






'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	str:
       lowerCamelCase__ :     Dict	  =	int(UpperCamelCase				)
       assert noofclusters < len(UpperCamelCase				)

       # Find out the dimensionality
       lowerCamelCase__ :     Any	  =	len(vectors[0]				)

       # Will help select random centroids from among the available vectors
       lowerCamelCase__ :     Any	  =	list(range(len(UpperCamelCase				)				)				)
       shuffle(UpperCamelCase				)

       # 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.

       lowerCamelCase__ :     Optional[Any]	  =	tf.Graph()

       with graph.as_default():
              # SESSION OF COMPUTATION

              lowerCamelCase__ :     Tuple	  =	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
              lowerCamelCase__ :     Tuple	  =	[
                  tf.Variable(vectors[vector_indices[i]]				) for i in range(UpperCamelCase				)
              ]
              ##These nodes will assign the centroid Variables the appropriate
              ##values
              lowerCamelCase__ :     Union[str, Any]	  =	tf.placeholder("""float64"""    ,      [dim]				)
              lowerCamelCase__ :     Optional[Any]	  =	[]
              for centroid in centroids:
                     cent_assigns.append(tf.assign(UpperCamelCase    ,      UpperCamelCase				)				)

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

              ##Now lets construct the node that will compute the mean
              # The placeholder for the input
              lowerCamelCase__ :     Any	  =	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
              lowerCamelCase__ :     int	  =	tf.reduce_mean(UpperCamelCase    ,      0				)

              ##Node for computing Euclidean distances
              # Placeholders for input
              lowerCamelCase__ :     Tuple	  =	tf.placeholder("""float"""    ,      [dim]				)
              lowerCamelCase__ :     int	  =	tf.placeholder("""float"""    ,      [dim]				)
              lowerCamelCase__ :     Tuple	  =	tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCamelCase    ,      UpperCamelCase				)    ,      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
              lowerCamelCase__ :     str	  =	tf.placeholder("""float"""    ,      [noofclusters]				)
              lowerCamelCase__ :     List[Any]	  =	tf.argmin(UpperCamelCase    ,      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.
              lowerCamelCase__ :     List[Any]	  =	tf.initialize_all_variables()

              # Initialize all variables
              sess.run(UpperCamelCase				)

              ##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.
              lowerCamelCase__ :     List[Any]	  =	100
              for _ in range(UpperCamelCase				):
                     ##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(UpperCamelCase				)				):
                            lowerCamelCase__ :     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.
                            lowerCamelCase__ :     List[Any]	  =	[
                                sess.run(UpperCamelCase    ,      feed_dict={va: vect, va: sess.run(UpperCamelCase				)}				)
                                for centroid in centroids
                            ]
                            # Now use the cluster assignment node, with the distances
                            # as the input
                            lowerCamelCase__ :     Optional[int]	  =	sess.run(
                                UpperCamelCase    ,      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(UpperCamelCase				):
                            # Collect all the vectors assigned to this cluster
                            lowerCamelCase__ :     Any	  =	[
                                vectors[i]
                                for i in range(len(UpperCamelCase				)				)
                                if sess.run(assignments[i]				) == cluster_n
                            ]
                            # Compute new centroid location
                            lowerCamelCase__ :     Dict	  =	sess.run(
                                UpperCamelCase    ,      feed_dict={mean_input: array(UpperCamelCase				)}				)
                            # Assign value to appropriate variable
                            sess.run(
                                cent_assigns[cluster_n]    ,      feed_dict={centroid_value: new_location}				)

        # Return centroids and assignments
              lowerCamelCase__ :     Optional[int]	  =	sess.run(UpperCamelCase				)
              lowerCamelCase__ :     Tuple	  =	sess.run(UpperCamelCase				)
              return centroids, assignments









'''simple docstring'''







from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


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

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	feature_size
														lowerCamelCase__ :     Tuple	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     Union[str, Any]	  =	hop_length
														lowerCamelCase__ :     Any	  =	win_length
														lowerCamelCase__ :     Tuple	  =	frame_signal_scale
														lowerCamelCase__ :     Dict	  =	preemphasis_coeff
														lowerCamelCase__ :     Optional[Any]	  =	mel_floor
														lowerCamelCase__ :     Any	  =	normalize_means
														lowerCamelCase__ :     Optional[int]	  =	normalize_vars
														lowerCamelCase__ :     Dict	  =	win_function
														lowerCamelCase__ :     Dict	  =	return_attention_mask

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[Any]	  =	mel_filter_bank(
														    num_frequency_bins=self.n_freqs      ,							num_mel_filters=self.feature_size      ,							min_frequency=0.0      ,							max_frequency=self.sampling_rate / 2.0      ,							sampling_rate=self.sampling_rate      ,							)

														lowerCamelCase__ :     Tuple	  =	spectrogram(
														    one_waveform * self.frame_signal_scale      ,							window=UpperCamelCase__      ,							frame_length=self.sample_size      ,							hop_length=self.sample_stride      ,							fft_length=self.n_fft      ,							center=UpperCamelCase__      ,							preemphasis=self.preemphasis_coeff      ,							mel_filters=UpperCamelCase__      ,							mel_floor=self.mel_floor      ,							log_mel="""log"""      ,							)
														return msfc_features.T




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

														# make sure array is in float32
														lowerCamelCase__ :     str	  =	x.astype(np.floataa					)

														return x




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[np.ndarray]      ,							UpperCamelCase__:    Optional[np.ndarray] = None					):
														lowerCamelCase__ :     Optional[int]	  =	attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(UpperCamelCase__      ,							UpperCamelCase__      ,							self.padding_value					) for x, n in zip(UpperCamelCase__      ,							UpperCamelCase__					)]




							def __call__(							self:    List[str]      ,							UpperCamelCase__:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,							UpperCamelCase__:    Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    Optional[bool] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    Optional[int] = None      ,							**UpperCamelCase__:    Tuple      ,							):

														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 `raw_speech` 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."""					)

														lowerCamelCase__ :     List[str]	  =	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}'''					)
														lowerCamelCase__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

														if is_batched:
																					lowerCamelCase__ :     str	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for speech in raw_speech]
														elif not is_batched and not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					lowerCamelCase__ :     List[Any]	  =	np.asarray(UpperCamelCase__      ,							dtype=np.floataa					)
														elif isinstance(UpperCamelCase__      ,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																					lowerCamelCase__ :     Optional[Any]	  =	raw_speech.astype(np.floataa					)

														# always return batch
														if not is_batched:
																					lowerCamelCase__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

														# convert into correct format for padding
														lowerCamelCase__ :     str	  =	BatchFeature({"""input_features""": features}					)

														lowerCamelCase__ :     str	  =	self.pad(
														    UpperCamelCase__      ,							padding=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							**UpperCamelCase__      ,							)
														# make sure list is in array format
														lowerCamelCase__ :     str	  =	padded_inputs.get("""input_features"""					)
														if isinstance(input_features[0]      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     Dict	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for feature in input_features]

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					lowerCamelCase__ :     str	  =	(
																					    np.array(UpperCamelCase__      ,							dtype=np.intaa					)
																					    if self._get_padding_strategies(UpperCamelCase__      ,							max_length=UpperCamelCase__					) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					lowerCamelCase__ :     Union[str, Any]	  =	self.normalize(
																					    padded_inputs["""input_features"""]      ,							attention_mask=UpperCamelCase__					)

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

