"""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						,      lowerCAmelCase						,      lowerCAmelCase=sys.maxsize      ):






										"""simple docstring"""

										snake_case        =     'bilinear'
										snake_case        =     max_size
										snake_case        =     short_edge_length

				def __call__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

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

										return img_augs





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     tuple(max(lowerCAmelCase      ) for s in zip(*[img.shape for img in images]      )      )
										snake_case        =     [im.shape[-2:] for im in images]
										snake_case        =     [
										    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      ):






										"""simple docstring"""

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

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[str]   )   ->			Optional[int]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Tuple[int, int]   )   ->			Dict:





						"""simple docstring"""






						assert torch.isfinite(_UpperCamelCase   ).all(), "Box tensor contains infinite or NaN!"
						snake_case   ,snake_case        =     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"""
from __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


