


'''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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


UpperCAmelCase_      :		Any          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[Any]							  =		["""pixel_values"""]




 def __init__(    self		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = PILImageResampling.BILINEAR		,      lowerCamelCase_ = True		,      lowerCamelCase_ = 1 / 2_5_5		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    None:
    super().__init__(**lowerCamelCase_      )
    _a					:  List[str]						=  size if size is not None else {'shortest_edge': 3_8_4}
    _a					:  str						=  get_size_dict(lowerCamelCase_		,      default_to_square=lowerCamelCase_      )

    _a					:  str						=  do_resize
    _a					:  Union[str, Any]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    _a					:  str						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    _a					:  Dict						=  resample
    _a					:  List[str]						=  do_rescale
    _a					:  Any						=  rescale_factor
    _a					:  Union[str, Any]						=  do_normalize
    _a					:  str						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _a					:  List[Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = PILImageResampling.BICUBIC		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    np.ndarray:
    _a					:  List[str]						=  get_size_dict(lowerCamelCase_		,      default_to_square=lowerCamelCase_      )
    if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
    _a					:  Tuple						=  size['shortest_edge']

    if shortest_edge < 3_8_4:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       _a					:  Optional[int]						=  int(shortest_edge / crop_pct      )
       _a					:  List[Any]						=  get_resize_output_image_size(lowerCamelCase_		,      size=lowerCamelCase_		,      default_to_square=lowerCamelCase_      )
       _a					:  Optional[Any]						=  resize(image=lowerCamelCase_		,      size=lowerCamelCase_		,      resample=lowerCamelCase_		,      data_format=lowerCamelCase_		,      **lowerCamelCase_      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=lowerCamelCase_		,      size=(shortest_edge, shortest_edge)		,      data_format=lowerCamelCase_		,      **lowerCamelCase_      )
    else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           lowerCamelCase_		,      size=(shortest_edge, shortest_edge)		,      resample=lowerCamelCase_		,      data_format=lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    int:
    return rescale(lowerCamelCase_		,      scale=lowerCamelCase_		,      data_format=lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    np.ndarray:
    return normalize(lowerCamelCase_		,      mean=lowerCamelCase_		,      std=lowerCamelCase_		,      data_format=lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = ChannelDimension.FIRST		,      **lowerCamelCase_		,      )      ->    PIL.Image.Image:
    _a					:  Union[str, Any]						=  do_resize if do_resize is not None else self.do_resize
    _a					:  Tuple						=  crop_pct if crop_pct is not None else self.crop_pct
    _a					:  Optional[Any]						=  resample if resample is not None else self.resample
    _a					:  Optional[Any]						=  do_rescale if do_rescale is not None else self.do_rescale
    _a					:  Any						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    _a					:  int						=  do_normalize if do_normalize is not None else self.do_normalize
    _a					:  int						=  image_mean if image_mean is not None else self.image_mean
    _a					:  List[str]						=  image_std if image_std is not None else self.image_std

    _a					:  List[Any]						=  size if size is not None else self.size
    _a					:  Any						=  get_size_dict(lowerCamelCase_		,      default_to_square=lowerCamelCase_      )

    _a					:  List[str]						=  make_list_of_images(lowerCamelCase_      )

    if not valid_images(lowerCamelCase_      ):
       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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

    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.
    _a					:  Optional[int]						=  [to_numpy_array(lowerCamelCase_      ) for image in images]

    if do_resize:
       _a					:  Dict						=  [self.resize(image=lowerCamelCase_		,      size=lowerCamelCase_		,      crop_pct=lowerCamelCase_		,      resample=lowerCamelCase_      ) for image in images]

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

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

    _a					:  Any						=  [to_channel_dimension_format(lowerCamelCase_		,      lowerCamelCase_      ) for image in images]

    _a					:  int						=  {'pixel_values': images}
    return BatchFeature(data=lowerCamelCase_		,      tensor_type=lowerCamelCase_      )






'''simple docstring'''




from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: int
 __lowerCAmelCase						: TreeNode | None							  =		None
 __lowerCAmelCase						: TreeNode | None							  =		None





UpperCAmelCase_      :		Dict          =		namedtuple("CoinsDistribResult", "moves excess")


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   if root is None:
      return 0

   # Validation
   def count_nodes(A      ) -> int:
      if node is None:
         return 0

      return count_nodes(node.left      ) + count_nodes(node.right      ) + 1

   def count_coins(A      ) -> int:
      if node is None:
         return 0

      return count_coins(node.left      ) + count_coins(node.right      ) + node.data

   if count_nodes(A      ) != count_coins(A      ):
      raise ValueError('The nodes number should be same as the number of coins'      )

   # Main calculation
   def get_distrib(A      ) -> CoinsDistribResult:

      if node is None:
         return CoinsDistribResult(0		,	1      )

      _a		,						_a					:  Optional[Any]						=  get_distrib(node.left      )
      _a		,						_a					:  Tuple						=  get_distrib(node.right      )

      _a					:  List[str]						=  1 - left_distrib_excess
      _a					:  str						=  1 - right_distrib_excess

      _a					:  Any						=  (
          left_distrib_moves
          + right_distrib_moves
          + abs(A      )
          + abs(A      )
      )
      _a					:  Optional[int]						=  node.data - coins_to_left - coins_to_right

      return CoinsDistribResult(A		,	A      )

   return get_distrib(A      )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()


