

"""simple docstring"""

from __future__ import annotations

from math import pi, pow



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





 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(__a				,				2      ) * (3 * radius - height)



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	float:





 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(__a				,				__a      )      )

 lowerCAmelCase__      :							Dict    =							(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 lowerCAmelCase__      :							Tuple    =							(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(__a				,				__a      ) + vol_spherical_cap(__a				,				__a      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	float:





 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(__a				,				__a      )      )

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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





 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 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





 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 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	float:





 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(__a				,				2      ) - pow(__a				,				2      )) * height



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	float:





 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(__a				,				2      ) + pow(__a				,				2      ) + radius_a * radius_a)
 )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	float:





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



def 			SCREAMING_SNAKE_CASE       (   ) ->	List[Any]:
 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
_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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

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

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

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






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							[]
        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:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          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:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							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" ,				)
         lowerCAmelCase__      :							Any    =							output_dict.pop("normalized_boxes"      )
         if not TEST:
          lowerCAmelCase__      :							Optional[int]    =							np.array(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							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)






