

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_      :


  """simple docstring"""



  def __init__(						self     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Any=sys.maxsize						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    "bilinear"
      SCREAMING_SNAKE_CASE__         =    max_size
      SCREAMING_SNAKE_CASE__         =    short_edge_length







  def __call__(						self     :					str  ,	A_     :					Union[str, Any]						)       ->			Optional[int]:
      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  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Union[str, Any]  ,	A_     :					Tuple						)       ->			Tuple:
      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     :					Union[str, Any]  ,	A_     :					int						)       ->			List[str]:
      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_     :					Any  ,	A_     :					List[Any]=False						)       ->			Any:
      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 				__a							(       __A			,							__A						):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes





def 				__a							(       __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						)





from __future__ import annotations





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    {1}
    SCREAMING_SNAKE_CASE__         =    [1]
    for _ in range(2			,							depth + 1						):
        SCREAMING_SNAKE_CASE__         =    [*previous_coefficients, 0]
        SCREAMING_SNAKE_CASE__         =    [0, *previous_coefficients]
        SCREAMING_SNAKE_CASE__         =    []
        for x, y in zip(__A			,							__A						):
            coefficients.add(x + y						)
            previous_coefficients.append(x + y						)
    return coefficients





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    set()
    for number in unique_coefficients:
        SCREAMING_SNAKE_CASE__         =    2
        SCREAMING_SNAKE_CASE__         =    number
        while divisor**2 <= copy_number:
            SCREAMING_SNAKE_CASE__         =    0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(__A						)
                break
            divisor += 1

    return unique_coefficients.difference(__A						)





def 				__a							(       __A = 51						):
    SCREAMING_SNAKE_CASE__         =    get_pascal_triangle_unique_coefficients(__A						)
    SCREAMING_SNAKE_CASE__         =    get_squarefrees(__A						)
    return sum(__A						)


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


