


'''simple docstring'''



from random import shuffle

import tensorflow as tf
from numpy import array







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     int(UpperCamelCase__					)
			assert noofclusters < len(UpperCamelCase__					)

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

			# Will help select random centroids from among the available vectors
			UpperCAmelCase__  :					Dict        =     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.

			UpperCAmelCase__  :					int        =     tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

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

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

						##Node for computing Euclidean distances
						# Placeholders for input
						UpperCAmelCase__  :					Optional[Any]        =     tf.placeholder("""float""" ,      [dim]					)
						UpperCAmelCase__  :					Optional[int]        =     tf.placeholder("""float""" ,      [dim]					)
						UpperCAmelCase__  :					Optional[Any]        =     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
						UpperCAmelCase__  :					List[str]        =     tf.placeholder("""float""" ,      [noofclusters]					)
						UpperCAmelCase__  :					Optional[Any]        =     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.
						UpperCAmelCase__  :					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.
						UpperCAmelCase__  :					Optional[int]        =     1_0_0
						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__					)					):
												UpperCAmelCase__  :					Tuple        =     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__  :					Optional[int]        =     [
												    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
												UpperCAmelCase__  :					Dict        =     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
												UpperCAmelCase__  :					Optional[int]        =     [
												    vectors[i]
												    for i in range(len(UpperCamelCase__					)					)
												    if sess.run(assignments[i]					) == cluster_n
												]
												# Compute new centroid location
												UpperCAmelCase__  :					Optional[int]        =     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
						UpperCAmelCase__  :					Dict        =     sess.run(UpperCamelCase__					)
						UpperCAmelCase__  :					List[Any]        =     sess.run(UpperCamelCase__					)
						return centroids, assignments



'''simple docstring'''



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A	=logging.getLogger()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     get_gpu_count()

								if n_gpu > 1:
											pass
											# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
											# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
											# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
											# cmd = [sys.executable] + distributed_args + args
											# execute_subprocess_async(cmd, env=self.get_env())
											# XXX: test the results - need to save them first into .json file
								else:
											args.insert(0							,							"""run_glue_deebert.py""")
											with patch.object(_lowerCamelCase							,							"""argv"""							,							_lowerCamelCase):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)