


'''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 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   ["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 / 2_55		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     **lowerCamelCase__     :   List[str]		,     )      ->None:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__       )
					_UpperCAmelCase						:				Union[str, Any]	    =					size if size is not None else {"height": 2_24, "width": 2_24}
					_UpperCAmelCase						:				List[str]	    =					get_size_dict(lowerCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					_UpperCAmelCase						:				str	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__		,     param_name="crop_size"       )

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




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



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					get_size_dict(lowerCamelCase__       )

					if "shortest_edge" in size:
							_UpperCAmelCase						:				Optional[int]	    =					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:
							_UpperCAmelCase						:				Optional[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 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Dict[str, int]		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Any		,     )      ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, 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 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   float		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   List[Any]       )      ->np.ndarray:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     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:



					'''simple docstring'''







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






			def 							lowerCAmelCase__			(						self     :   Any		,     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__     :   Union[str, Any]		,     )      ->BatchFeature:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					do_resize if do_resize is not None else self.do_resize
					_UpperCAmelCase						:				str	    =					do_rescale if do_rescale is not None else self.do_rescale
					_UpperCAmelCase						:				str	    =					do_normalize if do_normalize is not None else self.do_normalize
					_UpperCAmelCase						:				Dict	    =					do_center_crop if do_center_crop is not None else self.do_center_crop
					_UpperCAmelCase						:				Dict	    =					crop_size if crop_size is not None else self.crop_size
					_UpperCAmelCase						:				Tuple	    =					get_size_dict(lowerCamelCase__		,     param_name="crop_size"		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				Union[str, Any]	    =					resample if resample is not None else self.resample
					_UpperCAmelCase						:				Optional[Any]	    =					rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCAmelCase						:				List[Any]	    =					image_mean if image_mean is not None else self.image_mean
					_UpperCAmelCase						:				str	    =					image_std if image_std is not None else self.image_std

					_UpperCAmelCase						:				List[str]	    =					size if size is not None else self.size
					_UpperCAmelCase						:				Optional[int]	    =					get_size_dict(lowerCamelCase__       )

					if not is_batched(lowerCamelCase__       ):
							_UpperCAmelCase						:				List[str]	    =					[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.
					_UpperCAmelCase						:				int	    =					[to_numpy_array(lowerCamelCase__       ) for image in images]

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

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

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

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

					_UpperCAmelCase						:				List[Any]	    =					[to_channel_dimension_format(lowerCamelCase__		,     lowerCamelCase__       ) for image in images]

					_UpperCAmelCase						:				Dict	    =					{"pixel_values": images}
					return BatchFeature(data=lowerCamelCase__		,     tensor_type=lowerCamelCase__       )







'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase = 50      ):
		_UpperCAmelCase						:				Tuple	    =					[[0] * 3 for _ in range(length + 1      )]

		for row_length in range(length + 1      ):
				for tile_length in range(2					,       5      ):
						for tile_start in range(row_length - tile_length + 1      ):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]      )


if __name__ == "__main__":
						print(F'''{solution() = }''')



