






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

a__       =     logging.get_logger(__name__)

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							if isinstance(a  ,					(list, tuple) ) and isinstance(videos[0]  ,					(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(a  ,					(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(a ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Dict      =  ['''pixel_values''']



				def __init__(      self    :    Any			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    PILImageResampling = PILImageResampling.BILINEAR			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Union[int, float] = 1 / 2_5_5			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		**UpperCamelCase__    :    int			,		):






											'''simple docstring'''







											super().__init__(**UpperCamelCase__)
											snake_case__	  =       size if size is not None else {"""shortest_edge""": 2_5_6}
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											snake_case__	  =       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		param_name="""crop_size""")

											snake_case__	  =       do_resize
											snake_case__	  =       size
											snake_case__	  =       do_center_crop
											snake_case__	  =       crop_size
											snake_case__	  =       resample
											snake_case__	  =       do_rescale
											snake_case__	  =       rescale_factor
											snake_case__	  =       offset
											snake_case__	  =       do_normalize
											snake_case__	  =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											snake_case__	  =       image_std if image_std is not None else IMAGENET_STANDARD_STD



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Dict[str, int]			,		UpperCamelCase__    :    PILImageResampling = PILImageResampling.BILINEAR			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    int			,		):






											'''simple docstring'''







											snake_case__	  =       get_size_dict(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											if "shortest_edge" in size:
																		snake_case__	  =       get_resize_output_image_size(UpperCamelCase__			,		size["""shortest_edge"""]			,		default_to_square=UpperCamelCase__)
											elif "height" in size and "width" in size:
																		snake_case__	  =       (size["""height"""], size["""width"""])
											else:
																		raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
											return resize(UpperCamelCase__			,		size=UpperCamelCase__			,		resample=UpperCamelCase__			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Dict[str, int]			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    Union[str, Any]			,		):






											'''simple docstring'''







											snake_case__	  =       get_size_dict(UpperCamelCase__)
											if "height" not in size or "width" not in size:
																		raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
											return center_crop(UpperCamelCase__			,		size=(size["""height"""], size["""width"""])			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Union[int, float]			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    str			,		):






											'''simple docstring'''







											snake_case__	  =       image.astype(np.floataa)
											if offset:
																		snake_case__	  =       image - (scale / 2)
											return rescale(UpperCamelCase__			,		scale=UpperCamelCase__			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Union[float, List[float]]			,		UpperCamelCase__    :    Union[float, List[float]]			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    ImageInput			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    PILImageResampling = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    float = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[ChannelDimension] = ChannelDimension.FIRST			,		):






											'''simple docstring'''







											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_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.""")

											if offset and not do_rescale:
																		raise ValueError("""For offset, do_rescale must also be set to True.""")

											# All transformations expect numpy arrays.
											snake_case__	  =       to_numpy_array(UpperCamelCase__)

											if do_resize:
																		snake_case__	  =       self.resize(image=UpperCamelCase__			,		size=UpperCamelCase__			,		resample=UpperCamelCase__)

											if do_center_crop:
																		snake_case__	  =       self.center_crop(UpperCamelCase__			,		size=UpperCamelCase__)

											if do_rescale:
																		snake_case__	  =       self.rescale(image=UpperCamelCase__			,		scale=UpperCamelCase__			,		offset=UpperCamelCase__)

											if do_normalize:
																		snake_case__	  =       self.normalize(image=UpperCamelCase__			,		mean=UpperCamelCase__			,		std=UpperCamelCase__)

											snake_case__	  =       to_channel_dimension_format(UpperCamelCase__			,		UpperCamelCase__)
											return image



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    ImageInput			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    PILImageResampling = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    float = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		UpperCamelCase__    :    ChannelDimension = ChannelDimension.FIRST			,		**UpperCamelCase__    :    Tuple			,		):






											'''simple docstring'''







											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__	  =       offset if offset is not None else self.offset
											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(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											snake_case__	  =       crop_size if crop_size is not None else self.crop_size
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		param_name="""crop_size""")

											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.""")

											snake_case__	  =       make_batched(UpperCamelCase__)

											snake_case__	  =       [
											    [
											        self._preprocess_image(
											            image=UpperCamelCase__			,		do_resize=UpperCamelCase__			,		size=UpperCamelCase__			,		resample=UpperCamelCase__			,		do_center_crop=UpperCamelCase__			,		crop_size=UpperCamelCase__			,		do_rescale=UpperCamelCase__			,		rescale_factor=UpperCamelCase__			,		offset=UpperCamelCase__			,		do_normalize=UpperCamelCase__			,		image_mean=UpperCamelCase__			,		image_std=UpperCamelCase__			,		data_format=UpperCamelCase__			,		)
											        for img in video
											    ]
											    for video in videos
											]

											snake_case__	  =       {"""pixel_values""": videos}
											return BatchFeature(data=UpperCamelCase__			,		tensor_type=UpperCamelCase__)








from __future__ import annotations


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       data
											snake_case__	  =       None
											snake_case__	  =       None


def _UpperCAmelCase   (	a						:	Node | None ):  # In Order traversal of the tree
							if tree:
														display(tree.left )
														print(tree.data )
														display(tree.right )

def _UpperCAmelCase   (	a						:	Node | None ):
							return 1 + max(depth_of_tree(tree.left )  ,					depth_of_tree(tree.right ) ) if tree else 0

def _UpperCAmelCase   (	a						:	Node ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
							else:
														return not tree.left and not tree.right

def _UpperCAmelCase   (	):  # Main function for testing.
							snake_case__	  =       Node(1 )
							snake_case__	  =       Node(2 )
							snake_case__	  =       Node(3 )
							snake_case__	  =       Node(4 )
							snake_case__	  =       Node(5 )
							snake_case__	  =       Node(6 )
							snake_case__	  =       Node(7 )
							snake_case__	  =       Node(8 )
							snake_case__	  =       Node(9 )

							print(is_full_binary_tree(a ) )
							print(depth_of_tree(a ) )
							print("""Tree is: """ )
							display(a )


if __name__ == "__main__":
	main()
