


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=sys.maxsize)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      "bilinear"
								snake_case__  :						Optional[int]		=      max_size
								snake_case__  :						Optional[int]		=      short_edge_length







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







								'''simple docstring'''




								snake_case__  :						List[Any]		=      []
								for img in imgs:
									snake_case__,					snake_case__  :						Optional[int]		=      img.shape[:2]
									# later: provide list and randomly choose index for resize
									snake_case__  :						Optional[Any]		=      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(lowerCamelCase__	,					lowerCamelCase__)
									if h < w:
										snake_case__,					snake_case__  :						Tuple		=      size, scale * w
									else:
										snake_case__,					snake_case__  :						Dict		=      scale * h, size
									if max(lowerCamelCase__	,					lowerCamelCase__) > self.max_size:
										snake_case__  :						Optional[Any]		=      self.max_size * 1.0 / max(lowerCamelCase__	,					lowerCamelCase__)
										snake_case__  :						List[str]		=      newh * scale
										snake_case__  :						Optional[int]		=      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__  :						List[Any]		=      Image.fromarray(lowerCamelCase__)
										snake_case__  :						Any		=      pil_image.resize((neww, newh)	,					PILImageResampling.BILINEAR)
										snake_case__  :						Tuple		=      np.asarray(lowerCamelCase__)
									else:
										snake_case__  :						Optional[Any]		=      img.permute(2	,					0	,					1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
										snake_case__  :						Optional[Any]		=      nn.functional.interpolate(
										    lowerCamelCase__	,					(newh, neww)	,					mode=self.interp_method	,					align_corners=lowerCamelCase__).squeeze(0)
									img_augs.append(lowerCamelCase__)

								return img_augs


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						int		=      tuple(max(lowerCamelCase__) for s in zip(*[img.shape for img in images]))
								snake_case__  :						Optional[int]		=      [im.shape[-2:] for im in images]
								snake_case__  :						Tuple		=      [
								    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	,					lowerCamelCase__	,					lowerCamelCase__=False)   ->	Optional[Any]:







								'''simple docstring'''




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

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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Tuple[int, int]	)							->	Any:

	'''simple docstring'''


	assert torch.isfinite(_UpperCAmelCase	).all(), "Box tensor contains infinite or NaN!"
	snake_case__,					snake_case__  :						List[str]		=      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	)










"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       int = 998001      ):
			"""simple docstring"""

			# fetches the next number
			for number in range(n - 1				,						9999				,						-1      ):
						lowerCamelCase__						:						Optional[int]						      =str(__lowerCamelCase      )

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									lowerCamelCase__						:						Optional[int]						      =999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor      )      ) == 3.0):
															return number
												divisor -= 1
			raise ValueError('''That number is larger than our acceptable range.'''      )


if __name__ == "__main__":
		print(f'{solution() = }')




