


'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array




def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      Optional[Any]):
      A_						:  str          =				int(lowerCamelCase)
      assert noofclusters < len(lowerCamelCase)

      # Find out the dimensionality
      A_						:  Union[str, Any]          =				len(vectors[0])

      # Will help select random centroids from among the available vectors
      A_						:  int          =				list(range(len(lowerCamelCase)))
      shuffle(lowerCamelCase)

      # GRAPH OF COMPUTATION
      # We initialize a new graph and set it as the default during each run
      # of this algorithm. This ensures that as this function is called
      # multiple times, the default graph doesn't keep getting crowded with
      # unused ops and Variables from previous function calls.

      A_						:  Dict          =				tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

            A_						:  Union[str, Any]          =				tf.Session()

            ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

            ##First lets ensure we have a Variable vector for each centroid,
            ##initialized to one of the vectors from the available data points
            A_						:  List[Any]          =				[
                tf.Variable(vectors[vector_indices[i]]) for i in range(lowerCamelCase)
            ]
            ##These nodes will assign the centroid Variables the appropriate
            ##values
            A_						:  str          =				tf.placeholder("""float64"""							,						[dim])
            A_						:  Dict          =				[]
            for centroid in centroids:
                  cent_assigns.append(tf.assign(lowerCamelCase							,						lowerCamelCase))

            ##Variables for cluster assignments of individual vectors(initialized
            ##to 0 at first)
            A_						:  List[Any]          =				[tf.Variable(0) for i in range(len(lowerCamelCase))]
            ##These nodes will assign an assignment Variable the appropriate
            ##value
            A_						:  List[str]          =				tf.placeholder("""int32""")
            A_						:  Tuple          =				[]
            for assignment in assignments:
                  cluster_assigns.append(tf.assign(lowerCamelCase							,						lowerCamelCase))

            ##Now lets construct the node that will compute the mean
            # The placeholder for the input
            A_						:  Optional[int]          =				tf.placeholder("""float"""							,						[None, dim])
            # The Node/op takes the input and computes a mean along the 0th
            # dimension, i.e. the list of input vectors
            A_						:  str          =				tf.reduce_mean(lowerCamelCase							,						0)

            ##Node for computing Euclidean distances
            # Placeholders for input
            A_						:  List[Any]          =				tf.placeholder("""float"""							,						[dim])
            A_						:  str          =				tf.placeholder("""float"""							,						[dim])
            A_						:  str          =				tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCamelCase							,						lowerCamelCase)							,						2)))

            ##This node will figure out which cluster to assign a vector to,
            ##based on Euclidean distances of the vector from the centroids.
            # Placeholder for input
            A_						:  int          =				tf.placeholder("""float"""							,						[noofclusters])
            A_						:  Optional[int]          =				tf.argmin(lowerCamelCase							,						0)

            ##INITIALIZING STATE VARIABLES

            ##This will help initialization of all Variables defined with respect
            ##to the graph. The Variable-initializer should be defined after
            ##all the Variables have been constructed, so that each of them
            ##will be included in the initialization.
            A_						:  List[Any]          =				tf.initialize_all_variables()

            # Initialize all variables
            sess.run(lowerCamelCase)

            ##CLUSTERING ITERATIONS

            # Now perform the Expectation-Maximization steps of K-Means clustering
            # iterations. To keep things simple, we will only do a set number of
            # iterations, instead of using a Stopping Criterion.
            A_						:  Tuple          =				100
            for _ in range(lowerCamelCase):
                  ##EXPECTATION STEP
                  ##Based on the centroid locations till last iteration, compute
                  ##the _expected_ centroid assignments.
                  # Iterate over each vector
                  for vector_n in range(len(lowerCamelCase)):
                        A_						:  Tuple          =				vectors[vector_n]
                        # Compute Euclidean distance between this vector and each
                        # centroid. Remember that this list cannot be named
                        #'centroid_distances', since that is the input to the
                        # cluster assignment node.
                        A_						:  Dict          =				[
                            sess.run(lowerCamelCase							,						feed_dict={va: vect, va: sess.run(lowerCamelCase)})
                            for centroid in centroids
                        ]
                        # Now use the cluster assignment node, with the distances
                        # as the input
                        A_						:  int          =				sess.run(
                            lowerCamelCase							,						feed_dict={centroid_distances: distances})
                        # Now assign the value to the appropriate state variable
                        sess.run(
                            cluster_assigns[vector_n]							,						feed_dict={assignment_value: assignment})

                  ##MAXIMIZATION STEP
                  # Based on the expected state computed from the Expectation Step,
                  # compute the locations of the centroids so as to maximize the
                  # overall objective of minimizing within-cluster Sum-of-Squares
                  for cluster_n in range(lowerCamelCase):
                        # Collect all the vectors assigned to this cluster
                        A_						:  str          =				[
                            vectors[i]
                            for i in range(len(lowerCamelCase))
                            if sess.run(assignments[i]) == cluster_n
                        ]
                        # Compute new centroid location
                        A_						:  str          =				sess.run(
                            lowerCamelCase							,						feed_dict={mean_input: array(lowerCamelCase)})
                        # Assign value to appropriate variable
                        sess.run(
                            cent_assigns[cluster_n]							,						feed_dict={centroid_value: new_location})

        # Return centroids and assignments
            A_						:  Optional[Any]          =				sess.run(lowerCamelCase)
            A_						:  Dict          =				sess.run(lowerCamelCase)
            return centroids, assignments




'''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


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_features""", """is_longer"""]






  def __init__(							self :       Dict	,_a :       Optional[int]=64	,_a :       List[Any]=48000	,_a :       str=480	,_a :       Optional[Any]=10	,_a :       Optional[int]=1024	,_a :       Tuple=0.0	,_a :       str=False	,_a :       float = 0	,_a :       float = 14000	,_a :       int = None	,_a :       str = "fusion"	,_a :       str = "repeatpad"	,**_a :       Tuple	,):

        '''simple docstring'''




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






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				copy.deepcopy(self.__dict__ )
        A_						:  Tuple          =				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        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[np.array] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				spectrogram(
            _a	,window_function(self.fft_window_size	,"""hann""" )	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=_a	,log_mel="""dB"""	,)
        return log_mel_spectrogram.T






  def        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				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
              A_						:  List[Any]          =				[0]
        if len(ranges[2] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  int          =				[0]
        # randomly choose index for each part
        A_						:  List[str]          =				np.random.choice(ranges[0] )
        A_						:  int          =				np.random.choice(ranges[1] )
        A_						:  Optional[int]          =				np.random.choice(ranges[2] )

        A_						:  Tuple          =				mel[idx_front : idx_front + chunk_frames, :]
        A_						:  Dict          =				mel[idx_middle : idx_middle + chunk_frames, :]
        A_						:  Dict          =				mel[idx_back : idx_back + chunk_frames, :]

        A_						:  Optional[int]          =				torch.tensor(mel[None, None, :] )
        A_						:  Dict          =				torch.nn.functional.interpolate(
            _a	,size=[chunk_frames, 64]	,mode="""bilinear"""	,align_corners=_a )
        A_						:  str          =				mel_shrink[0][0].numpy()
        A_						:  Tuple          =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0 )
        return mel_fusion






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[Any]	,_a :       int	,_a :       Dict ):

        '''simple docstring'''




        if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                    A_						:  Dict          =				True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    A_						:  Tuple          =				len(_a ) - max_length
                    A_						:  Optional[int]          =				np.random.randint(0	,overflow + 1 )
                    A_						:  List[Any]          =				waveform[idx : idx + max_length]
                    A_						:  Optional[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]
              elif truncation == "fusion":
                    A_						:  Dict          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Tuple          =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    A_						:  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.
                          A_						:  Optional[int]          =				np.stack([mel, mel, mel, mel]	,axis=0 )
                          A_						:  str          =				False
                    else:
                          A_						:  str          =				self._random_mel_fusion(_a	,_a	,_a )
                          A_						:  Optional[Any]          =				True
              else:
                    raise NotImplementedError(f'data_truncating {truncation} not implemented' )

        else:
              A_						:  Optional[int]          =				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":
                          A_						:  int          =				int(max_length / len(_a ) )
                          A_						:  Any          =				np.stack(np.tile(_a	,n_repeat + 1 ) )[:max_length]
                    if padding == "repeatpad":
                          A_						:  List[str]          =				int(max_length / len(_a ) )
                          A_						:  Optional[Any]          =				np.stack(np.tile(_a	,_a ) )
                    A_						:  Any          =				np.pad(_a	,(0, max_length - waveform.shape[0])	,mode="""constant"""	,constant_values=0 )

              if truncation == "fusion":
                    A_						:  List[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Optional[Any]          =				np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0 )
              else:
                    A_						:  Union[str, Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]

        return input_mel, longer






  def __call__(							self :       List[Any]	,_a :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_a :       str = None	,_a :       Optional[str] = None	,_a :       Optional[int] = None	,_a :       Optional[int] = None	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Any	,):

        '''simple docstring'''




        A_						:  List[str]          =				truncation if truncation is not None else self.truncation
        A_						:  List[Any]          =				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.""" )

        A_						:  Any          =				isinstance(_a	,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_						:  int          =				is_batched_numpy or (
            isinstance(_a	,(list, tuple) ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list) ))
        )

        if is_batched:
              A_						:  Optional[int]          =				[np.asarray(_a	,dtype=np.floataa ) for speech in raw_speech]
        elif not is_batched and not isinstance(_a	,np.ndarray ):
              A_						:  str          =				np.asarray(_a	,dtype=np.floataa )
        elif isinstance(_a	,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
              A_						:  Tuple          =				raw_speech.astype(np.floataa )

        # always return batch
        if not is_batched:
              A_						:  Any          =				[np.asarray(_a )]

        # convert to mel spectrogram, truncate and pad if needed.
        A_						:  str          =				[
            self._get_input_mel(_a	,max_length if max_length else self.nb_max_samples	,_a	,_a )
            for waveform in raw_speech
        ]

        A_						:  int          =				[]
        A_						:  Any          =				[]
        for mel, longer in padded_inputs:
              input_mel.append(_a )
              is_longer.append(_a )

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

        if isinstance(input_mel[0]	,_a ):
              A_						:  Tuple          =				[np.asarray(_a	,dtype=np.floataa ) for feature in input_mel]

        # is_longer is a list of bool
        A_						:  List[str]          =				[[longer] for longer in is_longer]

        A_						:  Optional[Any]          =				{"""input_features""": input_mel, """is_longer""": is_longer}
        A_						:  int          =				BatchFeature(_a )

        if return_tensors is not None:
              A_						:  int          =				input_features.convert_to_tensors(_a )

        return input_features
