

"""simple docstring"""




A_				:      Dict           =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   set()
			# keep track of all the paths to be checked
			SCREAMING_SNAKE_CASE__										=   [[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
						SCREAMING_SNAKE_CASE__										=   queue.pop(0     )
						# get the last node from the path
						SCREAMING_SNAKE_CASE__										=   path[-1]
						if node not in explored:
									SCREAMING_SNAKE_CASE__										=   graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												SCREAMING_SNAKE_CASE__										=   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 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			SCREAMING_SNAKE_CASE__										=   [start]
			SCREAMING_SNAKE_CASE__										=   set(snake_case__     )
			# Keep tab on distances from `start` node.
			SCREAMING_SNAKE_CASE__										=   {start: 0, target: -1}
			while queue:
						SCREAMING_SNAKE_CASE__										=   queue.pop(0     )
						if node == target:
									SCREAMING_SNAKE_CASE__										=   (
									    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__     )
												SCREAMING_SNAKE_CASE__										=   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





'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)



@add_end_docstrings(a_			)
class 				_snake_case					(   a_			):

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      requires_backends(self      , 'vision'	)
      self.check_model_type(
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      if top_k is not None:
          lowerCAmelCase 						=							top_k
      return {}, {}, postprocess_params

  def __call__(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return super().__call__(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							load_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors=self.framework	)
      return model_inputs

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model(**_SCREAMING_SNAKE_CASE	)
      return model_outputs


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=5	):





      '''simple docstring'''

      if top_k > self.model.config.num_labels:
          lowerCAmelCase 						=							self.model.config.num_labels

      if self.framework == "pt":
          lowerCAmelCase 						=							model_outputs.logits.softmax(-1	)[0]
          lowerCAmelCase						,   lowerCAmelCase 						=							probs.topk(_SCREAMING_SNAKE_CASE	)
      elif self.framework == "tf":
          lowerCAmelCase 						=							stable_softmax(model_outputs.logits      , axis=-1	)[0]
          lowerCAmelCase 						=							tf.math.top_k(_SCREAMING_SNAKE_CASE      , k=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase						,   lowerCAmelCase 						=							topk.values.numpy(), topk.indices.numpy()
      else:
          raise ValueError(F'Unsupported framework: {self.framework}'	)

      lowerCAmelCase 						=							scores.tolist()
      lowerCAmelCase 						=							ids.tolist()
      return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)]





