
from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
      import PIL


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["pixel_values"]
   def __init__(self :					str					,				snake_case_ :					bool = True					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = PILImageResampling.BILINEAR					,				snake_case_ :					bool = True					,				snake_case_ :					Union[int, float] = 1 / 2_5_5					,				snake_case_ :					bool = True					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				**snake_case_ :					Tuple					,				):
    super().__init__(**snake_case_   )
    __a :  Optional[Any]						=  size if size is not None else {'''shortest_edge''': 3_8_4}
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  Tuple						=  do_resize
    __a :  List[str]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    __a :  List[str]						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    __a :  Tuple						=  resample
    __a :  Any						=  do_rescale
    __a :  Optional[Any]						=  rescale_factor
    __a :  int						=  do_normalize
    __a :  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a :  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					np.ndarray					,				snake_case_ :					Dict[str, int]					,				snake_case_ :					float					,				snake_case_ :					PILImageResampling = PILImageResampling.BICUBIC					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					str					,				):
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )
    if "shortest_edge" not in size:
     raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"   )
    __a :  Any						=  size['''shortest_edge''']

    if shortest_edge < 3_8_4:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __a :  Union[str, Any]						=  int(shortest_edge / crop_pct   )
     __a :  Tuple						=  get_resize_output_image_size(snake_case_					,				size=snake_case_					,				default_to_square=snake_case_   )
     __a :  List[str]						=  resize(image=snake_case_					,				size=snake_case_					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=snake_case_					,				size=(shortest_edge, shortest_edge)					,				data_format=snake_case_					,				**snake_case_   )
    else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         snake_case_					,				size=(shortest_edge, shortest_edge)					,				resample=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Union[float, List[float]]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return normalize(snake_case_					,				mean=snake_case_					,				std=snake_case_					,				data_format=snake_case_					,				**snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					ImageInput					,				snake_case_ :					bool = None					,				snake_case_ :					Dict[str, int] = None					,				snake_case_ :					float = None					,				snake_case_ :					PILImageResampling = None					,				snake_case_ :					bool = None					,				snake_case_ :					float = None					,				snake_case_ :					bool = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[float, List[float]]] = None					,				snake_case_ :					Optional[Union[str, TensorType]] = None					,				snake_case_ :					ChannelDimension = ChannelDimension.FIRST					,				**snake_case_ :					Dict					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  Dict						=  crop_pct if crop_pct is not None else self.crop_pct
    __a :  Tuple						=  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 :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  int						=  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 :  Optional[int]						=  image_std if image_std is not None else self.image_std

    __a :  Dict						=  size if size is not None else self.size
    __a :  List[str]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  List[str]						=  make_list_of_images(snake_case_   )

    if not valid_images(snake_case_   ):
     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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
     raise ValueError('''crop_pct must be specified if size < 384.'''   )

    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(snake_case_   ) for image in images]

    if do_resize:
     __a :  List[str]						=  [self.resize(image=snake_case_					,				size=snake_case_					,				crop_pct=snake_case_					,				resample=snake_case_   ) for image in images]

    if do_rescale:
     __a :  List[Any]						=  [self.rescale(image=snake_case_					,				scale=snake_case_   ) for image in images]

    if do_normalize:
     __a :  Optional[int]						=  [self.normalize(image=snake_case_					,				mean=snake_case_					,				std=snake_case_   ) for image in images]

    __a :  str						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

    __a :  Tuple						=  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_					,				tensor_type=snake_case_   )


from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  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 :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[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 :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

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

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





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
