




from __future__ import annotations

from math import pi, pow




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						float:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  2   ) * (3 * radius - height)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  lowercase_   )   )

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

    return vol_spherical_cap(lowercase_       ,  lowercase_   ) + vol_spherical_cap(lowercase_       ,  lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  lowercase_   )   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						float:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						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(lowercase_       ,  3   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						float:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  2   ) * height * pi




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  2   ) - pow(lowercase_       ,  2   )) * height




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						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(lowercase_       ,  2   ) + pow(lowercase_       ,  2   ) + radius_a * radius_a)
    )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						float:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[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()











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_048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

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






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None
					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							Union[str, Any]					     =   1
					_snake_case     , _snake_case					:							Dict					     =   getopt.getopt(SCREAMING_SNAKE_CASE__						,	"""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					:							Dict					     =   arg
						elif opt in ("-b", "--batch_size"):
							_snake_case					:							Union[str, Any]					     =   int(SCREAMING_SNAKE_CASE__      )
						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(SCREAMING_SNAKE_CASE__      ), f'''{outputfile}'''
					if subset_list is not None:
						with open(os.path.realpath(SCREAMING_SNAKE_CASE__      )      ) as f:
							_snake_case					:							int					     =   {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE__      )}
					else:
						_snake_case					:							List[str]					     =   None

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   []
					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(SCREAMING_SNAKE_CASE__      )      )
						if len(SCREAMING_SNAKE_CASE__      ) == self.batch:
							_snake_case					:							Union[str, Any]					     =   batch
							_snake_case					:							Optional[Any]					     =   []
							yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )

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







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[Any]					     =   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					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
						_snake_case					:							Dict					     =   output_dict.pop("""normalized_boxes"""      )
						if not TEST:
							_snake_case					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						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   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   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)





