


'''simple docstring'''



from random import shuffle

import tensorflow as tf
from numpy import array







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
				UpperCAmelCase__  :					Optional[Any]        =     int(UpperCamelCase__					)
				assert noofclusters < len(UpperCamelCase__					)

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

				# Will help select random centroids from among the available vectors
				UpperCAmelCase__  :					Dict        =     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.

				UpperCAmelCase__  :					int        =     tf.Graph()

				with graph.as_default():
								# SESSION OF COMPUTATION

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

								##Variables for cluster assignments of individual vectors(initialized
								##to 0 at first)
								UpperCAmelCase__  :					Any        =     [tf.Variable(0					) for i in range(len(UpperCamelCase__					)					)]
								##These nodes will assign an assignment Variable the appropriate
								##value
								UpperCAmelCase__  :					str        =     tf.placeholder("""int32"""					)
								UpperCAmelCase__  :					str        =     []
								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
								UpperCAmelCase__  :					Tuple        =     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__  :					List[Any]        =     tf.reduce_mean(UpperCamelCase__ ,      0					)

								##Node for computing Euclidean distances
								# Placeholders for input
								UpperCAmelCase__  :					Optional[Any]        =     tf.placeholder("""float""" ,      [dim]					)
								UpperCAmelCase__  :					Optional[int]        =     tf.placeholder("""float""" ,      [dim]					)
								UpperCAmelCase__  :					Optional[Any]        =     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
								UpperCAmelCase__  :					List[str]        =     tf.placeholder("""float""" ,      [noofclusters]					)
								UpperCAmelCase__  :					Optional[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.
								UpperCAmelCase__  :					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.
								UpperCAmelCase__  :					Optional[int]        =     1_0_0
								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__					)					):
																UpperCAmelCase__  :					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.
																UpperCAmelCase__  :					Optional[int]        =     [
																    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
																UpperCAmelCase__  :					Dict        =     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
																UpperCAmelCase__  :					Optional[int]        =     [
																    vectors[i]
																    for i in range(len(UpperCamelCase__					)					)
																    if sess.run(assignments[i]					) == cluster_n
																]
																# Compute new centroid location
																UpperCAmelCase__  :					Optional[int]        =     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
								UpperCAmelCase__  :					Dict        =     sess.run(UpperCamelCase__					)
								UpperCAmelCase__  :					List[Any]        =     sess.run(UpperCamelCase__					)
								return centroids, assignments



'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__A	='scheduler_config.json'


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Union[str, Any]   =					1
					lowerCAmelCase      :Union[str, Any]   =					2
					lowerCAmelCase      :Optional[int]   =					3
					lowerCAmelCase      :Optional[int]   =					4
					lowerCAmelCase      :Any   =					5
					lowerCAmelCase      :Tuple   =					6
					lowerCAmelCase      :List[Any]   =					7
					lowerCAmelCase      :str   =					8
					lowerCAmelCase      :List[Any]   =					9
					lowerCAmelCase      :List[str]   =					10
					lowerCAmelCase      :Union[str, Any]   =					11
					lowerCAmelCase      :Optional[int]   =					12
					lowerCAmelCase      :str   =					13
					lowerCAmelCase      :Dict   =					14


@dataclass
class 		_snake_case					(     a__	):
					lowerCAmelCase      :torch.FloatTensor


class 		_snake_case					:
					lowerCAmelCase      :str   =					SCHEDULER_CONFIG_NAME
					lowerCAmelCase      :Union[str, Any]   =					[]
					lowerCAmelCase      :List[str]   =					True




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     cls.load_config(
								    pretrained_model_name_or_path=_lowerCamelCase							,							subfolder=_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							return_commit_hash=_lowerCamelCase							,							**_lowerCamelCase							,							)
								return cls.from_config(_lowerCamelCase							,							return_unused_kwargs=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							**_lowerCamelCase):
								self.save_config(save_directory=_lowerCamelCase							,							push_to_hub=_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 				snake_case__						(      self):
								return self._get_compatibles()




					@classmethod
					def 				snake_case__						(      cls):
								UpperCAmelCase__  :					List[Any]        =     list(set([cls.__name__] + cls._compatibles))
								UpperCAmelCase__  :					int        =     importlib.import_module(__name__.split(""".""")[0])
								UpperCAmelCase__  :					Dict        =     [
								    getattr(_lowerCamelCase							,							_lowerCamelCase) for c in compatible_classes_str if hasattr(_lowerCamelCase							,							_lowerCamelCase)
								]
								return compatible_classes