




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


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'speech_to_text'
     lowerCAmelCase    :	Dict       		=  ['past_key_values']
     lowerCAmelCase    :	Optional[int]       		=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
     def __init__(     self       :      Dict ,   __snake_case       :      Tuple=1_00_00 ,   __snake_case       :      Union[str, Any]=12 ,   __snake_case       :      str=20_48 ,   __snake_case       :      Tuple=4 ,   __snake_case       :      Any=6 ,   __snake_case       :      List[Any]=20_48 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      Optional[int]=0.0 ,   __snake_case       :      Optional[int]=0.0 ,   __snake_case       :      Dict=True ,   __snake_case       :      List[Any]=True ,   __snake_case       :      List[str]="relu" ,   __snake_case       :      Union[str, Any]=2_56 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Any=0.0 ,   __snake_case       :      Any=0.0 ,   __snake_case       :      Any=0.02 ,   __snake_case       :      List[str]=2 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Dict=1 ,   __snake_case       :      Union[str, Any]=0 ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      List[str]=60_00 ,   __snake_case       :      Union[str, Any]=10_24 ,   __snake_case       :      Union[str, Any]=2 ,   __snake_case       :      Dict=(5, 5) ,   __snake_case       :      Optional[Any]=10_24 ,   __snake_case       :      Dict=80 ,   __snake_case       :      int=1 ,   **__snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					d_model
         UpperCAmelCase_										=					encoder_ffn_dim
         UpperCAmelCase_										=					encoder_layers
         UpperCAmelCase_										=					encoder_attention_heads
         UpperCAmelCase_										=					decoder_ffn_dim
         UpperCAmelCase_										=					decoder_layers
         UpperCAmelCase_										=					decoder_attention_heads
         UpperCAmelCase_										=					dropout
         UpperCAmelCase_										=					attention_dropout
         UpperCAmelCase_										=					activation_dropout
         UpperCAmelCase_										=					activation_function
         UpperCAmelCase_										=					init_std
         UpperCAmelCase_										=					encoder_layerdrop
         UpperCAmelCase_										=					decoder_layerdrop
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					encoder_layers
         UpperCAmelCase_										=					scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_										=					max_source_positions
         UpperCAmelCase_										=					max_target_positions
         UpperCAmelCase_										=					num_conv_layers
         UpperCAmelCase_										=					list(__snake_case    )
         UpperCAmelCase_										=					conv_channels
         UpperCAmelCase_										=					input_feat_per_channel
         UpperCAmelCase_										=					input_channels

         if len(self.conv_kernel_sizes    ) != self.num_conv_layers:
             raise ValueError(
                 '''Configuration for convolutional module is incorrect. '''
                 '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
                 F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes    )}`, '
                 F'`config.num_conv_layers = {self.num_conv_layers}`.'    )

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











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					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:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


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





