




"""simple docstring"""






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class   a__			:
						snake_case__             = 42  # [batch_size x 3]
						snake_case__             = 42  # [batch_size x 3]
						snake_case__             = 42  # [batch_size x 3]
						snake_case__             = 42  # [batch_size x 3]
						snake_case__             = 42
						snake_case__             = 42
						snake_case__             = 42
						snake_case__             = 42
						snake_case__             = 42





						def       __UpperCamelCase     (							self      :	Optional[int])						->       List[str]:




											"""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)						->       Any:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Any)						->       List[str]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	int)						->       torch.Tensor:




											"""simple docstring"""







											_lowerCAmelCase:int									=  torch.arange(self.height * self.width)
											_lowerCAmelCase:str									=  torch.stack(
											    [
											        pixel_indices % self.width,
											        torch.div(a__     ,self.width     ,rounding_mode='''trunc'''),
											    ]     ,axis=1     ,)
											return coords





						@property
						def       __UpperCamelCase     (							self      :	int)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase							,   *_lowerCAmelCase:Dict									=  self.shape
											_lowerCAmelCase:List[Any]									=  int(np.prod(a__))

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

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

											return rays





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	torch.Tensor)						->       torch.Tensor:




											"""simple docstring"""







											_lowerCAmelCase							,   *_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  coords.shape
											assert n_coords == 2
											assert batch_size == self.origin.shape[0]

											_lowerCAmelCase:int									=  coords.view(a__     ,-1     ,2)

											_lowerCAmelCase:Optional[int]									=  self.resolution()
											_lowerCAmelCase:Union[str, Any]									=  self.fov()

											_lowerCAmelCase:Tuple									=  (flat.float() / (res - 1)) * 2 - 1
											_lowerCAmelCase:Union[str, Any]									=  fracs * torch.tan(fov / 2)

											_lowerCAmelCase:str									=  fracs.view(a__     ,-1     ,2)
											_lowerCAmelCase:Tuple									=  (
											    self.z.view(a__     ,1     ,3)
											    + self.x.view(a__     ,1     ,3) * fracs[:, :, :1]
											    + self.y.view(a__     ,1     ,3) * fracs[:, :, 1:]
											)
											_lowerCAmelCase:str									=  directions / directions.norm(dim=-1     ,keepdim=a__)
											_lowerCAmelCase:List[str]									=  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      :	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 							UpperCAmelCase		(		snake_case			:					int    ):
					_lowerCAmelCase:Dict									=  []
					_lowerCAmelCase:int									=  []
					_lowerCAmelCase:Union[str, Any]									=  []
					_lowerCAmelCase:Union[str, Any]									=  []
					for theta in np.linspace(0      ,       2 * np.pi      ,       num=20    ):
										_lowerCAmelCase:int									=  np.array([np.sin(snake_case    ), np.cos(snake_case    ), -0.5]    )
										z /= np.sqrt(np.sum(z**2    )    )
										_lowerCAmelCase:Union[str, Any]									=  -z * 4
										_lowerCAmelCase:List[str]									=  np.array([np.cos(snake_case    ), -np.sin(snake_case    ), 0.0]    )
										_lowerCAmelCase:int									=  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"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






