


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     42  # [batch_size x 3]
						lowerCamelCase_         =     42  # [batch_size x 3]
						lowerCamelCase_         =     42  # [batch_size x 3]
						lowerCamelCase_         =     42  # [batch_size x 3]
						lowerCamelCase_         =     42
						lowerCamelCase_         =     42
						lowerCamelCase_         =     42
						lowerCamelCase_         =     42
						lowerCamelCase_         =     42




						def  _snake_case					(	self						:   Dict						)      ->Optional[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  _snake_case					(	self						:   str						)      ->Dict:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   List[str]						)      ->str:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   List[str]						)      ->torch.Tensor:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    torch.arange(self.height * self.width						)
									a__ :List[Any]						  =    torch.stack(
									    [
									        pixel_indices % self.width,
									        torch.div(__A   ,     self.width   ,     rounding_mode="trunc"						),
									    ]   ,     axis=1   ,     )
									return coords




						@property
						def  _snake_case					(	self						:   List[str]						)      ->Tuple:






									"""simple docstring"""






									a__ :int						  =    self.shape
									a__ :Any						  =    int(np.prod(__A						)						)

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

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

									return rays




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   torch.Tensor						)      ->torch.Tensor:






									"""simple docstring"""






									a__ :str						  =    coords.shape
									assert n_coords == 2
									assert batch_size == self.origin.shape[0]

									a__ :Optional[int]						  =    coords.view(__A   ,     -1   ,     2						)

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

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

									a__ :Optional[Any]						  =    fracs.view(__A   ,     -1   ,     2						)
									a__ :int						  =    (
									    self.z.view(__A   ,     1   ,     3						)
									    + self.x.view(__A   ,     1   ,     3						) * fracs[:, :, :1]
									    + self.y.view(__A   ,     1   ,     3						) * fracs[:, :, 1:]
									)
									a__ :Tuple						  =    directions / directions.norm(dim=-1   ,     keepdim=__A						)
									a__ :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  _snake_case					(	self						:   List[Any]   ,     __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 				lowerCamelCase__	(      a	:   int				)    ->	DifferentiableProjectiveCamera:





			"""simple docstring"""
			a__ :Dict						  =    []
			a__ :Any						  =    []
			a__ :Optional[int]						  =    []
			a__ :List[Any]						  =    []
			for theta in np.linspace(0	,  2 * np.pi	,  num=20				):
						a__ :Optional[int]						  =    np.array([np.sin(a				), np.cos(a				), -0.5]				)
						z /= np.sqrt(np.sum(z**2				)				)
						a__ :str						  =    -z * 4
						a__ :List[Any]						  =    np.array([np.cos(a				), -np.sin(a				), 0.0]				)
						a__ :Tuple						  =    np.cross(a	,  a				)
						origins.append(a				)
						xs.append(a				)
						ys.append(a				)
						zs.append(a				)
			return DifferentiableProjectiveCamera(
			    origin=torch.from_numpy(np.stack(a	,  axis=0				)				).float()	,  x=torch.from_numpy(np.stack(a	,  axis=0				)				).float()	,  y=torch.from_numpy(np.stack(a	,  axis=0				)				).float()	,  z=torch.from_numpy(np.stack(a	,  axis=0				)				).float()	,  width=a	,  height=a	,  x_fov=0.7	,  y_fov=0.7	,  shape=(1, len(a				))	,  )






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case__			 =						'''scheduler_config.json'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     3
						lowerCamelCase_         =     4
						lowerCamelCase_         =     5
						lowerCamelCase_         =     6
						lowerCamelCase_         =     7
						lowerCamelCase_         =     8
						lowerCamelCase_         =     9
						lowerCamelCase_         =     10
						lowerCamelCase_         =     11
						lowerCamelCase_         =     12
						lowerCamelCase_         =     13
						lowerCamelCase_         =     14






@dataclass
class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ :
						lowerCamelCase_         =     SCHEDULER_CONFIG_NAME
						lowerCamelCase_         =     []
						lowerCamelCase_         =     True




						@classmethod
						def  _snake_case					(	cls						:   Union[str, Any]   ,     __A						:   Dict[str, Any] = None   ,     __A						:   Optional[str] = None   ,     __A						:   Optional[int]=False   ,     **__A						:   Union[str, Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__  ,  a__  ,  a__ :List[Any]						  =    cls.load_config(
									    pretrained_model_name_or_path=__A   ,     subfolder=__A   ,     return_unused_kwargs=__A   ,     return_commit_hash=__A   ,     **__A   ,     )
									return cls.from_config(__A   ,     return_unused_kwargs=__A   ,     **__A						)




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, os.PathLike]   ,     __A						:   bool = False   ,     **__A						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									self.save_config(save_directory=__A   ,     push_to_hub=__A   ,     **__A						)




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






									"""simple docstring"""






									return self._get_compatibles()






						@classmethod
						def  _snake_case					(	cls						:   Dict						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    list(set([cls.__name__] + cls._compatibles						)						)
									a__ :Union[str, Any]						  =    importlib.import_module(__name__.split("."						)[0]						)
									a__ :Optional[int]						  =    [
									    getattr(__A   ,     __A						) for c in compatible_classes_str if hasattr(__A   ,     __A						)
									]
									return compatible_classes


