




'''simple docstring'''







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A     =logging.get_logger(__name__)


class 			_a  (	__a ):
       __a				:     Dict   				=					["""pixel_values"""]
       def __init__(	self						:	int   ,				lowercase						:	bool = True   ,				lowercase						:	Union[int, float] = 1 / 255   ,				lowercase						:	bool = True   ,				lowercase						:	int = 8   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)

           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_pad
           UpperCAmelCase			       =		pad_size
       def     A      (	self						:	Any   ,				lowercase						:	np.ndarray   ,				lowercase						:	float   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	str				):



           '''simple docstring'''

           return rescale(lowercase   ,				scale=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	np.ndarray   ,				lowercase						:	int   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		get_image_size(lowercase				)
           UpperCAmelCase			       =		(old_height // size + 1) * size - old_height
           UpperCAmelCase			       =		(old_width // size + 1) * size - old_width

           return pad(lowercase   ,				((0, pad_height), (0, pad_width))   ,				mode='''symmetric'''   ,				data_format=lowercase				)







       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	ImageInput   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[float] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				lowercase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**lowercase						:	Optional[Any]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase			       =		rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase			       =		do_pad if do_pad is not None else self.do_pad
           UpperCAmelCase			       =		pad_size if pad_size is not None else self.pad_size

           UpperCAmelCase			       =		make_list_of_images(lowercase				)

           if not valid_images(lowercase				):
               raise ValueError(
                   '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                   '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

           if do_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

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

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

           if do_pad:
               UpperCAmelCase			       =		[self.pad(lowercase   ,				size=lowercase				) for image in images]

           UpperCAmelCase			       =		[to_channel_dimension_format(lowercase   ,				lowercase				) for image in images]

           UpperCAmelCase			       =		{'''pixel_values''': images}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)








def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
