






from random import shuffle

import tensorflow as tf
from numpy import array







def      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Any   ):
				A	 =      int(UpperCAmelCase   )
				assert noofclusters < len(UpperCAmelCase   )

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

				# Will help select random centroids from among the available vectors
				A	 =      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.

				A	 =      tf.Graph()

				with graph.as_default():
								# SESSION OF COMPUTATION

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

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

								##Node for computing Euclidean distances
								# Placeholders for input
								A	 =      tf.placeholder('float',						[dim]   )
								A	 =      tf.placeholder('float',						[dim]   )
								A	 =      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
								A	 =      tf.placeholder('float',						[noofclusters]   )
								A	 =      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.
								A	 =      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.
								A	 =      1_00
								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   )   ):
																A	 =      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	 =      [
																    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
																A	 =      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
																A	 =      [
																    vectors[i]
																    for i in range(len(UpperCAmelCase   )   )
																    if sess.run(assignments[i]   ) == cluster_n
																]
																# Compute new centroid location
																A	 =      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
								A	 =      sess.run(UpperCAmelCase   )
								A	 =      sess.run(UpperCAmelCase   )
								return centroids, assignments













import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				A	 =      [
				    'encoder.version',
				    'decoder.version',
				    'model.encoder.version',
				    'model.decoder.version',
				    'decoder.output_projection.weight',
				    '_float_tensor',
				    'encoder.embed_positions._float_tensor',
				    'decoder.embed_positions._float_tensor',
				]
				for k in ignore_keys:
								state_dict.pop(UpperCAmelCase,						UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      list(s_dict.keys()   )
				for key in keys:
								if "transformer_layers" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )
								elif "subsample" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A ,		A	 =      emb.weight.shape
				A	 =      nn.Linear(UpperCAmelCase,						UpperCAmelCase,						bias=UpperCAmelCase   )
				A	 =      emb.weight.data
				return lin_layer







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )
				A	 =      mam_aaa['args']
				A	 =      mam_aaa['model']
				A	 =      state_dict['decoder.output_projection.weight']

				remove_ignore_keys_(UpperCAmelCase   )
				rename_keys(UpperCAmelCase   )

				A	 =      state_dict['decoder.embed_tokens.weight'].shape[0]

				A	 =      args.share_decoder_input_output_embed

				A	 =      [int(UpperCAmelCase   ) for i in args.conv_kernel_sizes.split(','   )]
				A	 =      SpeechaTextConfig(
				    vocab_size=UpperCAmelCase,						max_source_positions=args.max_source_positions,						max_target_positions=args.max_target_positions,						encoder_layers=args.encoder_layers,						decoder_layers=args.decoder_layers,						encoder_attention_heads=args.encoder_attention_heads,						decoder_attention_heads=args.decoder_attention_heads,						encoder_ffn_dim=args.encoder_ffn_embed_dim,						decoder_ffn_dim=args.decoder_ffn_embed_dim,						d_model=args.encoder_embed_dim,						dropout=args.dropout,						attention_dropout=args.attention_dropout,						activation_dropout=args.activation_dropout,						activation_function='relu',						num_conv_layers=len(UpperCAmelCase   ),						conv_channels=args.conv_channels,						conv_kernel_sizes=UpperCAmelCase,						input_feat_per_channel=args.input_feat_per_channel,						input_channels=args.input_channels,						tie_word_embeddings=UpperCAmelCase,						num_beams=5,						max_length=2_00,						use_cache=UpperCAmelCase,						decoder_start_token_id=2,						early_stopping=UpperCAmelCase,						)

				A	 =      SpeechaTextForConditionalGeneration(UpperCAmelCase   )
				A ,		A	 =      model.model.load_state_dict(UpperCAmelCase,						strict=UpperCAmelCase   )
				if len(UpperCAmelCase   ) > 0 and not set(UpperCAmelCase   ) <= {
				    "encoder.embed_positions.weights",
				    "decoder.embed_positions.weights",
				}:
								raise ValueError(
								    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
								    f' but all the following weights are missing {missing}'   )

				if tie_embeds:
								A	 =      make_linear_from_emb(model.model.decoder.embed_tokens   )
				else:
								A	 =      lm_head_weights

				model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					lowerCAmelCase_           = parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





