




"""simple docstring"""


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


def       UpperCAmelCase__ (snake_case__							:      dict	,     snake_case__							:      List[Any]	,     snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	str			=						set()
  # keep track of all the paths to be checked
  _snake_case							:	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
    _snake_case							:	int			=						queue.pop(0					)
    # get the last node from the path
    _snake_case							:	Dict			=						path[-1]
    if node not in explored:
      _snake_case							:	Any			=						graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        _snake_case							:	Optional[Any]			=						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       UpperCAmelCase__ (snake_case__							:      dict	,     snake_case__							:      Tuple	,     snake_case__							:      str					):
  """simple docstring"""

  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  _snake_case							:	int			=						[start]
  _snake_case							:	Optional[int]			=						set(snake_case__					)
  # Keep tab on distances from `start` node.
  _snake_case							:	List[str]			=						{start: 0, target: -1}
  while queue:
    _snake_case							:	List[str]			=						queue.pop(0					)
    if node == target:
      _snake_case							:	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(snake_case__					)
        queue.append(snake_case__					)
        _snake_case							:	Dict			=						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   if self.task == "multiple-choice":
     _snake_case							:	str			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _snake_case							:	List[str]			=						{0: """batch""", 1: """sequence"""}
   _snake_case							:	List[Any]			=						{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]	)


