



from random import shuffle

import tensorflow as tf
from numpy import array







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Dict  		=	int(_UpperCAmelCase   )
						assert noofclusters < len(_UpperCAmelCase   )

						# Find out the dimensionality
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	len(vectors[0]   )

						# Will help select random centroids from among the available vectors
						SCREAMING_SNAKE_CASE_:			int  		=	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.

						SCREAMING_SNAKE_CASE_:			str  		=	tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

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

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

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









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCAmelCase							:  Tuple            =						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

						env.seed(0)
						lowerCAmelCase							:  Optional[Any]            =						env.reset()
						lowerCAmelCase							:  Union[str, Any]            =						0
						lowerCAmelCase							:  Optional[int]            =						0
						lowerCAmelCase							:  Optional[int]            =						1000
						lowerCAmelCase							:  Dict            =						[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		lowerCAmelCase							:  Union[str, Any]            =						pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    f''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

						print(f'''Total reward: {total_reward}''')






