


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 						lowerCamelCase       :







							"""simple docstring"""




							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  A_     ( self     :					str						)				->			Optional[int]:





														"""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  A_     ( self     :					str						)				->			Dict:





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






							def  A_     ( self     :					Union[str, Any]						)				->			List[str]:





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






							def  A_     ( self     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.arange(self.height * self.width						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.stack(
														    [
														        pixel_indices % self.width,
														        torch.div(__a,			self.width,			rounding_mode="trunc"						),
														    ],			axis=1,			)
														return coords






							@property
							def  A_     ( self     :					List[str]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,*SCREAMING_SNAKE_CASE__       :      str     =						self.shape
														SCREAMING_SNAKE_CASE__       :      int     =						int(np.prod(__a						)						)

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

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

														return rays






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					torch.Tensor						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,*SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						coords.shape
														assert n_coords == 2
														assert batch_size == self.origin.shape[0]

														SCREAMING_SNAKE_CASE__       :      Dict     =						coords.view(__a,			-1,			2						)

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

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

														SCREAMING_SNAKE_CASE__       :      Tuple     =						fracs.view(__a,			-1,			2						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						(
														    self.z.view(__a,			1,			3						)
														    + self.x.view(__a,			1,			3						) * fracs[:, :, :1]
														    + self.y.view(__a,			1,			3						) * fracs[:, :, 1:]
														)
														SCREAMING_SNAKE_CASE__       :      Dict     =						directions / directions.norm(dim=-1,			keepdim=__a						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int						)				->			int:





														"""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 _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    DifferentiableProjectiveCamera:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[]
							SCREAMING_SNAKE_CASE__       :      int     =						[]
							SCREAMING_SNAKE_CASE__       :      Dict     =						[]
							for theta in np.linspace(0			,				2 * np.pi			,				num=20       ):
														SCREAMING_SNAKE_CASE__       :      Tuple     =						np.array([np.sin(snake_case_       ), np.cos(snake_case_       ), -0.5]       )
														z /= np.sqrt(np.sum(z**2       )       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-z * 4
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						np.array([np.cos(snake_case_       ), -np.sin(snake_case_       ), 0.0]       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						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_       ))			,				)













import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any]=sys.maxsize						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






							def __call__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
														for img in imgs:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						img.shape[:2]
																					# later: provide list and randomly choose index for resize
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						np.random.randint(self.short_edge_length[0],			self.short_edge_length[1] + 1						)
																					if size == 0:
																												return img
																					SCREAMING_SNAKE_CASE__       :      int     =						size * 1.0 / min(_UpperCAmelCase,			_UpperCAmelCase						)
																					if h < w:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size, scale * w
																					else:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						scale * h, size
																					if max(_UpperCAmelCase,			_UpperCAmelCase						) > self.max_size:
																												SCREAMING_SNAKE_CASE__       :      str     =						self.max_size * 1.0 / max(_UpperCAmelCase,			_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Any     =						newh * scale
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						neww * scale
																					SCREAMING_SNAKE_CASE__       :      Any     =						int(neww + 0.5						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						int(newh + 0.5						)

																					if img.dtype == np.uinta:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						Image.fromarray(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pil_image.resize((neww, newh),			PILImageResampling.BILINEAR						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.asarray(_UpperCAmelCase						)
																					else:
																												SCREAMING_SNAKE_CASE__       :      str     =						img.permute(2,			0,			1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						nn.functional.interpolate(
																												    _UpperCAmelCase,			(newh, neww),			mode=self.interp_method,			align_corners=_UpperCAmelCase						).squeeze(0						)
																					img_augs.append(_UpperCAmelCase						)

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],			cfg.INPUT.MAX_SIZE_TEST						)
														SCREAMING_SNAKE_CASE__       :      Any     =						cfg.INPUT.FORMAT
														SCREAMING_SNAKE_CASE__       :      List[str]     =						cfg.SIZE_DIVISIBILITY
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cfg.PAD_VALUE
														SCREAMING_SNAKE_CASE__       :      Dict     =						cfg.INPUT.MAX_SIZE_TEST
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cfg.MODEL.DEVICE
														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						lambda _UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

														return torch.stack(_UpperCAmelCase						), torch.tensor(_UpperCAmelCase						)






							def __call__( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			Optional[Any]:





														"""simple docstring"""
														with torch.no_grad():
																					if not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						[images]
																					if single_image:
																												assert len(_UpperCAmelCase						) == 1
																					for i in range(len(_UpperCAmelCase						)						):
																												if isinstance(images[i],			torch.Tensor						):
																																			images.insert(_UpperCAmelCase,			images.pop(_UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i],			torch.Tensor						):
																																			images.insert(
																																			    _UpperCAmelCase,			torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase						),			input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float(),			)
            # resize smallest edge
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor([im.shape[:2] for im in images]						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.aug(_UpperCAmelCase						)
																					# transpose images and convert to torch tensors
																					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																					# now normalize before pad to avoid useless arithmetic
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						[self.normalizer(_UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(_UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.true_divide(_UpperCAmelCase,			_UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






							assert torch.isfinite(SCREAMING_SNAKE_CASE__       ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						box_size
							tensor[:, 0].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 1].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 2].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 3].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )






