
'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array







def 		A_(     A						:		Any		,     A						:		List[Any]):
							UpperCamelCase   			=     int(A)
							assert noofclusters < len(A)

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

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

							# 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(A)
														]
														##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(A		,     A))

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

														##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(A		,     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(A		,     A)		,     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(A		,     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(A)

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

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






'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




