


'''simple docstring'''

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				:Union[str, Any]        =      logging.get_logger(__name__)





def lowerCamelCase      (      lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     Union[int, Iterable[int]]		,		lowerCAmelCase    :     bool		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	def constraint_to_multiple_of(lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]=0		,		lowerCAmelCase    :     Tuple=None		):
		__magic_name__	:   Tuple					     =					round(val / multiple		) * multiple

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

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

		return x

	__magic_name__	:   Union[str, Any]					     =					(output_size, output_size) if isinstance(lowerCAmelCase		,		lowerCAmelCase		) else output_size

	__magic_name__	:   Any					     =					get_image_size(lowerCAmelCase		)
	__magic_name__	:   int					     =					output_size

	# determine new height and width
	__magic_name__	:   Optional[Any]					     =					output_height / input_height
	__magic_name__	:   List[Any]					     =					output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width		) < abs(1 - scale_height		):
			# fit width
			__magic_name__	:   List[Any]					     =					scale_width
		else:
			# fit height
			__magic_name__	:   int					     =					scale_height

	__magic_name__	:   Dict					     =					constraint_to_multiple_of(scale_height * input_height		,		multiple=lowerCAmelCase		)
	__magic_name__	:   Any					     =					constraint_to_multiple_of(scale_width * input_width		,		multiple=lowerCAmelCase		)

	return (new_height, new_width)





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    ["""pixel_values"""]







		def __init__( self		:						Tuple  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						PILImageResampling = PILImageResampling.BILINEAR  ,					_A		:						bool = False  ,					_A		:						int = 1  ,					_A		:						bool = True  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Tuple  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'height': 384, 'width': 384}
			__magic_name__	:   Any					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[str]					     =					size
			__magic_name__	:   int					     =					keep_aspect_ratio
			__magic_name__	:   str					     =					ensure_multiple_of
			__magic_name__	:   Optional[int]					     =					resample
			__magic_name__	:   Optional[int]					     =					do_rescale
			__magic_name__	:   int					     =					rescale_factor
			__magic_name__	:   Optional[Any]					     =					do_normalize
			__magic_name__	:   Dict					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   List[Any]					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						bool = False  ,					_A		:						int = 1  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[Any]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			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()}'      )
			__magic_name__	:   int					     =					get_resize_output_image_size(
			    _A  ,					output_size=(size['height'], size['width'])  ,					keep_aspect_ratio=_A  ,					multiple=_A  ,					)
			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Union[int, float]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[Any]  ,					)  ->			Union[str, Any]:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[Any]  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						ImageInput  ,					_A		:						bool = None  ,					_A		:						int = None  ,					_A		:						bool = None  ,					_A		:						int = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						float = None  ,					_A		:						bool = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[str, TensorType]] = None  ,					_A		:						ChannelDimension = ChannelDimension.FIRST  ,					**_A		:						Optional[int]  ,					)  ->			PIL.Image.Image:
			__magic_name__	:   str					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   int					     =					size if size is not None else self.size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   List[str]					     =					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
			__magic_name__	:   Union[str, Any]					     =					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[int]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   str					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   List[Any]					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   List[Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   Union[str, Any]					     =					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 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.
			__magic_name__	:   Tuple					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   str					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   Union[str, Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

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

			__magic_name__	:   Dict					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   Optional[int]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						List[Tuple] = None      )  ->			Optional[Any]:
			__magic_name__	:   Tuple					     =					outputs.logits

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

				if is_torch_tensor(_A      ):
					__magic_name__	:   List[Any]					     =					target_sizes.numpy()

				__magic_name__	:   List[str]					     =					[]

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

			return semantic_segmentation



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent