





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			:  int										=				False
__UpperCAmelCase			:  Any										=				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
__UpperCAmelCase			:  str										=				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        __snake_case			:





       '''simple docstring'''







       def __init__(    self					:       str				,							A					:       List[Any]=sys.argv[1:]						):
         __snake_case:		Optional[int]					    =		None
         __snake_case:		Any					    =		None
         __snake_case:		int					    =		None
         __snake_case:		int					    =		1
         __snake_case    ,  __snake_case:		str					    =		getopt.getopt(A				,							"""i:o:b:s"""				,							["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]						)
         for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
             __snake_case:		List[Any]					    =		arg
           elif opt in ("-o", "--outfile"):
             __snake_case:		Tuple					    =		arg
           elif opt in ("-b", "--batch_size"):
             __snake_case:		int					    =		int(A						)
           elif opt in ("-s", "--subset_list"):
             __snake_case:		List[Any]					    =		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:
             __snake_case:		Optional[int]					    =		{self._vqa_file_split()[0] for x in tryload(A						)}
         else:
           __snake_case:		Any					    =		None

         __snake_case:		List[Any]					    =		CONFIG
         if torch.cuda.is_available():
           __snake_case:		List[Any]					    =		"""cuda"""
         __snake_case:		Tuple					    =		os.path.realpath(A						)
         __snake_case:		List[str]					    =		os.path.realpath(A						)
         __snake_case:		Union[str, Any]					    =		Preprocess(self.config						)
         __snake_case:		List[str]					    =		GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""				,							config=self.config						)
         __snake_case:		List[Any]					    =		batch_size if batch_size != 0 else 1
         __snake_case:		Any					    =		DEFAULT_SCHEMA




       def 				UpperCAmelCase__					(    self					:       Any				,							A					:       List[Any]						):
         __snake_case:		Union[str, Any]					    =		int(file.split("""."""						)[0].split("""_"""						)[-1]						)
         __snake_case:		Optional[int]					    =		os.path.join(self.inputdir				,							A						)
         return (img_id, filepath)




       @property
       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		Dict					    =		[]
         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:
             __snake_case:		Dict					    =		batch
             __snake_case:		List[Any]					    =		[]
             yield list(map(A				,							zip(*A						)						)						)

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






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







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       List[Any]:
  try:
    __snake_case:		Union[str, Any]					    =		json.load(SCREAMING_SNAKE_CASE__)
    try:
      __snake_case:		Dict					    =		list(data.keys())
    except Exception:
      __snake_case:		Tuple					    =		[d["""img_id"""] for d in data]
  except Exception:
    try:
      __snake_case:		List[Any]					    =		eval(stream.read())
    except Exception:
      __snake_case:		Any					    =		stream.read().split("""\n""")
  return data


if __name__ == "__main__":
      __UpperCAmelCase			:  Dict										=				Extract(sys.argv[1:])
      extract()
      if not TEST:
            __UpperCAmelCase			:  Optional[int]										=				datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)













from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1
       lowerCAmelCase__ 						=	2
       lowerCAmelCase__ 						=	3




       def __repr__(    self					:       Optional[int]						):
         return f'''{self.__class__.__name__}.{self.name}'''







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       float:
  __snake_case    ,  __snake_case:		Union[str, Any]					    =		point
  return degrees(atana(y - miny   ,   x - minx))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Direction:
  __snake_case    ,  __snake_case:		Dict					    =		starting
  __snake_case    ,  __snake_case:		List[str]					    =		via
  __snake_case    ,  __snake_case:		Optional[int]					    =		target
  __snake_case:		int					    =		degrees(atana(ya - ya   ,   xa - xa))
  via_angle %= 360
  __snake_case:		List[Any]					    =		degrees(atana(ya - ya   ,   xa - xa))
  target_angle %= 360
  # t-
  #  \ \
  #   \ v
  #    \|
  #     s
  # via_angle is always lower than target_angle, if direction is left.
  # If they are same, it means they are on a same line of convex hull.
  if target_angle > via_angle:
    return Direction.left
  elif target_angle == via_angle:
    return Direction.straight
  else:
    return Direction.right







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       list[tuple[int, int]]:

  if len(SCREAMING_SNAKE_CASE__) <= 2:
    # There is no convex hull
    raise ValueError("""graham_scan: argument must contain more than 3 points.""")
  if len(SCREAMING_SNAKE_CASE__) == 3:
    return points
  # find the lowest and the most left point
  __snake_case:		Optional[int]					    =		0
  __snake_case    ,  __snake_case:		Optional[int]					    =		maxsize, maxsize
  for i, point in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:		Tuple					    =		point[0]
    __snake_case:		Optional[Any]					    =		point[1]
    if y < miny:
      __snake_case:		Optional[Any]					    =		y
      __snake_case:		Union[str, Any]					    =		x
      __snake_case:		Any					    =		i
    if y == miny and x < minx:
      __snake_case:		Optional[Any]					    =		x
      __snake_case:		str					    =		i

    # remove the lowest and the most left point from points for preparing for sort
  points.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:		Optional[int]					    =		sorted(SCREAMING_SNAKE_CASE__   ,   key=lambda SCREAMING_SNAKE_CASE__: angle_comparer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  # This insert actually costs complexity,
  # and you should instead add (minx, miny) into stack later.
  # I'm using insert just for easy understanding.
  sorted_points.insert(0   ,   (minx, miny))

  __snake_case:		deque[tuple[int, int]]					    =		deque()
  stack.append(sorted_points[0])
  stack.append(sorted_points[1])
  stack.append(sorted_points[2])
  # In any ways, the first 3 points line are towards left.
  # Because we sort them the angle from minx, miny.
  __snake_case:		Optional[Any]					    =		Direction.left

  for i in range(3   ,   len(SCREAMING_SNAKE_CASE__)):
    while True:
      __snake_case:		Optional[Any]					    =		stack[-2]
      __snake_case:		List[Any]					    =		stack[-1]
      __snake_case:		int					    =		sorted_points[i]
      __snake_case:		Union[str, Any]					    =		check_direction(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

      if next_direction == Direction.left:
        __snake_case:		Dict					    =		Direction.left
        break
      if next_direction == Direction.straight:
        if current_direction == Direction.left:
          # We keep current_direction as left.
          # Because if the straight line keeps as straight,
          # we want to know if this straight line is towards left.
          break
        elif current_direction == Direction.right:
          # If the straight line is towards right,
          # every previous points on those straigh line is not convex hull.
          stack.pop()
      if next_direction == Direction.right:
        stack.pop()
    stack.append(sorted_points[i])
  return list(SCREAMING_SNAKE_CASE__)






