


'''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


a__  :					List[str]        =       logging.get_logger(__name__)


if is_vision_available():
  import PIL
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	['pixel_values']


    def __init__(     self  ,					a = True  ,					a = None  ,					a = PILImageResampling.BICUBIC  ,					a = True  ,					a = None  ,					a = True  ,					a = 1 / 2_55  ,					a = True  ,					a = None  ,					a = None  ,					a = True  ,					**a  ,					):
          super().__init__(**a    )
          UpperCamelCase__										=				size if size is not None else {"shortest_edge": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          UpperCamelCase__										=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a  ,					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 OPENAI_CLIP_MEAN
          UpperCamelCase__										=				image_std if image_std is not None else OPENAI_CLIP_STD
          UpperCamelCase__										=				do_convert_rgb


    def    __a							(     self  ,					a  ,					a  ,					a = PILImageResampling.BICUBIC  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          if "shortest_edge" not in size:
                raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
          UpperCamelCase__										=				get_resize_output_image_size(a  ,					size=size["shortest_edge"]  ,					default_to_square=a    )
          return resize(a  ,					size=a  ,					resample=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a    )
          if "height" not in size or "width" not in size:
                raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
          return center_crop(a  ,					size=(size["height"], size["width"])  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return rescale(a  ,					scale=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return normalize(a  ,					mean=a  ,					std=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = ChannelDimension.FIRST  ,					**a  ,					):
          UpperCamelCase__										=				do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__										=				size if size is not None else self.size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="size"  ,					default_to_square=a    )
          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__										=				crop_size if crop_size is not None else self.crop_size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="crop_size"  ,					default_to_square=a    )
          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__										=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCamelCase__										=				make_list_of_images(a    )

          if not valid_images(a    ):
                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:
                UpperCamelCase__										=				[convert_to_rgb(a    ) for image in images]

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

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

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

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

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

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

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









from __future__ import annotations

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


