





SCREAMING_SNAKE_CASE__		:					Optional[int]						=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE__		:					str						=					[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__		:					str						=					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class       snake_case					:
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




  def 	__lowercase(      self     :	List[Any]				)->       Union[str, 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 	__lowercase(      self     :	Dict				)->       Tuple:




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




  def 	__lowercase(      self     :	Dict				)->       Union[str, Any]:




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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		torch.arange(self.height * self.width				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a_     , self.width     , rounding_mode='trunc'				),
           ]     , axis=1     , )
       return coords




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.shape
       SCREAMING_SNAKE_CASE__				:				Tuple			=		int(np.prod(a_				)				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_image_coords()
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.broadcast_to(coords.unsqueeze(0				)     , [batch_size * inner_batch_size, *coords.shape]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_camera_rays(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		rays.view(a_     , inner_batch_size * self.height * self.width     , 2     , 3				)

       return rays




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




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

       SCREAMING_SNAKE_CASE__				:				str			=		coords.view(a_     , -1     , 2				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.resolution()
       SCREAMING_SNAKE_CASE__				:				str			=		self.fov()

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

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




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	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=a_     , height=a_     , x_fov=self.x_fov     , y_fov=self.y_fov     , )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				str			=		[]
     for theta in np.linspace(0     ,  2 * np.pi     ,  num=20 ):
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
          z /= np.sqrt(np.sum(z**2 ) )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		-z * 4
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__ ))     ,  )



