



'''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


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Dict   				=					['pixel_values']







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

									_a     :      Any        =							do_resize
									_a     :      str        =							do_rescale
									_a     :      Optional[Any]        =							do_normalize
									_a     :      Optional[Any]        =							do_center_crop
									_a     :      Any        =							crop_size
									_a     :      Optional[Any]        =							size
									_a     :      Optional[int]        =							resample
									_a     :      str        =							rescale_factor
									_a     :      Optional[int]        =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									_a     :      int        =							image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       np.ndarray	,_UpperCAmelCase					:       Dict[str, int]	,_UpperCAmelCase					:       PILImageResampling = PILImageResampling.BILINEAR	,_UpperCAmelCase					:       Optional[Union[str, ChannelDimension]] = None	,**_UpperCAmelCase					:       Optional[Any]	,):
									_a     :      Dict        =							get_size_dict(_UpperCAmelCase		)

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







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       np.ndarray	,_UpperCAmelCase					:       Dict[str, int]	,_UpperCAmelCase					:       Optional[Union[str, ChannelDimension]] = None	,**_UpperCAmelCase					:       Any	,):
									_a     :      Union[str, Any]        =							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 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       np.ndarray	,_UpperCAmelCase					:       float	,_UpperCAmelCase					:       Optional[Union[str, ChannelDimension]] = None	,**_UpperCAmelCase					:       str		):
									return rescale(_UpperCAmelCase	,scale=_UpperCAmelCase	,data_format=_UpperCAmelCase	,**_UpperCAmelCase		)







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







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

									_a     :      Union[str, Any]        =							size if size is not None else self.size
									_a     :      Optional[int]        =							get_size_dict(_UpperCAmelCase		)

									if not is_batched(_UpperCAmelCase		):
											_a     :      Tuple        =							[images]

									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.'		)

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

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

									if do_center_crop:
											_a     :      Tuple        =							[self.center_crop(image=_UpperCAmelCase	,size=_UpperCAmelCase		) for image in images]

									if do_rescale:
											_a     :      Optional[Any]        =							[self.rescale(image=_UpperCAmelCase	,scale=_UpperCAmelCase		) for image in images]

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

									_a     :      Optional[Any]        =							[to_channel_dimension_format(_UpperCAmelCase	,_UpperCAmelCase		) for image in images]

									_a     :      Optional[Any]        =							{'pixel_values': images}
									return BatchFeature(data=_UpperCAmelCase	,tensor_type=_UpperCAmelCase		)










def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     1

	while len(_A							) < 1E6:
		constant.append(str(_A							)							)
		i += 1

	snake_case_		    =     "".join(_A							)

	return (
	    int(constant[0]							)
	    * int(constant[9]							)
	    * int(constant[99]							)
	    * int(constant[999]							)
	    * int(constant[9999]							)
	    * int(constant[99999]							)
	    * int(constant[999999]							)
	)


if __name__ == "__main__":
		print(solution())
