




'''simple docstring'''

from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a  :				Tuple									=  logging.get_logger(__name__)






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		["pixel_values"]


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

									snake_case_									=				do_resize
									snake_case_									=				size
									snake_case_									=				resample
									snake_case_									=				do_center_crop
									snake_case_									=				crop_size
									snake_case_									=				do_rescale
									snake_case_									=				rescale_factor
									snake_case_									=				do_normalize
									snake_case_									=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									snake_case_									=				image_std if image_std is not None else IMAGENET_DEFAULT_STD


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Dict[str, int]       ,				lowercase_    :					PILImageResampling = PILImageResampling.BICUBIC       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None       ,				**lowercase_    :					Any       ,				):
									snake_case_									=				get_size_dict(lowercase_       ,				default_to_square=lowercase_					)
									# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
									if "shortest_edge" in size:
														snake_case_									=				int((256 / 224) * size['''shortest_edge''']					)
														snake_case_									=				get_resize_output_image_size(lowercase_       ,				size=lowercase_       ,				default_to_square=lowercase_					)
														snake_case_									=				{'''height''': output_size[0], '''width''': output_size[1]}
									if "height" not in size_dict or "width" not in size_dict:
														raise ValueError(
														    F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"					)
									return resize(
									    lowercase_       ,				size=(size_dict['''height'''], size_dict['''width'''])       ,				resample=lowercase_       ,				data_format=lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Dict[str, int]       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None       ,				**lowercase_    :					Dict       ,				):
									snake_case_									=				get_size_dict(lowercase_					)
									if "height" not in size or "width" not in size:
														raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}"					)
									return center_crop(lowercase_       ,				size=(size['''height'''], size['''width'''])       ,				data_format=lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					np.ndarray       ,				lowercase_    :					Union[int, float]       ,				lowercase_    :					Optional[Union[str, ChannelDimension]] = None       ,				**lowercase_    :					Optional[int]       ,				):
									return rescale(lowercase_       ,				scale=lowercase_       ,				data_format=lowercase_       ,				**lowercase_					)


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


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					ImageInput       ,				lowercase_    :					Optional[bool] = None       ,				lowercase_    :					Optional[Dict[str, int]] = None       ,				lowercase_    :					PILImageResampling = None       ,				lowercase_    :					Optional[bool] = None       ,				lowercase_    :					Optional[Dict[str, int]] = None       ,				lowercase_    :					Optional[bool] = None       ,				lowercase_    :					Optional[float] = None       ,				lowercase_    :					Optional[bool] = None       ,				lowercase_    :					Optional[Union[float, Iterable[float]]] = None       ,				lowercase_    :					Optional[Union[float, Iterable[float]]] = None       ,				lowercase_    :					Optional[TensorType] = None       ,				lowercase_    :					ChannelDimension = ChannelDimension.FIRST       ,				**lowercase_    :					int       ,				):
									snake_case_									=				do_resize if do_resize is not None else self.do_resize
									snake_case_									=				resample if resample is not None else self.resample
									snake_case_									=				do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_									=				do_rescale if do_rescale is not None else self.do_rescale
									snake_case_									=				rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_									=				do_normalize if do_normalize is not None else self.do_normalize
									snake_case_									=				image_mean if image_mean is not None else self.image_mean
									snake_case_									=				image_std if image_std is not None else self.image_std

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

									snake_case_									=				make_list_of_images(lowercase_					)

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

									# All transformations expect numpy arrays.
									snake_case_									=				[to_numpy_array(lowercase_					) for image in images]

									if do_resize:
														snake_case_									=				[self.resize(lowercase_       ,				lowercase_       ,				lowercase_					) for image in images]

									if do_center_crop:
														snake_case_									=				[self.center_crop(lowercase_       ,				lowercase_					) for image in images]

									if do_rescale:
														snake_case_									=				[self.rescale(lowercase_       ,				lowercase_					) for image in images]

									if do_normalize:
														snake_case_									=				[self.normalize(lowercase_       ,				lowercase_       ,				lowercase_					) for image in images]

									snake_case_									=				[to_channel_dimension_format(lowercase_       ,				lowercase_					) for image in images]

									snake_case_									=				{'''pixel_values''': images}
									return BatchFeature(data=lowercase_       ,				tensor_type=lowercase_					)







'''simple docstring'''

from __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

