






import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__snake_case												=  logging.get_logger(__name__)







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					List[str]           =['input_values', 'attention_mask']
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_ = 1    ,				SCREAMING_SNAKE_CASE_ = 1_6000    ,				SCREAMING_SNAKE_CASE_ = 0.0    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = 80    ,				SCREAMING_SNAKE_CASE_ = 16    ,				SCREAMING_SNAKE_CASE_ = 64    ,				SCREAMING_SNAKE_CASE_ = "hann_window"    ,				SCREAMING_SNAKE_CASE_ = 1.0    ,				SCREAMING_SNAKE_CASE_ = 80    ,				SCREAMING_SNAKE_CASE_ = 7600    ,				SCREAMING_SNAKE_CASE_ = 1e-10    ,				SCREAMING_SNAKE_CASE_ = 2    ,				SCREAMING_SNAKE_CASE_ = True    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Any:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_    ,				sampling_rate=SCREAMING_SNAKE_CASE_    ,				padding_value=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :str								=   do_normalize
          UpperCamelCase       :Optional[int]								=   return_attention_mask

          UpperCamelCase       :List[str]								=   num_mel_bins
          UpperCamelCase       :str								=   hop_length
          UpperCamelCase       :Dict								=   win_length
          UpperCamelCase       :Tuple								=   win_function
          UpperCamelCase       :Optional[int]								=   frame_signal_scale
          UpperCamelCase       :Union[str, Any]								=   fmin
          UpperCamelCase       :int								=   fmax
          UpperCamelCase       :Any								=   mel_floor
          UpperCamelCase       :Any								=   reduction_factor

          UpperCamelCase       :List[Any]								=   win_length * sampling_rate // 1000
          UpperCamelCase       :Any								=   hop_length * sampling_rate // 1000
          UpperCamelCase       :Union[str, Any]								=   optimal_fft_length(self.sample_size )
          UpperCamelCase       :List[Any]								=   (self.n_fft // 2) + 1

          UpperCamelCase       :Optional[int]								=   window_function(window_length=self.sample_size    ,				name=self.win_function    ,				periodic=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :str								=   mel_filter_bank(
              num_frequency_bins=self.n_freqs    ,				num_mel_filters=self.num_mel_bins    ,				min_frequency=self.fmin    ,				max_frequency=self.fmax    ,				sampling_rate=self.sampling_rate    ,				norm='''slaney'''    ,				mel_scale='''slaney'''    ,				)

          if frame_signal_scale != 1.0:
              warnings.warn(
                  '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,				SCREAMING_SNAKE_CASE_    ,				)
          if reduction_factor != 2.0:
              warnings.warn(
                  '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''    ,				SCREAMING_SNAKE_CASE_    ,				)
      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def     UpperCAmelCase						(				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = 0.0 )				->      List[np.ndarray]:
          if attention_mask is not None:
              UpperCamelCase       :Optional[Any]								=   np.array(SCREAMING_SNAKE_CASE_    ,				np.intaa )
              UpperCamelCase       :Optional[int]								=   []

              for vector, length in zip(SCREAMING_SNAKE_CASE_    ,				attention_mask.sum(-1 ) ):
                  UpperCamelCase       :Tuple								=   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                  if length < normed_slice.shape[0]:
                      UpperCamelCase       :Union[str, Any]								=   padding_value

                  normed_input_values.append(SCREAMING_SNAKE_CASE_ )
          else:
              UpperCamelCase       :List[str]								=   [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

          return normed_input_values
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				)				->      np.ndarray:
          UpperCamelCase       :Any								=   spectrogram(
              SCREAMING_SNAKE_CASE_    ,				window=self.window    ,				frame_length=self.sample_size    ,				hop_length=self.sample_stride    ,				fft_length=self.n_fft    ,				mel_filters=self.mel_filters    ,				mel_floor=self.mel_floor    ,				log_mel='''log10'''    ,				)
          return log_mel_spec.T
      def __call__(				self    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      BatchFeature:
          if audio is None and audio_target is None:
              raise ValueError('''You must provide either `audio` or `audio_target` values.''' )

          if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                      F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                      F''' {self.sampling_rate} and not {sampling_rate}.''' )
          else:
              logger.warning(
                  '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
                  '''Failing to do so can result in silent errors that might be hard to debug.''' )

          if audio is not None:
              UpperCamelCase       :Optional[int]								=   self._process_audio(
                  SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_    ,				)
          else:
              UpperCamelCase       :Optional[Any]								=   None

          if audio_target is not None:
              UpperCamelCase       :Optional[Any]								=   self._process_audio(
                  SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_    ,				)

              if inputs is None:
                  return inputs_target
              else:
                  UpperCamelCase       :Optional[Any]								=   inputs_target['''input_values''']
                  UpperCamelCase       :str								=   inputs_target.get('''attention_mask''' )
                  if decoder_attention_mask is not None:
                      UpperCamelCase       :Optional[int]								=   decoder_attention_mask

          return inputs
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      BatchFeature:
          UpperCamelCase       :int								=   isinstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray ) and len(speech.shape ) > 1
          if is_batched_numpy and len(speech.shape ) > 2:
              raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
          UpperCamelCase       :Optional[Any]								=   is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_    ,				(list, tuple) ) and (isinstance(speech[0]    ,				(np.ndarray, tuple, list) ))
          )

          if is_batched:
              UpperCamelCase       :Optional[Any]								=   [np.asarray(SCREAMING_SNAKE_CASE_    ,				dtype=np.floataa ) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray ):
              UpperCamelCase       :List[Any]								=   np.asarray(SCREAMING_SNAKE_CASE_    ,				dtype=np.floataa )
          elif isinstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
              UpperCamelCase       :Tuple								=   speech.astype(np.floataa )

          # always return batch
          if not is_batched:
              UpperCamelCase       :Union[str, Any]								=   [speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase       :Optional[Any]								=   self.feature_size

          # convert into correct format for padding
          if is_target:
              UpperCamelCase       :List[Any]								=   [self._extract_mel_features(SCREAMING_SNAKE_CASE_ ) for waveform in speech]
              UpperCamelCase       :Optional[Any]								=   BatchFeature({'''input_values''': features} )
              UpperCamelCase       :Any								=   self.num_mel_bins
          else:
              UpperCamelCase       :Union[str, Any]								=   BatchFeature({'''input_values''': speech} )

          UpperCamelCase       :Tuple								=   self.pad(
              SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				max_length=SCREAMING_SNAKE_CASE_    ,				truncation=SCREAMING_SNAKE_CASE_    ,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,				return_attention_mask=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_    ,				)

          UpperCamelCase       :Optional[Any]								=   feature_size_hack

          # convert input values to correct format
          UpperCamelCase       :Optional[int]								=   padded_inputs['''input_values''']
          if not isinstance(input_values[0]    ,				np.ndarray ):
              UpperCamelCase       :int								=   [np.asarray(SCREAMING_SNAKE_CASE_    ,				dtype=np.floataa ) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray )
              and isinstance(input_values[0]    ,				np.ndarray )
              and input_values[0].dtype is np.dtype(np.floataa )
          ):
              UpperCamelCase       :Optional[Any]								=   [array.astype(np.floataa ) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_    ,				np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
              UpperCamelCase       :int								=   input_values.astype(np.floataa )

          # convert attention_mask to correct format
          UpperCamelCase       :Union[str, Any]								=   padded_inputs.get('''attention_mask''' )
          if attention_mask is not None:
              UpperCamelCase       :List[Any]								=   [np.asarray(SCREAMING_SNAKE_CASE_    ,				dtype=np.intaa ) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
              UpperCamelCase       :Optional[int]								=   (
                  attention_mask
                  if self._get_padding_strategies(SCREAMING_SNAKE_CASE_    ,				max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              UpperCamelCase       :str								=   self.zero_mean_unit_var_norm(
                  padded_inputs['''input_values''']    ,				attention_mask=SCREAMING_SNAKE_CASE_    ,				padding_value=self.padding_value )

          if return_tensors is not None:
              UpperCamelCase       :Optional[Any]								=   padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ )

          return padded_inputs




      def     UpperCAmelCase						(				self )				->      Dict[str, Any]:
          UpperCamelCase       :List[Any]								=   super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase       :Tuple								=   ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
          for name in names:
              if name in output:
                  del output[name]

          return output










"""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() = }")


