

'''simple docstring'''






from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"prophetnet"
    _A         =							["past_key_values"]
    _A         =							{
        "num_attention_heads": "num_encoder_attention_heads",
    }




    def __init__( self    ,					lowercase__ = 0.1    ,					lowercase__ = "gelu"    ,					lowercase__ = 3_0522    ,					lowercase__ = 1024    ,					lowercase__ = 4096    ,					lowercase__ = 12    ,					lowercase__ = 16    ,					lowercase__ = 4096    ,					lowercase__ = 12    ,					lowercase__ = 16    ,					lowercase__ = 0.1    ,					lowercase__ = 0.1    ,					lowercase__ = 512    ,					lowercase__ = 0.02    ,					lowercase__ = True    ,					lowercase__ = True    ,					lowercase__ = 0    ,					lowercase__ = 2    ,					lowercase__ = 32    ,					lowercase__ = 128    ,					lowercase__ = False    ,					lowercase__ = 0.0    ,					lowercase__ = True    ,					lowercase__ = 0    ,					lowercase__ = 1    ,					lowercase__ = 2    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	encoder_ffn_dim
          SCREAMING_SNAKE_CASE_				:      str					=	num_encoder_layers
          SCREAMING_SNAKE_CASE_				:      Dict					=	num_encoder_attention_heads
          SCREAMING_SNAKE_CASE_				:      str					=	decoder_ffn_dim
          SCREAMING_SNAKE_CASE_				:      Tuple					=	num_decoder_layers
          SCREAMING_SNAKE_CASE_				:      int					=	num_decoder_attention_heads
          SCREAMING_SNAKE_CASE_				:      int					=	max_position_embeddings
          SCREAMING_SNAKE_CASE_				:      str					=	init_std  # Normal(0, this parameter)
          SCREAMING_SNAKE_CASE_				:      str					=	activation_function

          # parameters for prophetnet
          SCREAMING_SNAKE_CASE_				:      str					=	ngram
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	num_buckets
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	relative_max_distance
          SCREAMING_SNAKE_CASE_				:      Any					=	disable_ngram_loss
          SCREAMING_SNAKE_CASE_				:      Any					=	eps

          # 3 Types of Dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Any					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      str					=	dropout

          SCREAMING_SNAKE_CASE_				:      Dict					=	use_cache

          super().__init__(
              pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__    ,					is_encoder_decoder=lowercase__    ,					add_cross_attention=lowercase__    ,					decoder_start_token_id=lowercase__    ,					**lowercase__    ,					)




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          raise NotImplementedError(
              "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
              " `num_decoder_layers`."      )




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[(0, start)]  # cost from start node,end node
      SCREAMING_SNAKE_CASE_				:      str					=	set()
      while heap:
            ((SCREAMING_SNAKE_CASE_),						(SCREAMING_SNAKE_CASE_))				:      Optional[int]					=	heapq.heappop(SCREAMING_SNAKE_CASE_   )
            if u in visited:
                  continue
            visited.add(SCREAMING_SNAKE_CASE_   )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  SCREAMING_SNAKE_CASE_				:      int					=	cost + c
                  heapq.heappush(SCREAMING_SNAKE_CASE_  ,  (next_item, v)   )
      return -1


snake_case_					  =	{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

