




from random import shuffle

import tensorflow as tf
from numpy import array

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[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     =				1_00
          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









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase		, map_location=torch.device("cpu"))
     lightning_model.load_state_dict(ckpt["state_dict"])

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # transfer weights
     longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
     longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
     longformer_for_qa.eval()

     # save model
     longformer_for_qa.save_pretrained(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

