
"""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


_snake_case          =						logging.get_logger(__name__)


if is_vision_available():
	import PIL
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



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


				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Union[int, float] = 1 / 255				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			bool = True				,     **UpperCAmelCase_	:			Optional[int]				,     )		->  None:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )
											_lowerCAmelCase  	=			size if size is not None else {'shortest_edge': 224}
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_  )
											_lowerCAmelCase  	=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_				,     param_name='crop_size'  )

											_lowerCAmelCase  	=			do_resize
											_lowerCAmelCase  	=			size
											_lowerCAmelCase  	=			resample
											_lowerCAmelCase  	=			do_center_crop
											_lowerCAmelCase  	=			crop_size
											_lowerCAmelCase  	=			do_rescale
											_lowerCAmelCase  	=			rescale_factor
											_lowerCAmelCase  	=			do_normalize
											_lowerCAmelCase  	=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											_lowerCAmelCase  	=			image_std if image_std is not None else OPENAI_CLIP_STD
											_lowerCAmelCase  	=			do_convert_rgb


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Dict[str, int]				,     UpperCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			List[str]				,     )		->  np.ndarray:







											"""simple docstring"""




											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_  )
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
											_lowerCAmelCase  	=			get_resize_output_image_size(UpperCAmelCase_				,     size=size['shortest_edge']				,     default_to_square=UpperCAmelCase_  )
											return resize(UpperCAmelCase_				,     size=UpperCAmelCase_				,     resample=UpperCAmelCase_				,     data_format=UpperCAmelCase_				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Dict[str, int]				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			Tuple				,     )		->  np.ndarray:







											"""simple docstring"""




											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_  )
											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(UpperCAmelCase_				,     size=(size['height'], size['width'])				,     data_format=UpperCAmelCase_				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Union[int, float]				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Union[float, List[float]]				,     UpperCAmelCase_	:			Union[float, List[float]]				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			List[Any]				,     )		->  np.ndarray:







											"""simple docstring"""




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







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			ImageInput				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			PILImageResampling = None				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			int = None				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			float = None				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			Optional[Union[str, TensorType]] = None				,     UpperCAmelCase_	:			Optional[ChannelDimension] = ChannelDimension.FIRST				,     **UpperCAmelCase_	:			int				,     )		->  PIL.Image.Image:







											"""simple docstring"""




											_lowerCAmelCase  	=			do_resize if do_resize is not None else self.do_resize
											_lowerCAmelCase  	=			size if size is not None else self.size
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     param_name='size'				,     default_to_square=UpperCAmelCase_  )
											_lowerCAmelCase  	=			resample if resample is not None else self.resample
											_lowerCAmelCase  	=			do_center_crop if do_center_crop is not None else self.do_center_crop
											_lowerCAmelCase  	=			crop_size if crop_size is not None else self.crop_size
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     param_name='crop_size'				,     default_to_square=UpperCAmelCase_  )
											_lowerCAmelCase  	=			do_rescale if do_rescale is not None else self.do_rescale
											_lowerCAmelCase  	=			rescale_factor if rescale_factor is not None else self.rescale_factor
											_lowerCAmelCase  	=			do_normalize if do_normalize is not None else self.do_normalize
											_lowerCAmelCase  	=			image_mean if image_mean is not None else self.image_mean
											_lowerCAmelCase  	=			image_std if image_std is not None else self.image_std
											_lowerCAmelCase  	=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_lowerCAmelCase  	=			make_list_of_images(UpperCAmelCase_  )

											if not valid_images(UpperCAmelCase_  ):
																		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:
																		_lowerCAmelCase  	=			[convert_to_rgb(UpperCAmelCase_  ) for image in images]

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

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

											if do_center_crop:
																		_lowerCAmelCase  	=			[self.center_crop(image=UpperCAmelCase_				,     size=UpperCAmelCase_  ) for image in images]

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

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

											_lowerCAmelCase  	=			[to_channel_dimension_format(UpperCAmelCase_				,     UpperCAmelCase_  ) for image in images]

											_lowerCAmelCase  	=			{'pixel_values': images}
											return BatchFeature(data=UpperCAmelCase_				,     tensor_type=UpperCAmelCase_  )








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0

							# if input_string is "aba" than new_input_string become "a|b|a"
							_lowerCAmelCase  	=			''
							_lowerCAmelCase  	=			''

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(SCREAMING_SNAKE_CASE						) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							# length[i] shows the length of palindromic substring with center i
							_lowerCAmelCase  	=			[1 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# for each character in new_string find corresponding palindromic string
							_lowerCAmelCase  	=			0
							for j in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			1 if j > r else min(length[l + r - j] // 2     ,  r - j + 1						)
														while (
														    j - k >= 0
														    and j + k < len(SCREAMING_SNAKE_CASE						)
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														_lowerCAmelCase  	=			2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					_lowerCAmelCase  	=			j - k + 1  # noqa: E741
																					_lowerCAmelCase  	=			j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					_lowerCAmelCase  	=			length[j]
																					_lowerCAmelCase  	=			j

    # create that string
							_lowerCAmelCase  	=			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
	import doctest

	doctest.testmod()






