






'''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 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase=sys.maxsize   )				->Optional[int]:



         '''simple docstring'''
         __a					  =     'bilinear'
         __a					  =     max_size
         __a					  =     short_edge_length






      def __call__(	self			, lowerCamelCase   )				->Tuple:



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

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

         return img_augs







class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase   )				->Dict:



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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



         '''simple docstring'''
         __a					  =     tuple(max(lowerCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
         __a					  =     [im.shape[-2:] for im in images]
         __a					  =     [
             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   )				->List[Any]:



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

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







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						str     )	->      Dict:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Tuple[int, int]     )	->      str:

   """simple docstring"""
   assert torch.isfinite(SCREAMING_SNAKE_CASE__     ).all(), "Box tensor contains infinite or NaN!"
   __a  ,      __a					  =     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__     )







'''simple docstring'''



__UpperCamelCase   :							List[Any]         =   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						bytes     )	->      bytes:

   """simple docstring"""
   # Make sure the supplied data is a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   __a					  =     ''.join(bin(SCREAMING_SNAKE_CASE__     )[2:].zfill(8     ) for byte in data     )

   __a					  =     len(SCREAMING_SNAKE_CASE__     ) % 6 != 0

   if padding_needed:
      # The padding that will be added later
      __a					  =     b'=' * ((6 - len(SCREAMING_SNAKE_CASE__     ) % 6) // 2)

      # Append binary_stream with arbitrary binary digits (0's by default) to make its
      # length a multiple of 6.
      binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE__     ) % 6)
   else:
      __a					  =     b''

   # Encode every 6 binary digits to their corresponding Base64 character
   return (
       "".join(
           B64_CHARSET[int(binary_stream[index : index + 6],       2     )]
           for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       6     )     ).encode()
       + padding
   )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bytes:

   """simple docstring"""
   # Make sure encoded_data is either a string or a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) and not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     (
          'argument should be a bytes-like object or ASCII string, '
          f"""not '{encoded_data.__class__.__name__}'"""
      )
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   # In case encoded_data is a bytes-like object, make sure it contains only
   # ASCII characters so we convert it to a string object
   if isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      try:
         __a					  =     encoded_data.decode('utf-8'     )
      except UnicodeDecodeError:
         raise ValueError('base64 encoded data should only contain ASCII characters'     )

   __a					  =     encoded_data.count('='     )

   # Check if the encoded string contains non base64 characters
   if padding:
      assert all(
          char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
   else:
      assert all(
          char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

   # Check the padding
   assert len(SCREAMING_SNAKE_CASE__     ) % 4 == 0 and padding < 3, "Incorrect padding"

   if padding:
      # Remove padding if there is one
      __a					  =     encoded_data[:-padding]

      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
   else:
      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )

   __a					  =     [
       int(binary_stream[index : index + 8],       2     )
       for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       8     )
   ]

   return bytes(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()