





'''simple docstring'''




import functools
import operator

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


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Tuple       =				{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[str]           =							"""sew-d"""




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Tuple=3_2	,			snake_case_       :    Optional[Any]=7_6_8	,			snake_case_       :    Tuple=1_2	,			snake_case_       :    Union[str, Any]=1_2	,			snake_case_       :    Tuple=3_0_7_2	,			snake_case_       :    Tuple=2	,			snake_case_       :    int=5_1_2	,			snake_case_       :    Optional[int]=2_5_6	,			snake_case_       :    Union[str, Any]=True	,			snake_case_       :    Any=True	,			snake_case_       :    str=("p2c", "c2p")	,			snake_case_       :    Dict="layer_norm"	,			snake_case_       :    str="gelu_python"	,			snake_case_       :    Dict=0.1	,			snake_case_       :    List[str]=0.1	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Any=0.0	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Union[str, Any]=0.0_2	,			snake_case_       :    str=1e-7	,			snake_case_       :    Optional[Any]=1e-5	,			snake_case_       :    Optional[Any]="group"	,			snake_case_       :    Tuple="gelu"	,			snake_case_       :    Tuple=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,			snake_case_       :    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,			snake_case_       :    Dict=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,			snake_case_       :    int=False	,			snake_case_       :    Union[str, Any]=1_2_8	,			snake_case_       :    int=1_6	,			snake_case_       :    Any=True	,			snake_case_       :    Tuple=0.0_5	,			snake_case_       :    Tuple=1_0	,			snake_case_       :    Dict=2	,			snake_case_       :    Tuple=0.0	,			snake_case_       :    List[Any]=1_0	,			snake_case_       :    Union[str, Any]=0	,			snake_case_       :    Any="mean"	,			snake_case_       :    Optional[Any]=False	,			snake_case_       :    Any=False	,			snake_case_       :    Tuple=2_5_6	,			snake_case_       :    int=0	,			snake_case_       :    Optional[Any]=1	,			snake_case_       :    List[str]=2	,			**snake_case_       :    List[str]	,			):
      super().__init__(**snake_case_	,			pad_token_id=snake_case_	,			bos_token_id=snake_case_	,			eos_token_id=snake_case_    )
      _UpperCAmelCase     							=   hidden_size
      _UpperCAmelCase     							=   feat_extract_norm
      _UpperCAmelCase     							=   feat_extract_activation
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   conv_bias
      _UpperCAmelCase     							=   num_conv_pos_embeddings
      _UpperCAmelCase     							=   num_conv_pos_embedding_groups
      _UpperCAmelCase     							=   len(self.conv_dim    )
      _UpperCAmelCase     							=   num_hidden_layers
      _UpperCAmelCase     							=   intermediate_size
      _UpperCAmelCase     							=   squeeze_factor
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   position_buckets
      _UpperCAmelCase     							=   share_att_key
      _UpperCAmelCase     							=   relative_attention
      _UpperCAmelCase     							=   norm_rel_ebd
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   num_attention_heads
      _UpperCAmelCase     							=   hidden_dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   feat_proj_dropout
      _UpperCAmelCase     							=   final_dropout
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   feature_layer_norm_eps
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   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
      _UpperCAmelCase     							=   apply_spec_augment
      _UpperCAmelCase     							=   mask_time_prob
      _UpperCAmelCase     							=   mask_time_length
      _UpperCAmelCase     							=   mask_time_min_masks
      _UpperCAmelCase     							=   mask_feature_prob
      _UpperCAmelCase     							=   mask_feature_length
      _UpperCAmelCase     							=   mask_feature_min_masks

      # ctc loss
      _UpperCAmelCase     							=   ctc_loss_reduction
      _UpperCAmelCase     							=   ctc_zero_infinity

      # sequence classification
      _UpperCAmelCase     							=   use_weighted_layer_sum
      _UpperCAmelCase     							=   classifier_proj_size




 @property
 def      lowercase	(					self       :    Any    ):
      return functools.reduce(operator.mul	,			self.conv_stride	,			1    )









"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





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

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  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:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





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

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

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

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

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

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

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

    return initial_total - optimal_total


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


