



"""simple docstring"""







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class 				UpperCamelCase__       :




      """simple docstring"""




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



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



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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    Any								=			torch.arange(self.height * self.width	)
        lowerCAmelCase_       :    Any								=			torch.stack(
            [
                pixel_indices % self.width,
                torch.div(SCREAMING_SNAKE_CASE_   ,      self.width   ,      rounding_mode='trunc'	),
            ]   ,      axis=1   ,      )
        return coords



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_						,*lowerCAmelCase_       :    Optional[int]								=			self.shape
        lowerCAmelCase_       :    List[Any]								=			int(np.prod(SCREAMING_SNAKE_CASE_	)	)

        lowerCAmelCase_       :    Any								=			self.get_image_coords()
        lowerCAmelCase_       :    Optional[Any]								=			torch.broadcast_to(coords.unsqueeze(0	)   ,      [batch_size * inner_batch_size, *coords.shape]	)
        lowerCAmelCase_       :    Optional[Any]								=			self.get_camera_rays(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[int]								=			rays.view(SCREAMING_SNAKE_CASE_   ,      inner_batch_size * self.height * self.width   ,      2   ,      3	)

        return rays



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				torch.Tensor	):
        lowerCAmelCase_						,*lowerCAmelCase_						,lowerCAmelCase_       :    List[str]								=			coords.shape
        assert n_coords == 2
        assert batch_size == self.origin.shape[0]

        lowerCAmelCase_       :    int								=			coords.view(SCREAMING_SNAKE_CASE_   ,      -1   ,      2	)

        lowerCAmelCase_       :    Any								=			self.resolution()
        lowerCAmelCase_       :    Optional[int]								=			self.fov()

        lowerCAmelCase_       :    Union[str, Any]								=			(flat.float() / (res - 1)) * 2 - 1
        lowerCAmelCase_       :    Optional[Any]								=			fracs * torch.tan(fov / 2	)

        lowerCAmelCase_       :    Optional[int]								=			fracs.view(SCREAMING_SNAKE_CASE_   ,      -1   ,      2	)
        lowerCAmelCase_       :    List[Any]								=			(
            self.z.view(SCREAMING_SNAKE_CASE_   ,      1   ,      3	)
            + self.x.view(SCREAMING_SNAKE_CASE_   ,      1   ,      3	) * fracs[:, :, :1]
            + self.y.view(SCREAMING_SNAKE_CASE_   ,      1   ,      3	) * fracs[:, :, 1:]
        )
        lowerCAmelCase_       :    Union[str, Any]								=			directions / directions.norm(dim=-1   ,      keepdim=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			torch.stack(
            [
                torch.broadcast_to(self.origin.view(SCREAMING_SNAKE_CASE_   ,      1   ,      3	)   ,      [batch_size, directions.shape[1], 3]	),
                directions,
            ]   ,      dim=2   ,      )
        return rays.view(SCREAMING_SNAKE_CASE_   ,      *SCREAMING_SNAKE_CASE_   ,      2   ,      3	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        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=SCREAMING_SNAKE_CASE_   ,      height=SCREAMING_SNAKE_CASE_   ,      x_fov=self.x_fov   ,      y_fov=self.y_fov   ,      )




def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       DifferentiableProjectiveCamera:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			[]
  lowerCAmelCase_       :    Optional[int]								=			[]
  lowerCAmelCase_       :    int								=			[]
  lowerCAmelCase_       :    List[str]								=			[]
  for theta in np.linspace(0   ,       2 * np.pi   ,       num=20						):
    lowerCAmelCase_       :    Optional[int]								=			np.array([np.sin(lowerCAmelCase__						), np.cos(lowerCAmelCase__						), -0.5]						)
    z /= np.sqrt(np.sum(z**2						)						)
    lowerCAmelCase_       :    List[str]								=			-z * 4
    lowerCAmelCase_       :    Dict								=			np.array([np.cos(lowerCAmelCase__						), -np.sin(lowerCAmelCase__						), 0.0]						)
    lowerCAmelCase_       :    int								=			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     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str						)						->       int:

  """simple docstring"""




  if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
    raise ValueError('String lengths must match!'						)

  lowerCAmelCase_       :    List[Any]								=			0

  for chara, chara in zip(lowerCAmelCase__   ,       lowerCAmelCase__						):
    if chara != chara:
      count += 1

  return count


if __name__ == "__main__":
     import doctest

     doctest.testmod()





