

"""simple docstring"""





from __future__ import annotations

from math import pi, pow




def 						_A						(						UpperCamelCase_  : Optional[int])			->		float:
   '''simple docstring'''


   if side_length < 0:
      raise ValueError("vol_cube() only accepts non-negative values")
   return pow(__lowerCAmelCase,    3)




def 						_A						(						UpperCamelCase_  : Any,    UpperCamelCase_  : Optional[Any])			->		float:
   '''simple docstring'''


   if height < 0 or radius < 0:
      raise ValueError("vol_spherical_cap() only accepts non-negative values")
   # Volume is 1/3 pi * height squared * (3 * radius - height)
   return 1 / 3 * pi * pow(__lowerCAmelCase,    2) * (3 * radius - height)




def 						_A						(						UpperCamelCase_  : Dict,    UpperCamelCase_  : List[Any],    UpperCamelCase_  : List[Any])			->		float:
   '''simple docstring'''


   if radius_a < 0 or radius_a < 0 or centers_distance < 0:
      raise ValueError("vol_spheres_intersect() only accepts non-negative values")
   if centers_distance == 0:
      return vol_sphere(min(__lowerCAmelCase,    __lowerCAmelCase))

   __lowercase			=		(
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )
   __lowercase			=		(
       (radius_a - radius_a + centers_distance)
       * (radius_a + radius_a - centers_distance)
       / (2 * centers_distance)
   )

   return vol_spherical_cap(__lowerCAmelCase,    __lowerCAmelCase) + vol_spherical_cap(__lowerCAmelCase,    __lowerCAmelCase)




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : List[Any],    UpperCamelCase_  : List[Any])			->		float:
   '''simple docstring'''


   if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
      raise ValueError(
          "vol_spheres_union() only accepts non-negative values, non-zero radius")

   if centers_distance == 0:
      return vol_sphere(max(__lowerCAmelCase,    __lowerCAmelCase))

   return (
       vol_sphere(__lowerCAmelCase)
       + vol_sphere(__lowerCAmelCase)
       - vol_spheres_intersect(__lowerCAmelCase,    __lowerCAmelCase,    __lowerCAmelCase)
   )




def 						_A						(						UpperCamelCase_  : Optional[int],    UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any])			->		float:
   '''simple docstring'''


   if width < 0 or height < 0 or length < 0:
      raise ValueError("vol_cuboid() only accepts non-negative values")
   return float(width * height * length)




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any])			->		float:
   '''simple docstring'''


   if height < 0 or area_of_base < 0:
      raise ValueError("vol_cone() only accepts non-negative values")
   return area_of_base * height / 3.0




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : int)			->		float:
   '''simple docstring'''


   if height < 0 or radius < 0:
      raise ValueError("vol_right_circ_cone() only accepts non-negative values")
   return pi * pow(__lowerCAmelCase,    2) * height / 3.0




def 						_A						(						UpperCamelCase_  : List[str],    UpperCamelCase_  : str)			->		float:
   '''simple docstring'''


   if height < 0 or area_of_base < 0:
      raise ValueError("vol_prism() only accepts non-negative values")
   return float(area_of_base * height)




def 						_A						(						UpperCamelCase_  : Tuple,    UpperCamelCase_  : List[Any])			->		float:
   '''simple docstring'''


   if height < 0 or area_of_base < 0:
      raise ValueError("vol_pyramid() only accepts non-negative values")
   return area_of_base * height / 3.0




def 						_A						(						UpperCamelCase_  : Any)			->		float:
   '''simple docstring'''


   if radius < 0:
      raise ValueError("vol_sphere() only accepts non-negative values")
   # Volume is 4/3 * pi * radius cubed
   return 4 / 3 * pi * pow(__lowerCAmelCase,    3)




def 						_A						(						UpperCamelCase_  : int)			->		float:
   '''simple docstring'''


   if radius < 0:
      raise ValueError("vol_hemisphere() only accepts non-negative values")
   # Volume is radius cubed * pi * 2/3
   return pow(__lowerCAmelCase,    3) * pi * 2 / 3




def 						_A						(						UpperCamelCase_  : List[str],    UpperCamelCase_  : List[str])			->		float:
   '''simple docstring'''


   if height < 0 or radius < 0:
      raise ValueError("vol_circular_cylinder() only accepts non-negative values")
   # Volume is radius squared * height * pi
   return pow(__lowerCAmelCase,    2) * height * pi




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : List[Any],    UpperCamelCase_  : str)			->		float:
   '''simple docstring'''


   if inner_radius < 0 or outer_radius < 0 or height < 0:
      raise ValueError(
          "vol_hollow_circular_cylinder() only accepts non-negative values")
   if outer_radius <= inner_radius:
      raise ValueError("outer_radius must be greater than inner_radius")
   return pi * (pow(__lowerCAmelCase,    2) - pow(__lowerCAmelCase,    2)) * height




def 						_A						(						UpperCamelCase_  : Any,    UpperCamelCase_  : Dict,    UpperCamelCase_  : Union[str, Any])			->		float:
   '''simple docstring'''


   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError("vol_conical_frustum() only accepts non-negative values")
   return (
       1
       / 3
       * pi
       * height
       * (pow(__lowerCAmelCase,    2) + pow(__lowerCAmelCase,    2) + radius_a * radius_a)
   )




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : List[Any])			->		float:
   '''simple docstring'''


   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("vol_torus() only accepts non-negative values")
   return 2 * pow(__lowerCAmelCase,    2) * torus_radius * pow(__lowerCAmelCase,    2)




def 						_A						(						)			->		Any:
   '''simple docstring'''


   print("Volumes:")
   print(F"""Cube: {vol_cube(2) = }""")  # = 8
   print(F"""Cuboid: {vol_cuboid(2,    2,    2) = }""")  # = 8
   print(F"""Cone: {vol_cone(2,    2) = }""")  # ~= 1.33
   print(F"""Right Circular Cone: {vol_right_circ_cone(2,    2) = }""")  # ~= 8.38
   print(F"""Prism: {vol_prism(2,    2) = }""")  # = 4
   print(F"""Pyramid: {vol_pyramid(2,    2) = }""")  # ~= 1.33
   print(F"""Sphere: {vol_sphere(2) = }""")  # ~= 33.5
   print(F"""Hemisphere: {vol_hemisphere(2) = }""")  # ~= 16.75
   print(F"""Circular Cylinder: {vol_circular_cylinder(2,    2) = }""")  # ~= 25.1
   print(F"""Torus: {vol_torus(2,    2) = }""")  # ~= 157.9
   print(F"""Conical Frustum: {vol_conical_frustum(2,    2,    4) = }""")  # ~= 58.6
   print(F"""Spherical cap: {vol_spherical_cap(1,    2) = }""")  # ~= 5.24
   print(F"""Spheres intersetion: {vol_spheres_intersect(2,    2,    1) = }""")  # ~= 21.21
   print(F"""Spheres union: {vol_spheres_union(2,    2,    1) = }""")  # ~= 45.81
   print(
       F"""Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,    2,    3) = }""")  # ~= 28.3


if __name__ == "__main__":
   main()







"""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, 20_48), 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							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]=sys.argv[1:]			):
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		None
    __lowercase			=		1
    __lowercase	,__lowercase			=		getopt.getopt(UpperCAmelCase__,      "i:o:b:s",      ["inputdir=", "outfile=", "batch_size=", "subset_list="]			)
    for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
          __lowercase			=		arg
       elif opt in ("-o", "--outfile"):
          __lowercase			=		arg
       elif opt in ("-b", "--batch_size"):
          __lowercase			=		int(UpperCAmelCase__			)
       elif opt in ("-s", "--subset_list"):
          __lowercase			=		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:
          __lowercase			=		{self._vqa_file_split()[0] for x in tryload(UpperCAmelCase__			)}
    else:
       __lowercase			=		None

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


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


 @property
 def       _lowercase				(    self							:    int			):
    __lowercase			=		[]
    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:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



 def __call__(    self							:    Optional[Any]			):
    # make writer
    if not TEST:
       __lowercase			=		datasets.ArrowWriter(features=self.schema,      path=self.outputfile			)
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator			):
       __lowercase	,__lowercase	,__lowercase			=		self.preprocess(UpperCAmelCase__			)
       __lowercase			=		self.model(
           UpperCAmelCase__,      UpperCAmelCase__,      scales_yx=UpperCAmelCase__,      padding="max_detections",      max_detections=self.config.MAX_DETECTIONS,      pad_value=0,      return_tensors="np",      location="cpu",      )
       __lowercase			=		output_dict.pop("normalized_boxes"			)
       if not TEST:
          __lowercase			=		np.array(UpperCAmelCase__			)
          __lowercase			=		self.schema.encode_batch(UpperCAmelCase__			)
          writer.write_batch(UpperCAmelCase__			)
       if TEST:
          break
       # finalizer the writer
    if not TEST:
       __lowercase	,__lowercase			=		writer.finalize()
       print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      try:
         __lowercase			=		list(data.keys())
      except Exception:
         __lowercase			=		[d["img_id"] for d in data]
   except Exception:
      try:
         __lowercase			=		eval(stream.read())
      except Exception:
         __lowercase			=		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)




