





from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


lowerCAmelCase_						   =    logging.get_logger(__name__)



class        _lowerCAmelCase    (			_UpperCAmelCase							):


		'''simple docstring'''
		a_							:       Tuple      					=['pixel_values']







		def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[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_55      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[Union[float, List[float]]] = None      ,				UpperCamelCase      :					Optional[Union[float, List[float]]] = None      ,				**UpperCamelCase      :					List[str]      ,				):


				'''simple docstring'''





				super().__init__(**__UpperCamelCase							)
				_snake_case   :				str														=	size if size is not None else {'shortest_edge': 2_56}
				_snake_case   :				Optional[Any]														=	get_size_dict(__UpperCamelCase      ,				default_to_square=__UpperCamelCase							)
				_snake_case   :				Dict														=	crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
				_snake_case   :				Any														=	get_size_dict(__UpperCamelCase      ,				param_name='crop_size'							)
				_snake_case   :				Optional[Any]														=	do_resize
				_snake_case   :				Dict														=	size
				_snake_case   :				List[Any]														=	resample
				_snake_case   :				List[str]														=	do_center_crop
				_snake_case   :				List[str]														=	crop_size
				_snake_case   :				Optional[Any]														=	do_rescale
				_snake_case   :				Optional[Any]														=	rescale_factor
				_snake_case   :				Tuple														=	do_normalize
				_snake_case   :				Tuple														=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				_snake_case   :				int														=	image_std if image_std is not None else IMAGENET_STANDARD_STD







		def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					np.ndarray      ,				UpperCamelCase      :					Dict[str, int]      ,				UpperCamelCase      :					PILImageResampling = PILImageResampling.BICUBIC      ,				UpperCamelCase      :					Optional[Union[str, ChannelDimension]] = None      ,				**UpperCamelCase      :					List[str]      ,				):


				'''simple docstring'''





				_snake_case   :				Optional[Any]														=	get_size_dict(__UpperCamelCase      ,				default_to_square=__UpperCamelCase							)
				if "shortest_edge" not in size:
						raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
				_snake_case   :				List[str]														=	get_resize_output_image_size(__UpperCamelCase      ,				size=size['shortest_edge']      ,				default_to_square=__UpperCamelCase							)
				return resize(__UpperCamelCase      ,				size=__UpperCamelCase      ,				resample=__UpperCamelCase      ,				data_format=__UpperCamelCase      ,				**__UpperCamelCase							)







		def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					np.ndarray      ,				UpperCamelCase      :					Dict[str, int]      ,				UpperCamelCase      :					Optional[Union[str, ChannelDimension]] = None      ,				**UpperCamelCase      :					Tuple      ,				):


				'''simple docstring'''





				_snake_case   :				Optional[int]														=	get_size_dict(__UpperCamelCase							)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""							)
				return center_crop(__UpperCamelCase      ,				size=(size['height'], size['width'])      ,				data_format=__UpperCamelCase      ,				**__UpperCamelCase							)







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


				'''simple docstring'''





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







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


				'''simple docstring'''





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

				_snake_case   :				Optional[int]														=	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.'							)

				# All transformations expect numpy arrays.
				_snake_case   :				Optional[Any]														=	[to_numpy_array(__UpperCamelCase							) for image in images]

				if do_resize:
						_snake_case   :				Union[str, Any]														=	[self.resize(image=__UpperCamelCase      ,				size=__UpperCamelCase      ,				resample=__UpperCamelCase							) for image in images]

				if do_center_crop:
						_snake_case   :				Any														=	[self.center_crop(image=__UpperCamelCase      ,				size=__UpperCamelCase							) for image in images]

				if do_rescale:
						_snake_case   :				int														=	[self.rescale(image=__UpperCamelCase      ,				scale=__UpperCamelCase							) for image in images]

				if do_normalize:
						_snake_case   :				List[Any]														=	[self.normalize(image=__UpperCamelCase      ,				mean=__UpperCamelCase      ,				std=__UpperCamelCase							) for image in images]

				_snake_case   :				Optional[Any]														=	[to_channel_dimension_format(__UpperCamelCase      ,				__UpperCamelCase							) for image in images]

				_snake_case   :				int														=	{'pixel_values': images}
				return BatchFeature(data=__UpperCamelCase      ,				tensor_type=__UpperCamelCase							)







		def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					List[Any]      ,				UpperCamelCase      :					List[Tuple] = None							):


				'''simple docstring'''





				_snake_case   :				Optional[int]														=	outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
						if len(__UpperCamelCase							) != len(__UpperCamelCase							):
								raise ValueError(
								    'Make sure that you pass in as many target sizes as the batch dimension of the logits'							)

						if is_torch_tensor(__UpperCamelCase							):
								_snake_case   :				Tuple														=	target_sizes.numpy()

						_snake_case   :				Union[str, Any]														=	[]

						for idx in range(len(__UpperCamelCase							)							):
								_snake_case   :				List[Any]														=	torch.nn.functional.interpolate(
								    logits[idx].unsqueeze(dim=0							)      ,				size=target_sizes[idx]      ,				mode='bilinear'      ,				align_corners=__UpperCamelCase							)
								_snake_case   :				Dict														=	resized_logits[0].argmax(dim=0							)
								semantic_segmentation.append(__UpperCamelCase							)
				else:
						_snake_case   :				Dict														=	logits.argmax(dim=1							)
						_snake_case   :				int														=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''simple docstring'''





    from pprint import pformat

    if self.left is None and self.right is None:
      return f"""'{self.value}: {self.prior:.5}'"""
    else:
      return pformat(
          {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1							)







  def __str__(      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




