'''simple docstring'''


from __future__ import annotations






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = vertices
       UpperCAmelCase_				:  Dict  = [[0] * vertices for _ in range(snake_case_		)]


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       print('Vertex \t Distance from Source'		)
       for vertex in range(self.vertices		):
            print(snake_case_ ,						'\t\t' ,						distances_from_source[vertex]		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 1E7
       UpperCAmelCase_				:  List[Any]  = 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:
                 UpperCAmelCase_				:  str  = distances_from_source[vertex]
                 UpperCAmelCase_				:  Dict  = vertex
       return min_index






  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = [int(1E7		)] * self.vertices  # distances from the source
       UpperCAmelCase_				:  List[Any]  = 0
       UpperCAmelCase_				:  Dict  = [False] * self.vertices

       for _ in range(self.vertices		):
            UpperCAmelCase_				:  int  = self.minimum_distance(snake_case_ ,						snake_case_		)
            UpperCAmelCase_				:  Dict  = 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]
                 ):
                      UpperCAmelCase_				:  Optional[int]  = distances[u] + self.graph[u][v]

       self.print_solution(snake_case_		)


if __name__ == "__main__":
 snake_case__	:     Optional[Any]      =    Graph(9)
 snake_case__	:     Tuple      =    [
     [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)









"""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, 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


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: 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					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     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_center_crop and crop_size is None:
                raise ValueError('''Crop size 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.
         __magic_name__			:		Union[str, Any]        =     [to_numpy_array(snake_case      ) for image in images]

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

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

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

         if do_normalize:
                __magic_name__			:		Union[str, Any]        =     [self.normalize(image=snake_case       ,       mean=snake_case       ,       std=snake_case      ) for image in images]

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

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

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
