



from random import shuffle

import tensorflow as tf
from numpy import array






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					int(_lowerCamelCase						)
							assert noofclusters < len(_lowerCamelCase						)

							# Find out the dimensionality
							_lowerCAmelCase  :							Dict				     =					len(vectors[0]						)

							# Will help select random centroids from among the available vectors
							_lowerCAmelCase  :							List[str]				     =					list(range(len(_lowerCamelCase						)						)						)
							shuffle(_lowerCamelCase						)

							# 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  :							str				     =					tf.Graph()

							with graph.as_default():
														# SESSION OF COMPUTATION

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

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

														##Now lets construct the node that will compute the mean
														# The placeholder for the input
														_lowerCAmelCase  :							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
														_lowerCAmelCase  :							Optional[int]				     =					tf.reduce_mean(_lowerCamelCase       ,				0						)

														##Node for computing Euclidean distances
														# Placeholders for input
														_lowerCAmelCase  :							Optional[int]				     =					tf.placeholder("float"       ,				[dim]						)
														_lowerCAmelCase  :							Tuple				     =					tf.placeholder("float"       ,				[dim]						)
														_lowerCAmelCase  :							str				     =					tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase       ,				_lowerCamelCase						)       ,				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  :							Optional[int]				     =					tf.placeholder("float"       ,				[noofclusters]						)
														_lowerCAmelCase  :							List[str]				     =					tf.argmin(_lowerCamelCase       ,				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  :							Dict				     =					tf.initialize_all_variables()

														# Initialize all variables
														sess.run(_lowerCamelCase						)

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

        # Return centroids and assignments
														_lowerCAmelCase  :							str				     =					sess.run(_lowerCamelCase						)
														_lowerCAmelCase  :							int				     =					sess.run(_lowerCamelCase						)
														return centroids, assignments





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


_snake_case	=						{
    "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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					XLNetConfig.from_json_file(_lowerCamelCase						)

							_lowerCAmelCase  :							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}"						)
														_lowerCAmelCase  :							Any				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					GLUE_TASKS_NUM_LABELS[finetuning_task]
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetForSequenceClassification(_lowerCamelCase						)
							elif "squad" in finetuning_task:
														_lowerCAmelCase  :							Union[str, Any]				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					XLNetForQuestionAnswering(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetLMHeadModel(_lowerCamelCase						)

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

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


if __name__ == "__main__":
						_snake_case	=						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",
						)
						_snake_case	=						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
						)
