


"""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 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


snake_case__		:     Dict    				=				logging.get_logger(__name__)



class 		snake_case_(       a__			):
     __UpperCamelCase  =					['''pixel_values''']






     def __init__(	self  :					Tuple					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Dict[str, int] = None					,				UpperCamelCase_  :					PILImageResampling = PILImageResampling.BICUBIC					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Union[int, float] = 1 / 2_5_5					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[Union[float, List[float]]] = None					,				UpperCamelCase_  :					Optional[Union[float, List[float]]] = None					,				UpperCamelCase_  :					bool = True					,				**UpperCamelCase_  :					Any					,				):
           super().__init__(**UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
           lowerCAmelCase					:	str       =   get_size_dict(UpperCamelCase_					,				default_to_square=UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   do_resize
           lowerCAmelCase					:	int       =   size
           lowerCAmelCase					:	Any       =   resample
           lowerCAmelCase					:	Optional[Any]       =   do_rescale
           lowerCAmelCase					:	Union[str, Any]       =   rescale_factor
           lowerCAmelCase					:	List[str]       =   do_normalize
           lowerCAmelCase					:	str       =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCAmelCase					:	Optional[Any]       =   image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCAmelCase					:	str       =   do_convert_rgb






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					np.ndarray					,				UpperCamelCase_  :					Dict[str, int]					,				UpperCamelCase_  :					PILImageResampling = PILImageResampling.BICUBIC					,				UpperCamelCase_  :					Optional[Union[str, ChannelDimension]] = None					,				**UpperCamelCase_  :					Union[str, Any]					,				):
           lowerCAmelCase					:	Tuple       =   get_size_dict(UpperCamelCase_					,				default_to_square=UpperCamelCase_				)
           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()}'''				)
           lowerCAmelCase					:	Optional[int]       =   (size['''height'''], size['''width'''])
           return resize(UpperCamelCase_					,				size=UpperCamelCase_					,				resample=UpperCamelCase_					,				data_format=UpperCamelCase_					,				**UpperCamelCase_				)






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






     def      lowerCamelCase__     (	self  :					Optional[int]					,				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      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					ImageInput					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					Optional[Dict[str, int]] = None					,				UpperCamelCase_  :					PILImageResampling = 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_  :					bool = None					,				UpperCamelCase_  :					ChannelDimension = ChannelDimension.FIRST					,				**UpperCamelCase_  :					List[Any]					,				):
           lowerCAmelCase					:	Optional[int]       =   do_resize if do_resize is not None else self.do_resize
           lowerCAmelCase					:	Optional[Any]       =   resample if resample is not None else self.resample
           lowerCAmelCase					:	Union[str, Any]       =   do_rescale if do_rescale is not None else self.do_rescale
           lowerCAmelCase					:	str       =   rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCAmelCase					:	Optional[int]       =   do_normalize if do_normalize is not None else self.do_normalize
           lowerCAmelCase					:	Tuple       =   image_mean if image_mean is not None else self.image_mean
           lowerCAmelCase					:	Optional[int]       =   image_std if image_std is not None else self.image_std
           lowerCAmelCase					:	Dict       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCAmelCase					:	str       =   size if size is not None else self.size
           lowerCAmelCase					:	Dict       =   get_size_dict(UpperCamelCase_					,				default_to_square=UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   make_list_of_images(UpperCamelCase_				)

           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 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:
                 lowerCAmelCase					:	str       =   [convert_to_rgb(UpperCamelCase_				) for image in images]

           # All transformations expect numpy arrays.
           lowerCAmelCase					:	List[str]       =   [to_numpy_array(UpperCamelCase_				) for image in images]

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

           if do_rescale:
                 lowerCAmelCase					:	List[str]       =   [self.rescale(image=UpperCamelCase_					,				scale=UpperCamelCase_				) for image in images]

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

           lowerCAmelCase					:	Dict       =   [to_channel_dimension_format(UpperCamelCase_					,				UpperCamelCase_				) for image in images]

           lowerCAmelCase					:	List[str]       =   BatchFeature(data={'''pixel_values''': images}					,				tensor_type=UpperCamelCase_				)

           return encoded_outputs






"""simple docstring"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


def      _snake_case						(						_snake_case				:			Optional[Any]			):
      # Build BST
      if len(_snake_case			) == 0:
            return arr
      lowerCAmelCase					:	Optional[Any]       =   Node(arr[0]			)
      for i in range(1 ,   len(_snake_case			)			):
            root.insert(arr[i]			)
      # Traverse BST in order.
      lowerCAmelCase					:	Optional[int]       =   []
      inorder(_snake_case ,   _snake_case			)
      return res


if __name__ == "__main__":
 print(tree_sort([10, 1, 3, 2, 9, 14, 13]))


