



"""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 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=sys.maxsize						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    'bilinear'
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    max_size
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    short_edge_length







	def __call__(			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


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

									if img.dtype == np.uinta:
													SCREAMING_SNAKE_CASE	:       List[Any] 							=    Image.fromarray(_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    pil_image.resize((neww, newh)    ,					PILImageResampling.BILINEAR						)
													SCREAMING_SNAKE_CASE	:       List[str] 							=    np.asarray(_SCREAMING_SNAKE_CASE						)
									else:
													SCREAMING_SNAKE_CASE	:       List[str] 							=    img.permute(2    ,					0    ,					1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    nn.functional.interpolate(
													    _SCREAMING_SNAKE_CASE    ,					(newh, neww)    ,					mode=self.interp_method    ,					align_corners=_SCREAMING_SNAKE_CASE						).squeeze(0						)
									img_augs.append(_SCREAMING_SNAKE_CASE						)

					return img_augs




class 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]						)	->  Optional[Any]:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					List[str]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    tuple(max(_SCREAMING_SNAKE_CASE						) for s in zip(*[img.shape for img in images]						)						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [im.shape[-2:] for im in images]
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [
					    nn.functional.pad(
					        _SCREAMING_SNAKE_CASE    ,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,					value=self.pad_value    ,					)
					    for size, im in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					]

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







	def __call__(			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=False						)	->  Tuple:





					"""simple docstring"""


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

									if self.size_divisibility > 0:
													raise NotImplementedError()
									# pad
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.true_divide(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
									if single_image:
													return images[0], sizes[0], scales_yx[0]
									else:
													return images, sizes, scales_yx




def    __snake_case       (  __A			:	Dict       ,		__A			:	str    )    ->					str:






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







def    __snake_case       (  __A			:	str       ,		__A			:	Tuple[int, int]    )    ->					Tuple:






				'''simple docstring'''
				assert torch.isfinite(__A    ).all(), "Box tensor contains infinite or NaN!"
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    box_size
				tensor[:, 0].clamp_(min=0       ,		max=__A    )
				tensor[:, 1].clamp_(min=0       ,		max=__A    )
				tensor[:, 2].clamp_(min=0       ,		max=__A    )
				tensor[:, 3].clamp_(min=0       ,		max=__A    )






"""simple docstring"""

def    __snake_case       (  __A			:	int    )    ->					int:






				'''simple docstring'''
				if not isinstance(__A       ,		__A    ):
								raise ValueError('double_factorial() only accepts integral values'    )
				if num < 0:
								raise ValueError('double_factorial() not defined for negative values'    )
				SCREAMING_SNAKE_CASE	:       List[str] 							=    1
				for i in range(__A       ,		0       ,		-2    ):
								value *= i
				return value


if __name__ == "__main__":
							import doctest

							doctest.testmod()





