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	:	Optional[int]  ,  lowercase__	:	str  ,  lowercase__	:	Tuple=sys.maxsize						):
										_lowerCAmelCase							=							'bilinear'
										_lowerCAmelCase							=							max_size
										_lowerCAmelCase							=							short_edge_length





			def __call__(       self	:	int  ,  lowercase__	:	Optional[Any]						):
										_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(lowercase__  ,  lowercase__						)
																	if h < w:
																								_lowerCAmelCase			,		_lowerCAmelCase							=							size, scale * w
																	else:
																								_lowerCAmelCase			,		_lowerCAmelCase							=							scale * h, size
																	if max(lowercase__  ,  lowercase__						) > self.max_size:
																								_lowerCAmelCase							=							self.max_size * 1.0 / max(lowercase__  ,  lowercase__						)
																								_lowerCAmelCase							=							newh * scale
																								_lowerCAmelCase							=							neww * scale
																	_lowerCAmelCase							=							int(neww + 0.5						)
																	_lowerCAmelCase							=							int(newh + 0.5						)

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

										return img_augs







class        lowerCamelCase__				:





			def __init__(       self	:	int  ,  lowercase__	:	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 lowercase__						: (x - self.pixel_mean) / self.pixel_std





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

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





			def __call__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	str=False						):
										with torch.no_grad():
																	if not isinstance(lowercase__  ,  lowercase__						):
																								_lowerCAmelCase							=							[images]
																	if single_image:
																								assert len(lowercase__						) == 1
																	for i in range(len(lowercase__						)						):
																								if isinstance(images[i]  ,  torch.Tensor						):
																															images.insert(lowercase__  ,  images.pop(lowercase__						).to(self.device						).float()						)
																								elif not isinstance(images[i]  ,  torch.Tensor						):
																															images.insert(
																															    lowercase__  ,  torch.as_tensor(img_tensorize(images.pop(lowercase__						)  ,  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(lowercase__						)
																	# 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(lowercase__						) for x in images]
																	# now pad them to do the following operations
																	_lowerCAmelCase			,		_lowerCAmelCase							=							self.pad(lowercase__						)
																	# Normalize

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







def _lowerCamelCase  (		snake_case     , snake_case  ):
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes






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







from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






