



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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


__UpperCAmelCase			  =			logging.get_logger(__name__)



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Dict  	=    ["pixel_values"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = 1 / 255,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = True,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          super().__init__(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      size if size is not None else {'height': 384, 'width': 384}
          UpperCamelCase       :    Optional[Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    int		      =      do_resize
          UpperCamelCase       :    Tuple		      =      size
          UpperCamelCase       :    Optional[int]		      =      resample
          UpperCamelCase       :    Union[str, Any]		      =      do_rescale
          UpperCamelCase       :    Union[str, Any]		      =      rescale_factor
          UpperCamelCase       :    Optional[int]		      =      do_normalize
          UpperCamelCase       :    Tuple		      =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          UpperCamelCase       :    Any		      =      image_std if image_std is not None else OPENAI_CLIP_STD
          UpperCamelCase       :    Dict		      =      do_convert_rgb
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          UpperCamelCase       :    str		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          if "height" not in size or "width" not in size:
             raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""			)
          UpperCamelCase       :    Optional[int]		      =      (size['height'], size['width'])
          return resize(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						Tuple:
          return rescale(SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,     **SCREAMING_SNAKE_CASE_,     )	->						PIL.Image.Image:
          UpperCamelCase       :    Tuple		      =      do_resize if do_resize is not None else self.do_resize
          UpperCamelCase       :    Optional[Any]		      =      resample if resample is not None else self.resample
          UpperCamelCase       :    Union[str, Any]		      =      do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase       :    Tuple		      =      rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase       :    Optional[int]		      =      do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase       :    Optional[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       :    Tuple		      =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCamelCase       :    Dict		      =      size if size is not None else self.size
          UpperCamelCase       :    Optional[int]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Optional[Any]		      =      make_list_of_images(SCREAMING_SNAKE_CASE_			)

          if not valid_images(SCREAMING_SNAKE_CASE_			):
             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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize 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.'			)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             UpperCamelCase       :    Any		      =      [convert_to_rgb(SCREAMING_SNAKE_CASE_			) for image in images]

          # All transformations expect numpy arrays.
          UpperCamelCase       :    Any		      =      [to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

          if do_resize:
             UpperCamelCase       :    Optional[int]		      =      [self.resize(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_			) for image in images]

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

          if do_normalize:
             UpperCamelCase       :    List[Any]		      =      [self.normalize(image=SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_			) for image in images]

          UpperCamelCase       :    Optional[int]		      =      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) for image in images]

          UpperCamelCase       :    Tuple		      =      BatchFeature(data={'pixel_values': images},     tensor_type=SCREAMING_SNAKE_CASE_			)

          return encoded_outputs







'''simple docstring'''
from __future__ import annotations

import math


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      int:



					'''simple docstring'''






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

					if len(__a  ) == 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					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )

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


def      a (     )    ->      None:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__  :List[Any]				   =					math.log(len(__a  )					,   2  )
					print('''Optimal value : '''					,   end=''''''  )
					print(minimax(0					,   0					,   __a					,   __a					,   __a  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()