





lowercase      :       Union[str, Any]									=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}



def A_	(		A__		,      A__		,      A__				)  ->		list[str]:
       a__						:	List[str] =				set()
       # keep track of all the paths to be checked
       a__						:	Union[str, Any] =				[[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
              a__						:	Tuple =				queue.pop(0				)
              # get the last node from the path
              a__						:	Optional[int] =				path[-1]
              if node not in explored:
                     a__						:	List[str] =				graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            a__						:	Optional[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 A_	(		A__		,      A__		,      A__				)  ->		int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       a__						:	Tuple =				[start]
       a__						:	Union[str, Any] =				set(A__				)
       # Keep tab on distances from `start` node.
       a__						:	Optional[Any] =				{start: 0, target: -1}
       while queue:
              a__						:	str =				queue.pop(0				)
              if node == target:
                     a__						:	List[Any] =				(
                         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__				)
                            a__						:	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







'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



