






'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array
def     __lowercase  (_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Optional[int]				   =				int(_lowercase     )
     assert noofclusters < len(_lowercase     )

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

     # Will help select random centroids from among the available vectors
     __lowerCamelCase						:       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.

     __lowerCamelCase						:       Dict				   =				tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

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











'''simple docstring'''

import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

     # Load weights from tf checkpoint
     load_tf_weights_in_rembert(_lowercase,						_lowercase,						_lowercase     )

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



