


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					**lowercase_			)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       Any  						=  AutoConfig.from_pretrained(lowercase_			,					**lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  AutoModelForSeqaSeqLM.from_config(lowercase_			)
						model.save_pretrained(lowercase_			)
						AutoTokenizer.from_pretrained(lowercase_			).save_pretrained(lowercase_			)
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)





from random import shuffle

import tensorflow as tf
from numpy import array




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[Any]  						=  int(lowercase_			)
						assert noofclusters < len(lowercase_			)

						# Find out the dimensionality
						__UpperCAmelCase :       str  						=  len(vectors[0]			)

						# Will help select random centroids from among the available vectors
						__UpperCAmelCase :       Union[str, Any]  						=  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 :       Union[str, Any]  						=  tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

												##Variables for cluster assignments of individual vectors(initialized
												##to 0 at first)
												__UpperCAmelCase :       Union[str, Any]  						=  [tf.Variable(0			) for i in range(len(lowercase_			)			)]
												##These nodes will assign an assignment Variable the appropriate
												##value
												__UpperCAmelCase :       Dict  						=  tf.placeholder('''int32'''			)
												__UpperCAmelCase :       Optional[Any]  						=  []
												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 :       Union[str, 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
												__UpperCAmelCase :       Any  						=  tf.reduce_mean(lowercase_			,					0			)

												##Node for computing Euclidean distances
												# Placeholders for input
												__UpperCAmelCase :       Tuple  						=  tf.placeholder('''float'''			,					[dim]			)
												__UpperCAmelCase :       Any  						=  tf.placeholder('''float'''			,					[dim]			)
												__UpperCAmelCase :       Any  						=  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 :       Union[str, Any]  						=  tf.placeholder('''float'''			,					[noofclusters]			)
												__UpperCAmelCase :       Optional[Any]  						=  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 :       Optional[Any]  						=  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 :       Union[str, Any]  						=  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 :       List[str]  						=  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 :       List[Any]  						=  [
																								    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 :       Optional[Any]  						=  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 :       Optional[Any]  						=  [
																								    vectors[i]
																								    for i in range(len(lowercase_			)			)
																								    if sess.run(assignments[i]			) == cluster_n
																								]
																								# Compute new centroid location
																								__UpperCAmelCase :       str  						=  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 :       List[str]  						=  sess.run(lowercase_			)
												__UpperCAmelCase :       Tuple  						=  sess.run(lowercase_			)
												return centroids, assignments

