"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

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



def   a_						(					lowerCamelCase       ):
				if isinstance(lowerCamelCase					,			(list, tuple)       ) and isinstance(videos[0]					,			(list, tuple)       ) and is_valid_image(videos[0][0]       ):
								return videos

				elif isinstance(lowerCamelCase					,			(list, tuple)       ) and is_valid_image(videos[0]       ):
								return [videos]

				elif is_valid_image(lowerCamelCase       ):
								return [[videos]]

				raise ValueError(f'''Could not make batched video from {videos}'''       )







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	Dict    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[int, float] = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,**lowerCamelCase__   :	List[Any]    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'shortest_edge': 256}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									UpperCAmelCase__             =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,param_name='crop_size'			)

									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									UpperCAmelCase__             =							do_center_crop
									UpperCAmelCase__             =							crop_size
									UpperCAmelCase__             =							resample
									UpperCAmelCase__             =							do_rescale
									UpperCAmelCase__             =							rescale_factor
									UpperCAmelCase__             =							offset
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__             =							image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[str]    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									if "shortest_edge" in size:
													UpperCAmelCase__             =							get_resize_output_image_size(lowerCamelCase__    ,size['shortest_edge']    ,default_to_square=lowerCamelCase__			)
									elif "height" in size and "width" in size:
													UpperCAmelCase__             =							(size['height'], size['width'])
									else:
													raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
									return resize(lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''			)
									return center_crop(lowerCamelCase__    ,size=(size['height'], size['width'])    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[int, float]    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									UpperCAmelCase__             =							image.astype(np.floataa			)
									if offset:
													UpperCAmelCase__             =							image - (scale / 2)
									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__   :	Dict    ,):
									return normalize(lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[ChannelDimension] = ChannelDimension.FIRST    ,):
									if do_resize and size is None or resample is None:
													raise ValueError('Size and resample 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.'			)

									if offset and not do_rescale:
													raise ValueError('For offset, do_rescale must also be set to True.'			)

									# All transformations expect numpy arrays.
									UpperCAmelCase__             =							to_numpy_array(lowerCamelCase__			)

									if do_resize:
													UpperCAmelCase__             =							self.resize(image=lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__			)

									if do_center_crop:
													UpperCAmelCase__             =							self.center_crop(lowerCamelCase__    ,size=lowerCamelCase__			)

									if do_rescale:
													UpperCAmelCase__             =							self.rescale(image=lowerCamelCase__    ,scale=lowerCamelCase__    ,offset=lowerCamelCase__			)

									if do_normalize:
													UpperCAmelCase__             =							self.normalize(image=lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__			)

									UpperCAmelCase__             =							to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			)
									return image







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	ChannelDimension = ChannelDimension.FIRST    ,**lowerCamelCase__   :	Dict    ,):
									UpperCAmelCase__             =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									UpperCAmelCase__             =							do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__             =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__             =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__             =							offset if offset is not None else self.offset
									UpperCAmelCase__             =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__             =							image_std if image_std is not None else self.image_std

									UpperCAmelCase__             =							size if size is not None else self.size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									UpperCAmelCase__             =							crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,param_name='crop_size'			)

									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.'			)

									UpperCAmelCase__             =							make_batched(lowerCamelCase__			)

									UpperCAmelCase__             =							[
									    [
									        self._preprocess_image(
									            image=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,do_center_crop=lowerCamelCase__    ,crop_size=lowerCamelCase__    ,do_rescale=lowerCamelCase__    ,rescale_factor=lowerCamelCase__    ,offset=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,image_mean=lowerCamelCase__    ,image_std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,)
									        for img in video
									    ]
									    for video in videos
									]

									UpperCAmelCase__             =							{'pixel_values': videos}
									return BatchFeature(data=lowerCamelCase__    ,tensor_type=lowerCamelCase__			)




"""simple docstring"""






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = None
					snake_case__				    = None






lowerCAmelCase__  :  Union[str, Any]						     =	namedtuple('CoinsDistribResult', 'moves excess')



def   a_						(					lowerCamelCase       ):

				if root is None:
								return 0

				# Validation
				def count_nodes(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_nodes(node.left       ) + count_nodes(node.right       ) + 1

				def count_coins(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_coins(node.left       ) + count_coins(node.right       ) + node.data

				if count_nodes(lowerCamelCase       ) != count_coins(lowerCamelCase       ):
								raise ValueError('The nodes number should be same as the number of coins'       )

				# Main calculation
				def get_distrib(lowerCamelCase       ) -> CoinsDistribResult:

								if node is None:
												return CoinsDistribResult(0					,			1       )

								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.left       )
								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.right       )

								UpperCAmelCase__             =							1 - left_distrib_excess
								UpperCAmelCase__             =							1 - right_distrib_excess

								UpperCAmelCase__             =							(
								    left_distrib_moves
								    + right_distrib_moves
								    + abs(lowerCamelCase       )
								    + abs(lowerCamelCase       )
								)
								UpperCAmelCase__             =							node.data - coins_to_left - coins_to_right

								return CoinsDistribResult(lowerCamelCase					,			lowerCamelCase       )

				return get_distrib(lowerCamelCase       )[0]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



