


'''simple docstring'''



import functools
import operator

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


A__						:   str				=    logging.get_logger(__name__)

A__						:   int				=    {
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     'sew'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_="group"     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)     ,	SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,	SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1_28     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=0.0_5     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_="mean"     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=2_56     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=2     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Tuple:
         super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						hidden_size
         __lowerCamelCase  :      int														=						feat_extract_norm
         __lowerCamelCase  :      Optional[int]														=						feat_extract_activation
         __lowerCamelCase  :      Any														=						list(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						list(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						list(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						conv_bias
         __lowerCamelCase  :      Dict														=						num_conv_pos_embeddings
         __lowerCamelCase  :      Optional[Any]														=						num_conv_pos_embedding_groups
         __lowerCamelCase  :      Dict														=						len(self.conv_dim      )
         __lowerCamelCase  :      Optional[Any]														=						num_hidden_layers
         __lowerCamelCase  :      Tuple														=						intermediate_size
         __lowerCamelCase  :      List[Any]														=						squeeze_factor
         __lowerCamelCase  :      List[str]														=						hidden_act
         __lowerCamelCase  :      Dict														=						num_attention_heads
         __lowerCamelCase  :      Dict														=						hidden_dropout
         __lowerCamelCase  :      Tuple														=						attention_dropout
         __lowerCamelCase  :      Dict														=						activation_dropout
         __lowerCamelCase  :      Optional[int]														=						feat_proj_dropout
         __lowerCamelCase  :      Tuple														=						final_dropout
         __lowerCamelCase  :      str														=						layerdrop
         __lowerCamelCase  :      int														=						layer_norm_eps
         __lowerCamelCase  :      int														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						vocab_size

         if (
             (len(self.conv_stride      ) != self.num_feat_extract_layers)
             or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
             or (len(self.conv_dim      ) != self.num_feat_extract_layers)
         ):
              raise ValueError(
                  'Configuration for convolutional layers is incorrect.'
                  'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                  f'but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)'
                  f'= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'      )

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __lowerCamelCase  :      Optional[Any]														=						apply_spec_augment
         __lowerCamelCase  :      Tuple														=						mask_time_prob
         __lowerCamelCase  :      Any														=						mask_time_length
         __lowerCamelCase  :      int														=						mask_time_min_masks
         __lowerCamelCase  :      int														=						mask_feature_prob
         __lowerCamelCase  :      Dict														=						mask_feature_length
         __lowerCamelCase  :      List[str]														=						mask_feature_min_masks

         # ctc loss
         __lowerCamelCase  :      Any														=						ctc_loss_reduction
         __lowerCamelCase  :      List[Any]														=						ctc_zero_infinity

         # sequence classification
         __lowerCamelCase  :      List[Any]														=						use_weighted_layer_sum
         __lowerCamelCase  :      Union[str, Any]														=						classifier_proj_size







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         return functools.reduce(operator.mul     ,	self.conv_stride     ,	1      )










'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







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

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      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:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

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

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

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

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

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

     return initial_total - optimal_total


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






