


'''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    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=sys.maxsize	)				->     Optional[int]:
         __lowerCamelCase   :	int								=		'bilinear'
         __lowerCamelCase   :	int								=		max_size
         __lowerCamelCase   :	Optional[int]								=		short_edge_length







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

              if img.dtype == np.uinta:
                   __lowerCamelCase   :	Any								=		Image.fromarray(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Dict								=		pil_image.resize((neww, newh)			,     PILImageResampling.BILINEAR	)
                   __lowerCamelCase   :	int								=		np.asarray(SCREAMING_SNAKE_CASE_	)
              else:
                   __lowerCamelCase   :	Optional[Any]								=		img.permute(2			,     0			,     1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
                   __lowerCamelCase   :	Optional[Any]								=		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    UpperCAmelCase_  :





    """simple docstring"""






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







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

              if self.size_divisibility > 0:
                   raise NotImplementedError()
              # pad
              __lowerCamelCase   :	Optional[int]								=		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 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				Tuple[int, int]							)     ->		Optional[int]:
     assert torch.isfinite(UpperCAmelCase_							).all(), "Box tensor contains infinite or NaN!"
     __lowerCamelCase				,       __lowerCamelCase   :	List[Any]								=		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_							)










'''simple docstring'''



from importlib import import_module

from .logging import get_logger


A__		:					str												=  get_logger(__name__)





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         __lowerCamelCase   :	List[str]								=		attrs or []
         if module is not None:
              for key in module.__dict__:
                   if key in attrs or not key.startswith('__'	):
                        setattr(self			,     SCREAMING_SNAKE_CASE_			,     getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	Optional[int]								=		module._original_module if isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) else module







class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			[]







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		obj
         __lowerCamelCase   :	List[Any]								=		target
         __lowerCamelCase   :	Union[str, Any]								=		new
         __lowerCamelCase   :	Union[str, Any]								=		target.split('.'	)[0]
         __lowerCamelCase   :	Dict								=		{}
         __lowerCamelCase   :	Dict								=		attrs or []







    def __enter__(							self	)				->     Optional[int]:
         *__lowerCamelCase				,       __lowerCamelCase   :	int								=		self.target.split('.'	)

         # Patch modules:
         # it's used to patch attributes of submodules like "os.path.join";
         # in this case we need to patch "os" and "os.path"

         for i in range(len(SCREAMING_SNAKE_CASE_	)	):
              try:
                   __lowerCamelCase   :	Optional[int]								=		import_module('.'.join(submodules[: i + 1]	)	)
              except ModuleNotFoundError:
                   continue
              # We iterate over all the globals in self.obj in case we find "os" or "os.path"
              for attr in self.obj.__dir__():
                   __lowerCamelCase   :	List[Any]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) and obj_attr._original_module is submodule)
                   ):
                        __lowerCamelCase   :	Optional[Any]								=		obj_attr
                        # patch at top level
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(SCREAMING_SNAKE_CASE_			,     attrs=self.attrs	)	)
                        __lowerCamelCase   :	str								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        # construct lower levels patches
                        for key in submodules[i + 1 :]:
                             setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     attrs=self.attrs	)	)
                             __lowerCamelCase   :	List[Any]								=		getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                        # finally set the target attribute
                        setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

         if submodules:  # if it's an attribute of a submodule like "os.path.join"
              try:
                   __lowerCamelCase   :	Union[str, Any]								=		getattr(import_module('.'.join(SCREAMING_SNAKE_CASE_	)	)			,     SCREAMING_SNAKE_CASE_	)
              except (AttributeError, ModuleNotFoundError):
                   return
              # We iterate over all the globals in self.obj in case we find "os.path.join"
              for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj			,     SCREAMING_SNAKE_CASE_	) is attr_value:
                        __lowerCamelCase   :	Optional[int]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
              __lowerCamelCase   :	List[Any]								=		globals()['__builtins__'][target_attr]
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         else:
              raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.'	)







    def __exit__(							self			,     *SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         for attr in list(self.original	):
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.original.pop(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     Optional[int]:
         self.__enter__()
         self._active_patches.append(self	)







    def       lowercase_				(							self	)				->     str:
         try:
              self._active_patches.remove(self	)
         except ValueError:
              # If the patch hasn't been started this will fail
              return None

         return self.__exit__()






