




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
__SCREAMING_SNAKE_CASE			:      Optional[Any]      			=False
__SCREAMING_SNAKE_CASE			:      Dict      			=Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE			:      Optional[Any]      			=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 	A	:
     """simple docstring"""

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

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




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      int	) ->				List[str]:
           lowerCamelCase__					     = int(file.split('.'	)[0].split('_'	)[-1]	)
           lowerCamelCase__					     = os.path.join(self.inputdir		,     A__	)
           return (img_id, filepath)




     @property
     def        __snake_case      (			self     :      Any	) ->				List[Any]:
           lowerCamelCase__					     = []
           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:
                       lowerCamelCase__					     = batch
                       lowerCamelCase__					     = []
                       yield list(map(A__		,     zip(*A__	)	)	)

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




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

def 	lowerCAmelCase_	(     lowerCamelCase:				Any  )    ->	Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__					     = json.load(lowerCamelCase  )
            try:
                  lowerCamelCase__					     = list(data.keys()  )
            except Exception:
                  lowerCamelCase__					     = [d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__					     = eval(stream.read()  )
            except Exception:
                  lowerCamelCase__					     = stream.read().split('\n'  )
      return data


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





'''simple docstring'''







from __future__ import annotations

from math import pi, pow






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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_  )  )

		_snake_case	       =       (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)
		_snake_case	       =       (
		    (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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_  ):



		'''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 		_lowercase		(	lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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 		_lowercase		(	):



		'''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()





