


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		:	str     ,  lowercase_		:	List[str]     ,  lowercase_		:	Optional[Any]=sys.maxsize					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						str            ='bilinear'
             _lowerCamelCase			:						str            =max_size
             _lowerCamelCase			:						int            =short_edge_length






      def __call__(   self		:	List[str]     ,  lowercase_		:	Any					)   ->		List[str]:
             """simple docstring"""




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

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

             return img_augs




class A    :






      def __init__(   self		:	str     ,  lowercase_		:	int					)   ->		Optional[Any]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Dict     ,  lowercase_		:	List[str]					)   ->		List[str]:
             """simple docstring"""




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

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






      def __call__(   self		:	Dict     ,  lowercase_		:	str     ,  lowercase_		:	Tuple=False					)   ->		Tuple:
             """simple docstring"""




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

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




def       a_ (       SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : Optional[int]	):
       '''simple docstring'''





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




def       a_ (       SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Tuple[int, int]	):
       '''simple docstring'''





       assert torch.isfinite(SCREAMING_SNAKE_CASE__	).all(), "Box tensor contains infinite or NaN!"
       _lowerCamelCase      ,	_lowerCamelCase			:						Any            =box_size
       tensor[:, 0].clamp_(min=0						,     max=SCREAMING_SNAKE_CASE__	)
       tensor[:, 1].clamp_(min=0						,     max=SCREAMING_SNAKE_CASE__	)
       tensor[:, 2].clamp_(min=0						,     max=SCREAMING_SNAKE_CASE__	)
       tensor[:, 3].clamp_(min=0						,     max=SCREAMING_SNAKE_CASE__	)






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


