


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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=sys.maxsize		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					'''bilinear'''
         A_      :				Optional[Any]		     =					max_size
         A_      :				Optional[Any]		     =					short_edge_length







       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





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

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

         return img_augs






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





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

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







       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Dict:




         '''simple docstring'''





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

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




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  assert torch.isfinite(SCREAMING_SNAKE_CASE       ).all(), "Box tensor contains infinite or NaN!"
  A_     ,       A_      :				int		     =					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       )








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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=sys.maxsize		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					'''bilinear'''
         A_      :				Optional[Any]		     =					max_size
         A_      :				Optional[Any]		     =					short_edge_length







       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





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

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

         return img_augs






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





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

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







       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Dict:




         '''simple docstring'''





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

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




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  assert torch.isfinite(SCREAMING_SNAKE_CASE       ).all(), "Box tensor contains infinite or NaN!"
  A_     ,       A_      :				int		     =					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       )




