





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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						list[str]:







  """simple docstring"""





  lowerCamelCase__: Optional[int]         =set()
  # keep track of all the paths to be checked
  lowerCamelCase__: Tuple         =[[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
    lowerCamelCase__: Optional[Any]         =queue.pop(0						)
    # get the last node from the path
    lowerCamelCase__: Any         =path[-1]
    if node not in explored:
      lowerCamelCase__: Tuple         =graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        lowerCamelCase__: Any         =list(__a						)
        new_path.append(__a						)
        queue.append(__a						)
        # return path if neighbour is goal
        if neighbour == goal:
          return new_path

            # mark node as explored
      explored.add(__a						)

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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						int:







  """simple docstring"""





  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  lowerCamelCase__: Tuple         =[start]
  lowerCamelCase__: str         =set(__a						)
  # Keep tab on distances from `start` node.
  lowerCamelCase__: Any         ={start: 0, target: -1}
  while queue:
    lowerCamelCase__: List[Any]         =queue.pop(0						)
    if node == target:
      lowerCamelCase__: List[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(__a						)
        queue.append(__a						)
        lowerCamelCase__: Optional[int]         =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








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''laion/clap-htsat-unfused'''
       A_          =       tempfile.mkdtemp()


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return RobertaTokenizer.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return ClapFeatureExtractor.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_tokenizer()
       A_          =       self.get_feature_extractor()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       processor.save_pretrained(self.tmpdirname )
       A_          =       ClapProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ClapProcessor(tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() )
       processor.save_pretrained(self.tmpdirname )

       A_          =       self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
       A_          =       self.get_feature_extractor(do_normalize=__snake_case ,padding_value=1.0 )

       A_          =       ClapProcessor.from_pretrained(
           self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=__snake_case ,padding_value=1.0 )

       self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
       self.assertIsInstance(processor.tokenizer ,__snake_case )

       self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor_add_kwargs.to_json_string() )
       self.assertIsInstance(processor.feature_extractor ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       floats_list((3, 1_0_0_0) )

       A_          =       feature_extractor(__snake_case ,return_tensors='''np''' )
       A_          =       processor(audios=__snake_case ,return_tensors='''np''' )

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       '''This is a test string'''

       A_          =       processor(text=__snake_case )

       A_          =       tokenizer(__snake_case )

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_          =       processor.batch_decode(__snake_case )
       A_          =       tokenizer.batch_decode(__snake_case )

       self.assertListEqual(__snake_case ,__snake_case )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       self.assertListEqual(
           processor.model_input_names[2:] ,feature_extractor.model_input_names ,msg='''`processor` and `feature_extractor` model input names do not match''' ,)



