





'''simple docstring'''






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class  UpperCAmelCase     :
      _lowercase:			int						     =   42  # [batch_size x 3]
      _lowercase:			Tuple						     =   42  # [batch_size x 3]
      _lowercase:			Dict						     =   42  # [batch_size x 3]
      _lowercase:			Tuple						     =   42  # [batch_size x 3]
      _lowercase:			List[str]						     =   42
      _lowercase:			int						     =   42
      _lowercase:			Optional[int]						     =   42
      _lowercase:			Optional[int]						     =   42
      _lowercase:			Union[str, Any]						     =   42




      def 				lowercase__    (			self			: List[str]  )				->      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 				lowercase__    (			self			: Optional[int]  )				->      str:
            return torch.from_numpy(np.array([self.width, self.height]			,			dtype=np.floataa  )  )




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




      def 				lowercase__    (			self			: List[Any]  )				->      torch.Tensor:
            _lowerCAmelCase							     =							torch.arange(self.height * self.width  )
            _lowerCAmelCase							     =							torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(__snake_case			,			self.width			,			rounding_mode="""trunc"""  ),
                ]			,			axis=1			,			)
            return coords




      @property
      def 				lowercase__    (			self			: Any  )				->      Optional[Any]:
            _lowerCAmelCase     ,							*_lowerCAmelCase							     =							self.shape
            _lowerCAmelCase							     =							int(np.prod(__snake_case  )  )

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

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

            return rays




      def 				lowercase__    (			self			: List[str]			,			__snake_case			: int  )				->      torch.Tensor:
            _lowerCAmelCase     ,							*_lowerCAmelCase     ,							_lowerCAmelCase							     =							coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

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

            _lowerCAmelCase							     =							self.resolution()
            _lowerCAmelCase							     =							self.fov()

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

            _lowerCAmelCase							     =							fracs.view(__snake_case			,			-1			,			2  )
            _lowerCAmelCase							     =							(
                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:]
            )
            _lowerCAmelCase							     =							directions / directions.norm(dim=-1			,			keepdim=__snake_case  )
            _lowerCAmelCase							     =							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 				lowercase__    (			self			: Any			,			__snake_case			: int			,			__snake_case			: List[str]  )				->      "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    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							[]
      for theta in np.linspace(0							,	2 * np.pi							,	num=20  ):
            _lowerCAmelCase							     =							np.array([np.sin(__lowerCamelCase  ), np.cos(__lowerCamelCase  ), -0.5]  )
            z /= np.sqrt(np.sum(z**2  )  )
            _lowerCAmelCase							     =							-z * 4
            _lowerCAmelCase							     =							np.array([np.cos(__lowerCamelCase  ), -np.sin(__lowerCamelCase  ), 0.0]  )
            _lowerCAmelCase							     =							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  ))							,	)













'''simple docstring'''

from typing import List

from .keymap import KEYMAP, get_character





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[int]:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += [key]
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator





def lowerCamelCase						(     *__lowerCamelCase :    List[str]    )						->Dict:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += keys
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __new__(		cls ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			super().__new__(cls ,		A ,		A ,		A       )
        if not hasattr(A ,		"""key_handler"""       ):
            setattr(A ,		"""key_handler""" ,		{}       )
        setattr(A ,		"""handle_input""" ,		KeyHandler.handle_input       )

        for value in attrs.values():
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""handle_key""" ,		[]       )
            for key in handled_keys:
                _SCREAMING_SNAKE_CASE									=			value
        return new_cls





    @staticmethod
    def 			snake_case_(		cls       )		->   str:
        _SCREAMING_SNAKE_CASE									=			get_character()
        if char != KEYMAP["undefined"]:
            _SCREAMING_SNAKE_CASE									=			ord(A       )
        _SCREAMING_SNAKE_CASE									=			cls.key_handler.get(A       )
        if handler:
            _SCREAMING_SNAKE_CASE									=			char
            return handler(cls       )
        else:
            return None





def lowerCamelCase						(     cls :    Any    )						->Dict:
    return KeyHandler(cls.__name__					,       cls.__bases__					,       cls.__dict__.copy()    )



