"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


A :    str										=      logging.get_logger(__name__)




class     lowerCAmelCase	(	UpperCAmelCase__    ):

     '''simple docstring'''







     A 							=			['pixel_values']







     def __init__(			self  :int				,      lowerCamelCase_  :bool = True				,      lowerCamelCase_  :Dict[str, int] = None				,      lowerCamelCase_  :PILImageResampling = PIL.Image.BICUBIC				,      lowerCamelCase_  :bool = True				,      lowerCamelCase_  :Dict[str, int] = None				,      lowerCamelCase_  :Union[int, float] = 1 / 2_5_5				,      lowerCamelCase_  :bool = True				,      lowerCamelCase_  :bool = True				,      lowerCamelCase_  :Optional[Union[float, List[float]]] = None				,      lowerCamelCase_  :Optional[Union[float, List[float]]] = None				,      **lowerCamelCase_  :str				,      )			->      Union[str, Any]:




           """simple docstring"""



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

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







     def     lowerCamelCase__     (			self  :Optional[Any]				,      lowerCamelCase_  :np.ndarray				,      lowerCamelCase_  :Dict[str, int]				,      lowerCamelCase_  :PILImageResampling = PIL.Image.BICUBIC				,      lowerCamelCase_  :Optional[Union[str, ChannelDimension]] = None				,      **lowerCamelCase_  :int				,      )			->      Dict:




           """simple docstring"""



           UpperCamelCase__										=			get_size_dict(__lowercase     )
           if "height" not in size or "width" not in size:
                 raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'     )
           return resize(
               __lowercase				,      size=(size["height"], size["width"])				,      resample=__lowercase				,      data_format=__lowercase				,      **__lowercase     )







     def     lowerCamelCase__     (			self  :Dict				,      lowerCamelCase_  :np.ndarray				,      lowerCamelCase_  :Dict[str, int]				,      lowerCamelCase_  :Optional[Union[str, ChannelDimension]] = None				,      **lowerCamelCase_  :Optional[int]				,      )			->      Optional[Any]:




           """simple docstring"""



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







     def     lowerCamelCase__     (			self  :List[Any]				,      lowerCamelCase_  :np.ndarray				,      lowerCamelCase_  :Union[int, float]				,      lowerCamelCase_  :Optional[Union[str, ChannelDimension]] = None				,      **lowerCamelCase_  :List[str]				,      )			->      Optional[Any]:




           """simple docstring"""



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







     def     lowerCamelCase__     (			self  :Optional[Any]				,      lowerCamelCase_  :np.ndarray				,      lowerCamelCase_  :Union[float, List[float]]				,      lowerCamelCase_  :Union[float, List[float]]				,      lowerCamelCase_  :Optional[Union[str, ChannelDimension]] = None				,      **lowerCamelCase_  :Dict				,      )			->      Union[str, Any]:




           """simple docstring"""



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







     def     lowerCamelCase__     (			self  :Dict				,      lowerCamelCase_  :ImageInput				,      lowerCamelCase_  :bool = None				,      lowerCamelCase_  :Dict[str, int] = None				,      lowerCamelCase_  :int=None				,      lowerCamelCase_  :bool = None				,      lowerCamelCase_  :Dict[str, int] = None				,      lowerCamelCase_  :bool = None				,      lowerCamelCase_  :float = None				,      lowerCamelCase_  :bool = None				,      lowerCamelCase_  :Optional[Union[float, List[float]]] = None				,      lowerCamelCase_  :Optional[Union[float, List[float]]] = None				,      lowerCamelCase_  :Optional[Union[str, TensorType]] = None				,      lowerCamelCase_  :ChannelDimension = ChannelDimension.FIRST				,      **lowerCamelCase_  :Optional[int]				,      )			->      List[str]:




           """simple docstring"""



           UpperCamelCase__										=			do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__										=			resample if resample is not None else self.resample
           UpperCamelCase__										=			do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase__										=			do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__										=			rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__										=			do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__										=			image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__										=			image_std if image_std is not None else self.image_std

           UpperCamelCase__										=			size if size is not None else self.size
           UpperCamelCase__										=			get_size_dict(__lowercase     )
           UpperCamelCase__										=			crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__										=			get_size_dict(__lowercase				,      param_name="crop_size"     )

           UpperCamelCase__										=			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_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."     )

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

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

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

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

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

           UpperCamelCase__										=			[to_channel_dimension_format(__lowercase				,      __lowercase     ) for image in images]

           UpperCamelCase__										=			{"pixel_values": images}
           return BatchFeature(data=__lowercase				,      tensor_type=__lowercase     )





from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	def is_valid_tree(_A				) -> bool:
		if node is None:
			return True

		if not isinstance(_A	,      _A				):
			return False

		try:
			float(node.data				)
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left				) and is_valid_tree(node.right				)

	if not is_valid_tree(_A				):
		raise ValueError(
		    "Each node should be type of TreeNode and data should be float."				)

	def is_binary_search_tree_recursive_check(
	    _A	,      _A	,      _A				) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left	,      _A	,      node.data				)
		    and is_binary_search_tree_recursive_check(
		        node.right	,      node.data	,      _A				)
		)

	return is_binary_search_tree_recursive_check(_A	,      -float("inf"				)	,      float("inf"				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
