





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 							_a:







		def __init__(     self			,    __snake_case			,    __snake_case=sys.maxsize					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= "bilinear"
							_snake_case							:       str												= max_size
							_snake_case							:       Optional[int]												= short_edge_length







		def __call__(     self			,    __snake_case					)   ->   List[Any]:


							'''simple docstring'''

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

												if img.dtype == np.uinta:
																	_snake_case							:       Union[str, Any]												= Image.fromarray(__snake_case					)
																	_snake_case							:       List[str]												= pil_image.resize((neww, newh)			,    PILImageResampling.BILINEAR					)
																	_snake_case							:       List[Any]												= np.asarray(__snake_case					)
												else:
																	_snake_case							:       List[Any]												= img.permute(2			,    0			,    1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																	_snake_case							:       str												= 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 							_a:







		def __init__(     self			,    __snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       int												= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,    cfg.INPUT.MAX_SIZE_TEST					)
							_snake_case							:       str												= cfg.INPUT.FORMAT
							_snake_case							:       Union[str, Any]												= cfg.SIZE_DIVISIBILITY
							_snake_case							:       Tuple												= cfg.PAD_VALUE
							_snake_case							:       str												= cfg.INPUT.MAX_SIZE_TEST
							_snake_case							:       Optional[int]												= cfg.MODEL.DEVICE
							_snake_case							:       Optional[Any]												= torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,    1			,    1					)
							_snake_case							:       int												= torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,    1			,    1					)
							_snake_case							:       Optional[int]												= lambda __snake_case					: (x - self.pixel_mean) / self.pixel_std







		def 							lowercase							(     self			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= tuple(max(__snake_case					) for s in zip(*[img.shape for img in images]					)					)
							_snake_case							:       str												= [im.shape[-2:] for im in images]
							_snake_case							:       str												= [
							    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			,    __snake_case			,    __snake_case=False					)   ->   Tuple:


							'''simple docstring'''

							with torch.no_grad():
												if not isinstance(__snake_case			,    __snake_case					):
																	_snake_case							:       Dict												= [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
												_snake_case							:       List[str]												= torch.tensor([im.shape[:2] for im in images]					)
												_snake_case							:       Any												= 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
												_snake_case							:       str												= [self.normalizer(__snake_case					) for x in images]
												# now pad them to do the following operations
												_snake_case							,			_snake_case							:       Optional[int]												= self.pad(__snake_case					)
												# Normalize

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







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






from __future__ import annotations

from collections.abc import Generator







def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					) or n < 1:
										raise Exception("Sequence only defined for positive integers"					)

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_snake_case							:       Any												= 3 * n + 1
										yield n







def  A					(				):
					_snake_case							:       Dict												= 43
					_snake_case							:       List[str]												= tuple(collatz_sequence(UpperCAmelCase					)					)
					print(UpperCAmelCase					)
					print(F"""Collatz sequence from {n} took {len(UpperCAmelCase					)} steps."""					)


if __name__ == "__main__":
							main()