
"""simple docstring"""

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class      _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE   :							torch.Tensor  # [batch_size x 3]
      __SCREAMING_SNAKE_CASE   :							torch.Tensor  # [batch_size x 3]
      __SCREAMING_SNAKE_CASE   :							torch.Tensor  # [batch_size x 3]
      __SCREAMING_SNAKE_CASE   :							torch.Tensor  # [batch_size x 3]
      __SCREAMING_SNAKE_CASE   :							int
      __SCREAMING_SNAKE_CASE   :							int
      __SCREAMING_SNAKE_CASE   :							float
      __SCREAMING_SNAKE_CASE   :							float
      __SCREAMING_SNAKE_CASE   :							Tuple[int]




      def     a_      (							self							)						->					List[str]:
          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     a_      (							self							)						->					Union[str, Any]:
          return torch.from_numpy(np.array([self.width, self.height]   ,       dtype=np.floataa							)							)




      def     a_      (							self							)						->					Dict:
          return torch.from_numpy(np.array([self.x_fov, self.y_fov]   ,       dtype=np.floataa							)							)




      def     a_      (							self							)						->					torch.Tensor:
          UpperCAmelCase									=					torch.arange(self.height * self.width							)
          UpperCAmelCase									=					torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(lowercase_   ,       self.width   ,       rounding_mode='trunc'							),
              ]   ,       axis=1   ,       )
          return coords




      @property
      def     a_      (							self							)						->					Any:
          UpperCAmelCase			,							*UpperCAmelCase									=					self.shape
          UpperCAmelCase									=					int(np.prod(lowercase_							)							)

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

          UpperCAmelCase									=					rays.view(lowercase_   ,       inner_batch_size * self.height * self.width   ,       2   ,       3							)

          return rays




      def     a_      (							self   ,       lowercase_							)						->					torch.Tensor:
          UpperCAmelCase			,							*UpperCAmelCase			,							UpperCAmelCase									=					coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          UpperCAmelCase									=					coords.view(lowercase_   ,       -1   ,       2							)

          UpperCAmelCase									=					self.resolution()
          UpperCAmelCase									=					self.fov()

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

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




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					"DifferentiableProjectiveCamera":
          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=lowercase_   ,       height=lowercase_   ,       x_fov=self.x_fov   ,       y_fov=self.y_fov   ,       )




def     lowercase__						(					lowerCAmelCase				:					int ) ->      DifferentiableProjectiveCamera:






    """simple docstring"""




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





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number | (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number & ~(1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number ^ (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      bool:






    """simple docstring"""




    return ((number >> position) & 1) == 1




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return int((number & (1 << position)) != 0 )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



