


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		def constraint_to_multiple_of(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=0						,				SCREAMING_SNAKE_CASE=None     ):
				lowercase__            =      round(val / multiple     ) * multiple

				if max_val is not None and x > max_val:
						lowercase__            =      math.floor(val / multiple     ) * multiple

				if x < min_val:
						lowercase__            =      math.ceil(val / multiple     ) * multiple

				return x

		lowercase__            =      (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ) else output_size

		lowercase__     ,		lowercase__            =      get_image_size(SCREAMING_SNAKE_CASE     )
		lowercase__     ,		lowercase__            =      output_size

		# determine new height and width
		lowercase__            =      output_height / input_height
		lowercase__            =      output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width     ) < abs(1 - scale_height     ):
						# fit width
						lowercase__            =      scale_width
				else:
						# fit height
						lowercase__            =      scale_height

		lowercase__            =      constraint_to_multiple_of(scale_height * input_height						,				multiple=SCREAMING_SNAKE_CASE     )
		lowercase__            =      constraint_to_multiple_of(scale_width * input_width						,				multiple=SCREAMING_SNAKE_CASE     )

		return (new_height, new_width)


class        _a							(			UpperCamelCase__			):
							_lowercase	:			List[Any]        = ['''pixel_values''']

							def __init__(							self:	Optional[Any]    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Dict[str, int] = None    ,						UpperCamelCase_:	PILImageResampling = PILImageResampling.BILINEAR    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Union[int, float] = 1 / 255    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Optional[Union[float, List[float]]] = None    ,						UpperCamelCase_:	Optional[Union[float, List[float]]] = None    ,						**UpperCamelCase_:	List[Any]    ,						)			->   None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__            =      size if size is not None else {'''height''': 384, '''width''': 384}
									lowercase__            =      get_size_dict(UpperCamelCase_							)
									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      keep_aspect_ratio
									lowercase__            =      ensure_multiple_of
									lowercase__            =      resample
									lowercase__            =      do_rescale
									lowercase__            =      rescale_factor
									lowercase__            =      do_normalize
									lowercase__            =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__            =      image_std if image_std is not None else IMAGENET_STANDARD_STD

							def       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	np.ndarray    ,						UpperCamelCase_:	Dict[str, int]    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	PILImageResampling = PILImageResampling.BICUBIC    ,						UpperCamelCase_:	Optional[Union[str, ChannelDimension]] = None    ,						**UpperCamelCase_:	Dict    ,						)			->   np.ndarray:





									"""simple docstring"""







									lowercase__            =      get_size_dict(UpperCamelCase_							)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'							)
									lowercase__            =      get_resize_output_image_size(
									    UpperCamelCase_    ,						output_size=(size['''height'''], size['''width'''])    ,						keep_aspect_ratio=UpperCamelCase_    ,						multiple=UpperCamelCase_    ,						)
									return resize(UpperCamelCase_    ,						size=UpperCamelCase_    ,						resample=UpperCamelCase_    ,						data_format=UpperCamelCase_    ,						**UpperCamelCase_							)

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





									"""simple docstring"""







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

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





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	ImageInput    ,						UpperCamelCase_:	bool = None    ,						UpperCamelCase_:	int = None    ,						UpperCamelCase_:	bool = None    ,						UpperCamelCase_:	int = None    ,						UpperCamelCase_:	PILImageResampling = None    ,						UpperCamelCase_:	bool = None    ,						UpperCamelCase_:	float = 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_:	List[str]    ,						)			->   PIL.Image.Image:





									"""simple docstring"""







									lowercase__            =      do_resize if do_resize is not None else self.do_resize
									lowercase__            =      size if size is not None else self.size
									lowercase__            =      get_size_dict(UpperCamelCase_							)
									lowercase__            =      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									lowercase__            =      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									lowercase__            =      resample if resample is not None else self.resample
									lowercase__            =      do_rescale if do_rescale is not None else self.do_rescale
									lowercase__            =      rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__            =      do_normalize if do_normalize is not None else self.do_normalize
									lowercase__            =      image_mean if image_mean is not None else self.image_mean
									lowercase__            =      image_std if image_std is not None else self.image_std

									lowercase__            =      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 or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize 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.
									lowercase__            =      [to_numpy_array(UpperCamelCase_							) for image in images]

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

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

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

									lowercase__            =      [to_channel_dimension_format(UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									lowercase__            =      {'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase_    ,						tensor_type=UpperCamelCase_							)


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





									"""simple docstring"""







									lowercase__            =      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_							):
													lowercase__            =      target_sizes.numpy()

											lowercase__            =      []

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

									return semantic_segmentation






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()


