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



class 			UpperCamelCase					:

     '''simple docstring'''

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






           """simple docstring"""
           _lowerCamelCase           =					None
           _lowerCamelCase           =					None
           _lowerCamelCase           =					None
           _lowerCamelCase           =					1
           _lowerCamelCase     ,       _lowerCamelCase           =					getopt.getopt(A_					,					'''i:o:b:s'''					,					['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']    )
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       _lowerCamelCase           =					arg
                 elif opt in ("-o", "--outfile"):
                       _lowerCamelCase           =					arg
                 elif opt in ("-b", "--batch_size"):
                       _lowerCamelCase           =					int(A_    )
                 elif opt in ("-s", "--subset_list"):
                       _lowerCamelCase           =					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:
                       _lowerCamelCase           =					{self._vqa_file_split()[0] for x in tryload(A_    )}
           else:
                 _lowerCamelCase           =					None

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



     def 		UpperCamelCase_    (       self					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					int(file.split('''.'''    )[0].split('''_'''    )[-1]    )
           _lowerCamelCase           =					os.path.join(self.inputdir					,					A_    )
           return (img_id, filepath)



     @property
     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					[]
           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:
                       _lowerCamelCase           =					batch
                       _lowerCamelCase           =					[]
                       yield list(map(A_					,					zip(*A_    )    )    )

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





     def __call__(       self    )			->					Union[str, Any]:






           """simple docstring"""







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





def    __magic_name__(	__UpperCAmelCase		)    ->     Any:



      '''simple docstring'''

      try:
            _lowerCamelCase           =					json.load(__lowerCAmelCase		)
            try:
                  _lowerCamelCase           =					list(data.keys()		)
            except Exception:
                  _lowerCamelCase           =					[d['''img_id'''] for d in data]
      except Exception:
            try:
                  _lowerCamelCase           =					eval(stream.read()		)
            except Exception:
                  _lowerCamelCase           =					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)







from __future__ import annotations

import re


def __A       ( __lowerCAmelCase      )->    list[str]:





						"""simple docstring"""



						def alphanum_key(__lowerCAmelCase      ):
												return [int(__lowerCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'					,						__lowerCAmelCase      )]

						return sorted(__lowerCAmelCase					,						key=__lowerCAmelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
