



'''simple docstring'''






from typing import Mapping

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


lowerCAmelCase: List[Any]							=						logging.get_logger(__name__)

lowerCAmelCase: Optional[Any]							=						{
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """t5"""
    lowercase__			=   ["""past_key_values"""]
    lowercase__			=   {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







    def __init__(				self			:  List[Any]						,      __snake_case			:  List[Any]=3_21_28						,      __snake_case			:  Optional[int]=5_12						,      __snake_case			:  Optional[Any]=64						,      __snake_case			:  str=20_48						,      __snake_case			:  Dict=6						,      __snake_case			:  Union[str, Any]=None						,      __snake_case			:  Dict=8						,      __snake_case			:  Tuple=32						,      __snake_case			:  Any=1_28						,      __snake_case			:  Any=0.1						,      __snake_case			:  str=1e-6						,      __snake_case			:  Optional[int]=1.0						,      __snake_case			:  Dict="relu"						,      __snake_case			:  Optional[int]=True						,      __snake_case			:  Dict=True						,      __snake_case			:  Union[str, Any]=0						,      __snake_case			:  Dict=1						,      **__snake_case			:  Optional[Any]						,      ):
      a						: Dict					       =					vocab_size
      a						: Optional[Any]					       =					d_model
      a						: Tuple					       =					d_kv
      a						: Optional[int]					       =					d_ff
      a						: List[Any]					       =					num_layers
      a						: Optional[int]					       =					(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      a						: Union[str, Any]					       =					num_heads
      a						: Optional[int]					       =					relative_attention_num_buckets
      a						: Optional[int]					       =					relative_attention_max_distance
      a						: int					       =					dropout_rate
      a						: Optional[Any]					       =					layer_norm_epsilon
      a						: str					       =					initializer_factor
      a						: Union[str, Any]					       =					feed_forward_proj
      a						: str					       =					use_cache

      a						: str					       =					self.feed_forward_proj.split('-'     )
      a						: List[Any]					       =					act_info[-1]
      a						: Union[str, Any]					       =					act_info[0] == 'gated'

      if len(__snake_case     ) > 1 and act_info[0] != "gated" or len(__snake_case     ) > 2:
        raise ValueError(
            F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
            'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
            '\'gated-gelu\' or \'relu\''     )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
        a						: Tuple					       =					'gelu_new'

      super().__init__(
          pad_token_id=__snake_case						,      eos_token_id=__snake_case						,      is_encoder_decoder=__snake_case						,      **__snake_case						,      )

class 							a__( lowerCamelCase__     ):







    @property
    def       lowercase_	(				self			:  Dict     ):
      a						: Optional[int]					       =					{
          'input_ids': {0: 'batch', 1: 'encoder_sequence'},
          'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
      }
      if self.use_past:
        a						: Union[str, Any]					       =					'past_encoder_sequence + sequence'
        a						: str					       =					{0: 'batch'}
        a						: str					       =					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
        a						: Optional[int]					       =					{0: 'batch', 1: 'decoder_sequence'}
        a						: Dict					       =					{0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
        self.fill_with_past_key_values_(__snake_case						,      direction='inputs'     )

      return common_inputs







    @property
    def       lowercase_	(				self			:  Tuple     ):
      return 13




'''simple docstring'''






import math

class 							a__:







    def __init__(				self			:  int						,      __snake_case			:  Any=0     ):  # a graph with Node 0,1,...,N-1
      a						: List[Any]					       =					n
      a						: Optional[Any]					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # adjacency matrix for weight
      a						: Dict					       =					[
          [math.inf for j in range(0						,      __snake_case     )] for i in range(0						,      __snake_case     )
      ]  # dp[i][j] stores minimum distance from i to j







    def       lowercase_	(				self			:  int						,      __snake_case			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  int     ):
      a						: str					       =					w







    def       lowercase_	(				self			:  int     ):
      for k in range(0						,      self.n     ):
        for i in range(0						,      self.n     ):
          for j in range(0						,      self.n     ):
            a						: str					       =					min(self.dp[i][j]						,      self.dp[i][k] + self.dp[k][j]     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Any						,      __snake_case			:  List[str]     ):
      return self.dp[u][v]


if __name__ == "__main__":
     lowerCAmelCase: Any							=						Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 1_0)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 1_0)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)