


"""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_0_4_8), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class    __A       :






	"""simple docstring"""




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

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


	def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Any:
					a					=int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
					a					=os.path.join(self.inputdir				,					_lowerCAmelCase		)
					return (img_id, filepath)


	@property
	def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
					a					=[]
					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:
													a					=batch
													a					=[]
													yield list(map(_lowerCAmelCase				,					zip(*_lowerCAmelCase		)		)		)

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





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

def _A		(		lowercase	):







				"""simple docstring"""
				try:
								a					=json.load(__lowerCamelCase	)
								try:
												a					=list(data.keys()	)
								except Exception:
												a					=[d["""img_id"""] for d in data]
				except Exception:
								try:
												a					=eval(stream.read()	)
								except Exception:
												a					=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)



"""simple docstring"""







from cva import destroyAllWindows, imread, imshow, waitKey
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a	,   a					=img.shape[0], img.shape[1]

    def to_grayscale(lowercase ,					lowercase ,					lowercase	):
        return 0.2126 * red + 0.587 * green + 0.114 * blue

    def normalize(lowercase	):
        return min(lowercase ,					2_55	)

    for i in range(lowercase	):
        for j in range(lowercase	):
            a					=int(to_grayscale(*img[i][j]	)	)
            a					=[
                normalize(lowercase	),
                normalize(greyscale + factor	),
                normalize(greyscale + 2 * factor	),
            ]

    return img


if __name__ == "__main__":
       # read original image
       lowerCamelCase_     :     Any    		=						{
           percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'Original image with sepia (factor: {percentage})', img)

       waitKey(0)
       destroyAllWindows()