


'''simple docstring'''



import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
      import ctypes
      import msvcrt  # noqa



      class      a    (		ctypes.Structure	):


           """simple docstring"""






           SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       [("size", ctypes.c_int), ("visible", ctypes.c_byte)]




def      _a     (						):





     '''simple docstring'''






     if os.name == "nt":
          __UpperCAmelCase     :			Any  							=      CursorInfo()
          __UpperCAmelCase     :			List[Any]  							=      ctypes.windll.kernelaa.GetStdHandle(-11  )
          ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase      , ctypes.byref(_lowercase  )  )
          __UpperCAmelCase     :			Optional[Any]  							=      False
          ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase      , ctypes.byref(_lowercase  )  )
     elif os.name == "posix":
          sys.stdout.write('''\033[?25l'''  )
          sys.stdout.flush()




def      _a     (						):





     '''simple docstring'''






     if os.name == "nt":
          __UpperCAmelCase     :			Optional[int]  							=      CursorInfo()
          __UpperCAmelCase     :			str  							=      ctypes.windll.kernelaa.GetStdHandle(-11  )
          ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase      , ctypes.byref(_lowercase  )  )
          __UpperCAmelCase     :			int  							=      True
          ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase      , ctypes.byref(_lowercase  )  )
     elif os.name == "posix":
          sys.stdout.write('''\033[?25h'''  )
          sys.stdout.flush()




@contextmanager
def      _a     (						):





     '''simple docstring'''






     try:
          hide_cursor()
          yield
     finally:
          show_cursor()



'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      a    :


     """simple docstring"""






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

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

     def 			lowerCamelCase__			(    self					:		Any      )						->							torch.Tensor:
          __UpperCAmelCase     :			Dict  							=      torch.arange(self.height * self.width      )
          __UpperCAmelCase     :			Dict  							=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case							,			self.width							,			rounding_mode='''trunc'''      ),
              ]							,			axis=1							,			)
          return coords

     @property
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase							,       *__UpperCAmelCase     :			str  							=      self.shape
          __UpperCAmelCase     :			Dict  							=      int(np.prod(snake_case      )      )

          __UpperCAmelCase     :			Tuple  							=      self.get_image_coords()
          __UpperCAmelCase     :			List[Any]  							=      torch.broadcast_to(coords.unsqueeze(0      )							,			[batch_size * inner_batch_size, *coords.shape]      )
          __UpperCAmelCase     :			Any  							=      self.get_camera_rays(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      rays.view(snake_case							,			inner_batch_size * self.height * self.width							,			2							,			3      )

          return rays

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		torch.Tensor      )						->							torch.Tensor:
          __UpperCAmelCase							,       *__UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          __UpperCAmelCase     :			List[str]  							=      coords.view(snake_case							,			-1							,			2      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.resolution()
          __UpperCAmelCase     :			Tuple  							=      self.fov()

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

          __UpperCAmelCase     :			str  							=      fracs.view(snake_case							,			-1							,			2      )
          __UpperCAmelCase     :			Any  							=      (
              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:]
          )
          __UpperCAmelCase     :			Union[str, Any]  							=      directions / directions.norm(dim=-1							,			keepdim=snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      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					:		Any							,			snake_case					:		int							,			snake_case					:		int      )						->							"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=snake_case							,			height=snake_case							,			x_fov=self.x_fov							,			y_fov=self.y_fov							,			)




def      _a     (						_lowercase :     int  ):





     '''simple docstring'''






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