






"""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     UpperCamelCase_ :





	def __init__(   self      :				Any			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[Any]=sys.maxsize		)		->    List[str]:
				UpperCAmelCase_  :				Union[str, Any]      =      'bilinear'
				UpperCAmelCase_  :				Optional[int]      =      max_size
				UpperCAmelCase_  :				str      =      short_edge_length





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

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

				return img_augs







class     UpperCamelCase_ :





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Optional[int]		)		->    Dict:
				UpperCAmelCase_  :				int      =      tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
				UpperCAmelCase_  :				int      =      [im.shape[-2:] for im in images]
				UpperCAmelCase_  :				Dict      =      [
				    nn.functional.pad(
				        __a			,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,			value=self.pad_value			,			)
				    for size, im in zip(__a			,			__a		)
				]

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





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






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

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





def        snake_case   (     A__			,A__		):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def        snake_case   (     A__			,A__		):
			assert torch.isfinite(A__		).all(), "Box tensor contains infinite or NaN!"
			UpperCAmelCase_  :				Optional[int]      =      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"""
class     UpperCamelCase_ :





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int=None			,			lowerCAmelCase_      :				List[Any]=None		)		->    int:
				UpperCAmelCase_  :				int      =      data
				UpperCAmelCase_  :				Optional[int]      =      previous
				UpperCAmelCase_  :				int      =      next_node





	def __str__(   self      :				Dict		)		->    str:
				return f"""{self.data}"""





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				return self.data





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				return self.next





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







class     UpperCamelCase_ :





	def __init__(   self      :				Any			,			lowerCAmelCase_      :				str		)		->    List[str]:
				UpperCAmelCase_  :				Union[str, Any]      =      head





	def __iter__(   self      :				Any		)		->    List[str]:
				return self





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				if not self.current:
							raise StopIteration
				else:
							UpperCAmelCase_  :				Optional[int]      =      self.current.get_data()
							UpperCAmelCase_  :				Dict      =      self.current.get_next()
							return value







class     UpperCamelCase_ :





	def __init__(   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      None  # First node in list
				UpperCAmelCase_  :				Union[str, Any]      =      None  # Last node in list





	def __str__(   self      :				List[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				List[str]      =      self.head
				UpperCAmelCase_  :				int      =      []
				while current is not None:
							nodes.append(current.get_data()		)
							UpperCAmelCase_  :				Optional[Any]      =      current.get_next()
				return " ".join(str(lowerCAmelCase_		) for node in nodes		)





	def __contains__(   self      :				int			,			lowerCAmelCase_      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[int]      =      self.head
				while current:
							if current.get_data() == value:
										return True
							UpperCAmelCase_  :				Union[str, Any]      =      current.get_next()
				return False





	def __iter__(   self      :				int		)		->    Tuple:
				return LinkedListIterator(self.head		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				if self.head:
							return self.head.get_data()
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[Any]:
				if self.tail:
							return self.tail.get_data()
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Node		)		->    None:
				if self.head is None:
							UpperCAmelCase_  :				Optional[int]      =      node
							UpperCAmelCase_  :				Union[str, Any]      =      node
				else:
							self.insert_before_node(self.head			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Node		)		->    None:
				if self.head is None:
							self.set_head(lowerCAmelCase_		)
				else:
							self.insert_after_node(self.tail			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				int		)		->    None:
				UpperCAmelCase_  :				Optional[Any]      =      Node(lowerCAmelCase_		)
				if self.head is None:
							self.set_head(lowerCAmelCase_		)
				else:
							self.set_tail(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    None:
				UpperCAmelCase_  :				Any      =      node
				UpperCAmelCase_  :				Tuple      =      node.previous

				if node.get_previous() is None:
							UpperCAmelCase_  :				List[Any]      =      node_to_insert
				else:
							UpperCAmelCase_  :				Dict      =      node_to_insert

				UpperCAmelCase_  :				Dict      =      node_to_insert





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    None:
				UpperCAmelCase_  :				Dict      =      node
				UpperCAmelCase_  :				int      =      node.next

				if node.get_next() is None:
							UpperCAmelCase_  :				int      =      node_to_insert
				else:
							UpperCAmelCase_  :				Optional[Any]      =      node_to_insert

				UpperCAmelCase_  :				Any      =      node_to_insert





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int		)		->    None:
				UpperCAmelCase_  :				int      =      1
				UpperCAmelCase_  :				List[str]      =      Node(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      self.head
				while node:
							if current_position == position:
										self.insert_before_node(lowerCAmelCase_			,			lowerCAmelCase_		)
										return
							current_position += 1
							UpperCAmelCase_  :				Optional[Any]      =      node.next
				self.insert_after_node(self.tail			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				int		)		->    Node:
				UpperCAmelCase_  :				List[Any]      =      self.head
				while node:
							if node.get_data() == item:
										return node
							UpperCAmelCase_  :				Optional[int]      =      node.get_next()
				raise Exception("Node not found"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				List[str]		)		->    Union[str, Any]:
				if (node := self.get_node(lowerCAmelCase_		)) is not None:
							if node == self.head:
										UpperCAmelCase_  :				Tuple      =      self.head.get_next()

							if node == self.tail:
										UpperCAmelCase_  :				Optional[int]      =      self.tail.get_previous()

							self.remove_node_pointers(lowerCAmelCase_		)





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				Node		)		->    None:
				if node.get_next():
							UpperCAmelCase_  :				int      =      node.previous

				if node.get_previous():
							UpperCAmelCase_  :				Optional[Any]      =      node.next

				UpperCAmelCase_  :				Optional[int]      =      None
				UpperCAmelCase_  :				Dict      =      None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    str:
				return self.head is None





def        snake_case   (     ):
			pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()



