






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a	: Any      =				logging.get_logger(__name__)
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				int							 =			['pixel_values']
      def __init__(   self      :    str	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,   __lowercase      :    bool = True	,   __lowercase      :    Union[int, float] = 1 / 255	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   **__lowercase      :    Union[str, Any]	,   )							->   None:
        super().__init__(**__lowercase      )
        __UpperCAmelCase							:			List[str]     =						crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
        __UpperCAmelCase							:			int     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )
        __UpperCAmelCase							:			Dict     =						size if size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:			int     =						get_size_dict(__lowercase      )

        __UpperCAmelCase							:			Union[str, Any]     =						do_center_crop
        __UpperCAmelCase							:			List[Any]     =						crop_size
        __UpperCAmelCase							:			List[Any]     =						do_resize
        __UpperCAmelCase							:			List[str]     =						size
        __UpperCAmelCase							:			int     =						resample
        __UpperCAmelCase							:			List[str]     =						do_rescale
        __UpperCAmelCase							:			Dict     =						rescale_factor
        __UpperCAmelCase							:			Union[str, Any]     =						do_normalize
        __UpperCAmelCase							:			str     =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase							:			Union[str, Any]     =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    Optional[int] = None	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Tuple	,   )							->   np.ndarray:
        __UpperCAmelCase							:			Optional[int]     =						self.size if size is None else size
        __UpperCAmelCase							:			Tuple     =						get_size_dict(__lowercase      )
        __UpperCAmelCase							:			Dict     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )

        __UpperCAmelCase   ,				__UpperCAmelCase							:			Tuple     =						get_image_size(__lowercase      )
        __UpperCAmelCase							:			List[str]     =						min(__lowercase	,   __lowercase      )
        __UpperCAmelCase							:			Tuple     =						(size["""height"""] / crop_size["""height"""]) * min_dim
        __UpperCAmelCase							:			List[str]     =						(size["""width"""] / crop_size["""width"""]) * min_dim
        return center_crop(__lowercase	,   size=(cropped_height, cropped_width)	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    PILImageResampling = PIL.Image.BICUBIC	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    str	,   )							->   np.ndarray:
        __UpperCAmelCase							:			Tuple     =						get_size_dict(__lowercase      )
        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()}"""      )
        return resize(
            __lowercase	,   size=(size["""height"""], size["""width"""])	,   resample=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[int, float]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    List[str]	,   )							->   Any:
        return rescale(__lowercase	,   scale=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Tuple	,   )							->   np.ndarray:
        return normalize(__lowercase	,   mean=__lowercase	,   std=__lowercase	,   data_format=__lowercase	,   **__lowercase      )




      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    ImageInput	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Dict[str, int]] = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Dict[str, int]] = None	,   __lowercase      :    PILImageResampling = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[float] = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[str, TensorType]] = None	,   __lowercase      :    ChannelDimension = ChannelDimension.FIRST	,   **__lowercase      :    Union[str, Any]	,   )							->   PIL.Image.Image:
        __UpperCAmelCase							:			Dict     =						do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase							:			List[Any]     =						crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase							:			str     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )
        __UpperCAmelCase							:			List[str]     =						do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:			List[Any]     =						size if size is not None else self.size
        __UpperCAmelCase							:			Optional[Any]     =						get_size_dict(__lowercase      )
        __UpperCAmelCase							:			Optional[Any]     =						resample if resample is not None else self.resample
        __UpperCAmelCase							:			Any     =						do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:			int     =						rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:			List[Any]     =						do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase							:			Dict     =						image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase							:			Optional[int]     =						image_std if image_std is not None else self.image_std

        __UpperCAmelCase							:			int     =						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_center_crop and crop_size is None:
          raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""      )

        if do_resize and size is None:
          raise ValueError("""Size 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 image standard deviation must be specified if do_normalize is True."""      )

        # All transformations expect numpy arrays.
        __UpperCAmelCase							:			Tuple     =						[to_numpy_array(__lowercase      ) for image in images]

        if do_center_crop:
          __UpperCAmelCase							:			Optional[Any]     =						[self.center_crop(__lowercase	,   __lowercase	,   size=__lowercase      ) for image in images]

        if do_resize:
          __UpperCAmelCase							:			Dict     =						[self.resize(image=__lowercase	,   size=__lowercase	,   resample=__lowercase      ) for image in images]

        if do_rescale:
          __UpperCAmelCase							:			List[Any]     =						[self.rescale(image=__lowercase	,   scale=__lowercase      ) for image in images]

        if do_normalize:
          __UpperCAmelCase							:			List[str]     =						[self.normalize(image=__lowercase	,   mean=__lowercase	,   std=__lowercase      ) for image in images]

        __UpperCAmelCase							:			Dict     =						[to_channel_dimension_format(__lowercase	,   __lowercase      ) for image in images]

        __UpperCAmelCase							:			Optional[Any]     =						{"""pixel_values""": images}
        return BatchFeature(data=__lowercase	,   tensor_type=__lowercase      )










class 					a					:



      """simple docstring"""


      def __init__(   self      :    int	,   __lowercase      :    int      )							->   None:
        __UpperCAmelCase							:			str     =						data
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None






def        lowerCamelCase__   (					__lowerCamelCase    :  BinaryTreeNode | None						,	__lowerCamelCase    :  int	):
  if node is None:
    __UpperCAmelCase							:			Optional[int]     =						BinaryTreeNode(__lowerCamelCase	)
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase							:			int     =						insert(node.left_child						,	__lowerCamelCase	)
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase							:			Dict     =						insert(node.right_child						,	__lowerCamelCase	)
  return node






def        lowerCamelCase__   (					__lowerCamelCase    :  None | BinaryTreeNode	):  # if node is None,return
  if node:
    __UpperCAmelCase							:			Optional[Any]     =						inorder(node.left_child	)
    __UpperCAmelCase							:			str     =						[*inorder_array, node.data]
    __UpperCAmelCase							:			Optional[Any]     =						inorder_array + inorder(node.right_child	)
  else:
    __UpperCAmelCase							:			Tuple     =						[]
  return inorder_array






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Any     =						insert(__lowerCamelCase						,	15	)
  insert(__lowerCamelCase						,	10	)
  insert(__lowerCamelCase						,	25	)
  insert(__lowerCamelCase						,	6	)
  insert(__lowerCamelCase						,	14	)
  insert(__lowerCamelCase						,	20	)
  insert(__lowerCamelCase						,	60	)
  return root






def        lowerCamelCase__   (					):
  # main function
  __UpperCAmelCase							:			int     =						make_tree()
  print("""Printing values of binary search tree in Inorder Traversal."""	)
  inorder(__lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


