



'''simple docstring'''

from __future__ import annotations

import os
from collections.abc import Mapping

_A				:    str     						=tuple[int, int]



class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	set[int]     ,  UpperCamelCase_				:	Mapping[EdgeT, int]					)  ->       None:



						'''simple docstring'''





						_lowercase					:       set[int]       =		vertices
						_lowercase					:       dict[EdgeT, int]       =		{
						    (min(UpperCamelCase_					), max(UpperCamelCase_					)): weight for edge, weight in edges.items()
						}




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	EdgeT     ,  UpperCamelCase_				:	int					)  ->       None:



						'''simple docstring'''





						self.vertices.add(edge[0]					)
						self.vertices.add(edge[1]					)
						_lowercase					:       List[Any]       =		weight




					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Graph:



						'''simple docstring'''





						_lowercase					:       Graph       =		Graph({min(self.vertices					)}     ,  {}					)
						_lowercase					:       EdgeT
						_lowercase					:       int
						_lowercase					:       EdgeT
						_lowercase					:       int

						while len(subgraph.vertices					) < len(self.vertices					):
							_lowercase					:       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:
										_lowercase					:       Optional[Any]       =		edge
										_lowercase					:       List[Any]       =		weight

							subgraph.add_edge(UpperCamelCase_     ,  UpperCamelCase_					)

						return subgraph







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

	with open(_lowercase					) as f:
		_lowercase					:       List[str]       =		f.read().strip().split('\n'					)

	_lowercase					:       Optional[int]       =		[line.split(','					) for line in data]

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

	_lowercase					:       Graph       =		Graph(set(range(len(_lowercase					)					)					),				_lowercase					)

	_lowercase					:       Graph       =		graph.prims_algorithm()

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

	return initial_total - optimal_total


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





'''simple docstring'''

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

import numpy as np
import torch

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


_A				:    Optional[int]     						=logging.get_logger(__name__)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						["""input_features""", """is_longer"""]




					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	List[Any]=64     ,  UpperCamelCase_				:	int=4_8000     ,  UpperCamelCase_				:	Union[str, Any]=480     ,  UpperCamelCase_				:	Any=10     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[int]=0.0     ,  UpperCamelCase_				:	Tuple=False     ,  UpperCamelCase_				:	float = 0     ,  UpperCamelCase_				:	float = 1_4000     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	str = "fusion"     ,  UpperCamelCase_				:	str = "repeatpad"     ,  **UpperCamelCase_				:	Optional[Any]     ,  )  ->       Dict:



						'''simple docstring'''





						super().__init__(
						    feature_size=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  padding_value=UpperCamelCase_     ,  return_attention_mask=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						_lowercase					:       Tuple       =		top_db
						_lowercase					:       Any       =		truncation
						_lowercase					:       str       =		padding
						_lowercase					:       int       =		fft_window_size
						_lowercase					:       Any       =		(fft_window_size >> 1) + 1
						_lowercase					:       int       =		hop_length
						_lowercase					:       Any       =		max_length_s
						_lowercase					:       str       =		max_length_s * sampling_rate
						_lowercase					:       Any       =		sampling_rate
						_lowercase					:       List[Any]       =		frequency_min
						_lowercase					:       Tuple       =		frequency_max
						_lowercase					:       Tuple       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm=UpperCamelCase_     ,  mel_scale='htk'     ,  )
						_lowercase					:       Any       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict[str, Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       int       =		self.__class__.__name__
						if "mel_filters" in output:
							del output["mel_filters"]
						if "mel_filters_slaney" in output:
							del output["mel_filters_slaney"]
						return output




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	Optional[np.array] = None					)  ->       np.ndarray:



						'''simple docstring'''





						_lowercase					:       List[str]       =		spectrogram(
						    UpperCamelCase_     ,  window_function(self.fft_window_size     ,  'hann'					)     ,  frame_length=self.fft_window_size     ,  hop_length=self.hop_length     ,  power=2.0     ,  mel_filters=UpperCamelCase_     ,  log_mel='dB'     ,  )
						return log_mel_spectrogram.T




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		np.array_split(list(range(0     ,  total_frames - chunk_frames + 1					)					)     ,  3					)
						if len(ranges[1]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       int       =		[0]
						if len(ranges[2]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       Union[str, Any]       =		[0]
						# randomly choose index for each part
						_lowercase					:       Tuple       =		np.random.choice(ranges[0]					)
						_lowercase					:       int       =		np.random.choice(ranges[1]					)
						_lowercase					:       Any       =		np.random.choice(ranges[2]					)

						_lowercase					:       int       =		mel[idx_front : idx_front + chunk_frames, :]
						_lowercase					:       int       =		mel[idx_middle : idx_middle + chunk_frames, :]
						_lowercase					:       Tuple       =		mel[idx_back : idx_back + chunk_frames, :]

						_lowercase					:       List[Any]       =		torch.tensor(mel[None, None, :]					)
						_lowercase					:       Optional[int]       =		torch.nn.functional.interpolate(
						    UpperCamelCase_     ,  size=[chunk_frames, 64]     ,  mode='bilinear'     ,  align_corners=UpperCamelCase_					)
						_lowercase					:       str       =		mel_shrink[0][0].numpy()
						_lowercase					:       int       =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,  axis=0					)
						return mel_fusion




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Optional[int]					)  ->       np.array:



						'''simple docstring'''





						if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
								_lowercase					:       Tuple       =		True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								_lowercase					:       Any       =		len(UpperCamelCase_					) - max_length
								_lowercase					:       Dict       =		np.random.randint(0     ,  overflow + 1					)
								_lowercase					:       Optional[int]       =		waveform[idx : idx + max_length]
								_lowercase					:       Dict       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]
							elif truncation == "fusion":
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       List[Any]       =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								_lowercase					:       Optional[int]       =		mel.shape[0]
								if chunk_frames == total_frames:
									# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
									# In this case, we just use the whole audio.
									_lowercase					:       Optional[Any]       =		np.stack([mel, mel, mel, mel]     ,  axis=0					)
									_lowercase					:       List[Any]       =		False
								else:
									_lowercase					:       Union[str, Any]       =		self._random_mel_fusion(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
									_lowercase					:       int       =		True
							else:
								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''					)

						else:
							_lowercase					:       Any       =		False
							# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
							if waveform.shape[0] < max_length:
								if padding == "repeat":
									_lowercase					:       List[Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       List[str]       =		np.stack(np.tile(UpperCamelCase_     ,  n_repeat + 1					)					)[:max_length]
								if padding == "repeatpad":
									_lowercase					:       Union[str, Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       Union[str, Any]       =		np.stack(np.tile(UpperCamelCase_     ,  UpperCamelCase_					)					)
								_lowercase					:       Dict       =		np.pad(UpperCamelCase_     ,  (0, max_length - waveform.shape[0])     ,  mode='constant'     ,  constant_values=0					)

							if truncation == "fusion":
								_lowercase					:       str       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       Dict       =		np.stack([input_mel, input_mel, input_mel, input_mel]     ,  axis=0					)
							else:
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]

						return input_mel, longer




					def __call__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  UpperCamelCase_				:	str = None     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase_				:	Dict     ,  )  ->       BatchFeature:



						'''simple docstring'''





						_lowercase					:       Dict       =		truncation if truncation is not None else self.truncation
						_lowercase					:       int       =		padding if padding else self.padding

						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
								    F''' was sampled with {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.'					)

						_lowercase					:       Optional[Any]       =		isinstance(UpperCamelCase_     ,  np.ndarray					) and len(raw_speech.shape					) > 1
						if is_batched_numpy and len(raw_speech.shape					) > 2:
							raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''					)
						_lowercase					:       List[str]       =		is_batched_numpy or (
						    isinstance(UpperCamelCase_     ,  (list, tuple)					) and (isinstance(raw_speech[0]     ,  (np.ndarray, tuple, list)					))
						)

						if is_batched:
							_lowercase					:       Dict       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(UpperCamelCase_     ,  np.ndarray					):
							_lowercase					:       Any       =		np.asarray(UpperCamelCase_     ,  dtype=np.floataa					)
						elif isinstance(UpperCamelCase_     ,  np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
							_lowercase					:       Tuple       =		raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
							_lowercase					:       int       =		[np.asarray(UpperCamelCase_					)]

						# convert to mel spectrogram, truncate and pad if needed.
						_lowercase					:       Optional[Any]       =		[
						    self._get_input_mel(UpperCamelCase_     ,  max_length if max_length else self.nb_max_samples     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						    for waveform in raw_speech
						]

						_lowercase					:       List[Any]       =		[]
						_lowercase					:       Dict       =		[]
						for mel, longer in padded_inputs:
							input_mel.append(UpperCamelCase_					)
							is_longer.append(UpperCamelCase_					)

						if truncation == "fusion" and sum(UpperCamelCase_					) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							_lowercase					:       Optional[Any]       =		np.random.randint(0     ,  len(UpperCamelCase_					)					)
							_lowercase					:       str       =		True

						if isinstance(input_mel[0]     ,  UpperCamelCase_					):
							_lowercase					:       str       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for feature in input_mel]

						# is_longer is a list of bool
						_lowercase					:       Tuple       =		[[longer] for longer in is_longer]

						_lowercase					:       Optional[Any]       =		{'input_features': input_mel, 'is_longer': is_longer}
						_lowercase					:       Optional[int]       =		BatchFeature(UpperCamelCase_					)

						if return_tensors is not None:
							_lowercase					:       List[Any]       =		input_features.convert_to_tensors(UpperCamelCase_					)

						return input_features
