


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, 2_0_4_8), 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 ,  snake_case=sys.argv[1:]       ):

						'''simple docstring'''


						UpperCamelCase__   =    None
						UpperCamelCase__   =    None
						UpperCamelCase__   =    None
						UpperCamelCase__   =    1
						UpperCamelCase__, UpperCamelCase__   =    getopt.getopt(snake_case ,  "i:o:b:s" ,  ["inputdir=", "outfile=", "batch_size=", "subset_list="]       )
						for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
												UpperCamelCase__   =    arg
									elif opt in ("-o", "--outfile"):
												UpperCamelCase__   =    arg
									elif opt in ("-b", "--batch_size"):
												UpperCamelCase__   =    int(snake_case       )
									elif opt in ("-s", "--subset_list"):
												UpperCamelCase__   =    arg

						assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
						assert outputfile is not None and not os.path.isfile(snake_case       ), F'''{outputfile}'''
						if subset_list is not None:
									with open(os.path.realpath(snake_case       )       ) as f:
												UpperCamelCase__   =    {self._vqa_file_split()[0] for x in tryload(snake_case       )}
						else:
									UpperCamelCase__   =    None

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




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    int(file.split("."       )[0].split("_"       )[-1]       )
						UpperCamelCase__   =    os.path.join(self.inputdir ,  snake_case       )
						return (img_id, filepath)




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    []
						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(snake_case       )       )
									if len(snake_case       ) == self.batch:
												UpperCamelCase__   =    batch
												UpperCamelCase__   =    []
												yield list(map(snake_case ,  zip(*snake_case       )       )       )

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







			def __call__(							self       ):

						'''simple docstring'''


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



def     UpperCamelCase_(			_A				:List[Any]					)->						Tuple:
			try:
						UpperCamelCase__   =    json.load(_A					)
						try:
									UpperCamelCase__   =    list(data.keys()					)
						except Exception:
									UpperCamelCase__   =    [d["img_id"] for d in data]
			except Exception:
						try:
									UpperCamelCase__   =    eval(stream.read()					)
						except Exception:
									UpperCamelCase__   =    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)






import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			# sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# data position template + parity
			UpperCamelCase__   =    []
			# parity bit counter
			UpperCamelCase__   =    0
			# counter position of data bits
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / np.log(2					)).is_integer():
												data_out_gab.append("P"					)
												UpperCamelCase__   =    qtd_bp + 1
									else:
												data_out_gab.append("D"					)
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a given parity
						UpperCamelCase__   =    0
						# counter to control the loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			return data_out
def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0
			# list of parity received
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for x in range(1			,       len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]					)
						else:
									parity_received.append(data[cont_data]					)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			#  sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# Data position feedback + parity
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a certain parity
						UpperCamelCase__   =    0
						# Counter to control loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2					)					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # Parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			UpperCamelCase__   =    parity_received == parity
			return data_output, ack


