
"""simple docstring"""





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class __lowerCAmelCase					:




			"""simple docstring"""







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




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Any:







								"""simple docstring"""
								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   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								return torch.from_numpy(np.array([self.width, self.height]							, dtype=np.floataa						)						)




			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Union[str, Any]:







								"""simple docstring"""
								return torch.from_numpy(np.array([self.x_fov, self.y_fov]							, dtype=np.floataa						)						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			torch.Tensor:







								"""simple docstring"""
								A_								=    torch.arange(self.height * self.width						)
								A_								=    torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(_snake_case							, self.width							, rounding_mode="trunc"						),
								    ]							, axis=1							, )
								return coords




			@property
			def   lowerCamelCase__  (					self    :       str						)   ->			int:







								"""simple docstring"""
								A_			,     *A_								=    self.shape
								A_								=    int(np.prod(_snake_case						)						)

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

								A_								=    rays.view(_snake_case							, inner_batch_size * self.height * self.width							, 2							, 3						)

								return rays




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       torch.Tensor						)   ->			torch.Tensor:







								"""simple docstring"""
								A_			,     *A_			,     A_								=    coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								A_								=    coords.view(_snake_case							, -1							, 2						)

								A_								=    self.resolution()
								A_								=    self.fov()

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

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






			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       int							, _snake_case    :       int						)   ->			"DifferentiableProjectiveCamera":







								"""simple docstring"""
								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=_snake_case							, height=_snake_case							, x_fov=self.x_fov							, y_fov=self.y_fov							, )





def 				A_  (__a					):

					'''simple docstring'''





					A_								=    []
					A_								=    []
					A_								=    []
					A_								=    []
					for theta in np.linspace(0					, 2 * np.pi					, num=20					):
										A_								=    np.array([np.sin(__a					), np.cos(__a					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										A_								=    -z * 4
										A_								=    np.array([np.cos(__a					), -np.sin(__a					), 0.0]					)
										A_								=    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					))					, )






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




