
import math



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_=0	)					->		List[Any]:  # a graph with Node 0,1,...,N-1
          UpperCamelCase   :  List[str]						     =     n
          UpperCamelCase   :  Tuple						     =     [
              [math.inf for j in range(0,      SCREAMING_SNAKE_CASE_	)] for i in range(0,      SCREAMING_SNAKE_CASE_	)
          ]  # adjacency matrix for weight
          UpperCamelCase   :  Dict						     =     [
              [math.inf for j in range(0,      SCREAMING_SNAKE_CASE_	)] for i in range(0,      SCREAMING_SNAKE_CASE_	)
          ]  # dp[i][j] stores minimum distance from i to j
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  int						     =     w
       def  snake_case_	(      self	)					->		Union[str, Any]:







          for k in range(0,      self.n	):
             for i in range(0,      self.n	):
                for j in range(0,      self.n	):
                   UpperCamelCase   :  Tuple						     =     min(self.dp[i][j],      self.dp[i][k] + self.dp[k][j]	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          return self.dp[u][v]


if __name__ == "__main__":
    __UpperCAmelCase	       =	Graph(5)
    graph.add_edge(0, 2, 9)
    graph.add_edge(0, 4, 10)
    graph.add_edge(1, 3, 5)
    graph.add_edge(2, 3, 7)
    graph.add_edge(3, 0, 10)
    graph.add_edge(3, 1, 2)
    graph.add_edge(3, 2, 1)
    graph.add_edge(3, 4, 6)
    graph.add_edge(4, 1, 3)
    graph.add_edge(4, 2, 4)
    graph.add_edge(4, 3, 9)
    graph.floyd_warshall()
    graph.show_min(1, 4)
    graph.show_min(0, 3)








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


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ["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_,      )					->		Union[str, Any]:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,      sampling_rate=SCREAMING_SNAKE_CASE_,      padding_value=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     return_attention_mask

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

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

          UpperCamelCase   :  Any						     =     window_function(window_length=self.sample_size,      name=self.win_function,      periodic=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     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  snake_case_	(      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 0.0	)					->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase   :  Dict						     =     np.array(SCREAMING_SNAKE_CASE_,      np.intaa	)
             UpperCamelCase   :  int						     =     []

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

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

          return normed_input_values
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  int						     =     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   :  Dict						     =     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   :  str						     =     None

          if audio_target is not None:
             UpperCamelCase   :  str						     =     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   :  Dict						     =     inputs_target['input_values']
                UpperCamelCase   :  str						     =     inputs_target.get('attention_mask'	)
                if decoder_attention_mask is not None:
                   UpperCamelCase   :  str						     =     decoder_attention_mask

          return inputs
       def  snake_case_	(      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   :  Any						     =     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   :  Any						     =     is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,      (list, tuple)	) and (isinstance(speech[0],      (np.ndarray, tuple, list)	))
          )

          if is_batched:
             UpperCamelCase   :  str						     =     [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   :  int						     =     np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and speech.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Any						     =     speech.astype(np.floataa	)

          # always return batch
          if not is_batched:
             UpperCamelCase   :  str						     =     [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   :  Optional[int]						     =     [self._extract_mel_features(SCREAMING_SNAKE_CASE_	) for waveform in speech]
             UpperCamelCase   :  Union[str, Any]						     =     BatchFeature({'input_values': features}	)
             UpperCamelCase   :  List[str]						     =     self.num_mel_bins
          else:
             UpperCamelCase   :  Dict						     =     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   :  int						     =     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   :  Union[str, Any]						     =     [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[int]						     =     [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   :  Dict						     =     input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          UpperCamelCase   :  Dict						     =     padded_inputs.get('attention_mask'	)
          if attention_mask is not None:
             UpperCamelCase   :  int						     =     [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   :  Dict						     =     (
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase   :  Tuple						     =     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   :  int						     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

          return padded_inputs






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

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

          return output



