






"""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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A_    =							logging.get_logger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->List[List[ImageInput]]:
						if isinstance(UpperCAmelCase__,						(list, tuple)      ) and isinstance(videos[0],						(list, tuple)      ) and is_valid_image(videos[0][0]      ):
												return videos

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

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

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							['pixel_values']


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						bool = True			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						PILImageResampling = PILImageResampling.BILINEAR			,    snake_case      :						bool = True			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						bool = True			,    snake_case      :						Union[int, float] = 1 / 255			,    snake_case      :						bool = True			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    **snake_case      :						Optional[int]			,    ):
													'''simple docstring'''





													super().__init__(**snake_case							)
													A__						:  Optional[int]		  =  size if size is not None else {"""shortest_edge""": 224}
													A__						:  Dict		  =  get_size_dict(snake_case			,    default_to_square=snake_case							)
													A__						:  List[Any]		  =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
													A__						:  Optional[Any]		  =  get_size_dict(snake_case			,    param_name="""crop_size"""							)

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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						np.ndarray			,    snake_case      :						Dict[str, int]			,    snake_case      :						PILImageResampling = PILImageResampling.BILINEAR			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  int		  =  get_size_dict(snake_case			,    default_to_square=snake_case							)
													if "shortest_edge" in size:
																			A__						:  Optional[int]		  =  get_resize_output_image_size(snake_case			,    size["""shortest_edge"""]			,    default_to_square=snake_case							)
													elif "height" in size and "width" in size:
																			A__						:  Optional[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(snake_case			,    size=snake_case			,    resample=snake_case			,    data_format=snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						np.ndarray			,    snake_case      :						Dict[str, int]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  int		  =  get_size_dict(snake_case							)
													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(snake_case			,    size=(size["""height"""], size["""width"""])			,    data_format=snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						np.ndarray			,    snake_case      :						Union[int, float]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						List[Any]			,    ):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						np.ndarray			,    snake_case      :						Union[float, List[float]]			,    snake_case      :						Union[float, List[float]]			,    snake_case      :						Optional[Union[str, ChannelDimension]] = None			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						ImageInput			,    snake_case      :						bool = None			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						PILImageResampling = None			,    snake_case      :						bool = None			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						bool = None			,    snake_case      :						float = None			,    snake_case      :						bool = None			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    snake_case      :						Optional[ChannelDimension] = ChannelDimension.FIRST			,    ):
													'''simple docstring'''





													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."""							)

													# All transformations expect numpy arrays.
													A__						:  Optional[Any]		  =  to_numpy_array(snake_case							)

													if do_resize:
																			A__						:  Tuple		  =  self.resize(image=snake_case			,    size=snake_case			,    resample=snake_case							)

													if do_center_crop:
																			A__						:  Any		  =  self.center_crop(snake_case			,    size=snake_case							)

													if do_rescale:
																			A__						:  str		  =  self.rescale(image=snake_case			,    scale=snake_case							)

													if do_normalize:
																			A__						:  str		  =  self.normalize(image=snake_case			,    mean=snake_case			,    std=snake_case							)

													A__						:  Optional[Any]		  =  to_channel_dimension_format(snake_case			,    snake_case							)
													return image


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						ImageInput			,    snake_case      :						bool = None			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						PILImageResampling = None			,    snake_case      :						bool = None			,    snake_case      :						Dict[str, int] = None			,    snake_case      :						bool = None			,    snake_case      :						float = None			,    snake_case      :						bool = None			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    snake_case      :						Optional[Union[float, List[float]]] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    snake_case      :						ChannelDimension = ChannelDimension.FIRST			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  Dict		  =  do_resize if do_resize is not None else self.do_resize
													A__						:  Optional[int]		  =  resample if resample is not None else self.resample
													A__						:  Dict		  =  do_center_crop if do_center_crop is not None else self.do_center_crop
													A__						:  str		  =  do_rescale if do_rescale is not None else self.do_rescale
													A__						:  Tuple		  =  rescale_factor if rescale_factor is not None else self.rescale_factor
													A__						:  str		  =  do_normalize if do_normalize is not None else self.do_normalize
													A__						:  Optional[Any]		  =  image_mean if image_mean is not None else self.image_mean
													A__						:  List[Any]		  =  image_std if image_std is not None else self.image_std

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

													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."""							)

													A__						:  Tuple		  =  make_batched(snake_case							)

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

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














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


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

						A_    =							baseaa_decode(encoded)
						print(decoded)






