


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class        snake_case_   :
					UpperCAmelCase__					:    torch.Tensor  # [batch_size x 3]
					UpperCAmelCase__					:    torch.Tensor  # [batch_size x 3]
					UpperCAmelCase__					:    torch.Tensor  # [batch_size x 3]
					UpperCAmelCase__					:    torch.Tensor  # [batch_size x 3]
					UpperCAmelCase__					:    int
					UpperCAmelCase__					:    int
					UpperCAmelCase__					:    float
					UpperCAmelCase__					:    float
					UpperCAmelCase__					:    Tuple[int]
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Optional[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   :str      )					->    Any:
												return torch.from_numpy(np.array([self.width, self.height]			,dtype=np.floataa      )      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,dtype=np.floataa      )      )
					def  lowerCamelCase__(      self   :str      )					->    torch.Tensor:
												a__             =     torch.arange(self.height * self.width      )
												a__             =     torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(__snake_case			,self.width			,rounding_mode='trunc'      ),
												    ]			,axis=1			,)
												return coords
					@property
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Any:
												a__      ,					*a__             =     self.shape
												a__             =     int(np.prod(__snake_case      )      )

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

												a__             =     rays.view(__snake_case			,inner_batch_size * self.height * self.width			,2			,3      )

												return rays
					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :torch.Tensor      )					->    torch.Tensor:
												a__      ,					*a__      ,					a__             =     coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												a__             =     coords.view(__snake_case			,-1			,2      )

												a__             =     self.resolution()
												a__             =     self.fov()

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

												a__             =     fracs.view(__snake_case			,-1			,2      )
												a__             =     (
												    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:]
												)
												a__             =     directions / directions.norm(dim=-1			,keepdim=__snake_case      )
												a__             =     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   :Dict			,__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 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     []
							a__             =     []
							a__             =     []
							a__             =     []
							for theta in np.linspace(0					,    2 * np.pi					,    num=2_0   ):
														a__             =     np.array([np.sin(__lowerCAmelCase   ), np.cos(__lowerCAmelCase   ), -0.5]   )
														z /= np.sqrt(np.sum(z**2   )   )
														a__             =     -z * 4
														a__             =     np.array([np.cos(__lowerCAmelCase   ), -np.sin(__lowerCAmelCase   ), 0.0]   )
														a__             =     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   ))					,    )








import argparse


snake_case							:       int     			=      '''docs/source/_static/js/custom.js'''






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):
							with open(__lowerCAmelCase					,    encoding='utf-8'					,    newline='\n'   ) as f:
														a__             =     f.readlines()
							a__             =     0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion ='   ):
														index += 1
							a__             =     F'const stableVersion = "v{version}"\n'

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {'   ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}'   ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'    "v{version}": "v{version}",\n'

							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'					,    newline='\n'   ) as f:
														f.writelines(__lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Optional[Any]     			=      argparse.ArgumentParser()
						parser.add_argument('''--version''', help='''Release version.''')
						snake_case							:       Optional[int]     			=      parser.parse_args()
						update_custom_js(args.version)




