





'''simple docstring'''




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


lowercase__   :				List[str]      =   logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	['''input_values''', '''attention_mask''']


     def __init__(						self						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = 1_6000						,     _UpperCAmelCase = 0.0						,     _UpperCAmelCase = False						,     _UpperCAmelCase = 80						,     _UpperCAmelCase = 16						,     _UpperCAmelCase = 64						,     _UpperCAmelCase = "hann_window"						,     _UpperCAmelCase = 1.0						,     _UpperCAmelCase = 80						,     _UpperCAmelCase = 7600						,     _UpperCAmelCase = 1e-1_0						,     _UpperCAmelCase = 2						,     _UpperCAmelCase = True						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(feature_size=_UpperCAmelCase						,     sampling_rate=_UpperCAmelCase						,     padding_value=_UpperCAmelCase						,     **_UpperCAmelCase)
          __A						:			Union[str, Any]								=	do_normalize
          __A						:			Dict								=	return_attention_mask

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

          __A						:			Optional[Any]								=	win_length * sampling_rate // 1000
          __A						:			str								=	hop_length * sampling_rate // 1000
          __A						:			Tuple								=	optimal_fft_length(self.sample_size)
          __A						:			str								=	(self.n_fft // 2) + 1

          __A						:			Dict								=	window_function(window_length=self.sample_size						,     name=self.win_function						,     periodic=_UpperCAmelCase)

          __A						:			List[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'						,     _UpperCAmelCase						,     )
          if reduction_factor != 2.0:
               warnings.warn(
                   'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'						,     _UpperCAmelCase						,     )


     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = 0.0):

          '''simple docstring'''






          if attention_mask is not None:
               __A						:			List[Any]								=	np.array(_UpperCAmelCase						,     np.intaa)
               __A						:			int								=	[]

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

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

          return normed_input_values


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			Tuple								=	spectrogram(
              _UpperCAmelCase						,     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						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = False						,     _UpperCAmelCase = None						,     _UpperCAmelCase = False						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          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:
               __A						:			Dict								=	self._process_audio(
                   _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase						,     )
          else:
               __A						:			Union[str, Any]								=	None

          if audio_target is not None:
               __A						:			Optional[int]								=	self._process_audio(
                   _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase						,     )

               if inputs is None:
                    return inputs_target
               else:
                    __A						:			Any								=	inputs_target['input_values']
                    __A						:			Union[str, Any]								=	inputs_target.get('attention_mask')
                    if decoder_attention_mask is not None:
                         __A						:			List[str]								=	decoder_attention_mask

          return inputs


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = False						,     _UpperCAmelCase = False						,     _UpperCAmelCase = None						,     _UpperCAmelCase = False						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			Tuple								=	isinstance(_UpperCAmelCase						,     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}')
          __A						:			Optional[int]								=	is_batched_numpy or (
              isinstance(_UpperCAmelCase						,     (list, tuple)) and (isinstance(speech[0]						,     (np.ndarray, tuple, list)))
          )

          if is_batched:
               __A						:			Dict								=	[np.asarray(_UpperCAmelCase						,     dtype=np.floataa) for speech in speech]
          elif not is_batched and not isinstance(_UpperCAmelCase						,     np.ndarray):
               __A						:			List[str]								=	np.asarray(_UpperCAmelCase						,     dtype=np.floataa)
          elif isinstance(_UpperCAmelCase						,     np.ndarray) and speech.dtype is np.dtype(np.floataa):
               __A						:			List[Any]								=	speech.astype(np.floataa)

          # always return batch
          if not is_batched:
               __A						:			Tuple								=	[speech]

          # needed to make pad() work on spectrogram inputs
          __A						:			Tuple								=	self.feature_size

          # convert into correct format for padding
          if is_target:
               __A						:			Dict								=	[self._extract_mel_features(_UpperCAmelCase) for waveform in speech]
               __A						:			Dict								=	BatchFeature({'input_values': features})
               __A						:			Any								=	self.num_mel_bins
          else:
               __A						:			int								=	BatchFeature({'input_values': speech})

          __A						:			Union[str, Any]								=	self.pad(
              _UpperCAmelCase						,     padding=_UpperCAmelCase						,     max_length=_UpperCAmelCase						,     truncation=_UpperCAmelCase						,     pad_to_multiple_of=_UpperCAmelCase						,     return_attention_mask=_UpperCAmelCase						,     **_UpperCAmelCase						,     )

          __A						:			Any								=	feature_size_hack

          # convert input values to correct format
          __A						:			Union[str, Any]								=	padded_inputs['input_values']
          if not isinstance(input_values[0]						,     np.ndarray):
               __A						:			List[str]								=	[np.asarray(_UpperCAmelCase						,     dtype=np.floataa) for array in input_values]
          elif (
              not isinstance(_UpperCAmelCase						,     np.ndarray)
              and isinstance(input_values[0]						,     np.ndarray)
              and input_values[0].dtype is np.dtype(np.floataa)
          ):
               __A						:			Any								=	[array.astype(np.floataa) for array in input_values]
          elif isinstance(_UpperCAmelCase						,     np.ndarray) and input_values.dtype is np.dtype(np.floataa):
               __A						:			List[Any]								=	input_values.astype(np.floataa)

          # convert attention_mask to correct format
          __A						:			int								=	padded_inputs.get('attention_mask')
          if attention_mask is not None:
               __A						:			Optional[int]								=	[np.asarray(_UpperCAmelCase						,     dtype=np.intaa) for array in attention_mask]

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

          if return_tensors is not None:
               __A						:			Dict								=	padded_inputs.convert_to_tensors(_UpperCAmelCase)

          return padded_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          __A						:			str								=	['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'''




def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					str:
     __A						:			Optional[Any]								=	[0] * len(__snake_case      )
     __A						:			Dict								=	[]
     __A						:			Optional[int]								=	[1] * len(__snake_case      )

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(__snake_case      )      ):
          if indegree[i] == 0:
               queue.append(__snake_case      )

     while queue:
          __A						:			int								=	queue.pop(0      )
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    __A						:			str								=	long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(__snake_case      )

     print(max(__snake_case      )      )


# Adjacency list of Graph
lowercase__   :				Dict      =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}







longest_distance(graph)