




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 			a__    (					snake_case__      ):
						_a							:    Union[str, Any]					=    ["""pixel_values"""]






						def __init__(   self		,			_A = True		,			_A = 3_2		,			_A=PILImageResampling.BILINEAR		,			_A = True		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       do_rescale
											__lowerCAmelCase				   =       size_divisor
											__lowerCAmelCase				   =       resample
											super().__init__(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       get_image_size(_A			)
											# Rounds the height and width down to the closest multiple of size_divisor
											__lowerCAmelCase				   =       height // size_divisor * size_divisor
											__lowerCAmelCase				   =       width // size_divisor * size_divisor
											__lowerCAmelCase				   =       resize(_A		,			(new_h, new_w)		,			resample=_A		,			data_format=_A		,			**_A			)
											return image






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A			):




											"""simple docstring"""



											return rescale(image=_A		,			scale=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None		,			_A=None		,			_A = None		,			_A = None		,			_A = ChannelDimension.FIRST		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase				   =       do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase				   =       size_divisor if size_divisor is not None else self.size_divisor
											__lowerCAmelCase				   =       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"			)

											__lowerCAmelCase				   =       make_list_of_images(_A			)

											if not valid_images(_A			):
																raise ValueError("Invalid image(s)"			)

											# All transformations expect numpy arrays.
											__lowerCAmelCase				   =       [to_numpy_array(_A			) for img in images]

											if do_resize:
																__lowerCAmelCase				   =       [self.resize(_A		,			size_divisor=_A		,			resample=_A			) for image in images]

											if do_rescale:
																__lowerCAmelCase				   =       [self.rescale(_A		,			scale=1 / 2_5_5			) 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			)





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




