


"""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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__				    =       logging.get_logger(__name__)


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[Any]       =["pixel_values"]

       def __init__(						self		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = True		,					snake_case__ = 1 / 255		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(**snake_case__						)
         lowerCAmelCase					:					Any								=							size if size is not None else {"shortest_edge": 256}
         lowerCAmelCase					:					int								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					List[str]								=							crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase					:					List[Any]								=							get_size_dict(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							do_resize
         lowerCAmelCase					:					Tuple								=							size
         lowerCAmelCase					:					Optional[Any]								=							resample
         lowerCAmelCase					:					Dict								=							do_center_crop
         lowerCAmelCase					:					Union[str, Any]								=							crop_size
         lowerCAmelCase					:					List[str]								=							do_rescale
         lowerCAmelCase					:					List[str]								=							rescale_factor
         lowerCAmelCase					:					Tuple								=							do_normalize
         lowerCAmelCase					:					List[Any]								=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase					:					Optional[Any]								=							image_std if image_std is not None else IMAGENET_STANDARD_STD

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = PILImageResampling.BICUBIC		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					str								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         if "shortest_edge" not in size:
           raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
         lowerCAmelCase					:					Optional[int]								=							get_resize_output_image_size(snake_case__		,					size=size["shortest_edge"]		,					default_to_square=snake_case__						)
         return resize(snake_case__		,					size=snake_case__		,					resample=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							get_size_dict(snake_case__						)
         return center_crop(snake_case__		,					size=(size["height"], size["width"])		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





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







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = ChannelDimension.FIRST		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase					:					List[Any]								=							size if size is not None else self.size
         lowerCAmelCase					:					Union[str, Any]								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							resample if resample is not None else self.resample
         lowerCAmelCase					:					Any								=							do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase					:					str								=							crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase					:					Union[str, Any]								=							get_size_dict(snake_case__						)
         lowerCAmelCase					:					Optional[int]								=							do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase					:					Optional[Any]								=							rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase					:					List[str]								=							do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase					:					int								=							image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase					:					int								=							image_std if image_std is not None else self.image_std

         lowerCAmelCase					:					Union[str, Any]								=							make_list_of_images(snake_case__						)

         if not valid_images(snake_case__						):
           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."						)

         # All transformations expect numpy arrays.
         lowerCAmelCase					:					int								=							[to_numpy_array(snake_case__						) for image in images]

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

         if do_center_crop:
           lowerCAmelCase					:					Optional[int]								=							[self.center_crop(image=snake_case__		,					size=snake_case__						) for image in images]

         if do_rescale:
           lowerCAmelCase					:					Tuple								=							[self.rescale(image=snake_case__		,					scale=snake_case__						) for image in images]

         if do_normalize:
           lowerCAmelCase					:					Dict								=							[self.normalize(image=snake_case__		,					mean=snake_case__		,					std=snake_case__						) for image in images]

         lowerCAmelCase					:					str								=							[to_channel_dimension_format(snake_case__		,					snake_case__						) for image in images]

         lowerCAmelCase					:					Any								=							{"pixel_values": images}
         return BatchFeature(data=snake_case__		,					tensor_type=snake_case__						)









"""simple docstring"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





