





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


snake_case__					:							List[Any]													=     logging.get_logger(__name__)



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   Union[int, Iterable[int]]					,				lowerCamelCase:   bool					,				lowerCamelCase:   int )	->						Tuple[int, int]:







      '''simple docstring'''






      def constraint_to_multiple_of(lowerCamelCase:   str					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any=0					,				lowerCamelCase:   Optional[Any]=None ):
            __A          =				round(val / multiple ) * multiple

            if max_val is not None and x > max_val:
                  __A          =				math.floor(val / multiple ) * multiple

            if x < min_val:
                  __A          =				math.ceil(val / multiple ) * multiple

            return x

      __A          =				(output_size, output_size) if isinstance(lowerCamelCase					,				lowerCamelCase ) else output_size

      __A   ,						__A          =				get_image_size(lowerCamelCase )
      __A   ,						__A          =				output_size

      # determine new height and width
      __A          =				output_height / input_height
      __A          =				output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width ) < abs(1 - scale_height ):
                  # fit width
                  __A          =				scale_width
            else:
                  # fit height
                  __A          =				scale_height

      __A          =				constraint_to_multiple_of(scale_height * input_height					,				multiple=lowerCamelCase )
      __A          =				constraint_to_multiple_of(scale_width * input_width					,				multiple=lowerCamelCase )

      return (new_height, new_width)







class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""pixel_values"""]




   def __init__(self	:Optional[Any]    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BILINEAR    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Union[int, float] = 1 / 255    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   **_UpperCamelCase	:Any    ,   )->					None:
         super().__init__(**_UpperCamelCase )
         __A          =				size if size is not None else {'''height''': 384, '''width''': 384}
         __A          =				get_size_dict(_UpperCamelCase )
         __A          =				do_resize
         __A          =				size
         __A          =				keep_aspect_ratio
         __A          =				ensure_multiple_of
         __A          =				resample
         __A          =				do_rescale
         __A          =				rescale_factor
         __A          =				do_normalize
         __A          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __A          =				image_std if image_std is not None else IMAGENET_STANDARD_STD




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BICUBIC    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Dict    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase )
         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()}""" )
         __A          =				get_resize_output_image_size(
             _UpperCamelCase    ,   output_size=(size['''height'''], size['''width'''])    ,   keep_aspect_ratio=_UpperCamelCase    ,   multiple=_UpperCamelCase    ,   )
         return resize(_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[int, float]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:List[Any]    ,   )->					str:
         return rescale(_UpperCamelCase    ,   scale=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:List[str]    ,   )->					np.ndarray:
         return normalize(_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:ImageInput    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:int = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:int = None    ,   _UpperCamelCase	:PILImageResampling = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:float = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[str, TensorType]] = None    ,   _UpperCamelCase	:ChannelDimension = ChannelDimension.FIRST    ,   **_UpperCamelCase	:Optional[Any]    ,   )->					PIL.Image.Image:
         __A          =				do_resize if do_resize is not None else self.do_resize
         __A          =				size if size is not None else self.size
         __A          =				get_size_dict(_UpperCamelCase )
         __A          =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         __A          =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         __A          =				resample if resample is not None else self.resample
         __A          =				do_rescale if do_rescale is not None else self.do_rescale
         __A          =				rescale_factor if rescale_factor is not None else self.rescale_factor
         __A          =				do_normalize if do_normalize is not None else self.do_normalize
         __A          =				image_mean if image_mean is not None else self.image_mean
         __A          =				image_std if image_std is not None else self.image_std

         __A          =				make_list_of_images(_UpperCamelCase )

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

         # All transformations expect numpy arrays.
         __A          =				[to_numpy_array(_UpperCamelCase ) for image in images]

         if do_resize:
               __A          =				[self.resize(image=_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase ) for image in images]

         if do_rescale:
               __A          =				[self.rescale(image=_UpperCamelCase    ,   scale=_UpperCamelCase ) for image in images]

         if do_normalize:
               __A          =				[self.normalize(image=_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase ) for image in images]

         __A          =				[to_channel_dimension_format(_UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         __A          =				{'''pixel_values''': images}
         return BatchFeature(data=_UpperCamelCase    ,   tensor_type=_UpperCamelCase )






   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:List[Tuple] = None )->					int:
         __A          =				outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
               if len(_UpperCamelCase ) != len(_UpperCamelCase ):
                     raise ValueError(
                         '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

               if is_torch_tensor(_UpperCamelCase ):
                     __A          =				target_sizes.numpy()

               __A          =				[]

               for idx in range(len(_UpperCamelCase ) ):
                     __A          =				torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0 )    ,   size=target_sizes[idx]    ,   mode='''bilinear'''    ,   align_corners=_UpperCamelCase )
                     __A          =				resized_logits[0].argmax(dim=0 )
                     semantic_segmentation.append(_UpperCamelCase )
         else:
               __A          =				logits.argmax(dim=1 )
               __A          =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

         return semantic_segmentation












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





