



'''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 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_  ,a_=sys.maxsize							)		->   List[str]:
        _UpperCAmelCase  :						str			     = """bilinear"""
        _UpperCAmelCase  :						int			     = max_size
        _UpperCAmelCase  :						Union[str, Any]			     = short_edge_length







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

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

        return img_augs







class 							lowercase       :


 """simple docstring"""


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







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

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







def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


       assert torch.isfinite(snake_case__		).all(), "Box tensor contains infinite or NaN!"
       _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = box_size
       tensor[:, 0].clamp_(min=0       ,   max=snake_case__		)
       tensor[:, 1].clamp_(min=0       ,   max=snake_case__		)
       tensor[:, 2].clamp_(min=0       ,   max=snake_case__		)
       tensor[:, 3].clamp_(min=0       ,   max=snake_case__		)




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''resnet'''
 lowerCamelCase	:Any    			=		['''basic''', '''bottleneck''']



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types							)}'''							)
        _A        =  num_channels
        _A        =  embedding_size
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  layer_type
        _A        =  hidden_act
        _A        =  downsample_in_first_stage
        _A        =  ["""stem"""] + [F'''stage{idx}''' for idx in range(1		,				len(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




