

"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							["""pixel_values"""]



					def __init__( self							:						int,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Optional[Dict[str, int]] = None,						lowerCamelCase							:						PILImageResampling = PILImageResampling.BICUBIC,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Union[int, float] = 1 / 255,						lowerCamelCase							:						Dict[str, int] = None,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Optional[Union[float, List[float]]] = None,						lowerCamelCase							:						Optional[Union[float, List[float]]] = None,						**lowerCamelCase							:						Union[str, Any],						)->      None:
								super().__init__(**lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=size if size is not None else {'''height''': 224, '''width''': 224}
								lowerCamelCase__	:	Dict				=get_size_dict(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowerCamelCase__	:	Union[str, Any]				=get_size_dict(lowerCamelCase,						default_to_square=lowerCamelCase,						param_name='''crop_size'''  )

								lowerCamelCase__	:	str				=do_resize
								lowerCamelCase__	:	List[str]				=do_rescale
								lowerCamelCase__	:	List[Any]				=do_normalize
								lowerCamelCase__	:	Any				=do_center_crop
								lowerCamelCase__	:	Dict				=crop_size
								lowerCamelCase__	:	Tuple				=size
								lowerCamelCase__	:	Tuple				=resample
								lowerCamelCase__	:	Any				=rescale_factor
								lowerCamelCase__	:	Dict				=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								lowerCamelCase__	:	Union[str, Any]				=image_std if image_std is not None else IMAGENET_DEFAULT_STD



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Dict[str, int],						lowerCamelCase							:						PILImageResampling = PILImageResampling.BILINEAR,						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						Dict,						)->      np.ndarray:
								lowerCamelCase__	:	List[Any]				=get_size_dict(lowerCamelCase  )

								if "shortest_edge" in size:
											lowerCamelCase__	:	Union[str, Any]				=get_resize_output_image_size(lowerCamelCase,						size=size['''shortest_edge'''],						default_to_square=lowerCamelCase  )
											# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
								elif "height" in size and "width" in size:
											lowerCamelCase__	:	List[Any]				=(size['''height'''], size['''width'''])
								else:
											raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''  )
								return resize(lowerCamelCase,						size=lowerCamelCase,						resample=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Dict[str, int],						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						List[Any],						)->      np.ndarray:
								lowerCamelCase__	:	List[Any]				=get_size_dict(lowerCamelCase  )
								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(lowerCamelCase,						size=(size['''height'''], size['''width''']),						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						float,						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						Dict  )->      np.ndarray:
								return rescale(lowerCamelCase,						scale=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Union[float, List[float]],						lowerCamelCase							:						Union[float, List[float]],						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						Union[str, Any],						)->      np.ndarray:
								return normalize(lowerCamelCase,						mean=lowerCamelCase,						std=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )




					def 	snake_case ( self							:						int,						lowerCamelCase							:						ImageInput,						lowerCamelCase							:						Optional[bool] = None,						lowerCamelCase							:						Dict[str, int] = None,						lowerCamelCase							:						PILImageResampling = None,						lowerCamelCase							:						bool = None,						lowerCamelCase							:						int = None,						lowerCamelCase							:						Optional[bool] = None,						lowerCamelCase							:						Optional[float] = None,						lowerCamelCase							:						Optional[bool] = None,						lowerCamelCase							:						Optional[Union[float, List[float]]] = None,						lowerCamelCase							:						Optional[Union[float, List[float]]] = None,						lowerCamelCase							:						Optional[Union[str, TensorType]] = None,						lowerCamelCase							:						Union[str, ChannelDimension] = ChannelDimension.FIRST,						**lowerCamelCase							:						List[str],						)->      BatchFeature:
								lowerCamelCase__	:	int				=do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__	:	Union[str, Any]				=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__	:	List[Any]				=do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__	:	str				=do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase__	:	Optional[int]				=crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__	:	Optional[Any]				=get_size_dict(lowerCamelCase,						param_name='''crop_size''',						default_to_square=lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=resample if resample is not None else self.resample
								lowerCamelCase__	:	Dict				=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__	:	str				=image_mean if image_mean is not None else self.image_mean
								lowerCamelCase__	:	int				=image_std if image_std is not None else self.image_std

								lowerCamelCase__	:	Union[str, Any]				=size if size is not None else self.size
								lowerCamelCase__	:	Dict				=get_size_dict(lowerCamelCase  )

								if not is_batched(lowerCamelCase  ):
											lowerCamelCase__	:	Optional[Any]				=[images]

								if not valid_images(lowerCamelCase  ):
											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.'''  )

								# All transformations expect numpy arrays.
								lowerCamelCase__	:	int				=[to_numpy_array(lowerCamelCase  ) for image in images]

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

								if do_center_crop:
											lowerCamelCase__	:	List[Any]				=[self.center_crop(image=lowerCamelCase,						size=lowerCamelCase  ) for image in images]

								if do_rescale:
											lowerCamelCase__	:	Union[str, Any]				=[self.rescale(image=lowerCamelCase,						scale=lowerCamelCase  ) for image in images]

								if do_normalize:
											lowerCamelCase__	:	Dict				=[self.normalize(image=lowerCamelCase,						mean=lowerCamelCase,						std=lowerCamelCase  ) for image in images]

								lowerCamelCase__	:	List[str]				=[to_channel_dimension_format(lowerCamelCase,						lowerCamelCase  ) for image in images]

								lowerCamelCase__	:	Any				={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,						tensor_type=lowerCamelCase  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 10							,	__lowerCamelCase       :					int = 22			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=range(1							,	__lowerCamelCase			)
			lowerCamelCase__	:	str				=range(1							,	__lowerCamelCase			)
			return sum(
			    1 for power in powers for base in bases if len(str(base**power			)			) == power			)


if __name__ == "__main__":
		print(f'{solution(1_0, 2_2) = }')




