



'''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 				_snake_case			:




				def __init__(						self					:						Tuple,					__lowercase					:						Optional[Any],					__lowercase					:						Optional[Any]=sys.maxsize						):
							lowercase__					  =				"bilinear"
							lowercase__					  =				max_size
							lowercase__					  =				short_edge_length




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

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

							return img_augs
class 				_snake_case			:




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




				def 							A__      (						self					:						List[str],					__lowercase					:						int						):
							lowercase__					  =				tuple(max(snake_case_						) for s in zip(*[img.shape for img in images]						)						)
							lowercase__					  =				[im.shape[-2:] for im in images]
							lowercase__					  =				[
							    nn.functional.pad(
							        snake_case_,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],					value=self.pad_value,					)
							    for size, im in zip(snake_case_,					snake_case_						)
							]

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




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

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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			assert torch.isfinite(SCREAMING_SNAKE_CASE_  ).all(), "Box tensor contains infinite or NaN!"
			lowercase__  ,       lowercase__					  =				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_  )









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), len(grid[0]  )
			if (
			    min(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)  )

			lowercase__					  =				0
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row + 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row - 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col + 1						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col - 1						,  SCREAMING_SNAKE_CASE_  )

			visit.remove((row, col)  )
			return count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




