





from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase_     							= TypeVar('T')






class     lowercase__       (	Generic[T]				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      data
       UpperCamelCase__							:				Optional[Any]          =      self
       UpperCamelCase__							:				Tuple          =      0






class     lowercase__       (	Generic[T]				):

   '''simple docstring'''


   def __init__(    self							) ->      None:






       """simple docstring"""
       # map from node name to the node object
       UpperCamelCase__							:				dict[T, DisjointSetTreeNode[T]]          =      {}




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # create a new set with x as its member
       UpperCamelCase__							:				Tuple          =      DisjointSetTreeNode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      DisjointSetTreeNode[T]:






       """simple docstring"""
       # find the set x belongs to (with path-compression)
       UpperCamelCase__							:				Any          =      self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__							:				List[str]          =      self.find_set(elem_ref.parent.data							)
       return elem_ref.parent




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      None:






       """simple docstring"""
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__							:				List[str]          =      nodea
       else:
           UpperCamelCase__							:				List[str]          =      nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      None:






       """simple docstring"""
       # merge 2 disjoint sets
       self.link(self.find_set(__magic_name__							),		self.find_set(__magic_name__							)							)






class     lowercase__       (	Generic[T]				):

   '''simple docstring'''


   def __init__(    self							) ->      None:






       """simple docstring"""
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__							:				dict[T, dict[T, int]]          =      {}




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__							:				str          =      {}




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      None:






       """simple docstring"""
       # add an edge with the given weight
       self.add_node(__magic_name__							)
       self.add_node(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      weight
       UpperCamelCase__							:				str          =      weight




   def       UpperCamelCase__  (    self							) ->      GraphUndirectedWeighted[T]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      []
       UpperCamelCase__							:				List[Any]          =      set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)							)
                   edges.append((start, end, self.connections[start][end])							)
       edges.sort(key=lambda __magic_name__							: x[2]							)

       # creating the disjoint set
       UpperCamelCase__							:				str          =      DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(__magic_name__							)

       # MST generation
       UpperCamelCase__							:				str          =      0
       UpperCamelCase__							:				Optional[Any]          =      0
       UpperCamelCase__							:				str          =      GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections							) - 1:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Any          =      edges[index]
           index += 1
           UpperCamelCase__							:				Union[str, Any]          =      disjoint_set.find_set(__magic_name__							)
           UpperCamelCase__							:				Any          =      disjoint_set.find_set(__magic_name__							)
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(__magic_name__,		__magic_name__,		__magic_name__							)
               disjoint_set.union(__magic_name__,		__magic_name__							)
       return graph








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

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

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

