




'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

lowercase      :   str      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase				):
     if isinstance(__lowercase     ,   (list, tuple)				) and isinstance(videos[0]     ,   (list, tuple)				) and is_valid_image(videos[0][0]				):
          return videos

     elif isinstance(__lowercase     ,   (list, tuple)				) and is_valid_image(videos[0]				):
          return [videos]

     elif is_valid_image(__lowercase				):
          return [[videos]]

     raise ValueError(F'''Could not make batched video from {videos}'''				)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = None							,__a = True							,__a = 1 / 255							,__a = True							,__a = True							,__a = None							,__a = None							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""shortest_edge""": 256}
          snake_case			:      Dict        =			get_size_dict(__a							,default_to_square=__a							)
          snake_case			:      str        =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Tuple        =			get_size_dict(__a							,param_name="""crop_size"""							)

          snake_case			:      Tuple        =			do_resize
          snake_case			:      str        =			size
          snake_case			:      int        =			do_center_crop
          snake_case			:      List[Any]        =			crop_size
          snake_case			:      str        =			resample
          snake_case			:      List[Any]        =			do_rescale
          snake_case			:      Any        =			rescale_factor
          snake_case			:      List[str]        =			offset
          snake_case			:      Union[str, Any]        =			do_normalize
          snake_case			:      List[Any]        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      int        =			image_std if image_std is not None else IMAGENET_STANDARD_STD







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      Any        =			get_size_dict(__a							,default_to_square=__a							)
          if "shortest_edge" in size:
               snake_case			:      List[str]        =			get_resize_output_image_size(__a							,size["""shortest_edge"""]							,default_to_square=__a							)
          elif "height" in size and "width" in size:
               snake_case			:      List[Any]        =			(size["""height"""], size["""width"""])
          else:
               raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      Tuple        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''							)
          return center_crop(__a							,size=(size["""height"""], size["""width"""])							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = True							,__a = None							,**__a							,)      ->				str:
          snake_case			:      List[str]        =			image.astype(np.floataa							)
          if offset:
               snake_case			:      Any        =			image - (scale / 2)
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,)      ->				np.ndarray:
          if do_resize and size is None or resample is None:
               raise ValueError("""Size and resample must be specified if do_resize is True."""							)

          if do_center_crop and crop_size is None:
               raise ValueError("""Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."""							)

          if offset and not do_rescale:
               raise ValueError("""For offset, do_rescale must also be set to True."""							)

          # All transformations expect numpy arrays.
          snake_case			:      int        =			to_numpy_array(__a							)

          if do_resize:
               snake_case			:      List[str]        =			self.resize(image=__a							,size=__a							,resample=__a							)

          if do_center_crop:
               snake_case			:      str        =			self.center_crop(__a							,size=__a							)

          if do_rescale:
               snake_case			:      Tuple        =			self.rescale(image=__a							,scale=__a							,offset=__a							)

          if do_normalize:
               snake_case			:      Union[str, Any]        =			self.normalize(image=__a							,mean=__a							,std=__a							)

          snake_case			:      List[str]        =			to_channel_dimension_format(__a							,__a							)
          return image







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      Union[str, Any]        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case			:      int        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      Tuple        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      str        =			offset if offset is not None else self.offset
          snake_case			:      List[str]        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      List[Any]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      Dict        =			image_std if image_std is not None else self.image_std

          snake_case			:      Tuple        =			size if size is not None else self.size
          snake_case			:      List[Any]        =			get_size_dict(__a							,default_to_square=__a							)
          snake_case			:      Optional[int]        =			crop_size if crop_size is not None else self.crop_size
          snake_case			:      Any        =			get_size_dict(__a							,param_name="""crop_size"""							)

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

          snake_case			:      int        =			make_batched(__a							)

          snake_case			:      Dict        =			[
              [
                  self._preprocess_image(
                      image=__a							,do_resize=__a							,size=__a							,resample=__a							,do_center_crop=__a							,crop_size=__a							,do_rescale=__a							,rescale_factor=__a							,offset=__a							,do_normalize=__a							,image_mean=__a							,image_std=__a							,data_format=__a							,)
                  for img in video
              ]
              for video in videos
          ]

          snake_case			:      str        =			{"""pixel_values""": videos}
          return BatchFeature(data=__a							,tensor_type=__a							)









'''simple docstring'''







import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a							)      ->				Tuple:
          if dst_width < 0 or dst_height < 0:
               raise ValueError("""Destination width/height should be > 0"""							)

          snake_case			:      str        =			img
          snake_case			:      str        =			img.shape[1]
          snake_case			:      Union[str, Any]        =			img.shape[0]
          snake_case			:      Optional[int]        =			dst_width
          snake_case			:      List[str]        =			dst_height

          snake_case			:      List[str]        =			self.src_w / self.dst_w
          snake_case			:      int        =			self.src_h / self.dst_h

          snake_case			:      List[Any]        =			(
              np.ones((self.dst_h, self.dst_w, 3)							,np.uinta							) * 255
          )







     def 			snake_case_      (				self							)      ->				int:
          for i in range(self.dst_h							):
               for j in range(self.dst_w							):
                    snake_case			:      Optional[Any]        =			self.img[self.get_y(__a							)][self.get_x(__a							)]







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_x * x							)







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_y * y							)


if __name__ == "__main__":
      lowercase,	lowercase      :   Optional[int]      =       800, 600
      lowercase      :   Union[str, Any]      =       imread("""image_data/lena.jpg""", 1)
      lowercase      :   int      =       NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()



