



"""simple docstring"""
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class    a		(					enum.Enum    ):
  UpperCamelCase		:			Optional[int]         =   'timm'
  UpperCamelCase		:			Dict         =   'transformers'







def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   if stage_names is None:
      raise ValueError("""Stage_names must be set for transformers backbones"""	)

   if out_features is not None:
      if not isinstance(lowercase							,     (list,)	):
         raise ValueError(f'''out_features must be a list {type(lowercase	)}'''	)
      if any(feat not in stage_names for feat in out_features	):
         raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''	)

   if out_indices is not None:
      if not isinstance(lowercase							,     (list, tuple)	):
         raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase	)}'''	)
      if any(idx >= len(lowercase	) for idx in out_indices	):
         raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""	)

   if out_features is not None and out_indices is not None:
      if len(lowercase	) != len(lowercase	):
         raise ValueError("""out_features and out_indices should have the same length if both are set"""	)
      if out_features != [stage_names[idx] for idx in out_indices]:
         raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     ):
   if out_indices is None and out_features is None:
      SCREAMING_SNAKE_CASE_:	Any            =[len(lowercase	) - 1]
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      SCREAMING_SNAKE_CASE_:	str            =[stage_names.index(lowercase	) for layer in out_features]
   elif out_features is None and out_indices is not None:
      SCREAMING_SNAKE_CASE_:	Tuple            =[stage_names[idx] for idx in out_indices]
   return out_features, out_indices





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     ):
   verify_out_features_out_indices(out_features=lowercase							,     out_indices=lowercase							,     stage_names=lowercase	)
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =_align_output_features_output_indices(
       out_features=lowercase							,     out_indices=lowercase							,     stage_names=lowercase	)
   # Verify that the aligned out_features and out_indices are valid
   verify_out_features_out_indices(out_features=lowercase							,     out_indices=lowercase							,     stage_names=lowercase	)
   return output_features, output_indices
class    a		:
  UpperCamelCase		:			Optional[BackboneType]         =   None



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   Optional[Any]						)       ->   None:


     '''simple docstring'''




     if getattr(self    ,						"""_backbone"""    ,						lowerCAmelCase						) is None:
        raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""						)

     # These will diagree with the defaults for the transformers models e.g. for resnet50
     # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
     # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
     SCREAMING_SNAKE_CASE_:	str            =[stage["""module"""] for stage in self._backbone.feature_info.info]
     SCREAMING_SNAKE_CASE_:	Any            =[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =self._backbone.feature_info.out_indices
     SCREAMING_SNAKE_CASE_:	int            =self._backbone.feature_info.module_name()

     # We verify the out indices and out features are valid
     verify_out_features_out_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=self.stage_names						)
     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =out_features, out_indices



  def 		lowerCamelCase__    (  self	:   List[Any]    ,						lowerCAmelCase	:   Any						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =getattr(lowerCAmelCase    ,						"""stage_names"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =getattr(lowerCAmelCase    ,						"""out_features"""    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =getattr(lowerCAmelCase    ,						"""out_indices"""    ,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =stage_names
     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=lowerCAmelCase						)
     # Number of channels for each stage. This is set in the transformer backbone model init
     SCREAMING_SNAKE_CASE_:	List[str]            =None



  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Any						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =config

     SCREAMING_SNAKE_CASE_:	List[Any]            =getattr(lowerCAmelCase    ,						"""use_timm_backbone"""    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

     if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(lowerCAmelCase						)
     elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(lowerCAmelCase						)
     else:
        raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''						)



  @property
  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   int:


     '''simple docstring'''




     return self._out_features



  @out_features.setter
  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   List[str]						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Union[str, Any]            =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=self.stage_names						)



  @property
  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   str:


     '''simple docstring'''




     return self._out_indices



  @out_indices.setter
  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   Union[Tuple[int], List[int]]						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[Any]            =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=self.stage_names						)



  @property
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   List[Any]:


     '''simple docstring'''




     return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}



  @property
  def 		lowerCamelCase__    (  self	:   Dict						)       ->   List[str]:


     '''simple docstring'''




     return [self.out_feature_channels[name] for name in self.out_features]



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						*lowerCAmelCase	:   Optional[Any]    ,						**lowerCAmelCase	:   Tuple						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =dict(inspect.signature(self.forward						).parameters						)
     SCREAMING_SNAKE_CASE_:	Dict            ={k: v for k, v in kwargs.items() if k in signature}
     return self(*lowerCAmelCase    ,						**lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Optional[bool] = None    ,						lowerCAmelCase	:   Optional[bool] = None    ,						lowerCAmelCase	:   Optional[bool] = None    ,						)       ->   List[Any]:


     '''simple docstring'''




     raise NotImplementedError("""This method should be implemented by the derived class."""						)





  def 		lowerCamelCase__    (  self	:   str						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =super().to_dict()
     SCREAMING_SNAKE_CASE_:	str            =output.pop("""_out_features"""						)
     SCREAMING_SNAKE_CASE_:	Any            =output.pop("""_out_indices"""						)
     return output







class    a		:



  @property
  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   List[str]:


     '''simple docstring'''




     return self._out_features



  @out_features.setter
  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	int            =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=self.stage_names						)



  @property
  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     return self._out_indices



  @out_indices.setter
  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Union[Tuple[int], List[int]]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	int            =get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase    ,						out_indices=lowerCAmelCase    ,						stage_names=self.stage_names						)





  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =super().to_dict()
     SCREAMING_SNAKE_CASE_:	int            =output.pop("""_out_features"""						)
     SCREAMING_SNAKE_CASE_:	Any            =output.pop("""_out_indices"""						)
     return output








"""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    a		:



  def __init__(  self	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




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

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

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =tuple(max(lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[
         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	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]=False						)       ->   Optional[Any]:


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[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
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =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
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

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







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)



