


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 ,       A_ ,       A_=sys.maxsize   )  ->	str:
         __UpperCamelCase   							='''bilinear'''
         __UpperCamelCase   							=max_size
         __UpperCamelCase   							=short_edge_length






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

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

         return img_augs






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Tuple:
         __UpperCamelCase   							=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,       cfg.INPUT.MAX_SIZE_TEST   )
         __UpperCamelCase   							=cfg.INPUT.FORMAT
         __UpperCamelCase   							=cfg.SIZE_DIVISIBILITY
         __UpperCamelCase   							=cfg.PAD_VALUE
         __UpperCamelCase   							=cfg.INPUT.MAX_SIZE_TEST
         __UpperCamelCase   							=cfg.MODEL.DEVICE
         __UpperCamelCase   							=torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   ) ,       1 ,       1   )
         __UpperCamelCase   							=torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   ) ,       1 ,       1   )
         __UpperCamelCase   							=lambda A_   : (x - self.pixel_mean) / self.pixel_std






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

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






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

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Tuple[int, int]    ):
     assert torch.isfinite(lowerCAmelCase__    ).all(), "Box tensor contains infinite or NaN!"
     __UpperCamelCase   							=box_size
     tensor[:, 0].clamp_(min=0      ,					max=lowerCAmelCase__    )
     tensor[:, 1].clamp_(min=0      ,					max=lowerCAmelCase__    )
     tensor[:, 2].clamp_(min=0      ,					max=lowerCAmelCase__    )
     tensor[:, 3].clamp_(min=0      ,					max=lowerCAmelCase__    )










import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





