exception:
                  SCREAMING_SNAKE_CASE_					=    stream.read().split('\n'							)
      return data


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





def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5) + 32    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) + 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5) + 4_9_1.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) - 32) * 5 / 9    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(((float(__UpperCAmelCase							) - 32) * 5 / 9) + 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) + 4_5_9.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) - 2_7_3.1_5    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(((float(__UpperCAmelCase							) - 2_7_3.1_5) * 9 / 5) + 32    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 9 / 5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) - 4_9_1.6_7) * 5 / 9    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round(float(__UpperCAmelCase							) - 4_5_9.6_7    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 5 / 9)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 1.2_5 + 2_7_3.1_5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 2.2_5 + 32)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 1.2_5)    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			float    ,    __UpperCAmelCase		:			int = 2							)      ->				float:
      return round((float(__UpperCAmelCase							) * 2.2_5 + 32 + 4_5_9.6_7)    ,    __UpperCAmelCase							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()