



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     torch.Tensor  # [batch_size x 3]
						_snake_case			:     int
						_snake_case			:     int
						_snake_case			:     float
						_snake_case			:     float
						_snake_case			:     Tuple[int]



						def     _snake_case	(	self	:							List[str]	):






							'''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     _snake_case	(	self	:							Any	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



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



						@property
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase       ,    *__lowercase		       =			self.shape
							__lowercase		       =			int(np.prod(lowerCamelCase	)	)

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

							__lowercase		       =			rays.view(lowerCamelCase							,      inner_batch_size * self.height * self.width							,      2							,      3	)

							return rays



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							torch.Tensor	):






							'''simple docstring'''



							__lowercase       ,    *__lowercase       ,    __lowercase		       =			coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowercase		       =			coords.view(lowerCamelCase							,      -1							,      2	)

							__lowercase		       =			self.resolution()
							__lowercase		       =			self.fov()

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

							__lowercase		       =			fracs.view(lowerCamelCase							,      -1							,      2	)
							__lowercase		       =			(
							    self.z.view(lowerCamelCase							,      1							,      3	)
							    + self.x.view(lowerCamelCase							,      1							,      3	) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase							,      1							,      3	) * fracs[:, :, 1:]
							)
							__lowercase		       =			directions / directions.norm(dim=-1							,      keepdim=lowerCamelCase	)
							__lowercase		       =			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     _snake_case	(	self	:							Any							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''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=lowerCamelCase							,      height=lowerCamelCase							,      x_fov=self.x_fov							,      y_fov=self.y_fov							,      )



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]
	__lowercase		       =			[]
	for theta in np.linspace(0  ,	2 * np.pi  ,	num=2_0   ):
		__lowercase		       =			np.array([np.sin(_SCREAMING_SNAKE_CASE   ), np.cos(_SCREAMING_SNAKE_CASE   ), -0.5]   )
		z /= np.sqrt(np.sum(z**2   )   )
		__lowercase		       =			-z * 4
		__lowercase		       =			np.array([np.cos(_SCREAMING_SNAKE_CASE   ), -np.sin(_SCREAMING_SNAKE_CASE   ), 0.0]   )
		__lowercase		       =			np.cross(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		origins.append(_SCREAMING_SNAKE_CASE   )
		xs.append(_SCREAMING_SNAKE_CASE   )
		ys.append(_SCREAMING_SNAKE_CASE   )
		zs.append(_SCREAMING_SNAKE_CASE   )
	return DifferentiableProjectiveCamera(
	    origin=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	x=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	y=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	z=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE  ,	axis=0   )   ).float()  ,	width=_SCREAMING_SNAKE_CASE  ,	height=_SCREAMING_SNAKE_CASE  ,	x_fov=0.7  ,	y_fov=0.7  ,	shape=(1, len(_SCREAMING_SNAKE_CASE   ))  ,	)








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'''





							_snake_case			:     Optional[Any]      							=      [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]



def    snake_case_				(    ):
	if os.name == "nt":
		__lowercase		       =			CursorInfo()
		__lowercase		       =			ctypes.windll.kernelaa.GetStdHandle(-1_1   )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
		__lowercase		       =			False
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
	elif os.name == "posix":
		sys.stdout.write("\033[?25l"   )
		sys.stdout.flush()



def    snake_case_				(    ):
	if os.name == "nt":
		__lowercase		       =			CursorInfo()
		__lowercase		       =			ctypes.windll.kernelaa.GetStdHandle(-1_1   )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
		__lowercase		       =			True
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
	elif os.name == "posix":
		sys.stdout.write("\033[?25h"   )
		sys.stdout.flush()



@contextmanager
def    snake_case_				(    ):
	try:
		hide_cursor()
		yield
	finally:
		show_cursor()



