

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch






@dataclass
class        A_		:
							_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]):
								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):
								return torch.from_numpy(np.array([self.width, self.height]   ,dtype=np.floataa))
							def     lowerCAmelCase     (		self		:			int):
								return torch.from_numpy(np.array([self.x_fov, self.y_fov]   ,dtype=np.floataa))
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				int					       = torch.arange(self.height * self.width)
								__lowerCamelCase				:				Optional[Any]					       = torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(SCREAMING_SNAKE_CASE__   ,self.width   ,rounding_mode='trunc'),
								    ]   ,axis=1   ,)
								return coords
							@property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , *__lowerCamelCase				:				Union[str, Any]					       = self.shape
								__lowerCamelCase				:				Optional[Any]					       = int(np.prod(SCREAMING_SNAKE_CASE__))

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

								__lowerCamelCase				:				Optional[int]					       = rays.view(SCREAMING_SNAKE_CASE__   ,inner_batch_size * self.height * self.width   ,2   ,3)

								return rays
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			torch.Tensor):
								__lowerCamelCase       , *__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								__lowerCamelCase				:				Tuple					       = coords.view(SCREAMING_SNAKE_CASE__   ,-1   ,2)

								__lowerCamelCase				:				Optional[int]					       = self.resolution()
								__lowerCamelCase				:				Optional[int]					       = self.fov()

								__lowerCamelCase				:				Optional[int]					       = (flat.float() / (res - 1)) * 2 - 1
								__lowerCamelCase				:				int					       = fracs * torch.tan(fov / 2)

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


							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):
								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=SCREAMING_SNAKE_CASE__   ,height=SCREAMING_SNAKE_CASE__   ,x_fov=self.x_fov   ,y_fov=self.y_fov   ,)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     DifferentiableProjectiveCamera:
	__lowerCamelCase				:				Dict					       = []
	__lowerCamelCase				:				List[Any]					       = []
	__lowerCamelCase				:				Optional[Any]					       = []
	__lowerCamelCase				:				Optional[Any]					       = []
	for theta in np.linspace(0	, 2 * np.pi	, num=2_0			):
		__lowerCamelCase				:				Dict					       = np.array([np.sin(lowerCamelCase__			), np.cos(lowerCamelCase__			), -0.5]			)
		z /= np.sqrt(np.sum(z**2			)			)
		__lowerCamelCase				:				List[str]					       = -z * 4
		__lowerCamelCase				:				List[str]					       = np.array([np.cos(lowerCamelCase__			), -np.sin(lowerCamelCase__			), 0.0]			)
		__lowerCamelCase				:				int					       = 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__			))	, )










from typing import TYPE_CHECKING

from ....utils import _LazyModule


_UpperCAmelCase		:  Dict	=   {"tokenization_tapex": ["TapexTokenizer"]}


if TYPE_CHECKING:
     from .tokenization_tapex import TapexTokenizer


else:
     import sys

     _UpperCAmelCase		:  Optional[Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure)



