






from random import shuffle

import tensorflow as tf
from numpy import array
def 		_a       ( a					:str      ,				a					:List[str]					)				->     Union[str, Any]:
 a						    = int(a					)
 assert noofclusters < len(a					)

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

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

 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(a					)
  ]
  ##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(a      ,				a					)					)

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  a						    = [tf.Variable(0					) for i in range(len(a					)					)]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  a						    = tf.placeholder('''int32'''					)
  a						    = []
  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
  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(a      ,				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(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
  a						    = tf.placeholder('''float'''      ,				[noofclusters]					)
  a						    = 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.
  a						    = 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.
  a						    = 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					)					):
    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(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
    a						    = 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
    a						    = [
        vectors[i]
        for i in range(len(a					)					)
        if sess.run(assignments[i]					) == cluster_n
    ]
    # Compute new centroid location
    a						    = 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
  a						    = sess.run(a					)
  a						    = sess.run(a					)
  return centroids, assignments










from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
  restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
  a						    = {name: i for i, name in enumerate(a					)}
  restype_atomaa_to_atomaa_list.append(
      [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

  restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

 # Add dummy mapping for restype 'UNK'
 restype_atomaa_to_atomaa_list.append([0] * 14					)
 restype_atomaa_to_atomaa_list.append([0] * 37					)
 restype_atomaa_mask_list.append([0.0] * 14					)

 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device      ,				)
 a						    = protein['''aatype'''].to(torch.long					)

 # create the mapping for (residx, atom14) --> atom37, i.e. an array
 # with shape (num_res, 14) containing the atom37 indices for this protein
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = restype_atomaa_mask[protein_aatype]

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

 # create the gather indices for mapping back
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = residx_atomaa_to_atomaa.long()

 # create the corresponding mask
 a						    = torch.zeros([21, 37]      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device					)
 for restype, restype_letter in enumerate(rc.restypes					):
  a						    = rc.restype_atoa[restype_letter]
  a						    = rc.residue_atoms[restype_name]
  for atom_name in atom_names:
   a						    = rc.atom_order[atom_name]
   a						    = 1

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


