





'''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	:      Optional[Any]			,   lowercase	:      Optional[int]			,   lowercase	:      Dict=sys.maxsize		):





        """simple docstring"""




        lowercase_    :Tuple					    =				"bilinear"
        lowercase_    :Optional[int]					    =				max_size
        lowercase_    :Optional[int]					    =				short_edge_length







     def __call__(				self	:      str			,   lowercase	:      int		):





        """simple docstring"""




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

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

        return img_augs







class    a_     :





     def __init__(				self	:      List[Any]			,   lowercase	:      str		):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      int			,   lowercase	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :Dict					    =				tuple(max(lowercase		) for s in zip(*[img.shape for img in images]		)		)
        lowercase_    :Optional[int]					    =				[im.shape[-2:] for im in images]
        lowercase_    :int					    =				[
            nn.functional.pad(
                lowercase			,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,   value=self.pad_value			,   )
            for size, im in zip(lowercase			,   lowercase		)
        ]

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







     def __call__(				self	:      str			,   lowercase	:      int			,   lowercase	:      Optional[int]=False		):





        """simple docstring"""




        with torch.no_grad():
           if not isinstance(lowercase			,   lowercase		):
              lowercase_    :Optional[int]					    =				[images]
           if single_image:
              assert len(lowercase		) == 1
           for i in range(len(lowercase		)		):
              if isinstance(images[i]			,   torch.Tensor		):
                 images.insert(lowercase			,   images.pop(lowercase		).to(self.device		).float()		)
              elif not isinstance(images[i]			,   torch.Tensor		):
                 images.insert(
                     lowercase			,   torch.as_tensor(img_tensorize(images.pop(lowercase		)			,   input_format=self.input_format		)		)
                     .to(self.device		)
                     .float()			,   )
            # resize smallest edge
           lowercase_    :Union[str, Any]					    =				torch.tensor([im.shape[:2] for im in images]		)
           lowercase_    :Optional[Any]					    =				self.aug(lowercase		)
           # 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
           lowercase_    :Union[str, Any]					    =				[self.normalizer(lowercase		) for x in images]
           # now pad them to do the following operations
           lowercase_    ,							lowercase_    :Optional[Any]					    =				self.pad(lowercase		)
           # Normalize

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







def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[Any]			,__lowerCamelCase      :      int				):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes


def 							UpperCAmelCase_						(		__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      Tuple[int, int]				):
   assert torch.isfinite(__lowerCamelCase				).all(), "Box tensor contains infinite or NaN!"
   lowercase_    ,							lowercase_    :int					    =				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				)









'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase      :  Any      							=logging.get_logger(__name__)
class    a_     (       _lowerCAmelCase							):
     __A  				=    ["pixel_values"]





     def __init__(				self	:      Tuple			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      bool = True			,   lowercase	:      Dict[str, int] = None			,   lowercase	:      PILImageResampling = PILImageResampling.BICUBIC			,   lowercase	:      bool = True			,   lowercase	:      Union[int, float] = 1 / 255			,   lowercase	:      bool = True			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   **lowercase	:      int			,   ):





        """simple docstring"""




        super().__init__(**lowercase		)
        lowercase_    :Dict					    =				crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_    :str					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Optional[Any]					    =				size if size is not None else {"height": 224, "width": 224}
        lowercase_    :Union[str, Any]					    =				get_size_dict(lowercase		)

        lowercase_    :Union[str, Any]					    =				do_center_crop
        lowercase_    :str					    =				crop_size
        lowercase_    :List[str]					    =				do_resize
        lowercase_    :str					    =				size
        lowercase_    :Tuple					    =				resample
        lowercase_    :int					    =				do_rescale
        lowercase_    :Union[str, Any]					    =				rescale_factor
        lowercase_    :Tuple					    =				do_normalize
        lowercase_    :List[Any]					    =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_    :Optional[int]					    =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 							lowercase__   (				self	:      Any			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[Any]			,   ):





        """simple docstring"""




        lowercase_    :Dict					    =				self.size if size is None else size
        lowercase_    :Dict					    =				get_size_dict(lowercase		)
        lowercase_    :List[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)

        lowercase_    ,							lowercase_    :List[str]					    =				get_image_size(lowercase		)
        lowercase_    :List[str]					    =				min(lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				(size["height"] / crop_size["height"]) * min_dim
        lowercase_    :int					    =				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowercase			,   size=(cropped_height, cropped_width)			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      Union[str, Any]			,   lowercase	:      np.ndarray			,   lowercase	:      Dict[str, int]			,   lowercase	:      PILImageResampling = PIL.Image.BICUBIC			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      List[str]			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				get_size_dict(lowercase		)
        if "height" not in size or "width" not in size:
           raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
        return resize(
            lowercase			,   size=(size["height"], size["width"])			,   resample=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[int, float]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      Union[str, Any]			,   ):





        """simple docstring"""




        return rescale(lowercase			,   scale=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      str			,   ):





        """simple docstring"""




        return normalize(lowercase			,   mean=lowercase			,   std=lowercase			,   data_format=lowercase			,   **lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      ImageInput			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      PILImageResampling = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[float] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[float, List[float]]] = None			,   lowercase	:      Optional[Union[str, TensorType]] = None			,   lowercase	:      ChannelDimension = ChannelDimension.FIRST			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_    :Union[str, Any]					    =				crop_size if crop_size is not None else self.crop_size
        lowercase_    :Optional[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Union[str, Any]					    =				do_resize if do_resize is not None else self.do_resize
        lowercase_    :Any					    =				size if size is not None else self.size
        lowercase_    :List[Any]					    =				get_size_dict(lowercase		)
        lowercase_    :Optional[Any]					    =				resample if resample is not None else self.resample
        lowercase_    :Dict					    =				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_    :List[Any]					    =				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_    :str					    =				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_    :Dict					    =				image_mean if image_mean is not None else self.image_mean
        lowercase_    :List[Any]					    =				image_std if image_std is not None else self.image_std

        lowercase_    :Any					    =				make_list_of_images(lowercase		)

        if not valid_images(lowercase		):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_center_crop and crop_size is None:
           raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."		)

        if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."		)

        if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        lowercase_    :Optional[int]					    =				[to_numpy_array(lowercase		) for image in images]

        if do_center_crop:
           lowercase_    :str					    =				[self.center_crop(lowercase			,   lowercase			,   size=lowercase		) for image in images]

        if do_resize:
           lowercase_    :List[str]					    =				[self.resize(image=lowercase			,   size=lowercase			,   resample=lowercase		) for image in images]

        if do_rescale:
           lowercase_    :List[Any]					    =				[self.rescale(image=lowercase			,   scale=lowercase		) for image in images]

        if do_normalize:
           lowercase_    :Optional[Any]					    =				[self.normalize(image=lowercase			,   mean=lowercase			,   std=lowercase		) for image in images]

        lowercase_    :Optional[Any]					    =				[to_channel_dimension_format(lowercase			,   lowercase		) for image in images]

        lowercase_    :Union[str, Any]					    =				{"pixel_values": images}
        return BatchFeature(data=lowercase			,   tensor_type=lowercase		)


