
'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a__						:				Optional[Any]           =    logging.get_logger(__name__)


if is_vision_available():
 import PIL


class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE):
 UpperCAmelCase__      :      Dict  			=   ['pixel_values']






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







       '''simple docstring'''



       super().__init__(**_A     )
       __A    		=     size if size is not None else {'shortest_edge': 224}
       __A    		=     get_size_dict(_A		,			default_to_square=_A     )
       __A    		=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
       __A    		=     get_size_dict(_A     )

       __A    		=     do_resize
       __A    		=     size
       __A    		=     resample
       __A    		=     do_center_crop
       __A    		=     crop_size
       __A    		=     do_rescale
       __A    		=     rescale_factor
       __A    		=     do_normalize
       __A    		=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       __A    		=     image_std if image_std is not None else OPENAI_CLIP_STD
       __A    		=     do_convert_rgb






 def 		lowercase_  (     self					:Optional[Any]		,			_A					:np.ndarray		,			_A					:Dict[str, int]		,			_A					:PILImageResampling = PILImageResampling.BICUBIC		,			_A					:Optional[Union[str, ChannelDimension]] = None		,			**_A					:str		,			)			->     np.ndarray:







       '''simple docstring'''



       __A    		=     get_size_dict(_A		,			default_to_square=_A     )
       __A    		=     get_resize_output_image_size(
           _A		,			size=(size['height'], size['width'])		,			default_to_square=_A     )
       return resize(_A		,			size=_A		,			resample=_A		,			data_format=_A		,			**_A     )






 def 		lowercase_  (     self					:Optional[Any]		,			_A					:np.ndarray		,			_A					:Dict[str, int]		,			_A					:Optional[Union[str, ChannelDimension]] = None		,			**_A					:Tuple		,			)			->     np.ndarray:







       '''simple docstring'''



       __A    		=     get_size_dict(_A     )
       return center_crop(_A		,			size=(size['height'], size['width'])		,			data_format=_A		,			**_A     )






 def 		lowercase_  (     self					:Union[str, Any]		,			_A					:np.ndarray		,			_A					:Union[int, float]		,			_A					:Optional[Union[str, ChannelDimension]] = None		,			**_A					:int		,			)			->     str:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:Optional[Any]		,			_A					:np.ndarray		,			_A					:Union[float, List[float]]		,			_A					:Union[float, List[float]]		,			_A					:Optional[Union[str, ChannelDimension]] = None		,			**_A					:Tuple		,			)			->     np.ndarray:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:str		,			_A					:ImageInput		,			_A					:bool = None		,			_A					:Dict[str, int] = None		,			_A					:PILImageResampling = None		,			_A					:bool = None		,			_A					:int = None		,			_A					:bool = None		,			_A					:float = None		,			_A					:bool = None		,			_A					:Optional[Union[float, List[float]]] = None		,			_A					:Optional[Union[float, List[float]]] = None		,			_A					:bool = None		,			_A					:Optional[Union[str, TensorType]] = None		,			_A					:Optional[ChannelDimension] = ChannelDimension.FIRST		,			**_A					:int		,			)			->     PIL.Image.Image:







       '''simple docstring'''



       __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(_A		,			default_to_square=_A     )
       __A    		=     resample if resample is not None else self.resample
       __A    		=     do_center_crop if do_center_crop is not None else self.do_center_crop
       __A    		=     crop_size if crop_size is not None else self.crop_size
       __A    		=     get_size_dict(_A     )
       __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    		=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       __A    		=     make_list_of_images(_A     )

       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.'     )

       if do_resize and size is None:
             raise ValueError('Size 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.'     )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             __A    		=     [convert_to_rgb(_A     ) for image in images]

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

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

       if do_center_crop:
             __A    		=     [self.center_crop(image=_A		,			size=_A     ) for image in images]

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

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

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

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








'''simple docstring'''





import math
import os

import cva
import numpy as np

a__						:				List[Any]           =    255.0





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     float:







      """simple docstring"""
      __A    		=     np.mean((original - contrast) ** 2		)
      if mse == 0:
            return 1_0_0

      return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase		)		)





def     snake_case							(						)->     None:







      """simple docstring"""
      __A    		=     os.path.dirname(os.path.realpath(UpperCAmelCase		)		)
      # Loading images (original image and compressed image)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/original_image.png'		)		)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/compressed_image.png'		) ,					1		)

      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-base.png'		)		)
      __A    		=     cva.imread(
          os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-comp-10.jpg'		) ,					1		)

      # Value expected: 29.73dB
      print('-- First Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)

      # # Value expected: 31.53dB (Wikipedia Example)
      print('\n-- Second Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)


if __name__ == "__main__":
 main()






