
from __future__ import annotations




class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			int   )      ->     None:







    """simple docstring"""






    __magic_name__    					=	vertices
    __magic_name__    					=	[[0] * vertices for _ in range(UpperCamelCase__   )]
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			list[int]   )      ->     None:







    """simple docstring"""






    print("""Vertex \t Distance from Source"""   )
    for vertex in range(self.vertices   ):
      print(UpperCamelCase__      ,							"""\t\t"""      ,							distances_from_source[vertex]   )
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			list[int]      ,							UpperCamelCase__      :			list[bool]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	1E7
    __magic_name__    					=	0

    # Search not nearest vertex not in the shortest path tree
    for vertex in range(self.vertices   ):
      if distances_from_source[vertex] < minimum and visited[vertex] is False:
        __magic_name__    					=	distances_from_source[vertex]
        __magic_name__    					=	vertex
    return min_index





  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			int   )      ->     None:







    """simple docstring"""






    __magic_name__    					=	[int(1E7   )] * self.vertices  # distances from the source
    __magic_name__    					=	0
    __magic_name__    					=	[False] * self.vertices

    for _ in range(self.vertices   ):
      __magic_name__    					=	self.minimum_distance(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	True

      # Update dist value of the adjacent vertices
      # of the picked vertex only if the current
      # distance is greater than new distance and
      # the vertex in not in the shortest path tree
      for v in range(self.vertices   ):
        if (
            self.graph[u][v] > 0
            and visited[v] is False
            and distances[v] > distances[u] + self.graph[u][v]
        ):
          __magic_name__    					=	distances[u] + self.graph[u][v]

    self.print_solution(UpperCamelCase__   )


if __name__ == "__main__":
    __lowerCAmelCase      :					Any   	=       Graph(9)
    __lowerCAmelCase      :					Optional[int]   	=       [
        [0, 4, 0, 0, 0, 0, 0, 8, 0],
        [4, 0, 8, 0, 0, 0, 0, 11, 0],
        [0, 8, 0, 7, 0, 4, 0, 0, 2],
        [0, 0, 7, 0, 9, 14, 0, 0, 0],
        [0, 0, 0, 9, 0, 10, 0, 0, 0],
        [0, 0, 4, 14, 10, 0, 2, 0, 0],
        [0, 0, 0, 0, 0, 2, 0, 1, 6],
        [8, 11, 0, 0, 0, 0, 1, 0, 7],
        [0, 0, 2, 0, 0, 0, 6, 7, 0],
    ]
    graph.dijkstra(0)



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 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, logging


__lowerCAmelCase      :					str   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""pixel_values"""]
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Dict[str, int]] = None      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[int, float] = 1 / 255      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )
    __magic_name__    					=	size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    __magic_name__    					=	do_resize
    __magic_name__    					=	do_rescale
    __magic_name__    					=	do_normalize
    __magic_name__    					=	size
    __magic_name__    					=	resample
    __magic_name__    					=	rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__    					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Dict[str, int]      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Optional[int]      ,							)      ->     np.ndarray:







    """simple docstring"""






    __magic_name__    					=	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()}'''   )
    return resize(
        UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			float      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     np.ndarray:







    """simple docstring"""






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







    """simple docstring"""






    return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )





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







    """simple docstring"""






    __magic_name__    					=	do_resize if do_resize is not None else self.do_resize
    __magic_name__    					=	do_rescale if do_rescale is not None else self.do_rescale
    __magic_name__    					=	do_normalize if do_normalize is not None else self.do_normalize
    __magic_name__    					=	resample if resample is not None else self.resample
    __magic_name__    					=	rescale_factor if rescale_factor is not None else self.rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else self.image_mean
    __magic_name__    					=	image_std if image_std is not None else self.image_std

    __magic_name__    					=	size if size is not None else self.size
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )

    __magic_name__    					=	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:
      raise ValueError("""Size 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."""   )

    # All transformations expect numpy arrays.
    __magic_name__    					=	[to_numpy_array(UpperCamelCase__   ) for image in images]

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

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

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

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

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

