"""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


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


if is_vision_available():
       import PIL


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Optional[Any]										=						["pixel_values"]


      def __init__(     self	,    a = True	,    a = None	,    a = PILImageResampling.BICUBIC	,    a = True	,    a = None	,    a = True	,    a = 1 / 2_5_5	,    a = True	,    a = None	,    a = None	,    a = True	,    **a	,    ) ->      None:
             super().__init__(**a							)
             lowercase__    :	Union[str, Any]     					=			size if size is not None else {'shortest_edge': 2_2_4}
             lowercase__    :	Tuple     					=			get_size_dict(a	,    default_to_square=a							)
             lowercase__    :	List[str]     					=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             lowercase__    :	Optional[int]     					=			get_size_dict(a	,    default_to_square=a	,    param_name='crop_size'							)

             lowercase__    :	Union[str, Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	Optional[int]     					=			resample
             lowercase__    :	Tuple     					=			do_center_crop
             lowercase__    :	Tuple     					=			crop_size
             lowercase__    :	Dict     					=			do_rescale
             lowercase__    :	Any     					=			rescale_factor
             lowercase__    :	Dict     					=			do_normalize
             lowercase__    :	List[str]     					=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             lowercase__    :	Any     					=			image_std if image_std is not None else OPENAI_CLIP_STD
             lowercase__    :	List[Any]     					=			do_convert_rgb


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = PILImageResampling.BICUBIC	,    a = None	,    **a	,    ) ->      np.ndarray:
             lowercase__    :	Optional[Any]     					=			get_size_dict(a	,    default_to_square=a							)
             if "shortest_edge" not in size:
                    raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
             lowercase__    :	List[Any]     					=			get_resize_output_image_size(a	,    size=size['shortest_edge']	,    default_to_square=a							)
             return resize(a	,    size=a	,    resample=a	,    data_format=a	,    **a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = None	,    **a	,    ) ->      np.ndarray:
             lowercase__    :	Tuple     					=			get_size_dict(a							)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
             return center_crop(a	,    size=(size['height'], size['width'])	,    data_format=a	,    **a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = None	,    **a	,    ) ->      int:
             return rescale(a	,    scale=a	,    data_format=a	,    **a							)


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


      def 	_UpperCAmelCase (     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:
             lowercase__    :	int     					=			do_resize if do_resize is not None else self.do_resize
             lowercase__    :	Union[str, Any]     					=			size if size is not None else self.size
             lowercase__    :	Any     					=			get_size_dict(a	,    param_name='size'	,    default_to_square=a							)
             lowercase__    :	str     					=			resample if resample is not None else self.resample
             lowercase__    :	Any     					=			do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__    :	Optional[Any]     					=			crop_size if crop_size is not None else self.crop_size
             lowercase__    :	str     					=			get_size_dict(a	,    param_name='crop_size'	,    default_to_square=a							)
             lowercase__    :	Optional[int]     					=			do_rescale if do_rescale is not None else self.do_rescale
             lowercase__    :	int     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__    :	Any     					=			do_normalize if do_normalize is not None else self.do_normalize
             lowercase__    :	List[Any]     					=			image_mean if image_mean is not None else self.image_mean
             lowercase__    :	Union[str, Any]     					=			image_std if image_std is not None else self.image_std
             lowercase__    :	str     					=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowercase__    :	int     					=			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:
                    lowercase__    :	int     					=			[convert_to_rgb(a							) for image in images]

             # All transformations expect numpy arrays.
             lowercase__    :	Optional[Any]     					=			[to_numpy_array(a							) for image in images]

             if do_resize:
                    lowercase__    :	Any     					=			[self.resize(image=a	,    size=a	,    resample=a							) for image in images]

             if do_center_crop:
                    lowercase__    :	Optional[Any]     					=			[self.center_crop(image=a	,    size=a							) for image in images]

             if do_rescale:
                    lowercase__    :	Dict     					=			[self.rescale(image=a	,    scale=a							) for image in images]

             if do_normalize:
                    lowercase__    :	List[Any]     					=			[self.normalize(image=a	,    mean=a	,    std=a							) for image in images]

             lowercase__    :	str     					=			[to_channel_dimension_format(a	,    a							) for image in images]

             lowercase__    :	Tuple     					=			{'pixel_values': images}
             return BatchFeature(data=a	,    tensor_type=a							)


"""simple docstring"""





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			[0] * len(_lowerCAmelCase  )

       for i in range(1     ,				len(_lowerCAmelCase  )  ):
              # use last results for better performance - dynamic programming
              lowercase__    :	List[str]     					=			prefix_result[i - 1]
              while j > 0 and input_string[i] != input_string[j]:
                     lowercase__    :	Dict     					=			prefix_result[j - 1]

              if input_string[i] == input_string[j]:
                     j += 1
              lowercase__    :	Union[str, Any]     					=			j

       return prefix_result





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       return max(prefix_function(_lowerCAmelCase  )  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

