
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


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


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Any			  =       ["audio_values", "audio_mask"]



		def __init__(				self       ,   A_=2_048       ,   A_=1       ,   A_=[16, 16]       ,   A_=128       ,   A_=44_100       ,   A_=86       ,   A_=2_048       ,   A_=0.0       ,   **A_       ,   )						->      List[str]:
				"""simple docstring"""
				super().__init__(
				    feature_size=A_       ,   sampling_rate=A_       ,   padding_value=A_       ,   **A_       ,   )

				UpperCamelCase						=  spectrogram_length
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  patch_size
				UpperCamelCase						=  feature_size // self.patch_size[1]
				UpperCamelCase						=  n_fft
				UpperCamelCase						=  sampling_rate // hop_length_to_sampling_rate
				UpperCamelCase						=  sampling_rate
				UpperCamelCase						=  padding_value
				UpperCamelCase						=  mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2       ,   num_mel_filters=A_       ,   min_frequency=0.0       ,   max_frequency=2_2050.0       ,   sampling_rate=A_       ,   norm='slaney'       ,   mel_scale='slaney'       ,   ).T



		def __UpperCamelCase				(				self       ,   A_ )						->      np.ndarray:
				"""simple docstring"""
				UpperCamelCase						=  spectrogram(
				    A_       ,   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=80.0       ,   )
				UpperCamelCase						=  log_spec[:, :-1]
				UpperCamelCase						=  log_spec - 20.0
				UpperCamelCase						=  np.clip(log_spec / 40.0       ,   -2.0       ,   0.0 ) + 1.0
				return log_spec



		def __call__(				self       ,   A_       ,   A_ = None       ,   A_ = True       ,   A_ = None       ,   A_ = False       ,   A_ = False       ,   **A_       ,   )						->      BatchFeature:
				"""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.' )

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

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

				# Create audio attention mask
				UpperCamelCase						=  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:
						UpperCamelCase						=  [
						    (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
						]
						UpperCamelCase						=  np.array(A_ ).astype(np.floataa )

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

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

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


from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		assert noofclusters < len(lowercase				)

		# Find out the dimensionality
		UpperCamelCase						=  len(vectors[0]				)

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

		# 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.

		UpperCamelCase						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				UpperCamelCase						=  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
				UpperCamelCase						=  [
				    tf.Variable(vectors[vector_indices[i]]				) for i in range(lowercase				)
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				UpperCamelCase						=  tf.placeholder('float64'					, [dim]				)
				UpperCamelCase						=  []
				for centroid in centroids:
						cent_assigns.append(tf.assign(lowercase					, lowercase				)				)

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

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

				##Node for computing Euclidean distances
				# Placeholders for input
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase					, lowercase				)					, 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
				UpperCamelCase						=  tf.placeholder('float'					, [noofclusters]				)
				UpperCamelCase						=  tf.argmin(lowercase					, 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.
				UpperCamelCase						=  tf.initialize_all_variables()

				# Initialize all variables
				sess.run(lowercase				)

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

        # Return centroids and assignments
				UpperCamelCase						=  sess.run(lowercase				)
				UpperCamelCase						=  sess.run(lowercase				)
				return centroids, assignments
