


from __future__ import annotations

import os
from collections.abc import Mapping

lowerCAmelCase__      		= tuple[int, int]







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            _A      :       set[int]				    = vertices
            _A      :       dict[EdgeT, int]				    = {
                (min(__lowerCamelCase), max(__lowerCamelCase)): weight for edge, weight in edges.items()
            }


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            self.vertices.add(edge[0])
            self.vertices.add(edge[1])
            _A      :       int				    = weight


     def  _lowerCamelCase (						self)			->				Graph:
            _A      :       Graph				    = Graph({min(self.vertices)} ,						{})
            _A      :       EdgeT
            _A      :       int
            _A      :       EdgeT
            _A      :       int

            while len(subgraph.vertices) < len(self.vertices):
                   _A      :       Any				    = max(self.edges.values()) + 1
                   for edge, weight in self.edges.items():
                          if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                                 if weight < min_weight:
                                        _A      :       int				    = edge
                                        _A      :       int				    = weight

                   subgraph.add_edge(__lowerCamelCase ,						__lowerCamelCase)

            return subgraph




def 							_UpperCAmelCase				(UpperCamelCase__     :  str = "p107_network.txt"						):
       _A      :       str				    = os.path.abspath(os.path.dirname(UpperCamelCase__						)						)
       _A      :       str				    = os.path.join(UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       dict[EdgeT, int]				    = {}
       _A      :       list[str]
       _A      :       int
       _A      :       int

       with open(UpperCamelCase__						) as f:
              _A      :       Dict				    = f.read().strip().split("\n"						)

       _A      :       List[str]				    = [line.split(","						) for line in data]

       for edgea in range(1				,							len(UpperCamelCase__						)						):
              for edgea in range(UpperCamelCase__						):
                     if adjaceny_matrix[edgea][edgea] != "-":
                            _A      :       List[Any]				    = int(adjaceny_matrix[edgea][edgea]						)

       _A      :       Graph				    = Graph(set(range(len(UpperCamelCase__						)						)						)				,							UpperCamelCase__						)

       _A      :       Graph				    = graph.prims_algorithm()

       _A      :       int				    = sum(graph.edges.values()						)
       _A      :       int				    = sum(subgraph.edges.values()						)

       return initial_total - optimal_total


if __name__ == "__main__":
    print(f"{solution() = }")



from typing import Mapping

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "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						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 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__          =   'gelu_new'

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




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

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

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

