"""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_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


lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	List[Any]    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[int, float] = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'shortest_edge': 384}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)

									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									# Default value set here for backwards compatibility where the value in config is None
									UpperCAmelCase__             =							crop_pct if crop_pct is not None else 224 / 256
									UpperCAmelCase__             =							resample
									UpperCAmelCase__             =							do_rescale
									UpperCAmelCase__             =							rescale_factor
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__             =							image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	float    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BICUBIC    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									if "shortest_edge" not in size:
													raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
									UpperCAmelCase__             =							size['shortest_edge']

									if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													UpperCAmelCase__             =							int(shortest_edge / crop_pct			)
													UpperCAmelCase__             =							get_resize_output_image_size(lowerCamelCase__    ,size=lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
													UpperCAmelCase__             =							resize(image=lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=lowerCamelCase__    ,size=(shortest_edge, shortest_edge)    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)
									else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    lowerCamelCase__    ,size=(shortest_edge, shortest_edge)    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[int, float]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	str    ,):
									return rescale(lowerCamelCase__    ,scale=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Union[float, List[float]]    ,lowerCamelCase__   :	Union[float, List[float]]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	List[str]    ,):
									return normalize(lowerCamelCase__    ,mean=lowerCamelCase__    ,std=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	PILImageResampling = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	float = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	ChannelDimension = ChannelDimension.FIRST    ,**lowerCamelCase__   :	Any    ,):
									UpperCAmelCase__             =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__             =							crop_pct if crop_pct is not None else self.crop_pct
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									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_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__             =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__             =							image_std if image_std is not None else self.image_std

									UpperCAmelCase__             =							size if size is not None else self.size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)

									UpperCAmelCase__             =							make_list_of_images(lowerCamelCase__			)

									if not valid_images(lowerCamelCase__			):
													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_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError('crop_pct must be specified if size < 384.'			)

									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.
									UpperCAmelCase__             =							[to_numpy_array(lowerCamelCase__			) for image in images]

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

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

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

									UpperCAmelCase__             =							[to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			) for image in images]

									UpperCAmelCase__             =							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__    ,tensor_type=lowerCamelCase__			)







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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


