


"""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 convert_to_rgb, 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


if is_vision_available():
       import PIL


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						(		_a    ):
      a :     str											=		["""pixel_values"""]
      def __init__(		self	:	List[Any]      ,       A_	:	bool = True      ,       A_	:	Dict[str, int] = None      ,       A_	:	PILImageResampling = PILImageResampling.BICUBIC      ,       A_	:	bool = True      ,       A_	:	Union[int, float] = 1 / 2_5_5      ,       A_	:	bool = True      ,       A_	:	Optional[Union[float, List[float]]] = None      ,       A_	:	Optional[Union[float, List[float]]] = None      ,       A_	:	bool = True      ,       **A_	:	Any      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)
       __lowercase									=	size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)

       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	resample
       __lowercase									=	do_rescale
       __lowercase									=	rescale_factor
       __lowercase									=	do_normalize
       __lowercase									=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       __lowercase									=	image_std if image_std is not None else OPENAI_CLIP_STD
       __lowercase									=	do_convert_rgb
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	np.ndarray      ,       A_	:	Dict[str, int]      ,       A_	:	PILImageResampling = PILImageResampling.BICUBIC      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)
       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()}'''							)
       __lowercase									=	(size["""height"""], size["""width"""])
       return resize(A_      ,       size=A_      ,       resample=A_      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	np.ndarray      ,       A_	:	Union[int, float]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	Optional[Any]      ,       ):






       '''simple docstring'''


       return rescale(A_      ,       scale=A_      ,       data_format=A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	np.ndarray      ,       A_	:	Union[float, List[float]]      ,       A_	:	Union[float, List[float]]      ,       A_	:	Optional[Union[str, ChannelDimension]] = None      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	ImageInput      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[Dict[str, int]] = None      ,       A_	:	PILImageResampling = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[float] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[Union[float, List[float]]] = None      ,       A_	:	Optional[Union[float, List[float]]] = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	bool = None      ,       A_	:	ChannelDimension = ChannelDimension.FIRST      ,       **A_	:	List[str]      ,       ):






       '''simple docstring'''


       __lowercase									=	do_resize if do_resize is not None else self.do_resize
       __lowercase									=	resample if resample is not None else self.resample
       __lowercase									=	do_rescale if do_rescale is not None else self.do_rescale
       __lowercase									=	rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowercase									=	do_normalize if do_normalize is not None else self.do_normalize
       __lowercase									=	image_mean if image_mean is not None else self.image_mean
       __lowercase									=	image_std if image_std is not None else self.image_std
       __lowercase									=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       __lowercase									=	size if size is not None else self.size
       __lowercase									=	get_size_dict(A_      ,       default_to_square=A_							)

       __lowercase									=	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 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."""							)

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

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

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

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

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

       __lowercase									=	[to_channel_dimension_format(A_      ,       A_							) for image in images]

       __lowercase									=	BatchFeature(data={"""pixel_values""": images}      ,       tensor_type=A_							)

       return encoded_outputs




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 assert x is not None
 assert y is not None

 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # declaring the array for storing the dp values
 __lowercase									=	[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

 for i in range(1				,   m + 1 ):
  for j in range(1				,   n + 1 ):
   __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

   __lowercase									=	max(l[i - 1][j]				,   l[i][j - 1]				,   l[i - 1][j - 1] + match )

 __lowercase									=	""""""
 __lowercase							,		__lowercase									=	m, n
 while i > 0 and j > 0:
  __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

  if l[i][j] == l[i - 1][j - 1] + match:
   if match == 1:
    __lowercase									=	x[i - 1] + seq
   i -= 1
   j -= 1
  elif l[i][j] == l[i - 1][j]:
   i -= 1
  else:
   j -= 1

 return l[m][n], seq


if __name__ == "__main__":
       UpperCAmelCase__  							="AGGTAB"
       UpperCAmelCase__  							="GXTXAYB"
       UpperCAmelCase__  							=4
       UpperCAmelCase__  							="GTAB"

       UpperCAmelCase__							,      UpperCAmelCase__  							=longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()
