






"""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_			  =    False
A_			  =    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A_			  =    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 						__lowerCAmelCase   :







  '''simple docstring'''


  def __init__(      self:		Any				,			UpperCamelCase_:		Any=sys.argv[1:]					):
     UpperCamelCase_     =None
     UpperCamelCase_     =None
     UpperCamelCase_     =None
     UpperCamelCase_     =1
     UpperCamelCase_    ,							UpperCamelCase_     =getopt.getopt(UpperCamelCase_				,			"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(UpperCamelCase_					)
        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(UpperCamelCase_					), f"""{outputfile}"""
     if subset_list is not None:
        with open(os.path.realpath(UpperCamelCase_					)					) as f:
           UpperCamelCase_     ={self._vqa_file_split()[0] for x in tryload(UpperCamelCase_					)}
     else:
        UpperCamelCase_     =None

     UpperCamelCase_     =CONFIG
     if torch.cuda.is_available():
        UpperCamelCase_     ="cuda"
     UpperCamelCase_     =os.path.realpath(UpperCamelCase_					)
     UpperCamelCase_     =os.path.realpath(UpperCamelCase_					)
     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 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Dict					):
     UpperCamelCase_     =int(file.split("."					)[0].split("_"					)[-1]					)
     UpperCamelCase_     =os.path.join(self.inputdir				,			UpperCamelCase_					)
     return (img_id, filepath)
  @property
  def 					UpperCamelCase__   (      self:		Optional[int]					):
     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(UpperCamelCase_					)					)
        if len(UpperCamelCase_					) == self.batch:
           UpperCamelCase_     =batch
           UpperCamelCase_     =[]
           yield list(map(UpperCamelCase_				,			zip(*UpperCamelCase_					)					)					)

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





  def __call__(      self:		Dict					):
     # make writer
     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(UpperCamelCase_					)
        UpperCamelCase_     =self.model(
            UpperCamelCase_				,			UpperCamelCase_				,			scales_yx=UpperCamelCase_				,			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(UpperCamelCase_					)
           UpperCamelCase_     =self.schema.encode_batch(UpperCamelCase_					)
           writer.write_batch(UpperCamelCase_					)
        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			):
   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__":
    A_			  =    Extract(sys.argv[1:])
    extract()
    if not TEST:
        A_			  =    datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)








"""simple docstring"""






from __future__ import annotations

from pathlib import Path




def 						_UpperCamelCase			(  A    ,       A			):
   return pointa[0] * pointa[1] - pointa[1] * pointa[0]




def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
   UpperCamelCase_     =(xa, ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
       A    ,       A			)
   UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
       A    ,       A			)

   return a > 0 and b > 0 and a + b < 1




def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
   UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)

   UpperCamelCase_     =[]
   for line in data.strip().split("\n"			):
      triangles.append([int(A			) for number in line.split(","			)]			)

   UpperCamelCase_     =0
   UpperCamelCase_     =42

   for triangle in triangles:
      ret += contains_origin(*A			)

   return ret


if __name__ == "__main__":
    print(f'''{solution() = }''')
