
"""simple docstring"""





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch






@dataclass
class        _UpperCAmelCase:
		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 		UpperCAmelCase				(   self)			->			List[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 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






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



		def 		UpperCAmelCase				(   self)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    torch.arange(self.height * self.width)
							_UpperCamelCase									=    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(__a							,						self.width							,						rounding_mode='''trunc'''),
							    ]							,						axis=1							,						)
							return coords



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






							'''simple docstring'''
							_UpperCamelCase					,    *_UpperCamelCase									=    self.shape
							_UpperCamelCase									=    int(np.prod(__a))

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

							_UpperCamelCase									=    rays.view(__a							,						inner_batch_size * self.height * self.width							,						2							,						3)

							return rays



		def 		UpperCAmelCase				(   self							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase					,    *_UpperCamelCase					,    _UpperCamelCase									=    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							_UpperCamelCase									=    coords.view(__a							,						-1							,						2)

							_UpperCamelCase									=    self.resolution()
							_UpperCamelCase									=    self.fov()

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

							_UpperCamelCase									=    fracs.view(__a							,						-1							,						2)
							_UpperCamelCase									=    (
							    self.z.view(__a							,						1							,						3)
							    + self.x.view(__a							,						1							,						3) * fracs[:, :, :1]
							    + self.y.view(__a							,						1							,						3) * fracs[:, :, 1:]
							)
							_UpperCamelCase									=    directions / directions.norm(dim=-1							,						keepdim=__a)
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self							,						__a							,						__a)			->			"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  lowerCamelCase__						(     __snake_case      )				->						DifferentiableProjectiveCamera:


					"""simple docstring"""





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








"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


if __name__ == "__main__":
				fire.Fire(calculate_rouge_path)






