


from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class   A__							:
						UpperCAmelCase     =  ("_front", "_back", "_len")

						@dataclass
						class   A__							:
												UpperCAmelCase     =  None
												UpperCAmelCase     =  None
												UpperCAmelCase     =  None

						class   A__							:
												UpperCAmelCase     =  ("_cur",)




												def __init__(    self   : int			,      _a   : Deque._Node | None					)							->       None:


																"""simple docstring"""





																_SCREAMING_SNAKE_CASE     					=cur




												def __iter__(    self   : str					)							->       Deque._Iterator:


																"""simple docstring"""





																return self




												def 							__UpperCamelCase			(    self   : Any					)							->       Any:


																"""simple docstring"""





																if self._cur is None:
																				# finished iterating
																				raise StopIteration
																_SCREAMING_SNAKE_CASE     					=self._cur.val
																_SCREAMING_SNAKE_CASE     					=self._cur.next_node

																return val




						def __init__(    self   : str			,      _a   : Iterable[Any] | None = None					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=0

										if iterable is not None:
														# append every value to the deque
														for val in iterable:
																		self.append(_a					)




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self._Node(_a			,      _a			,      _a					)
										if self.is_empty():
														# front = back
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=1
										else:
														# connect nodes
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=self._back
														_SCREAMING_SNAKE_CASE     					=node  # assign new back to the new node

														self._len += 1

														# make sure there were no errors
														assert not self.is_empty(), "Error on appending value."




						def 							__UpperCamelCase			(    self   : List[str]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self._Node(_a			,      _a			,      _a					)
										if self.is_empty():
														# front = back
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=1
										else:
														# connect nodes
														_SCREAMING_SNAKE_CASE     					=self._front
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=node  # assign new front to the new node

														self._len += 1

														# make sure there were no errors
														assert not self.is_empty(), "Error on appending value."




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Iterable[Any]					)							->       None:


										"""simple docstring"""





										for val in iterable:
														self.append(_a					)




						def 							__UpperCamelCase			(    self   : int			,      _a   : Iterable[Any]					)							->       None:


										"""simple docstring"""





										for val in iterable:
														self.appendleft(_a					)




						def 							__UpperCamelCase			(    self   : Any					)							->       Any:


										"""simple docstring"""





										assert not self.is_empty(), "Deque is empty."

										_SCREAMING_SNAKE_CASE     					=self._back
										_SCREAMING_SNAKE_CASE     					=self._back.prev_node  # set new back
										# drop the last node - python will deallocate memory automatically
										_SCREAMING_SNAKE_CASE     					=None

										self._len -= 1

										return topop.val




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


										"""simple docstring"""





										assert not self.is_empty(), "Deque is empty."

										_SCREAMING_SNAKE_CASE     					=self._front
										_SCREAMING_SNAKE_CASE     					=self._front.next_node  # set new front and drop the first node
										_SCREAMING_SNAKE_CASE     					=None

										self._len -= 1

										return topop.val




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       bool:


										"""simple docstring"""





										return self._front is None




						def __len__(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										return self._len




						def __eq__(    self   : Dict			,      _a   : object					)							->       bool:


										"""simple docstring"""





										if not isinstance(_a			,      _a					):
														return NotImplemented

										_SCREAMING_SNAKE_CASE     					=self._front
										_SCREAMING_SNAKE_CASE     					=other._front

										# if the length of the dequeues are not the same, they are not equal
										if len(self					) != len(_a					):
														return False

										while me is not None and oth is not None:
														# compare every value
														if me.val != oth.val:
																		return False
														_SCREAMING_SNAKE_CASE     					=me.next_node
														_SCREAMING_SNAKE_CASE     					=oth.next_node

										return True




						def __iter__(    self   : Optional[Any]					)							->       Deque._Iterator:


										"""simple docstring"""





										return Deque._Iterator(self._front					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[]
										_SCREAMING_SNAKE_CASE     					=self._front
										while aux is not None:
														# append the values in a list to display
														values_list.append(aux.val					)
														_SCREAMING_SNAKE_CASE     					=aux.next_node

										return f"[{', '.join(repr(_a					) for val in values_list					)}]"


if __name__ == "__main__":
							import doctest

							doctest.testmod()



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   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





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

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

										return img_augs

class   A__							:




						def __init__(    self   : Any			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





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


										"""simple docstring"""





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

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







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







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