


from random import shuffle

import tensorflow as tf
from numpy import array
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


 _lowercase							      =int(__snake_case						)
 assert noofclusters < len(__snake_case						)

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

 # Will help select random centroids from among the available vectors
 _lowercase							      =list(range(len(__snake_case						)						)						)
 shuffle(__snake_case						)

 # 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.

 _lowercase							      =tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

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

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

  ##Now lets construct the node that will compute the mean
  # The placeholder for the input
  _lowercase							      =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
  _lowercase							      =tf.reduce_mean(__snake_case     ,						0						)

  ##Node for computing Euclidean distances
  # Placeholders for input
  _lowercase							      =tf.placeholder('''float'''     ,						[dim]						)
  _lowercase							      =tf.placeholder('''float'''     ,						[dim]						)
  _lowercase							      =tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__snake_case     ,						__snake_case						)     ,						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
  _lowercase							      =tf.placeholder('''float'''     ,						[noofclusters]						)
  _lowercase							      =tf.argmin(__snake_case     ,						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.
  _lowercase							      =tf.initialize_all_variables()

  # Initialize all variables
  sess.run(__snake_case						)

  ##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.
  _lowercase							      =100
  for _ in range(__snake_case						):
   ##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(__snake_case						)						):
    _lowercase							      =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.
    _lowercase							      =[
        sess.run(__snake_case     ,						feed_dict={va: vect, va: sess.run(__snake_case						)}						)
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    _lowercase							      =sess.run(
        __snake_case     ,						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(__snake_case						):
    # Collect all the vectors assigned to this cluster
    _lowercase							      =[
        vectors[i]
        for i in range(len(__snake_case						)						)
        if sess.run(assignments[i]						) == cluster_n
    ]
    # Compute new centroid location
    _lowercase							      =sess.run(
        __snake_case     ,						feed_dict={mean_input: array(__snake_case						)}						)
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n]     ,						feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
  _lowercase							      =sess.run(__snake_case						)
  _lowercase							      =sess.run(__snake_case						)
  return centroids, assignments





from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   	=							logging.get_logger(__name__)

# TODO Update this
UpperCAmelCase__   	=							{
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''esm'''





     def __init__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_2_6		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-12		,     UpperCAmelCase="absolute"		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Tuple:
      super().__init__(pad_token_id=UpperCAmelCase		,     mask_token_id=UpperCAmelCase		,     **UpperCAmelCase       )

      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =layer_norm_eps
      _lowercase							      =position_embedding_type
      _lowercase							      =use_cache
      _lowercase							      =emb_layer_norm_before
      _lowercase							      =token_dropout
      _lowercase							      =is_folding_model
      if is_folding_model:
       if esmfold_config is None:
        logger.info('''No esmfold_config supplied for folding model, using default values.'''       )
        _lowercase							      =EsmFoldConfig()
       elif isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =EsmFoldConfig(**UpperCAmelCase       )
       _lowercase							      =esmfold_config
       if vocab_list is None:
        logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''       )
        _lowercase							      =get_default_vocab_list()
       else:
        _lowercase							      =vocab_list
      else:
       _lowercase							      =None
       _lowercase							      =None
      if self.esmfold_config is not None and getattr(self.esmfold_config		,     '''use_esm_attn_map'''		,     UpperCAmelCase       ):
       raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =super().to_dict()
      if isinstance(self.esmfold_config		,     UpperCAmelCase       ):
       _lowercase							      =self.esmfold_config.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			None
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			0

     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False

     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   Union[str, Any]:
      if self.trunk is None:
       _lowercase							      =TrunkConfig()
      elif isinstance(self.trunk		,     UpperCAmelCase       ):
       _lowercase							      =TrunkConfig(**self.trunk       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.trunk.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			48
     SCREAMING_SNAKE_CASE__      			=			1024
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   List[str]:
      if self.structure_module is None:
       _lowercase							      =StructureModuleConfig()
      elif isinstance(self.structure_module		,     UpperCAmelCase       ):
       _lowercase							      =StructureModuleConfig(**self.structure_module       )

      if self.max_recycles <= 0:
       raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}."       )
      if self.sequence_state_dim % self.sequence_state_dim != 0:
       raise ValueError(
           '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
           f" {self.sequence_state_dim} and {self.sequence_state_dim}."       )
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
       raise ValueError(
           '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
           f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."       )

      _lowercase							      =self.sequence_state_dim // self.sequence_head_width
      _lowercase							      =self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
       raise ValueError(
           '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
           f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."       )
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
       raise ValueError(
           '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
           f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."       )
      if self.pairwise_state_dim % 2 != 0:
       raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."       )

      if self.dropout >= 0.4:
       raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}."       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.structure_module.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			384
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			16
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			12
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			0.1
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			1
     SCREAMING_SNAKE_CASE__      			=			2
     SCREAMING_SNAKE_CASE__      			=			7
     SCREAMING_SNAKE_CASE__      			=			10
     SCREAMING_SNAKE_CASE__      			=			1E-8
     SCREAMING_SNAKE_CASE__      			=			1E5





     def      __A					(self       )     ->   List[Any]:
      return asdict(self       )



def 				UpperCAmelCase_					(							)     ->			Tuple:







 """simple docstring"""


 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )

