






"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


UpperCamelCase					=     logging.get_logger(__name__)


if is_vision_available():
  import PIL







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							["""pixel_values"""]





   def __init__(       self					, UpperCAmelCase_ = True					, UpperCAmelCase_ = None					, UpperCAmelCase_ = PILImageResampling.BICUBIC					, UpperCAmelCase_ = True					, UpperCAmelCase_ = None					, UpperCAmelCase_ = True					, UpperCAmelCase_ = 1 / 255					, UpperCAmelCase_ = True					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = True					, **UpperCAmelCase_					, )			->   None:




          '''simple docstring'''




          super().__init__(**UpperCAmelCase_)
          lowercase__:							Dict         =     size if size is not None else {"shortest_edge": 224}
          lowercase__:							List[str]         =     get_size_dict(UpperCAmelCase_					, default_to_square=UpperCAmelCase_)
          lowercase__:							Union[str, Any]         =     crop_size if crop_size is not None else {"height": 224, "width": 224}
          lowercase__:							int         =     get_size_dict(UpperCAmelCase_)

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = PILImageResampling.BICUBIC					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   np.ndarray:




          '''simple docstring'''




          lowercase__:							int         =     get_size_dict(UpperCAmelCase_					, default_to_square=UpperCAmelCase_)
          lowercase__:							List[Any]         =     get_resize_output_image_size(
              UpperCAmelCase_					, size=(size["height"], size["width"])					, default_to_square=UpperCAmelCase_)
          return resize(UpperCAmelCase_					, size=UpperCAmelCase_					, resample=UpperCAmelCase_					, data_format=UpperCAmelCase_					, **UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   np.ndarray:




          '''simple docstring'''




          lowercase__:							int         =     get_size_dict(UpperCAmelCase_)
          return center_crop(UpperCAmelCase_					, size=(size["height"], size["width"])					, data_format=UpperCAmelCase_					, **UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   Optional[int]:




          '''simple docstring'''




          return rescale(UpperCAmelCase_					, scale=UpperCAmelCase_					, data_format=UpperCAmelCase_					, **UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   np.ndarray:




          '''simple docstring'''




          return normalize(UpperCAmelCase_					, mean=UpperCAmelCase_					, std=UpperCAmelCase_					, data_format=UpperCAmelCase_					, **UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = ChannelDimension.FIRST					, **UpperCAmelCase_					, )			->   PIL.Image.Image:




          '''simple docstring'''




          lowercase__:							List[str]         =     do_resize if do_resize is not None else self.do_resize
          lowercase__:							Optional[int]         =     size if size is not None else self.size
          lowercase__:							Dict         =     get_size_dict(UpperCAmelCase_					, default_to_square=UpperCAmelCase_)
          lowercase__:							int         =     resample if resample is not None else self.resample
          lowercase__:							int         =     do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase__:							List[str]         =     crop_size if crop_size is not None else self.crop_size
          lowercase__:							Union[str, Any]         =     get_size_dict(UpperCAmelCase_)
          lowercase__:							List[Any]         =     do_rescale if do_rescale is not None else self.do_rescale
          lowercase__:							Optional[Any]         =     rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase__:							Dict         =     do_normalize if do_normalize is not None else self.do_normalize
          lowercase__:							Tuple         =     image_mean if image_mean is not None else self.image_mean
          lowercase__:							Tuple         =     image_std if image_std is not None else self.image_std
          lowercase__:							Optional[Any]         =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowercase__:							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:
                 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.")

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

          # All transformations expect numpy arrays.
          lowercase__:							List[Any]         =     [to_numpy_array(UpperCAmelCase_) for image in images]

          if do_resize:
                 lowercase__:							List[str]         =     [self.resize(image=UpperCAmelCase_					, size=UpperCAmelCase_					, resample=UpperCAmelCase_) for image in images]

          if do_center_crop:
                 lowercase__:							List[str]         =     [self.center_crop(image=UpperCAmelCase_					, size=UpperCAmelCase_) for image in images]

          if do_rescale:
                 lowercase__:							Tuple         =     [self.rescale(image=UpperCAmelCase_					, scale=UpperCAmelCase_) for image in images]

          if do_normalize:
                 lowercase__:							Union[str, Any]         =     [self.normalize(image=UpperCAmelCase_					, mean=UpperCAmelCase_					, std=UpperCAmelCase_) for image in images]

          lowercase__:							List[Any]         =     [to_channel_dimension_format(UpperCAmelCase_					, UpperCAmelCase_) for image in images]

          lowercase__:							Optional[int]         =     {"pixel_values": images}
          return BatchFeature(data=UpperCAmelCase_					, tensor_type=UpperCAmelCase_)












def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
