"""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       :




					"""simple docstring"""

					def __init__(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	int=sys.maxsize			):
									UpperCAmelCase__             =							'bilinear'
									UpperCAmelCase__             =							max_size
									UpperCAmelCase__             =							short_edge_length







					def __call__(				self   :	List[Any]    ,lowerCamelCase__   :	str			):
									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(lowerCamelCase__    ,lowerCamelCase__			)
													if h < w:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							size, scale * w
													else:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							scale * h, size
													if max(lowerCamelCase__    ,lowerCamelCase__			) > self.max_size:
																	UpperCAmelCase__             =							self.max_size * 1.0 / max(lowerCamelCase__    ,lowerCamelCase__			)
																	UpperCAmelCase__             =							newh * scale
																	UpperCAmelCase__             =							neww * scale
													UpperCAmelCase__             =							int(neww + 0.5			)
													UpperCAmelCase__             =							int(newh + 0.5			)

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

									return img_augs






class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	Tuple			):
									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 lowerCamelCase__			: (x - self.pixel_mean) / self.pixel_std







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							tuple(max(lowerCamelCase__			) for s in zip(*[img.shape for img in images]			)			)
									UpperCAmelCase__             =							[im.shape[-2:] for im in images]
									UpperCAmelCase__             =							[
									    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   :	Dict    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	str=False			):
									with torch.no_grad():
													if not isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							[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
													UpperCAmelCase__             =							torch.tensor([im.shape[:2] for im in images]			)
													UpperCAmelCase__             =							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
													UpperCAmelCase__             =							[self.normalizer(lowerCamelCase__			) for x in images]
													# now pad them to do the following operations
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.pad(lowerCamelCase__			)
													# Normalize

													if self.size_divisibility > 0:
																	raise NotImplementedError()
													# pad
													UpperCAmelCase__             =							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       ):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				assert torch.isfinite(lowerCamelCase       ).all(), "Box tensor contains infinite or NaN!"
				UpperCAmelCase__      ,	UpperCAmelCase__             =							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       )




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



