




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       :






       def __init__(   self:					str       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					str=sys.maxsize				):
         lowercase    :str      	=						"bilinear"
         lowercase    :Optional[int]      	=						max_size
         lowercase    :List[str]      	=						short_edge_length






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

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

         return img_augs







class    __lowerCAmelCase       :






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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					str				):
         lowercase    :Any      	=						tuple(max(_lowercase				) for s in zip(*[img.shape for img in images]				)				)
         lowercase    :List[Any]      	=						[im.shape[-2:] for im in images]
         lowercase    :List[Any]      	=						[
             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:					Tuple       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					Dict=False				):
         with torch.no_grad():
           if not isinstance(_lowercase       ,					_lowercase				):
             lowercase    :Tuple      	=						[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
           lowercase    :Dict      	=						torch.tensor([im.shape[:2] for im in images]				)
           lowercase    :int      	=						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
           lowercase    :Tuple      	=						[self.normalizer(_lowercase				) for x in images]
           # now pad them to do the following operations
           lowercase	,    lowercase    :str      	=						self.pad(_lowercase				)
           # Normalize

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







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  assert torch.isfinite(UpperCamelCase__   ).all(), "Box tensor contains infinite or NaN!"
  lowercase	,    lowercase    :Dict      	=						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__   )









from abc import ABC, abstractmethod
from argparse import ArgumentParser




class    __lowerCAmelCase       (							lowerCAmelCase):






       @staticmethod
       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					ArgumentParser				):
         raise NotImplementedError()






       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         raise NotImplementedError()



