
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						:    List[Any]			,    _snake_case						:    str			,    _snake_case						:    Any=sys.maxsize			):
          __lowercase						:     int		      =    '''bilinear'''
          __lowercase						:     Union[str, Any]		      =    max_size
          __lowercase						:     Optional[int]		      =    short_edge_length




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

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

          return img_augs






class   __lowerCAmelCase		:



      """simple docstring"""







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




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

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




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

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






def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  List[Any]:
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes





def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Union[str, Any]:
    assert torch.isfinite(__lowerCAmelCase		).all(), "Box tensor contains infinite or NaN!"
    __lowercase      , __lowercase						:     int		      =    box_size
    tensor[:, 0].clamp_(min=0		,   max=__lowerCAmelCase		)
    tensor[:, 1].clamp_(min=0		,   max=__lowerCAmelCase		)
    tensor[:, 2].clamp_(min=0		,   max=__lowerCAmelCase		)
    tensor[:, 3].clamp_(min=0		,   max=__lowerCAmelCase		)




from __future__ import annotations

import math
from itertools import permutations


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,   int(math.sqrt(__lowerCAmelCase		) + 1		)		,   6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


def       UpperCAmelCase_					(  __lowerCAmelCase = 7		)	->  int:
    __lowercase						:     Union[str, Any]		      =    ''''''.join(str(__lowerCAmelCase		) for i in range(1		,   n + 1		)		)
    __lowercase						:     Dict		      =    [int(''''''.join(__lowerCAmelCase		)		) for i in permutations(__lowerCAmelCase		,   __lowerCAmelCase		)]
    __lowercase						:     int		      =    [num for num in perm_list if is_prime(__lowerCAmelCase		)]
    return max(__lowerCAmelCase		) if pandigitals else 0


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


