
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	:							int							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=sys.maxsize						)  ->							List[Any]:
						__lowerCamelCase					      =			'''bilinear'''
						__lowerCamelCase					      =			max_size
						__lowerCamelCase					      =			short_edge_length







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

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

						return img_augs





class      lowerCAmelCase__  :






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






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

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







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

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





def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Optional[int]							)    ->     Any:
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Tuple[int, int]							)    ->     Any:
				assert torch.isfinite(__lowerCAmelCase							).all(), "Box tensor contains infinite or NaN!"
				__lowerCamelCase      ,					__lowerCamelCase					      =			box_size
				tensor[:, 0].clamp_(min=0	,						max=__lowerCAmelCase							)
				tensor[:, 1].clamp_(min=0	,						max=__lowerCAmelCase							)
				tensor[:, 2].clamp_(min=0	,						max=__lowerCAmelCase							)
				tensor[:, 3].clamp_(min=0	,						max=__lowerCAmelCase							)





def 				__magic_name__				(     __lowerCAmelCase			:		list	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int							)    ->     int:
				__lowerCamelCase					      =			int((lower_bound + upper_bound) // 2							)
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(__lowerCAmelCase	,						r + 1	,						__lowerCAmelCase	,						__lowerCAmelCase							)
				else:
								return binary_search(__lowerCAmelCase	,						__lowerCAmelCase	,						r - 1	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		list							)    ->     list:
				__lowerCamelCase					      =			0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(__lowerCAmelCase							) and matrix[index][0] < value:
								__lowerCamelCase					      =			binary_search(matrix[index]	,						0	,						len(matrix[index]							) - 1	,						__lowerCAmelCase							)
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



