



from typing import Dict, Optional, Tuple

import torch

def       A_   (						__a  :					torch.Tensor		):

				"""simple docstring"""
				a__					   =		boundaries[1] - boundaries[0]
				a__					   =		boundaries + step / 2
				a__					   =		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1		)]			,		dim=0		)
				return bin_centers

def       A_   (						__a  :					torch.Tensor			,		__a  :					torch.Tensor			,		):

				"""simple docstring"""
				a__					   =		_calculate_bin_centers(__a		)
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers			,		dim=-1		),
				    bin_centers[-1],
				)

def       A_   (						__a  :					torch.Tensor			,		__a  :					int = 31			,		__a  :					int = 64			,		**__a  :					Any			,		):

				"""simple docstring"""
				a__					   =		torch.linspace(0			,		__a			,		steps=(no_bins - 1)			,		device=logits.device		)

				a__					   =		torch.nn.functional.softmax(__a			,		dim=-1		)
				a__   ,      a__					   =		_calculate_expected_aligned_error(
				    alignment_confidence_breaks=__a			,		aligned_distance_error_probs=__a			,		)

				return {
				    "aligned_confidence_probs": aligned_confidence_probs,
				    "predicted_aligned_error": predicted_aligned_error,
				    "max_predicted_aligned_error": max_predicted_aligned_error,
				}




def       A_   (						__a  :					torch.Tensor			,		__a  :					Optional[torch.Tensor] = None			,		__a  :					int = 31			,		__a  :					int = 64			,		__a  :					float = 1e-8			,		**__a  :					str			,		):

				"""simple docstring"""
				if residue_weights is None:
								a__					   =		logits.new_ones(logits.shape[-2]		)

				a__					   =		torch.linspace(0			,		__a			,		steps=(no_bins - 1)			,		device=logits.device		)

				a__					   =		_calculate_bin_centers(__a		)
				torch.sum(__a		)
				a__					   =		logits.shape[-2]
				a__					   =		max(__a			,		19		)

				a__					   =		1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

				a__					   =		torch.nn.functional.softmax(__a			,		dim=-1		)

				a__					   =		1.0 / (1 + (bin_centers**2) / (da**2))
				a__					   =		torch.sum(probs * tm_per_bin			,		dim=-1		)

				a__					   =		residue_weights / (eps + residue_weights.sum())
				a__					   =		torch.sum(predicted_tm_term * normed_residue_mask			,		dim=-1		)

				a__					   =		per_alignment * residue_weights

				a__					   =		(weighted == torch.max(__a		)).nonzero()[0]
				return per_alignment[tuple(__a		)]








"""simple docstring"""





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   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								UpperCamelCase =						[]
								for img in imgs:
										UpperCamelCase ,					UpperCamelCase =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										UpperCamelCase =						np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										UpperCamelCase =						size * 1.0 / min(__a ,				__a		)
										if h < w:
												UpperCamelCase ,					UpperCamelCase =						size, scale * w
										else:
												UpperCamelCase ,					UpperCamelCase =						scale * h, size
										if max(__a ,				__a		) > self.max_size:
												UpperCamelCase =						self.max_size * 1.0 / max(__a ,				__a		)
												UpperCamelCase =						newh * scale
												UpperCamelCase =						neww * scale
										UpperCamelCase =						int(neww + 0.5		)
										UpperCamelCase =						int(newh + 0.5		)

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

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




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# 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
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										UpperCamelCase =						torch.true_divide(__a ,				__a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		assert torch.isfinite(_SCREAMING_SNAKE_CASE     ).all(), "Box tensor contains infinite or NaN!"
		UpperCamelCase ,					UpperCamelCase =						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     )






