





"""simple docstring"""





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class        _lowerCamelCase      :
						UpperCAmelCase_ =			42  # [batch_size x 3]
						UpperCAmelCase_ =			42  # [batch_size x 3]
						UpperCAmelCase_ =			42  # [batch_size x 3]
						UpperCAmelCase_ =			42  # [batch_size x 3]
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42


						def     snake_case_   (self		) ->   str:
								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		) ->   Dict:
								return torch.from_numpy(np.array([self.width, self.height]   ,							dtype=np.floataa		)		)


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


						def     snake_case_   (self		) ->   torch.Tensor:
								UpperCamelCase   =	torch.arange(self.height * self.width		)
								UpperCamelCase   =	torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(__UpperCamelCase   ,							self.width   ,							rounding_mode="trunc"		),
								    ]   ,							axis=1   ,							)
								return coords


						@property
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   ,				*UpperCamelCase   =	self.shape
								UpperCamelCase   =	int(np.prod(__UpperCamelCase		)		)

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

								UpperCamelCase   =	rays.view(__UpperCamelCase   ,							inner_batch_size * self.height * self.width   ,							2   ,							3		)

								return rays


						def     snake_case_   (self   ,							__a		) ->   torch.Tensor:
								UpperCamelCase   ,				*UpperCamelCase   ,				UpperCamelCase   =	coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								UpperCamelCase   =	coords.view(__UpperCamelCase   ,							-1   ,							2		)

								UpperCamelCase   =	self.resolution()
								UpperCamelCase   =	self.fov()

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

								UpperCamelCase   =	fracs.view(__UpperCamelCase   ,							-1   ,							2		)
								UpperCamelCase   =	(
								    self.z.view(__UpperCamelCase   ,							1   ,							3		)
								    + self.x.view(__UpperCamelCase   ,							1   ,							3		) * fracs[:, :, :1]
								    + self.y.view(__UpperCamelCase   ,							1   ,							3		) * fracs[:, :, 1:]
								)
								UpperCamelCase   =	directions / directions.norm(dim=-1   ,							keepdim=__UpperCamelCase		)
								UpperCamelCase   =	torch.stack(
								    [
								        torch.broadcast_to(self.origin.view(__UpperCamelCase   ,							1   ,							3		)   ,							[batch_size, directions.shape[1], 3]		),
								        directions,
								    ]   ,							dim=2   ,							)
								return rays.view(__UpperCamelCase   ,							*__UpperCamelCase   ,							2   ,							3		)




						def     snake_case_   (self   ,							__a   ,							__a		) ->   "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=__UpperCamelCase   ,							height=__UpperCamelCase   ,							x_fov=self.x_fov   ,							y_fov=self.y_fov   ,							)



def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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









"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"upernet"


						def __init__(self   ,							__a=None   ,							__a=5_12   ,							__a=0.02   ,							__a=[1, 2, 3, 6]   ,							__a=True   ,							__a=0.4   ,							__a=3_84   ,							__a=2_56   ,							__a=1   ,							__a=False   ,							__a=2_55   ,							**__a   ,							) ->   Tuple:
								super().__init__(**__a		)

								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
										UpperCamelCase   =	CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]		)
								elif isinstance(__a   ,							__a		):
										UpperCamelCase   =	backbone_config.get("model_type"		)
										UpperCamelCase   =	CONFIG_MAPPING[backbone_model_type]
										UpperCamelCase   =	config_class.from_dict(__a		)

								UpperCamelCase   =	backbone_config
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	pool_scales
								UpperCamelCase   =	use_auxiliary_head
								UpperCamelCase   =	auxiliary_loss_weight
								UpperCamelCase   =	auxiliary_in_channels
								UpperCamelCase   =	auxiliary_channels
								UpperCamelCase   =	auxiliary_num_convs
								UpperCamelCase   =	auxiliary_concat_input
								UpperCamelCase   =	loss_ignore_index




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	copy.deepcopy(self.__dict__		)
								UpperCamelCase   =	self.backbone_config.to_dict()
								UpperCamelCase   =	self.__class__.model_type
								return output






