






'''simple docstring'''
from random import shuffle

import tensorflow as tf
from numpy import array




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     int(_UpperCamelCase							)
   assert noofclusters < len(_UpperCamelCase							)

   # Find out the dimensionality
   lowercase_	:    Optional[Any]           =     len(vectors[0]							)

   # Will help select random centroids from among the available vectors
   lowercase_	:    Union[str, Any]           =     list(range(len(_UpperCamelCase							)							)							)
   shuffle(_UpperCamelCase							)

   # 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_	:    str           =     tf.Graph()

   with graph.as_default():
      # SESSION OF COMPUTATION

      lowercase_	:    List[Any]           =     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_	:    Any           =     [
          tf.Variable(vectors[vector_indices[i]]							) for i in range(_UpperCamelCase							)
      ]
      ##These nodes will assign the centroid Variables the appropriate
      ##values
      lowercase_	:    Dict           =     tf.placeholder("float64"						,		[dim]							)
      lowercase_	:    int           =     []
      for centroid in centroids:
         cent_assigns.append(tf.assign(_UpperCamelCase						,		_UpperCamelCase							)							)

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

      ##Now lets construct the node that will compute the mean
      # The placeholder for the input
      lowercase_	:    Any           =     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_	:    Tuple           =     tf.reduce_mean(_UpperCamelCase						,		0							)

      ##Node for computing Euclidean distances
      # Placeholders for input
      lowercase_	:    Any           =     tf.placeholder("float"						,		[dim]							)
      lowercase_	:    Optional[int]           =     tf.placeholder("float"						,		[dim]							)
      lowercase_	:    str           =     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_UpperCamelCase						,		_UpperCamelCase							)						,		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_	:    str           =     tf.placeholder("float"						,		[noofclusters]							)
      lowercase_	:    Dict           =     tf.argmin(_UpperCamelCase						,		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_	:    Union[str, Any]           =     tf.initialize_all_variables()

      # Initialize all variables
      sess.run(_UpperCamelCase							)

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

        # Return centroids and assignments
      lowercase_	:    Union[str, Any]           =     sess.run(_UpperCamelCase							)
      lowercase_	:    Union[str, Any]           =     sess.run(_UpperCamelCase							)
      return centroids, assignments












'''simple docstring'''
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCamelCase__ =			{
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Tuple           =     XLNetConfig.from_json_file(_UpperCamelCase							)

   lowercase_	:    Union[str, Any]           =     finetuning_task.lower() if finetuning_task is not None else ""
   if finetuning_task in GLUE_TASKS_NUM_LABELS:
      print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""							)
      lowercase_	:    Dict           =     finetuning_task
      lowercase_	:    Any           =     GLUE_TASKS_NUM_LABELS[finetuning_task]
      lowercase_	:    Any           =     XLNetForSequenceClassification(_UpperCamelCase							)
   elif "squad" in finetuning_task:
      lowercase_	:    Optional[int]           =     finetuning_task
      lowercase_	:    Optional[int]           =     XLNetForQuestionAnswering(_UpperCamelCase							)
   else:
      lowercase_	:    Union[str, Any]           =     XLNetLMHeadModel(_UpperCamelCase							)

   # Load weights from tf checkpoint
   load_tf_weights_in_xlnet(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)

   # Save pytorch-model
   lowercase_	:    Optional[Any]           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Dict           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   print(F"""Save PyTorch model to {os.path.abspath(_UpperCamelCase							)}"""							)
   torch.save(model.state_dict()						,		_UpperCamelCase							)
   print(F"""Save configuration file to {os.path.abspath(_UpperCamelCase							)}"""							)
   with open(_UpperCamelCase						,		"w"						,		encoding="utf-8"							) as f:
      f.write(config.to_json_string()							)


if __name__ == "__main__":
 UpperCamelCase__ =			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(
     '--xlnet_config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained XLNet model. \n'
         'This specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     required=True,
     help='Path to the folder to store the PyTorch model or dataset/vocab.',
 )
 parser.add_argument(
     '--finetuning_task',
     default=None,
     type=str,
     help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
 )
 UpperCamelCase__ =			parser.parse_args()
 print(args)

 convert_xlnet_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
 )




