





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    42  # [batch_size x 3]
					A__				 =    42  # [batch_size x 3]
					A__				 =    42  # [batch_size x 3]
					A__				 =    42  # [batch_size x 3]
					A__				 =    42
					A__				 =    42
					A__				 =    42
					A__				 =    42
					A__				 =    42





					def 					__magic_name__ ( self	):
										assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
										assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
										assert len(self.x.shape	) == len(self.y.shape	) == len(self.z.shape	) == len(self.origin.shape	) == 2





					def 					__magic_name__ ( self	):
										return torch.from_numpy(np.array([self.width, self.height]	,  dtype=np.floataa	)	)





					def 					__magic_name__ ( self	):
										return torch.from_numpy(np.array([self.x_fov, self.y_fov]	,  dtype=np.floataa	)	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       torch.arange(self.height * self.width	)
										lowerCamelCase__       =       torch.stack(
										    [
										        pixel_indices % self.width,
										        torch.div(_lowerCAmelCase	,  self.width	,  rounding_mode="trunc"	),
										    ]	,  axis=1	,  )
										return coords





					@property
					def 					__magic_name__ ( self	):
										lowerCamelCase__    ,		*lowerCamelCase__       =       self.shape
										lowerCamelCase__       =       int(np.prod(_lowerCAmelCase	)	)

										lowerCamelCase__       =       self.get_image_coords()
										lowerCamelCase__       =       torch.broadcast_to(coords.unsqueeze(0	)	,  [batch_size * inner_batch_size, *coords.shape]	)
										lowerCamelCase__       =       self.get_camera_rays(_lowerCAmelCase	)

										lowerCamelCase__       =       rays.view(_lowerCAmelCase	,  inner_batch_size * self.height * self.width	,  2	,  3	)

										return rays





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		*lowerCamelCase__    ,		lowerCamelCase__       =       coords.shape
										assert n_coords == 2
										assert batch_size == self.origin.shape[0]

										lowerCamelCase__       =       coords.view(_lowerCAmelCase	,  -1	,  2	)

										lowerCamelCase__       =       self.resolution()
										lowerCamelCase__       =       self.fov()

										lowerCamelCase__       =       (flat.float() / (res - 1)) * 2 - 1
										lowerCamelCase__       =       fracs * torch.tan(fov / 2	)

										lowerCamelCase__       =       fracs.view(_lowerCAmelCase	,  -1	,  2	)
										lowerCamelCase__       =       (
										    self.z.view(_lowerCAmelCase	,  1	,  3	)
										    + self.x.view(_lowerCAmelCase	,  1	,  3	) * fracs[:, :, :1]
										    + self.y.view(_lowerCAmelCase	,  1	,  3	) * fracs[:, :, 1:]
										)
										lowerCamelCase__       =       directions / directions.norm(dim=-1	,  keepdim=_lowerCAmelCase	)
										lowerCamelCase__       =       torch.stack(
										    [
										        torch.broadcast_to(self.origin.view(_lowerCAmelCase	,  1	,  3	)	,  [batch_size, directions.shape[1], 3]	),
										        directions,
										    ]	,  dim=2	,  )
										return rays.view(_lowerCAmelCase	,  *_lowerCAmelCase	,  2	,  3	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										assert width * self.height == height * self.width, "The aspect ratio should not change."
										return DifferentiableProjectiveCamera(
										    origin=self.origin	,  x=self.x	,  y=self.y	,  z=self.z	,  width=_lowerCAmelCase	,  height=_lowerCAmelCase	,  x_fov=self.x_fov	,  y_fov=self.y_fov	,  )





def    __UpperCamelCase      (    a) ->DifferentiableProjectiveCamera:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					for theta in np.linspace(0,     2 * np.pi,     num=20):
										lowerCamelCase__       =       np.array([np.sin(a), np.cos(a), -0.5])
										z /= np.sqrt(np.sum(z**2))
										lowerCamelCase__       =       -z * 4
										lowerCamelCase__       =       np.array([np.cos(a), -np.sin(a), 0.0])
										lowerCamelCase__       =       np.cross(a,     a)
										origins.append(a)
										xs.append(a)
										ys.append(a)
										zs.append(a)
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(a,     axis=0)).float(),     x=torch.from_numpy(np.stack(a,     axis=0)).float(),     y=torch.from_numpy(np.stack(a,     axis=0)).float(),     z=torch.from_numpy(np.stack(a,     axis=0)).float(),     width=a,     height=a,     x_fov=0.7,     y_fov=0.7,     shape=(1, len(a)),     )








def    __UpperCamelCase      (    a) ->str:

					if isinstance(a,     a):
										raise TypeError("'float' object cannot be interpreted as an integer")
					if isinstance(a,     a):
										raise TypeError("'str' object cannot be interpreted as an integer")

					if num == 0:
										return "0b0"

					lowerCamelCase__       =       False

					if num < 0:
										lowerCamelCase__       =       True
										lowerCamelCase__       =       -num

					lowerCamelCase__       =       []
					while num > 0:
										binary.insert(0,     num % 2)
										num >>= 1

					if negative:
										return "-0b" + "".join(str(a) for e in binary)

					return "0b" + "".join(str(a) for e in binary)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

