





from math import ceil
from typing import List, Optional, Union

import numpy as np

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


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





class UpperCAmelCase_				( _UpperCamelCase ):
  __lowerCAmelCase   :						Any							=   ["""audio_values""", """audio_mask"""]


  def __init__(							self ,__snake_case=2_0_4_8 ,__snake_case=1 ,__snake_case=[1_6, 1_6] ,__snake_case=1_2_8 ,__snake_case=4_4_1_0_0 ,__snake_case=8_6 ,__snake_case=2_0_4_8 ,__snake_case=0.0 ,**__snake_case ,):



       """simple docstring"""





       super().__init__(
           feature_size=lowerCamelCase_ ,sampling_rate=lowerCamelCase_ ,padding_value=lowerCamelCase_ ,**lowerCamelCase_ ,)

       A_          =       spectrogram_length
       A_          =       num_channels
       A_          =       patch_size
       A_          =       feature_size // self.patch_size[1]
       A_          =       n_fft
       A_          =       sampling_rate // hop_length_to_sampling_rate
       A_          =       sampling_rate
       A_          =       padding_value
       A_          =       mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2 ,num_mel_filters=lowerCamelCase_ ,min_frequency=0.0 ,max_frequency=2_2_0_5_0.0 ,sampling_rate=lowerCamelCase_ ,norm='''slaney''' ,mel_scale='''slaney''' ,).T


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       spectrogram(
           lowerCamelCase_ ,window_function(self.n_fft ,'''hann''' ) ,frame_length=self.n_fft ,hop_length=self.hop_length ,power=2.0 ,mel_filters=self.mel_filters.T ,log_mel='''dB''' ,db_range=8_0.0 ,)
       A_          =       log_spec[:, :-1]
       A_          =       log_spec - 2_0.0
       A_          =       np.clip(log_spec / 4_0.0 ,-2.0 ,0.0 ) + 1.0
       return log_spec




  def __call__(							self ,__snake_case ,__snake_case = None ,__snake_case = True ,__snake_case = None ,__snake_case = False ,__snake_case = False ,**__snake_case ,):



       """simple docstring"""





       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     '''This feature extractor is set to support sampling rate'''
                     f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
                     f' 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.''' )

       A_          =       isinstance(lowerCamelCase_ ,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}' )
       A_          =       is_batched_numpy or (
           isinstance(lowerCamelCase_ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
       )
       if is_batched:
            A_          =       [np.asarray([speech] ,dtype=np.floataa ).T for speech in raw_speech]
       elif not is_batched and not isinstance(lowerCamelCase_ ,np.ndarray ):
            A_          =       np.asarray(lowerCamelCase_ ,dtype=np.floataa )
       elif isinstance(lowerCamelCase_ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
            A_          =       raw_speech.astype(np.floataa )
       # always return batch
       if not is_batched:
            A_          =       [np.asarray([raw_speech] ).T]

       # Convert audio signals to log mel spectrograms, truncate by time axis
       A_          =       [
           self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
       ]
       if isinstance(audio_features[0] ,lowerCamelCase_ ):
            A_          =       [np.asarray(lowerCamelCase_ ,dtype=np.floataa ) for feature in audio_features]

       # Create audio attention mask
       A_          =       max(
           [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
       if return_attention_mask:
            A_          =       [
                (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
                + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
                for feature in audio_features
            ]
            A_          =       np.array(lowerCamelCase_ ).astype(np.floataa )

       # convert into correct format for padding
       A_          =       max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
       A_          =       np.ones([len(lowerCamelCase_ ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
       A_          =       padded_audio_features * self.padding_value
       for i in range(len(lowerCamelCase_ ) ):
            A_          =       audio_features[i]
            A_          =       feature

       # return as BatchFeature
       if return_attention_mask:
            A_          =       {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
       else:
            A_          =       {'audio_values': padded_audio_features}

       A_          =       BatchFeature(data=lowerCamelCase_ ,tensor_type=lowerCamelCase_ )
       return encoded_inputs










from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




		'''simple docstring'''
		print(f"""Vertex\tShortest Distance from vertex {src}"""			)
		for i, d in enumerate(_lowercase			):
				print(f"""{i}\t\t{d}"""			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(graph[j][k] for k in ['src', 'dst', 'weight'])
				if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
						return True
		return False


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

			for i in range(E):
						print("""Edge """, i + 1)
						__UpperCamelCase,	__UpperCamelCase,	__UpperCamelCase:	List[str] = (
						    int(x)
						    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
						)
						__UpperCamelCase:	List[Any] = {"""src""": src, """dst""": dest, """weight""": weight}

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


