

'''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


__snake_case			:     Optional[int]										=      logging.get_logger(__name__)
def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		Union[int, Iterable[int]],		__snake_case					:		bool,		__snake_case					:		int	)	->							Optional[int]:






						"""simple docstring"""





						def constraint_to_multiple_of(__snake_case					:		int,		__snake_case					:		Dict,		__snake_case					:		Optional[Any]=0,		__snake_case					:		Any=None	):
												A__					:       Optional[Any]     =round(val / multiple	) * multiple

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

												if x < min_val:
																		A__					:       Optional[Any]     =math.ceil(val / multiple	) * multiple

												return x

						A__					:       Tuple     =(output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE__,		SCREAMING_SNAKE_CASE__	) else output_size

						A__   ,						A__					:       Tuple     =get_image_size(SCREAMING_SNAKE_CASE__	)
						A__   ,						A__					:       List[str]     =output_size

						# determine new height and width
						A__					:       Optional[int]     =output_height / input_height
						A__					:       Optional[int]     =output_width / input_width

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

						A__					:       Any     =constraint_to_multiple_of(scale_height * input_height,		multiple=SCREAMING_SNAKE_CASE__	)
						A__					:       List[Any]     =constraint_to_multiple_of(scale_width * input_width,		multiple=SCREAMING_SNAKE_CASE__	)

						return (new_height, new_width)



class lowerCamelCase			(  lowerCamelCase_						):



			'''simple docstring'''






			__snake_case        =	["""pixel_values"""]


			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Dict[str, int] = None		,      lowerCAmelCase_	:						PILImageResampling = PILImageResampling.BILINEAR		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						int = 1		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[int, float] = 1 / 2_55		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      **lowerCAmelCase_	:						List[Any]		,      )      ->		Dict:





									'''simple docstring'''
									super().__init__(**_UpperCAmelCase			)
									A__					:       List[str]     =size if size is not None else {"""height""": 3_84, """width""": 3_84}
									A__					:       Any     =get_size_dict(_UpperCAmelCase			)
									A__					:       Any     =do_resize
									A__					:       str     =size
									A__					:       int     =keep_aspect_ratio
									A__					:       Tuple     =ensure_multiple_of
									A__					:       List[Any]     =resample
									A__					:       Tuple     =do_rescale
									A__					:       List[str]     =rescale_factor
									A__					:       Optional[Any]     =do_normalize
									A__					:       Any     =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A__					:       Any     =image_std if image_std is not None else IMAGENET_STANDARD_STD


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





									'''simple docstring'''
									A__					:       Optional[int]     =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()}"			)
									A__					:       List[Any]     =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 lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						np.ndarray		,      lowerCAmelCase_	:						Union[int, float]		,      lowerCAmelCase_	:						Optional[Union[str, ChannelDimension]] = None		,      **lowerCAmelCase_	:						Dict		,      )      ->		Any:





									'''simple docstring'''
									return rescale(_UpperCAmelCase		,      scale=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase			)


			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						np.ndarray		,      lowerCAmelCase_	:						Union[float, List[float]]		,      lowerCAmelCase_	:						Union[float, List[float]]		,      lowerCAmelCase_	:						Optional[Union[str, ChannelDimension]] = None		,      **lowerCAmelCase_	:						Dict		,      )      ->		Tuple:





									'''simple docstring'''
									return normalize(_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase			)


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						ImageInput		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						int = None		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						int = None		,      lowerCAmelCase_	:						PILImageResampling = None		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						float = None		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      lowerCAmelCase_	:						ChannelDimension = ChannelDimension.FIRST		,      **lowerCAmelCase_	:						Tuple		,      )      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =do_resize if do_resize is not None else self.do_resize
									A__					:       Optional[int]     =size if size is not None else self.size
									A__					:       Optional[Any]     =get_size_dict(_UpperCAmelCase			)
									A__					:       Union[str, Any]     =keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									A__					:       Optional[int]     =ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									A__					:       List[str]     =resample if resample is not None else self.resample
									A__					:       Optional[int]     =do_rescale if do_rescale is not None else self.do_rescale
									A__					:       Tuple     =rescale_factor if rescale_factor is not None else self.rescale_factor
									A__					:       Dict     =do_normalize if do_normalize is not None else self.do_normalize
									A__					:       Optional[int]     =image_mean if image_mean is not None else self.image_mean
									A__					:       Union[str, Any]     =image_std if image_std is not None else self.image_std

									A__					:       Any     =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.
									A__					:       List[Any]     =[to_numpy_array(_UpperCAmelCase			) for image in images]

									if do_resize:
															A__					:       Dict     =[self.resize(image=_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase			) for image in images]

									if do_rescale:
															A__					:       Optional[int]     =[self.rescale(image=_UpperCAmelCase		,      scale=_UpperCAmelCase			) for image in images]

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

									A__					:       int     =[to_channel_dimension_format(_UpperCAmelCase		,      _UpperCAmelCase			) for image in images]

									A__					:       Any     ={"""pixel_values""": images}
									return BatchFeature(data=_UpperCAmelCase		,      tensor_type=_UpperCAmelCase			)



			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						List[Tuple] = None			)      ->		Dict:





									'''simple docstring'''
									A__					:       Tuple     =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			):
																					A__					:       Dict     =target_sizes.numpy()

															A__					:       Optional[int]     =[]

															for idx in range(len(_UpperCAmelCase			)			):
																					A__					:       Dict     =torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0			)		,      size=target_sizes[idx]		,      mode="""bilinear"""		,      align_corners=_UpperCAmelCase			)
																					A__					:       Union[str, Any]     =resized_logits[0].argmax(dim=0			)
																					semantic_segmentation.append(_UpperCAmelCase			)
									else:
															A__					:       str     =logits.argmax(dim=1			)
															A__					:       Optional[Any]     =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

									return semantic_segmentation









'''simple docstring'''



from __future__ import annotations

from typing import Any



class lowerCamelCase			:



			'''simple docstring'''






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





									'''simple docstring'''
									A__					:       Any     =num_of_nodes
									A__					:       list[list[int]]     =[]
									A__					:       dict[int, int]     ={}


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''
									self.m_edges.append([u_node, v_node, weight]			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int			)      ->		int:





									'''simple docstring'''

									if self.m_component[u_node] == u_node:
															return u_node
									return self.find_component(self.m_component[u_node]			)


			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if self.m_component[u_node] != u_node:
															for k in self.m_component:
																					A__					:       str     =self.find_component(lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						list[int]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if component_size[u_node] <= component_size[v_node]:
															A__					:       int     =v_node
															component_size[v_node] += component_size[u_node]
															self.set_component(lowerCAmelCase_			)

									elif component_size[u_node] >= component_size[v_node]:
															A__					:       List[str]     =self.find_component(lowerCAmelCase_			)
															component_size[u_node] += component_size[v_node]
															self.set_component(lowerCAmelCase_			)



			def lowercase__						(				self	:						str			)      ->		None:





									'''simple docstring'''
									A__					:       Union[str, Any]     =[]
									A__					:       List[str]     =0

									A__					:       list[Any]     =[-1] * self.m_num_of_nodes

									# A list of components (initialized to all of the nodes)
									for node in range(self.m_num_of_nodes			):
															self.m_component.update({node: node}			)
															component_size.append(1			)

									A__					:       List[str]     =self.m_num_of_nodes

									while num_of_components > 1:
															for edge in self.m_edges:
																					A__   ,						A__   ,						A__					:       Any     =edge

																					A__					:       Tuple     =self.m_component[u]
																					A__					:       Optional[Any]     =self.m_component[v]

																					if u_component != v_component:

																											for component in (u_component, v_component):
																																	if (
																																	    minimum_weight_edge[component] == -1
																																	    or minimum_weight_edge[component][2] > w
																																	):
																																							A__					:       Optional[int]     =[u, v, w]

															for edge in minimum_weight_edge:
																					if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
																											A__   ,						A__   ,						A__					:       Tuple     =edge

																											A__					:       Any     =self.m_component[u]
																											A__					:       Optional[Any]     =self.m_component[v]

																											if u_component != v_component:
																																	mst_weight += w
																																	self.union(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
																																	print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"			)
																																	num_of_components -= 1

															A__					:       int     =[-1] * self.m_num_of_nodes
									print(f"The total weight of the minimal spanning tree is: {mst_weight}"			)


def 						__lowerCamelCase	(       )	->							None:






						"""simple docstring"""







if __name__ == "__main__":
		import doctest

		doctest.testmod()

