






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  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       Any,    lowerCamelCase		:       Any,    lowerCamelCase		:       List[str]=sys.maxsize    ):

								'''simple docstring'''
								lowercase__  					=     '''bilinear'''
								lowercase__  					=     max_size
								lowercase__  					=     short_edge_length




				def __call__(	self		:       Any,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								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(lowerCamelCase,    lowerCamelCase    )
												if h < w:
																lowercase__					,		lowercase__  					=     size, scale * w
												else:
																lowercase__					,		lowercase__  					=     scale * h, size
												if max(lowerCamelCase,    lowerCamelCase    ) > self.max_size:
																lowercase__  					=     self.max_size * 1.0 / max(lowerCamelCase,    lowerCamelCase    )
																lowercase__  					=     newh * scale
																lowercase__  					=     neww * scale
												lowercase__  					=     int(neww + 0.5    )
												lowercase__  					=     int(newh + 0.5    )

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

								return img_augs



class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								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 lowerCamelCase    : (x - self.pixel_mean) / self.pixel_std




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Any    ):

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

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




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

								'''simple docstring'''
								with torch.no_grad():
												if not isinstance(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     [images]
												if single_image:
																assert len(lowerCamelCase    ) == 1
												for i in range(len(lowerCamelCase    )    ):
																if isinstance(images[i],    torch.Tensor    ):
																				images.insert(lowerCamelCase,    images.pop(lowerCamelCase    ).to(self.device    ).float()    )
																elif not isinstance(images[i],    torch.Tensor    ):
																				images.insert(
																				    lowerCamelCase,    torch.as_tensor(img_tensorize(images.pop(lowerCamelCase    ),    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(lowerCamelCase    )
												# 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(lowerCamelCase    ) for x in images]
												# now pad them to do the following operations
												lowercase__					,		lowercase__  					=     self.pad(lowerCamelCase    )
												# Normalize

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






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				assert torch.isfinite(lowerCamelCase_							).all(), "Box tensor contains infinite or NaN!"
				lowercase__					,		lowercase__  					=     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_							)












from __future__ import annotations

from typing import Any






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if not postfix_notation:
								return 0

				lowercase__  					=     {'''+''', '''-''', '''*''', '''/'''}
				lowercase__  					=     []

				for token in postfix_notation:
								if token in operations:
												lowercase__					,		lowercase__  					=     stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b							)
												elif token == "-":
																stack.append(a - b							)
												elif token == "*":
																stack.append(a * b							)
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1							)
																else:
																				stack.append(a // b							)
								else:
												stack.append(int(lowerCamelCase_							)							)

				return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




