



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        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=sys.maxsize  ) ->				int:
									"""simple docstring"""




									a__       :						List[str]							=  '''bilinear'''
									a__       :						List[str]							=  max_size
									a__       :						List[Any]							=  short_edge_length

						def __call__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




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

												if img.dtype == np.uinta:
															a__       :						Optional[Any]							=  Image.fromarray(snake_case_  )
															a__       :						str							=  pil_image.resize((neww, newh) ,       PILImageResampling.BILINEAR  )
															a__       :						Optional[Any]							=  np.asarray(snake_case_  )
												else:
															a__       :						Tuple							=  img.permute(2 ,       0 ,       1  ).unsqueeze(0  )  # 3, 0, 1)  # hw(c) -> nchw
															a__       :						Any							=  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        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						List[str]							=  tuple(max(snake_case_  ) for s in zip(*[img.shape for img in images]  )  )
									a__       :						List[Any]							=  [im.shape[-2:] for im in images]
									a__       :						Optional[int]							=  [
									    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 ,       __lowercase ,       __lowercase=False  ) ->				str:
									"""simple docstring"""




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

												if self.size_divisibility > 0:
															raise NotImplementedError()
												# pad
												a__       :						str							=  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      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Union[str, Any])							->	int:







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





def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       Tuple[int, int])							->	Optional[int]:







			"""simple docstring"""
			assert torch.isfinite(lowerCAmelCase__).all(), "Box tensor contains infinite or NaN!"
			a__       :						Optional[Any]							=  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_mobilevit import MobileViTImageProcessor


_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				None:
									"""simple docstring"""




									warnings.warn(
									    """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use MobileViTImageProcessor instead.""" ,       __lowercase ,       )
									super().__init__(*__lowercase ,       **__lowercase  )




