

'''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_    						=       logging.get_logger(__name__)


if is_vision_available():
							import PIL





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		Dict		,					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__     :		Any		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )
										lowercase :							Tuple    		=size if size is not None else {'''shortest_edge''': 224}
										lowercase :							List[str]    		=get_size_dict(UpperCAmelCase__		,					default_to_square=UpperCAmelCase__   )
										lowercase :							Dict    		=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										lowercase :							Tuple    		=get_size_dict(UpperCAmelCase__		,					default_to_square=UpperCAmelCase__		,					param_name='''crop_size'''   )

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







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Dict[str, int]		,					UpperCAmelCase__     :		PILImageResampling = PILImageResampling.BICUBIC		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Tuple		,					):
										'''simple docstring'''
										lowercase :							Any    		=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()}'''   )
										lowercase :							List[str]    		=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     :		Dict		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Dict[str, int]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		List[Any]		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=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     :		Any		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Union[int, float]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Any		,					):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Union[float, List[float]]		,					UpperCAmelCase__     :		Union[float, List[float]]		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Union[str, Any]		,					):
										'''simple docstring'''
										return normalize(UpperCAmelCase__		,					mean=UpperCAmelCase__		,					std=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					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__     :		str		,					):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=do_resize if do_resize is not None else self.do_resize
										lowercase :							Optional[int]    		=size if size is not None else self.size
										lowercase :							List[str]    		=get_size_dict(UpperCAmelCase__		,					param_name='''size'''		,					default_to_square=UpperCAmelCase__   )
										lowercase :							int    		=resample if resample is not None else self.resample
										lowercase :							Optional[int]    		=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 :							int    		=get_size_dict(UpperCAmelCase__		,					param_name='''crop_size'''		,					default_to_square=UpperCAmelCase__   )
										lowercase :							Dict    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							List[str]    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							Union[str, Any]    		=do_normalize if do_normalize is not None else self.do_normalize
										lowercase :							Union[str, Any]    		=image_mean if image_mean is not None else self.image_mean
										lowercase :							List[str]    		=image_std if image_std is not None else self.image_std
										lowercase :							Dict    		=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowercase :							Any    		=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:
																lowercase :							Tuple    		=[convert_to_rgb(UpperCAmelCase__   ) for image in images]

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

										if do_resize:
																lowercase :							Dict    		=[self.resize(image=UpperCAmelCase__		,					size=UpperCAmelCase__		,					resample=UpperCAmelCase__   ) for image in images]

										if do_center_crop:
																lowercase :							Optional[int]    		=[self.center_crop(image=UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

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

										if do_normalize:
																lowercase :							str    		=[self.normalize(image=UpperCAmelCase__		,					mean=UpperCAmelCase__		,					std=UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

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












'''simple docstring'''






def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


