


"""simple docstring"""


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =["""pixel_values"""]

							def __init__(	self       ,  __a = True       ,  __a = None       ,  __a = 0.9       ,  __a = PILImageResampling.BICUBIC       ,  __a = True       ,  __a = None       ,  __a = 1 / 2_55       ,  __a = True       ,  __a = True       ,  __a = None       ,  __a = None       ,  **__a       ,  ):
										super().__init__(**__a      )
										__lowerCAmelCase							       =     size if size is not None else {"shortest_edge": 2_24}
										__lowerCAmelCase							       =     get_size_dict(__a       ,  default_to_square=__a      )
										__lowerCAmelCase							       =     crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase							       =     get_size_dict(__a       ,  param_name="crop_size"      )

										__lowerCAmelCase							       =     do_resize
										__lowerCAmelCase							       =     size
										__lowerCAmelCase							       =     crop_pct
										__lowerCAmelCase							       =     resample
										__lowerCAmelCase							       =     do_center_crop
										__lowerCAmelCase							       =     crop_size
										__lowerCAmelCase							       =     do_rescale
										__lowerCAmelCase							       =     rescale_factor
										__lowerCAmelCase							       =     do_normalize
										__lowerCAmelCase							       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__lowerCAmelCase							       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a = None       ,  __a = PILImageResampling.BICUBIC       ,  __a = None       ,  **__a       ,  ):
										__lowerCAmelCase							       =     get_size_dict(__a       ,  default_to_square=__a      )
										if "shortest_edge" not in size and ("height" not in size or "width" not in size):
													raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"      )
										if crop_pct is not None:
													if "shortest_edge" in size:
																__lowerCAmelCase							       =     int(size["shortest_edge"] / crop_pct      )
													elif "height" in size and "width" in size:
																if size["height"] == size["width"]:
																			__lowerCAmelCase							       =     int(size["height"] / crop_pct      )
																else:
																			__lowerCAmelCase							       =     (int(size["height"] / crop_pct      ), int(size["width"] / crop_pct      ))
													else:
																raise ValueError("Invalid size for resize: {}".format(__a      )      )

													__lowerCAmelCase							       =     get_resize_output_image_size(__a       ,  size=__a       ,  default_to_square=__a      )
										else:
													if "shortest_edge" in size:
																__lowerCAmelCase							       =     get_resize_output_image_size(__a       ,  size=size["shortest_edge"]       ,  default_to_square=__a      )
													elif "height" in size and "width" in size:
																__lowerCAmelCase							       =     (size["height"], size["width"])
													else:
																raise ValueError("Invalid size for resize: {}".format(__a      )      )

										return resize(__a       ,  size=__a       ,  resample=__a       ,  data_format=__a       ,  **__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a = None       ,  **__a       ,  ):
										__lowerCAmelCase							       =     get_size_dict(__a      )
										if "height" not in size or "width" not in size:
													raise ValueError(f"size must contain 'height' and 'width' as keys. Got {size.keys()}"      )
										return center_crop(__a       ,  size=(size["height"], size["width"])       ,  data_format=__a       ,  **__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a = None       ,  **__a       ,  ):
										return rescale(__a       ,  scale=__a       ,  data_format=__a       ,  **__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  **__a       ,  ):
										return normalize(__a       ,  mean=__a       ,  std=__a       ,  data_format=__a       ,  **__a      )



							def      snake_case							(	self       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = ChannelDimension.FIRST       ,  **__a       ,  ):
										__lowerCAmelCase							       =     do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase							       =     crop_pct if crop_pct is not None else self.crop_pct
										__lowerCAmelCase							       =     resample if resample is not None else self.resample
										__lowerCAmelCase							       =     do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase							       =     do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =     rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase							       =     do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase							       =     image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase							       =     image_std if image_std is not None else self.image_std

										__lowerCAmelCase							       =     size if size is not None else self.size
										__lowerCAmelCase							       =     get_size_dict(__a       ,  default_to_square=__a      )
										__lowerCAmelCase							       =     crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase							       =     get_size_dict(__a       ,  param_name="crop_size"      )

										__lowerCAmelCase							       =     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_center_crop and crop_pct is None:
													raise ValueError("Crop_pct must be specified if do_center_crop 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.
										__lowerCAmelCase							       =     [to_numpy_array(__a      ) for image in images]

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

										if do_center_crop:
													__lowerCAmelCase							       =     [self.center_crop(image=__a       ,  size=__a      ) for image in images]

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

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

										__lowerCAmelCase							       =     [to_channel_dimension_format(__a       ,  __a      ) for image in images]

										__lowerCAmelCase							       =     {"pixel_values": images}
										return BatchFeature(data=__a       ,  tensor_type=__a      )








"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     str(id_      )
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     {}  # {vertex:distance}

							def __lt__(	self       ,  __a      ):
										return self.key < other.key

							def __repr__(	self      ):
										return self.id

							def      snake_case							(	self       ,  __a      ):
										self.neighbors.append(__a      )



							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     weight


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			graph[a - 1].add_neighbor(graph[b - 1] )
			graph[b - 1].add_neighbor(graph[a - 1] )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]       ,					_UpperCamelCase )
			graph[b - 1].add_edge(graph[a - 1]       ,					_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     []
			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0
			__lowerCAmelCase							       =     graph[:]
			while q:
						__lowerCAmelCase							       =     min(_UpperCamelCase )
						q.remove(_UpperCamelCase )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
			for i in range(1       ,					len(_UpperCamelCase ) ):
						a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
			return a


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0

			__lowerCAmelCase							       =     list(_UpperCamelCase )
			hq.heapify(_UpperCamelCase )

			while h:
						__lowerCAmelCase							       =     hq.heappop(_UpperCamelCase )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
												hq.heapify(_UpperCamelCase )

			for i in range(1       ,					len(_UpperCamelCase ) ):
						yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def _lowerCamelCase      (						):

			'''simple docstring'''






if __name__ == "__main__":
	import doctest

	doctest.testmod()




