




'''simple docstring'''







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A     =logging.get_logger(__name__)


class 			_a  (	__a ):
       __a				:     Dict   				=					["""pixel_values"""]
       def __init__(	self						:	int   ,				lowercase						:	bool = True   ,				lowercase						:	Union[int, float] = 1 / 255   ,				lowercase						:	bool = True   ,				lowercase						:	int = 8   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)

           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_pad
           UpperCAmelCase			       =		pad_size
       def     A      (	self						:	Any   ,				lowercase						:	np.ndarray   ,				lowercase						:	float   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	str				):



           '''simple docstring'''

           return rescale(lowercase   ,				scale=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	np.ndarray   ,				lowercase						:	int   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		get_image_size(lowercase				)
           UpperCAmelCase			       =		(old_height // size + 1) * size - old_height
           UpperCAmelCase			       =		(old_width // size + 1) * size - old_width

           return pad(lowercase   ,				((0, pad_height), (0, pad_width))   ,				mode='''symmetric'''   ,				data_format=lowercase				)







       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	ImageInput   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[float] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				lowercase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**lowercase						:	Optional[Any]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase			       =		rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase			       =		do_pad if do_pad is not None else self.do_pad
           UpperCAmelCase			       =		pad_size if pad_size is not None else self.pad_size

           UpperCAmelCase			       =		make_list_of_images(lowercase				)

           if not valid_images(lowercase				):
               raise ValueError(
                   '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                   '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

           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.
           UpperCAmelCase			       =		[to_numpy_array(lowercase				) for image in images]

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

           if do_pad:
               UpperCAmelCase			       =		[self.pad(lowercase   ,				size=lowercase				) for image in images]

           UpperCAmelCase			       =		[to_channel_dimension_format(lowercase   ,				lowercase				) for image in images]

           UpperCAmelCase			       =		{'''pixel_values''': images}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)












'''simple docstring'''







from __future__ import annotations

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

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				dict[str, list[str]]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       graph
														# mapping node to its parent in resulting breadth first tree
														lowerCamelCase__					:	dict[str, str | None]         =       {}
														lowerCamelCase__					:	Any         =       source_vertex




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       {self.source_vertex}
														lowerCamelCase__					:	List[str]         =       None
														lowerCamelCase__					:	Tuple         =       [self.source_vertex]  # first in first out queue

														while queue:
																					lowerCamelCase__					:	Tuple         =       queue.pop(0     )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(UpperCamelCase__     )
																																			lowerCamelCase__					:	List[str]         =       vertex
																																			queue.append(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):
														if target_vertex == self.source_vertex:
																					return self.source_vertex

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

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


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

