

"""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
A_							:     str     =	False
A_							:     int     =	Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A_							:     Dict     =	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  :

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Union[str, Any]=sys.argv[1:]    )				->      str:
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= None
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= getopt.getopt(__UpperCAmelCase      ,			"""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(__UpperCAmelCase    )
									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(__UpperCAmelCase    ), F"""{outputfile}"""
						if subset_list is not None:
									with open(os.path.realpath(__UpperCAmelCase    )    ) as f:
												SCREAMING_SNAKE_CASE__									= {self._vqa_file_split()[0] for x in tryload(__UpperCAmelCase    )}
						else:
									SCREAMING_SNAKE_CASE__									= None

						SCREAMING_SNAKE_CASE__									= CONFIG
						if torch.cuda.is_available():
									SCREAMING_SNAKE_CASE__									= """cuda"""
						SCREAMING_SNAKE_CASE__									= os.path.realpath(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= os.path.realpath(__UpperCAmelCase    )
						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    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  List[Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= int(file.split("""."""    )[0].split("""_"""    )[-1]    )
						SCREAMING_SNAKE_CASE__									= os.path.join(self.inputdir      ,			__UpperCAmelCase    )
						return (img_id, filepath)

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Tuple:
						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(__UpperCAmelCase    )    )
									if len(__UpperCAmelCase    ) == self.batch:
												SCREAMING_SNAKE_CASE__									= batch
												SCREAMING_SNAKE_CASE__									= []
												yield list(map(__UpperCAmelCase      ,			zip(*__UpperCAmelCase    )    )    )

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





			def __call__(   self			:  Union[str, Any]    )				->      List[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__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.preprocess(__UpperCAmelCase    )
									SCREAMING_SNAKE_CASE__									= self.model(
									    __UpperCAmelCase      ,			__UpperCAmelCase      ,			scales_yx=__UpperCAmelCase      ,			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(__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= self.schema.encode_batch(__UpperCAmelCase    )
												writer.write_batch(__UpperCAmelCase    )
									if TEST:
												break
									# finalizer the writer
						if not TEST:
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= writer.finalize()
									print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""    )

def 		A    (       snake_case__      ):




			'''simple docstring'''


			try:
						SCREAMING_SNAKE_CASE__									= json.load(snake_case__      )
						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__":
						A_							:     Optional[Any]     =	Extract(sys.argv[1:])
						extract()
						if not TEST:
												A_							:     Optional[int]     =	datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)





"""simple docstring"""




import math

def 		A    (       snake_case__      ):




			'''simple docstring'''


			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,			int(math.sqrt(snake_case__      ) + 1      )						,			6      ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True

def 		A    (       snake_case__ = 10_00						,			snake_case__ = 10_00      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [0, 0, 0]  # length, a, b
			for a in range((a_limit * -1) + 1						,			snake_case__      ):
						for b in range(2						,			snake_case__      ):
									if is_prime(snake_case__      ):
												SCREAMING_SNAKE_CASE__									= 0
												SCREAMING_SNAKE_CASE__									= 0
												while is_prime((n**2) + (a * n) + b      ):
															count += 1
															n += 1
												if count > longest[0]:
															SCREAMING_SNAKE_CASE__									= [count, a, b]
			SCREAMING_SNAKE_CASE__									= longest[1] * longest[2]
			return ans


if __name__ == "__main__":
						print(solution(1_000, 1_000))


