




import argparse

import torch

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


logging.set_verbosity_info()





def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      RemBertConfig.from_json_file(lowerCAmelCase__				)
     print('''Building PyTorch model from configuration: {}'''.format(str(lowerCAmelCase__				)				)				)
     UpperCAmelCase_      :							str       =      RemBertModel(lowerCAmelCase__				)

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

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


if __name__ == "__main__":
     __a          =			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.'
     )
     __a          =			parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





from random import shuffle

import tensorflow as tf
from numpy import array







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      int(_lowercase				)
     assert noofclusters < len(_lowercase				)

     # Find out the dimensionality
     UpperCAmelCase_      :							Optional[int]       =      len(vectors[0]				)

     # Will help select random centroids from among the available vectors
     UpperCAmelCase_      :							Optional[int]       =      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.

     UpperCAmelCase_      :							List[str]       =      tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

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

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