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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	###############################################################################
	# Copyright (C) 2020-2022 Habana Labs, Ltd. an Intel Company
	#
	# Changes:
	# - Added stdout/stderr flush in main
	# - Added default values for environment variables:
	# - HABANA_INITIAL_WORKSPACE_SIZE_MB
	# - TF_DISABLE_MKL
	# - TF_BF16_CONVERSION
	# - TF_PRELIMINARY_CLUSTER_SIZE
	# - TF_DISABLE_SCOPED_ALLOCATOR
	# - Added command-line flags:
	# - num_train_steps
	# - deterministic_run
	# - deterministic_seed
	# - Added prefetch of dataset
	# - Added support for HPU profiling (command-line flag - `profile`).
	# - Added line tf.get_logger().propagate = False
	###############################################################################

	"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""

	from __future__ import division
	from __future__ import print_function
	from __future__ import absolute_import

	import json
	import os
	import sys
	import socket

	curr_path = os.path.abspath(os.path.dirname(__file__))
	sys.path.append(os.path.join(curr_path, '..'))

	import tensorflow as tf
	import six
	import TensorFlow.nlp.bert.utils.habana_hooks as habana_hooks
	import TensorFlow.nlp.bert.data_preprocessing.tokenization as tokenization
	import TensorFlow.nlp.bert.optimization as optimization
	import TensorFlow.nlp.bert.modeling as modeling
	import numpy as np
	import random
	import math
	import json
	import collections
	from TensorFlow.common.tb_utils import write_hparams_v1, TBSummary, TensorBoardHook
	from TensorFlow.common.debug import dump_callback
	from habana_frameworks.tensorflow import load_habana_module

	try:
	import horovod.tensorflow as hvd
	except ImportError:
	hvd = None

	def horovod_enabled():
	return hvd is not None and hvd.is_initialized()

	tf.get_logger().propagate = False

	flags = tf.compat.v1.flags

	FLAGS = flags.FLAGS


	def init_squad_flags():
	# Required parameters
	flags.DEFINE_string(
	"bert_config_file", None,
	"The config json file corresponding to the pre-trained BERT model. "
	"This specifies the model architecture.")

	flags.DEFINE_string("vocab_file", None,
	"The vocabulary file that the BERT model was trained on.")

	flags.DEFINE_string(
	"output_dir", None,
	"The output directory where the model checkpoints will be written.")

	# Other parameters
	flags.DEFINE_string("train_file", None,
	"SQuAD json for training. E.g., train-v1.1.json")

	flags.DEFINE_string(
	"predict_file", None,
	"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")

	flags.DEFINE_string(
	"init_checkpoint", None,
	"Initial checkpoint (usually from a pre-trained BERT model).")

	flags.DEFINE_bool(
	"do_lower_case", True,
	"Whether to lower case the input text. Should be True for uncased "
	"models and False for cased models.")

	flags.DEFINE_integer(
	"max_seq_length", 384,
	"The maximum total input sequence length after WordPiece tokenization. "
	"Sequences longer than this will be truncated, and sequences shorter "
	"than this will be padded.")

	flags.DEFINE_integer(
	"doc_stride", 128,
	"When splitting up a long document into chunks, how much stride to "
	"take between chunks.")

	flags.DEFINE_integer(
	"max_query_length", 64,
	"The maximum number of tokens for the question. Questions longer than "
	"this will be truncated to this length.")

	flags.DEFINE_bool("do_train", False, "Whether to run training.")

	flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")

	flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

	flags.DEFINE_integer("train_batch_size", 32,
	"Total batch size for training.")

	flags.DEFINE_integer("predict_batch_size", 8,
	"Total batch size for predictions.")

	flags.DEFINE_float("learning_rate", 5e-5,
	"The initial learning rate for Adam.")

	flags.DEFINE_float("num_train_epochs", 3.0,
	"Total number of training epochs to perform.")

	flags.DEFINE_float("num_train_steps", None,
	"Total number of training steps to perform.")

	flags.DEFINE_float(
	"warmup_proportion", 0.1,
	"Proportion of training to perform linear learning rate warmup for. "
	"E.g., 0.1 = 10% of training.")

	flags.DEFINE_integer("save_checkpoints_steps", 1000,
	"How often to save the model checkpoint.")

	flags.DEFINE_integer("save_summary_steps", 1,
	"How often to save the summary data.")

	flags.DEFINE_integer("iterations_per_loop", 1000,
	"How many steps to make in each estimator call.")

	flags.DEFINE_integer(
	"n_best_size", 20,
	"The total number of n-best predictions to generate in the "
	"nbest_predictions.json output file.")

	flags.DEFINE_integer(
	"max_answer_length", 30,
	"The maximum length of an answer that can be generated. This is needed "
	"because the start and end predictions are not conditioned on one another.")

	flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

	flags.DEFINE_string(
	"tpu_name", None,
	"The Cloud TPU to use for training. This should be either the name "
	"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
	"url.")

	flags.DEFINE_string(
	"tpu_zone", None,
	"[Optional] GCE zone where the Cloud TPU is located in. If not "
	"specified, we will attempt to automatically detect the GCE project from "
	"metadata.")

	flags.DEFINE_string(
	"gcp_project", None,
	"[Optional] Project name for the Cloud TPU-enabled project. If not "
	"specified, we will attempt to automatically detect the GCE project from "
	"metadata.")

	flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

	flags.DEFINE_integer(
	"num_tpu_cores", 8,
	"Only used if `use_tpu` is True. Total number of TPU cores to use.")

	flags.DEFINE_bool(
	"verbose_logging", False,
	"If true, all of the warnings related to data processing will be printed. "
	"A number of warnings are expected for a normal SQuAD evaluation.")

	flags.DEFINE_bool(
	"version_2_with_negative", False,
	"If true, the SQuAD examples contain some that do not have an answer.")

	flags.DEFINE_float(
	"null_score_diff_threshold", 0.0,
	"If null_score - best_non_null is greater than the threshold predict null.")

	flags.DEFINE_bool("cpu_only", False, "Whether to run on CPU.")

	flags.DEFINE_bool("deterministic_run", False, "If set run will be deterministic (set random seed, read dataset in single thread, disable dropout)")

	flags.DEFINE_integer("deterministic_seed", 1, "Seed vaule to be used in deterministic mode for all pseudorandom sequences")

	flags.DEFINE_bool("use_horovod", False, "Run training using horovod.")

	flags.DEFINE_integer('horovod_fusion_threshold', 256, "Horovod Fusion Buffer size in MB.")

	flags.DEFINE_string("bf16_config_path", None, "Defines config for tensor converson to bf16 data type")

	flags.DEFINE_bool('enable_scoped_allocator', False, "Enable scoped allocator optimization")

	flags.DEFINE_string("profile", "", "Profile Steps range X-Y (e.g. --profile 7,10)")

	def set_random_seed(seed):
	tf.compat.v1.set_random_seed(seed)
	tf.random.set_seed(seed)
	random.seed(seed)
	np.random.seed(seed)

	class SquadExample(object):
	"""A single training/test example for simple sequence classification.

	For examples without an answer, the start and end position are -1.
	"""

	def __init__(self,
	qas_id,
	question_text,
	doc_tokens,
	orig_answer_text=None,
	start_position=None,
	end_position=None,
	is_impossible=False):
	self.qas_id = qas_id
	self.question_text = question_text
	self.doc_tokens = doc_tokens
	self.orig_answer_text = orig_answer_text
	self.start_position = start_position
	self.end_position = end_position
	self.is_impossible = is_impossible

	def __str__(self):
	return self.__repr__()

	def __repr__(self):
	s = ""
	s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
	s += ", question_text: %s" % (
	tokenization.printable_text(self.question_text))
	s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
	if self.start_position:
	s += ", start_position: %d" % (self.start_position)
	if self.start_position:
	s += ", end_position: %d" % (self.end_position)
	if self.start_position:
	s += ", is_impossible: %r" % (self.is_impossible)
	return s


	class InputFeatures(object):
	"""A single set of features of data."""

	def __init__(self,
	unique_id,
	example_index,
	doc_span_index,
	tokens,
	token_to_orig_map,
	token_is_max_context,
	input_ids,
	input_mask,
	segment_ids,
	start_position=None,
	end_position=None,
	is_impossible=None):
	self.unique_id = unique_id
	self.example_index = example_index
	self.doc_span_index = doc_span_index
	self.tokens = tokens
	self.token_to_orig_map = token_to_orig_map
	self.token_is_max_context = token_is_max_context
	self.input_ids = input_ids
	self.input_mask = input_mask
	self.segment_ids = segment_ids
	self.start_position = start_position
	self.end_position = end_position
	self.is_impossible = is_impossible


	def read_squad_examples(input_file, is_training, version_2_with_negative):
	"""Read a SQuAD json file into a list of SquadExample."""
	with tf.io.gfile.GFile(input_file, "r") as reader:
	input_data = json.load(reader)["data"]

	def is_whitespace(c):
	if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
	return True
	return False

	examples = []
	for entry in input_data:
	for paragraph in entry["paragraphs"]:
	paragraph_text = paragraph["context"]
	doc_tokens = []
	char_to_word_offset = []
	prev_is_whitespace = True
	for c in paragraph_text:
	if is_whitespace(c):
	prev_is_whitespace = True
	else:
	if prev_is_whitespace:
	doc_tokens.append(c)
	else:
	doc_tokens[-1] += c
	prev_is_whitespace = False
	char_to_word_offset.append(len(doc_tokens) - 1)

	for qa in paragraph["qas"]:
	qas_id = qa["id"]
	question_text = qa["question"]
	start_position = None
	end_position = None
	orig_answer_text = None
	is_impossible = False
	if is_training:

	if version_2_with_negative:
	is_impossible = qa["is_impossible"]
	if (len(qa["answers"]) != 1) and (not is_impossible):
	raise ValueError(
	"For training, each question should have exactly 1 answer.")
	if not is_impossible:
	answer = qa["answers"][0]
	orig_answer_text = answer["text"]
	answer_offset = answer["answer_start"]
	answer_length = len(orig_answer_text)
	start_position = char_to_word_offset[answer_offset]
	end_position = char_to_word_offset[answer_offset + answer_length -
	1]
	# Only add answers where the text can be exactly recovered from the
	# document. If this CAN'T happen it's likely due to weird Unicode
	# stuff so we will just skip the example.
	#
	# Note that this means for training mode, every example is NOT
	# guaranteed to be preserved.
	actual_text = " ".join(
	doc_tokens[start_position:(end_position + 1)])
	cleaned_answer_text = " ".join(
	tokenization.whitespace_tokenize(orig_answer_text))
	if actual_text.find(cleaned_answer_text) == -1:
	tf.compat.v1.logging.warning("Could not find answer: '%s' vs. '%s'",
	actual_text, cleaned_answer_text)
	continue
	else:
	start_position = -1
	end_position = -1
	orig_answer_text = ""

	example = SquadExample(
	qas_id=qas_id,
	question_text=question_text,
	doc_tokens=doc_tokens,
	orig_answer_text=orig_answer_text,
	start_position=start_position,
	end_position=end_position,
	is_impossible=is_impossible)
	examples.append(example)

	return examples


	def convert_examples_to_features(examples, tokenizer, max_seq_length,
	doc_stride, max_query_length, is_training,
	output_fn):
	"""Loads a data file into a list of `InputBatch`s."""

	unique_id = 1000000000

	for (example_index, example) in enumerate(examples):
	query_tokens = tokenizer.tokenize(example.question_text)

	if len(query_tokens) > max_query_length:
	query_tokens = query_tokens[0:max_query_length]

	tok_to_orig_index = []
	orig_to_tok_index = []
	all_doc_tokens = []
	for (i, token) in enumerate(example.doc_tokens):
	orig_to_tok_index.append(len(all_doc_tokens))
	sub_tokens = tokenizer.tokenize(token)
	for sub_token in sub_tokens:
	tok_to_orig_index.append(i)
	all_doc_tokens.append(sub_token)

	tok_start_position = None
	tok_end_position = None
	if is_training and example.is_impossible:
	tok_start_position = -1
	tok_end_position = -1
	if is_training and not example.is_impossible:
	tok_start_position = orig_to_tok_index[example.start_position]
	if example.end_position < len(example.doc_tokens) - 1:
	tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
	else:
	tok_end_position = len(all_doc_tokens) - 1
	(tok_start_position, tok_end_position) = _improve_answer_span(
	all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
	example.orig_answer_text)

	# The -3 accounts for [CLS], [SEP] and [SEP]
	max_tokens_for_doc = max_seq_length - len(query_tokens) - 3

	# We can have documents that are longer than the maximum sequence length.
	# To deal with this we do a sliding window approach, where we take chunks
	# of the up to our max length with a stride of `doc_stride`.
	_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
	"DocSpan", ["start", "length"])
	doc_spans = []
	start_offset = 0
	while start_offset < len(all_doc_tokens):
	length = len(all_doc_tokens) - start_offset
	if length > max_tokens_for_doc:
	length = max_tokens_for_doc
	doc_spans.append(_DocSpan(start=start_offset, length=length))
	if start_offset + length == len(all_doc_tokens):
	break
	start_offset += min(length, doc_stride)

	for (doc_span_index, doc_span) in enumerate(doc_spans):
	tokens = []
	token_to_orig_map = {}
	token_is_max_context = {}
	segment_ids = []
	tokens.append("[CLS]")
	segment_ids.append(0)
	for token in query_tokens:
	tokens.append(token)
	segment_ids.append(0)
	tokens.append("[SEP]")
	segment_ids.append(0)

	for i in range(doc_span.length):
	split_token_index = doc_span.start + i
	token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]

	is_max_context = _check_is_max_context(doc_spans, doc_span_index,
	split_token_index)
	token_is_max_context[len(tokens)] = is_max_context
	tokens.append(all_doc_tokens[split_token_index])
	segment_ids.append(1)
	tokens.append("[SEP]")
	segment_ids.append(1)

	input_ids = tokenizer.convert_tokens_to_ids(tokens)

	# The mask has 1 for real tokens and 0 for padding tokens. Only real
	# tokens are attended to.
	input_mask = [1] * len(input_ids)

	# Zero-pad up to the sequence length.
	while len(input_ids) < max_seq_length:
	input_ids.append(0)
	input_mask.append(0)
	segment_ids.append(0)

	assert len(input_ids) == max_seq_length
	assert len(input_mask) == max_seq_length
	assert len(segment_ids) == max_seq_length

	start_position = None
	end_position = None
	if is_training and not example.is_impossible:
	# For training, if our document chunk does not contain an annotation
	# we throw it out, since there is nothing to predict.
	doc_start = doc_span.start
	doc_end = doc_span.start + doc_span.length - 1
	out_of_span = False
	if not (tok_start_position >= doc_start and
	tok_end_position <= doc_end):
	out_of_span = True
	if out_of_span:
	start_position = 0
	end_position = 0
	else:
	doc_offset = len(query_tokens) + 2
	start_position = tok_start_position - doc_start + doc_offset
	end_position = tok_end_position - doc_start + doc_offset

	if is_training and example.is_impossible:
	start_position = 0
	end_position = 0

	if example_index < 20:
	tf.compat.v1.logging.info("* Example *")
	tf.compat.v1.logging.info("unique_id: %s" % (unique_id))
	tf.compat.v1.logging.info("example_index: %s" % (example_index))
	tf.compat.v1.logging.info("doc_span_index: %s" % (doc_span_index))
	tf.compat.v1.logging.info("tokens: %s" % " ".join(
	[tokenization.printable_text(x) for x in tokens]))
	tf.compat.v1.logging.info("token_to_orig_map: %s" % " ".join(
	["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
	tf.compat.v1.logging.info("token_is_max_context: %s" % " ".join([
	"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
	]))
	tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
	tf.compat.v1.logging.info(
	"input_mask: %s" % " ".join([str(x) for x in input_mask]))
	tf.compat.v1.logging.info(
	"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
	if is_training and example.is_impossible:
	tf.compat.v1.logging.info("impossible example")
	if is_training and not example.is_impossible:
	answer_text = " ".join(tokens[start_position:(end_position + 1)])
	tf.compat.v1.logging.info("start_position: %d" % (start_position))
	tf.compat.v1.logging.info("end_position: %d" % (end_position))
	tf.compat.v1.logging.info(
	"answer: %s" % (tokenization.printable_text(answer_text)))

	feature = InputFeatures(
	unique_id=unique_id,
	example_index=example_index,
	doc_span_index=doc_span_index,
	tokens=tokens,
	token_to_orig_map=token_to_orig_map,
	token_is_max_context=token_is_max_context,
	input_ids=input_ids,
	input_mask=input_mask,
	segment_ids=segment_ids,
	start_position=start_position,
	end_position=end_position,
	is_impossible=example.is_impossible)

	# Run callback
	output_fn(feature)

	unique_id += 1


	def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
	orig_answer_text):
	"""Returns tokenized answer spans that better match the annotated answer."""

	# The SQuAD annotations are character based. We first project them to
	# whitespace-tokenized words. But then after WordPiece tokenization, we can
	# often find a "better match". For example:
	#
	# Question: What year was John Smith born?
	# Context: The leader was John Smith (1895-1943).
	# Answer: 1895
	#
	# The original whitespace-tokenized answer will be "(1895-1943).". However
	# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
	# the exact answer, 1895.
	#
	# However, this is not always possible. Consider the following:
	#
	# Question: What country is the top exporter of electornics?
	# Context: The Japanese electronics industry is the lagest in the world.
	# Answer: Japan
	#
	# In this case, the annotator chose "Japan" as a character sub-span of
	# the word "Japanese". Since our WordPiece tokenizer does not split
	# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
	# in SQuAD, but does happen.
	tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))

	for new_start in range(input_start, input_end + 1):
	for new_end in range(input_end, new_start - 1, -1):
	text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
	if text_span == tok_answer_text:
	return (new_start, new_end)

	return (input_start, input_end)


	def _check_is_max_context(doc_spans, cur_span_index, position):
	"""Check if this is the 'max context' doc span for the token."""

	# Because of the sliding window approach taken to scoring documents, a single
	# token can appear in multiple documents. E.g.
	# Doc: the man went to the store and bought a gallon of milk
	# Span A: the man went to the
	# Span B: to the store and bought
	# Span C: and bought a gallon of
	# ...
	#
	# Now the word 'bought' will have two scores from spans B and C. We only
	# want to consider the score with "maximum context", which we define as
	# the minimum of its left and right context (the sum of left and
	# right context will always be the same, of course).
	#
	# In the example the maximum context for 'bought' would be span C since
	# it has 1 left context and 3 right context, while span B has 4 left context
	# and 0 right context.
	best_score = None
	best_span_index = None
	for (span_index, doc_span) in enumerate(doc_spans):
	end = doc_span.start + doc_span.length - 1
	if position < doc_span.start:
	continue
	if position > end:
	continue
	num_left_context = position - doc_span.start
	num_right_context = end - position
	score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
	if best_score is None or score > best_score:
	best_score = score
	best_span_index = span_index

	return cur_span_index == best_span_index


	def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
	use_one_hot_embeddings):
	"""Creates a classification model."""
	model = modeling.BertModel(
	config=bert_config,
	is_training=is_training,
	input_ids=input_ids,
	input_mask=input_mask,
	token_type_ids=segment_ids,
	use_one_hot_embeddings=use_one_hot_embeddings)

	final_hidden = model.get_sequence_output()

	final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
	batch_size = final_hidden_shape[0]
	seq_length = final_hidden_shape[1]
	hidden_size = final_hidden_shape[2]

	output_weights = tf.compat.v1.get_variable(
	"cls/squad/output_weights", [2, hidden_size],
	initializer=tf.compat.v1.truncated_normal_initializer(stddev=0.02))

	output_bias = tf.compat.v1.get_variable(
	"cls/squad/output_bias", [2], initializer=tf.compat.v1.zeros_initializer())

	final_hidden_matrix = tf.reshape(final_hidden,
	[batch_size * seq_length, hidden_size])
	logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
	logits = tf.nn.bias_add(logits, output_bias)

	logits = tf.reshape(logits, [batch_size, seq_length, 2])
	logits = tf.transpose(a=logits, perm=[2, 0, 1])

	unstacked_logits = tf.unstack(logits, axis=0)

	(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])

	return (start_logits, end_logits)


	def model_fn_builder(bert_config, init_checkpoint, learning_rate,
	num_train_steps, num_warmup_steps, use_tpu,
	use_one_hot_embeddings):
	"""Returns `model_fn` closure for TPUEstimator."""

	def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
	"""The `model_fn` for TPUEstimator."""

	tf.compat.v1.logging.info("* Features *")
	for name in sorted(features.keys()):
	tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape))

	unique_ids = features["unique_ids"]
	input_ids = features["input_ids"]
	input_mask = features["input_mask"]
	segment_ids = features["segment_ids"]

	is_training = (mode == tf.estimator.ModeKeys.TRAIN)

	(start_logits, end_logits) = create_model(
	bert_config=bert_config,
	is_training=is_training,
	input_ids=input_ids,
	input_mask=input_mask,
	segment_ids=segment_ids,
	use_one_hot_embeddings=use_one_hot_embeddings)

	tvars = tf.compat.v1.trainable_variables()

	initialized_variable_names = {}
	scaffold_fn = None
	if init_checkpoint:
	(assignment_map, initialized_variable_names
	) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
	if use_tpu:

	def tpu_scaffold():
	tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)
	return tf.compat.v1.train.Scaffold()

	scaffold_fn = tpu_scaffold
	else:
	tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)

	tf.compat.v1.logging.info("** Trainable Variables **")
	for var in tvars:
	init_string = ""
	if var.name in initialized_variable_names:
	init_string = ", INIT_FROM_CKPT"
	tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
	init_string)

	output_spec = None
	if mode == tf.estimator.ModeKeys.TRAIN:
	seq_length = modeling.get_shape_list(input_ids)[1]

	def compute_loss(logits, positions):
	one_hot_positions = tf.one_hot(
	positions, depth=seq_length, dtype=tf.float32)
	log_probs = tf.nn.log_softmax(logits, axis=-1)
	loss = -tf.reduce_mean(
	input_tensor=tf.reduce_sum(input_tensor=one_hot_positions * log_probs, axis=-1))
	return loss

	start_positions = features["start_positions"]
	end_positions = features["end_positions"]

	start_loss = compute_loss(start_logits, start_positions)
	end_loss = compute_loss(end_logits, end_positions)

	total_loss = (start_loss + end_loss) / 2.0
	total_loss = tf.compat.v1.Print(total_loss, [total_loss], message="LOSS: ")

	train_op = optimization.create_optimizer(
	total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

	output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
	mode=mode,
	loss=total_loss,
	train_op=train_op,
	scaffold_fn=scaffold_fn)
	elif mode == tf.estimator.ModeKeys.PREDICT:
	predictions = {
	"unique_ids": unique_ids,
	"start_logits": start_logits,
	"end_logits": end_logits,
	}
	output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
	mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
	else:
	raise ValueError(
	"Only TRAIN and PREDICT modes are supported: %s" % (mode))

	return output_spec

	return model_fn


	def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
	"""Creates an `input_fn` closure to be passed to TPUEstimator."""

	name_to_features = {
	"unique_ids": tf.io.FixedLenFeature([], tf.int64),
	"input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
	"input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
	"segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
	}

	if is_training:
	name_to_features["start_positions"] = tf.io.FixedLenFeature([], tf.int64)
	name_to_features["end_positions"] = tf.io.FixedLenFeature([], tf.int64)

	def _decode_record(record, name_to_features):
	"""Decodes a record to a TensorFlow example."""
	example = tf.io.parse_single_example(serialized=record, features=name_to_features)

	# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
	# So cast all int64 to int32.
	for name in list(example.keys()):
	t = example[name]
	if t.dtype == tf.int64:
	t = tf.cast(t, dtype=tf.int32)
	example[name] = t

	return example

	def input_fn(params):
	"""The actual input function."""
	batch_size = params["batch_size"]

	# For training, we want a lot of parallel reading and shuffling.
	# For eval, we want no shuffling and parallel reading doesn't matter.
	d = tf.data.TFRecordDataset(input_file)

	if FLAGS.deterministic_run:
	d = d.repeat()
	d = d.shuffle(buffer_size=100, seed=FLAGS.deterministic_seed)
	d = d.apply(
	tf.data.experimental.map_and_batch(
	lambda record: _decode_record(record, name_to_features),
	batch_size=batch_size,
	num_parallel_calls=1,
	drop_remainder=drop_remainder))

	else:
	if is_training:
	if horovod_enabled():
	d = d.shard(hvd.local_size(), hvd.local_rank())
	d = d.repeat()
	d = d.shuffle(buffer_size=100)

	d = d.apply(
	tf.data.experimental.map_and_batch(
	lambda record: _decode_record(record, name_to_features),
	batch_size=batch_size,
	drop_remainder=drop_remainder))

	d = d.prefetch(tf.data.experimental.AUTOTUNE)
	return d

	return input_fn


	RawResult = collections.namedtuple("RawResult",
	["unique_id", "start_logits", "end_logits"])


	def write_predictions(all_examples, all_features, all_results, n_best_size,
	max_answer_length, do_lower_case, output_prediction_file,
	output_nbest_file, output_null_log_odds_file, version_2_with_negative,
	verbose_logging):
	"""Write final predictions to the json file and log-odds of null if needed."""
	tf.compat.v1.logging.info("Writing predictions to: %s" % (output_prediction_file))
	tf.compat.v1.logging.info("Writing nbest to: %s" % (output_nbest_file))

	example_index_to_features = collections.defaultdict(list)
	for feature in all_features:
	example_index_to_features[feature.example_index].append(feature)

	unique_id_to_result = {}
	for result in all_results:
	unique_id_to_result[result.unique_id] = result

	_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
	"PrelimPrediction",
	["feature_index", "start_index", "end_index", "start_logit", "end_logit"])

	all_predictions = collections.OrderedDict()
	all_nbest_json = collections.OrderedDict()
	scores_diff_json = collections.OrderedDict()

	for (example_index, example) in enumerate(all_examples):
	features = example_index_to_features[example_index]

	prelim_predictions = []
	# keep track of the minimum score of null start+end of position 0
	score_null = 1000000 # large and positive
	min_null_feature_index = 0 # the paragraph slice with min mull score
	null_start_logit = 0 # the start logit at the slice with min null score
	null_end_logit = 0 # the end logit at the slice with min null score
	for (feature_index, feature) in enumerate(features):
	result = unique_id_to_result[feature.unique_id]
	start_indexes = _get_best_indexes(result.start_logits, n_best_size)
	end_indexes = _get_best_indexes(result.end_logits, n_best_size)
	# if we could have irrelevant answers, get the min score of irrelevant
	if version_2_with_negative:
	feature_null_score = result.start_logits[0] + result.end_logits[0]
	if feature_null_score < score_null:
	score_null = feature_null_score
	min_null_feature_index = feature_index
	null_start_logit = result.start_logits[0]
	null_end_logit = result.end_logits[0]
	for start_index in start_indexes:
	for end_index in end_indexes:
	# We could hypothetically create invalid predictions, e.g., predict
	# that the start of the span is in the question. We throw out all
	# invalid predictions.
	if start_index >= len(feature.tokens):
	continue
	if end_index >= len(feature.tokens):
	continue
	if start_index not in feature.token_to_orig_map:
	continue
	if end_index not in feature.token_to_orig_map:
	continue
	if not feature.token_is_max_context.get(start_index, False):
	continue
	if end_index < start_index:
	continue
	length = end_index - start_index + 1
	if length > max_answer_length:
	continue
	prelim_predictions.append(
	_PrelimPrediction(
	feature_index=feature_index,
	start_index=start_index,
	end_index=end_index,
	start_logit=result.start_logits[start_index],
	end_logit=result.end_logits[end_index]))

	if version_2_with_negative:
	prelim_predictions.append(
	_PrelimPrediction(
	feature_index=min_null_feature_index,
	start_index=0,
	end_index=0,
	start_logit=null_start_logit,
	end_logit=null_end_logit))
	prelim_predictions = sorted(
	prelim_predictions,
	key=lambda x: (x.start_logit + x.end_logit),
	reverse=True)

	_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
	"NbestPrediction", ["text", "start_logit", "end_logit"])

	seen_predictions = {}
	nbest = []
	for pred in prelim_predictions:
	if len(nbest) >= n_best_size:
	break
	feature = features[pred.feature_index]
	if pred.start_index > 0: # this is a non-null prediction
	tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
	orig_doc_start = feature.token_to_orig_map[pred.start_index]
	orig_doc_end = feature.token_to_orig_map[pred.end_index]
	orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
	tok_text = " ".join(tok_tokens)

	# De-tokenize WordPieces that have been split off.
	tok_text = tok_text.replace(" ##", "")
	tok_text = tok_text.replace("##", "")

	# Clean whitespace
	tok_text = tok_text.strip()
	tok_text = " ".join(tok_text.split())
	orig_text = " ".join(orig_tokens)

	final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
	if final_text in seen_predictions:
	continue

	seen_predictions[final_text] = True
	else:
	final_text = ""
	seen_predictions[final_text] = True

	nbest.append(
	_NbestPrediction(
	text=final_text,
	start_logit=pred.start_logit,
	end_logit=pred.end_logit))

	# if we didn't inlude the empty option in the n-best, inlcude it
	if version_2_with_negative:
	if "" not in seen_predictions:
	nbest.append(
	_NbestPrediction(
	text="", start_logit=null_start_logit,
	end_logit=null_end_logit))
	# In very rare edge cases we could have no valid predictions. So we
	# just create a nonce prediction in this case to avoid failure.
	if not nbest:
	nbest.append(
	_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))

	assert len(nbest) >= 1

	total_scores = []
	best_non_null_entry = None
	for entry in nbest:
	total_scores.append(entry.start_logit + entry.end_logit)
	if not best_non_null_entry:
	if entry.text:
	best_non_null_entry = entry

	probs = _compute_softmax(total_scores)

	nbest_json = []
	for (i, entry) in enumerate(nbest):
	output = collections.OrderedDict()
	output["text"] = entry.text
	output["probability"] = probs[i]
	output["start_logit"] = entry.start_logit
	output["end_logit"] = entry.end_logit
	nbest_json.append(output)

	assert len(nbest_json) >= 1

	if not version_2_with_negative:
	all_predictions[example.qas_id] = nbest_json[0]["text"]
	else:
	# predict "" iff the null score - the score of best non-null > threshold
	score_diff = score_null - best_non_null_entry.start_logit - (
	best_non_null_entry.end_logit)
	scores_diff_json[example.qas_id] = score_diff
	if score_diff > FLAGS.null_score_diff_threshold:
	all_predictions[example.qas_id] = ""
	else:
	all_predictions[example.qas_id] = best_non_null_entry.text

	all_nbest_json[example.qas_id] = nbest_json

	with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
	writer.write(json.dumps(all_predictions, indent=4) + "\n")

	with tf.io.gfile.GFile(output_nbest_file, "w") as writer:
	writer.write(json.dumps(all_nbest_json, indent=4) + "\n")

	if version_2_with_negative:
	with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer:
	writer.write(json.dumps(scores_diff_json, indent=4) + "\n")

	return [all_predictions, all_nbest_json, scores_diff_json]


	def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging):
	"""Project the tokenized prediction back to the original text."""

	# When we created the data, we kept track of the alignment between original
	# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
	# now `orig_text` contains the span of our original text corresponding to the
	# span that we predicted.
	#
	# However, `orig_text` may contain extra characters that we don't want in
	# our prediction.
	#
	# For example, let's say:
	# pred_text = steve smith
	# orig_text = Steve Smith's
	#
	# We don't want to return `orig_text` because it contains the extra "'s".
	#
	# We don't want to return `pred_text` because it's already been normalized
	# (the SQuAD eval script also does punctuation stripping/lower casing but
	# our tokenizer does additional normalization like stripping accent
	# characters).
	#
	# What we really want to return is "Steve Smith".
	#
	# Therefore, we have to apply a semi-complicated alignment heruistic between
	# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
	# can fail in certain cases in which case we just return `orig_text`.

	def _strip_spaces(text):
	ns_chars = []
	ns_to_s_map = collections.OrderedDict()
	for (i, c) in enumerate(text):
	if c == " ":
	continue
	ns_to_s_map[len(ns_chars)] = i
	ns_chars.append(c)
	ns_text = "".join(ns_chars)
	return (ns_text, ns_to_s_map)

	# We first tokenize `orig_text`, strip whitespace from the result
	# and `pred_text`, and check if they are the same length. If they are
	# NOT the same length, the heuristic has failed. If they are the same
	# length, we assume the characters are one-to-one aligned.
	tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)

	tok_text = " ".join(tokenizer.tokenize(orig_text))

	start_position = tok_text.find(pred_text)
	if start_position == -1:
	if verbose_logging:
	tf.compat.v1.logging.info(
	"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
	return orig_text
	end_position = start_position + len(pred_text) - 1

	(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
	(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)

	if len(orig_ns_text) != len(tok_ns_text):
	if verbose_logging:
	tf.compat.v1.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
	orig_ns_text, tok_ns_text)
	return orig_text

	# We then project the characters in `pred_text` back to `orig_text` using
	# the character-to-character alignment.
	tok_s_to_ns_map = {}
	for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
	tok_s_to_ns_map[tok_index] = i

	orig_start_position = None
	if start_position in tok_s_to_ns_map:
	ns_start_position = tok_s_to_ns_map[start_position]
	if ns_start_position in orig_ns_to_s_map:
	orig_start_position = orig_ns_to_s_map[ns_start_position]

	if orig_start_position is None:
	if verbose_logging:
	tf.compat.v1.logging.info("Couldn't map start position")
	return orig_text

	orig_end_position = None
	if end_position in tok_s_to_ns_map:
	ns_end_position = tok_s_to_ns_map[end_position]
	if ns_end_position in orig_ns_to_s_map:
	orig_end_position = orig_ns_to_s_map[ns_end_position]

	if orig_end_position is None:
	if verbose_logging:
	tf.compat.v1.logging.info("Couldn't map end position")
	return orig_text

	output_text = orig_text[orig_start_position:(orig_end_position + 1)]
	return output_text


	def _get_best_indexes(logits, n_best_size):
	"""Get the n-best logits from a list."""
	index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)

	best_indexes = []
	for i in range(len(index_and_score)):
	if i >= n_best_size:
	break
	best_indexes.append(index_and_score[i][0])
	return best_indexes


	def _compute_softmax(scores):
	"""Compute softmax probability over raw logits."""
	if not scores:
	return []

	max_score = None
	for score in scores:
	if max_score is None or score > max_score:
	max_score = score

	exp_scores = []
	total_sum = 0.0
	for score in scores:
	x = math.exp(score - max_score)
	exp_scores.append(x)
	total_sum += x

	probs = []
	for score in exp_scores:
	probs.append(score / total_sum)
	return probs


	class FeatureWriter(object):
	"""Writes InputFeature to TF example file."""

	def __init__(self, filename, is_training):
	self.filename = filename
	self.is_training = is_training
	self.num_features = 0
	self._writer = tf.io.TFRecordWriter(filename)

	def process_feature(self, feature):
	"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
	self.num_features += 1

	def create_int_feature(values):
	feature = tf.train.Feature(
	int64_list=tf.train.Int64List(value=list(values)))
	return feature

	features = collections.OrderedDict()
	features["unique_ids"] = create_int_feature([feature.unique_id])
	features["input_ids"] = create_int_feature(feature.input_ids)
	features["input_mask"] = create_int_feature(feature.input_mask)
	features["segment_ids"] = create_int_feature(feature.segment_ids)

	if self.is_training:
	features["start_positions"] = create_int_feature([feature.start_position])
	features["end_positions"] = create_int_feature([feature.end_position])
	impossible = 0
	if feature.is_impossible:
	impossible = 1
	features["is_impossible"] = create_int_feature([impossible])

	tf_example = tf.train.Example(features=tf.train.Features(feature=features))
	self._writer.write(tf_example.SerializeToString())

	def close(self):
	self._writer.close()


	def validate_flags_or_throw(bert_config):
	"""Validate the input FLAGS or throw an exception."""
	tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
	FLAGS.init_checkpoint)

	if not FLAGS.do_train and not FLAGS.do_predict:
	raise ValueError("At least one of `do_train` or `do_predict` must be True.")

	if FLAGS.do_train:
	if not FLAGS.train_file:
	raise ValueError(
	"If `do_train` is True, then `train_file` must be specified.")
	if FLAGS.do_predict:
	if not FLAGS.predict_file:
	raise ValueError(
	"If `do_predict` is True, then `predict_file` must be specified.")

	if FLAGS.max_seq_length > bert_config.max_position_embeddings:
	raise ValueError(
	"Cannot use sequence length %d because the BERT model "
	"was only trained up to sequence length %d" %
	(FLAGS.max_seq_length, bert_config.max_position_embeddings))

	if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
	raise ValueError(
	"The max_seq_length (%d) must be greater than max_query_length "
	"(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))


	def main(_):

	tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)

	bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

	validate_flags_or_throw(bert_config)

	tf.io.gfile.makedirs(FLAGS.output_dir)

	tokenizer = tokenization.FullTokenizer(
	vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

	tpu_cluster_resolver = None
	if FLAGS.use_tpu and FLAGS.tpu_name:
	tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
	FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

	model_dir = FLAGS.output_dir
	if horovod_enabled():
	model_dir = os.path.join(FLAGS.output_dir, "worker_" + str(hvd.rank()))

	is_per_host = tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V2

	# The Scoped Allocator Optimization is enabled by default unless disabled by a flag.
	if FLAGS.enable_scoped_allocator:
	from tensorflow.core.protobuf import rewriter_config_pb2 # pylint: disable=import-error

	session_config = tf.compat.v1.ConfigProto()
	session_config.graph_options.rewrite_options.scoped_allocator_optimization = rewriter_config_pb2.RewriterConfig.ON

	enable_op = session_config.graph_options.rewrite_options.scoped_allocator_opts.enable_op
	del enable_op[:]
	enable_op.append("HorovodAllreduce")
	else:
	session_config = None

	run_config = tf.compat.v1.estimator.tpu.RunConfig(
	cluster=tpu_cluster_resolver,
	master=FLAGS.master,
	model_dir=model_dir,
	keep_checkpoint_max=1,
	save_checkpoints_steps=FLAGS.save_checkpoints_steps,
	save_summary_steps=FLAGS.save_summary_steps,
	tpu_config=tf.compat.v1.estimator.tpu.TPUConfig(
	iterations_per_loop=FLAGS.iterations_per_loop,
	num_shards=FLAGS.num_tpu_cores,
	per_host_input_for_training=is_per_host),
	session_config=session_config)

	train_examples = None
	num_train_steps = None
	num_warmup_steps = None

	train_batch_size = FLAGS.train_batch_size
	if horovod_enabled():
	train_batch_size = train_batch_size * hvd.size()

	if FLAGS.do_train:
	train_examples = read_squad_examples(
	input_file=FLAGS.train_file, is_training=True, version_2_with_negative=FLAGS.version_2_with_negative)
	if FLAGS.num_train_steps is not None:
	num_train_steps = FLAGS.num_train_steps
	else:
	num_train_steps = int(
	len(train_examples) / train_batch_size * FLAGS.num_train_epochs)
	num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

	# Pre-shuffle the input to avoid having to make a very large shuffle
	# buffer in in the `input_fn`.
	rng = random.Random(12345)
	rng.shuffle(train_examples)

	start_index = 0
	end_index = len(train_examples)
	per_worker_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")]
	worker_id = 0

	if horovod_enabled():
	per_worker_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record_{}".format(i))
	for i in range(hvd.local_size())]
	num_examples_per_rank = len(train_examples) // hvd.size()
	remainder = len(train_examples) % hvd.size()
	worker_id = hvd.rank()
	if worker_id < remainder:
	start_index = worker_id * (num_examples_per_rank + 1)
	end_index = start_index + num_examples_per_rank + 1
	else:
	start_index = worker_id * num_examples_per_rank + remainder
	end_index = start_index + (num_examples_per_rank)

	learning_rate = FLAGS.learning_rate
	if horovod_enabled():
	learning_rate = learning_rate * hvd.size()

	model_fn = model_fn_builder(
	bert_config=bert_config,
	init_checkpoint=FLAGS.init_checkpoint,
	learning_rate=FLAGS.learning_rate,
	num_train_steps=num_train_steps,
	num_warmup_steps=num_warmup_steps,
	use_tpu=FLAGS.use_tpu,
	use_one_hot_embeddings=FLAGS.use_tpu)

	# If TPU is not available, this will fall back to normal Estimator on CPU
	# or GPU.
	estimator = tf.compat.v1.estimator.tpu.TPUEstimator(
	use_tpu=FLAGS.use_tpu,
	model_fn=model_fn,
	config=run_config,
	train_batch_size=FLAGS.train_batch_size,
	predict_batch_size=FLAGS.predict_batch_size)

	write_hparams_v1(FLAGS.output_dir, {
	'batch_size': FLAGS.train_batch_size,
	**{x: getattr(FLAGS, x) for x in FLAGS}
	})

	if FLAGS.do_train:
	# We write to a temporary file to avoid storing very large constant tensors
	# in memory.

	train_writer = FeatureWriter(
	filename=per_worker_filenames[hvd.local_rank() if horovod_enabled() else worker_id],
	is_training=True)
	convert_examples_to_features(
	examples=train_examples[start_index:end_index],
	tokenizer=tokenizer,
	max_seq_length=FLAGS.max_seq_length,
	doc_stride=FLAGS.doc_stride,
	max_query_length=FLAGS.max_query_length,
	is_training=True,
	output_fn=train_writer.process_feature)
	train_writer.close()
	num_features = train_writer.num_features

	tf.compat.v1.logging.info("*** Running training ***")
	tf.compat.v1.logging.info(" Num orig examples = %d", len(train_examples))
	tf.compat.v1.logging.info(" Num split examples = %d", num_features)
	tf.compat.v1.logging.info(" Per-worker batch size = %d", FLAGS.train_batch_size)
	tf.compat.v1.logging.info(" Total batch size = %d", train_batch_size)
	tf.compat.v1.logging.info(" Num steps = %d", num_train_steps)
	del train_examples

	train_input_fn = input_fn_builder(
	input_file=per_worker_filenames,
	seq_length=FLAGS.max_seq_length,
	is_training=True,
	drop_remainder=True)

	train_hooks = [habana_hooks.PerfLoggingHook(batch_size=train_batch_size, mode="train")]
	if len(FLAGS.profile) > 0:
	train_hooks.append(TensorBoardHook(output_dir=FLAGS.output_dir,profile_steps=FLAGS.profile))
	if horovod_enabled():
	train_hooks.append(hvd.BroadcastGlobalVariablesHook(0))

	with dump_callback():
	estimator.train(input_fn=train_input_fn, max_steps=num_train_steps, hooks=train_hooks)

	if FLAGS.do_predict:
	eval_examples = read_squad_examples(
	input_file=FLAGS.predict_file, is_training=False, version_2_with_negative=FLAGS.version_2_with_negative)

	eval_writer = FeatureWriter(
	filename=os.path.join(model_dir, "eval.tf_record"),
	is_training=False)
	eval_features = []

	def append_feature(feature):
	eval_features.append(feature)
	eval_writer.process_feature(feature)

	convert_examples_to_features(
	examples=eval_examples,
	tokenizer=tokenizer,
	max_seq_length=FLAGS.max_seq_length,
	doc_stride=FLAGS.doc_stride,
	max_query_length=FLAGS.max_query_length,
	is_training=False,
	output_fn=append_feature)
	eval_writer.close()

	tf.compat.v1.logging.info("*** Running predictions ***")
	tf.compat.v1.logging.info(" Num orig examples = %d", len(eval_examples))
	tf.compat.v1.logging.info(" Num split examples = %d", len(eval_features))
	tf.compat.v1.logging.info(" Batch size = %d", FLAGS.predict_batch_size)

	all_results = []

	predict_input_fn = input_fn_builder(
	input_file=eval_writer.filename,
	seq_length=FLAGS.max_seq_length,
	is_training=False,
	drop_remainder=False)

	eval_hooks = [habana_hooks.PerfLoggingHook(batch_size=FLAGS.predict_batch_size, mode="eval")]

	# If running eval on the TPU, you will need to specify the number of
	# steps.
	all_results = []
	for result in estimator.predict(
	predict_input_fn, yield_single_examples=True, hooks=eval_hooks):
	if len(all_results) % 1000 == 0:
	tf.compat.v1.logging.info("Processing example: %d" % (len(all_results)))
	unique_id = int(result["unique_ids"])
	start_logits = [float(x) for x in result["start_logits"].flat]
	end_logits = [float(x) for x in result["end_logits"].flat]
	all_results.append(
	RawResult(
	unique_id=unique_id,
	start_logits=start_logits,
	end_logits=end_logits))

	output_prediction_file = os.path.join(model_dir, "predictions.json")
	output_nbest_file = os.path.join(model_dir, "nbest_predictions.json")
	output_null_log_odds_file = os.path.join(model_dir, "null_odds.json")

	all_predictions, all_nbest_json, scores_diff_json = write_predictions(
	eval_examples, eval_features, all_results,
	FLAGS.n_best_size, FLAGS.max_answer_length,
	FLAGS.do_lower_case, output_prediction_file,
	output_nbest_file, output_null_log_odds_file,
	FLAGS.version_2_with_negative,
	FLAGS.verbose_logging)

	if FLAGS.do_eval:
	if FLAGS.version_2_with_negative:
	tf.compat.v1.logging.fatal("Eval for v2 is not supported")
	pass
	else:
	with tf.io.gfile.GFile(FLAGS.predict_file, 'r') as reader:
	dataset_json = json.load(reader)
	pred_dataset = dataset_json['data']
	f1 = exact_match = total = 0
	for article in pred_dataset:
	for paragraph in article["paragraphs"]:
	for qa in paragraph["qas"]:
	total += 1
	if qa["id"] not in all_predictions:
	message = "Unanswered question " + qa["id"] + " will receive score 0."
	tf.compat.v1.logging.error(message)
	continue
	ground_truths = [entry["text"] for entry in qa["answers"]]
	prediction = all_predictions[qa["id"]]
	exact_match += _metric_max_over_ground_truths(_exact_match_score,
	prediction, ground_truths)
	f1 += _metric_max_over_ground_truths(_f1_score, prediction,
	ground_truths)

	exact_match = exact_match / total
	f1 = f1 / total
	tf.compat.v1.logging.warning("Exact matches %s" % str(exact_match))
	tf.compat.v1.logging.warning("Final accuracy %s" % str(f1))

	with TBSummary(os.path.join(model_dir, 'eval')) as summary_writer:
	summary_writer.add_scalar('accuracy', f1, 0)
	summary_writer.add_scalar('exact_matches', exact_match, 0)


	def _metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
	"""Computes the max over all metric scores."""
	scores_for_ground_truths = []
	for ground_truth in ground_truths:
	score = metric_fn(prediction, ground_truth)
	scores_for_ground_truths.append(score)
	return max(scores_for_ground_truths)


	def _exact_match_score(prediction, ground_truth):
	"""Checks if predicted answer exactly matches ground truth answer."""
	return _normalize_answer(prediction) == _normalize_answer(ground_truth)


	def _f1_score(prediction, ground_truth):
	"""Computes F1 score by comparing prediction to ground truth."""
	prediction_tokens = _normalize_answer(prediction).split()
	ground_truth_tokens = _normalize_answer(ground_truth).split()
	prediction_counter = collections.Counter(prediction_tokens)
	ground_truth_counter = collections.Counter(ground_truth_tokens)
	common = prediction_counter & ground_truth_counter
	num_same = sum(common.values())
	if num_same == 0:
	return 0
	precision = 1.0 * num_same / len(prediction_tokens)
	recall = 1.0 * num_same / len(ground_truth_tokens)
	f1 = (2 * precision * recall) / (precision + recall)
	return f1


	def _normalize_answer(s):
	"""Lowers text and remove punctuation, articles and extra whitespace."""

	def remove_articles(text):
	import re
	return re.sub(r"\b(a\|an\|the)\b", " ", text)

	def white_space_fix(text):
	return " ".join(text.split())

	def remove_punc(text):
	import string
	exclude = set(string.punctuation)
	return "".join(ch for ch in text if ch not in exclude)

	def lower(text):
	return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(s))))


	if __name__ == "__main__":
	init_squad_flags()

	tf.compat.v1.enable_resource_variables()

	flags.mark_flag_as_required("vocab_file")
	flags.mark_flag_as_required("bert_config_file")
	flags.mark_flag_as_required("output_dir")
	if FLAGS.deterministic_run:
	set_random_seed(FLAGS.deterministic_seed)
	modeling.dropout = lambda input_tensor, dropout_prob: input_tensor # disable dropout

	if FLAGS.bf16_config_path is None:
	os.environ.setdefault('HABANA_INITIAL_WORKSPACE_SIZE_MB', '13271')
	else:
	os.environ['TF_BF16_CONVERSION'] = FLAGS.bf16_config_path
	os.environ.setdefault('HABANA_INITIAL_WORKSPACE_SIZE_MB', '17371')
	os.environ.setdefault("TF_DISABLE_MKL", "1")

	bert_config = json.load(open(FLAGS.bert_config_file, 'r'))
	if 'hidden_size' in bert_config:
	if bert_config['hidden_size'] == 768:
	# cluster slicing optimization tested for bert base, works well with this size
	os.environ.setdefault('TF_PRELIMINARY_CLUSTER_SIZE', '1000')
	elif bert_config['hidden_size'] >= 1024:
	if FLAGS.bf16_config_path is None:
	os.environ.setdefault('HABANA_INITIAL_WORKSPACE_SIZE_MB', '17257')
	else:
	os.environ.setdefault('HABANA_INITIAL_WORKSPACE_SIZE_MB', '21393')

	if not FLAGS.cpu_only:
	load_habana_module()

	from habana_frameworks.tensorflow.habana_device import get_type
	if get_type() == 'GAUDI2':
	os.environ['HOROVOD_FUSION_THRESHOLD'] = str(FLAGS.horovod_fusion_threshold)

	if FLAGS.use_horovod:
	assert not FLAGS.cpu_only, "Horovod without HPU is not supported in helpers."
	if hvd is None:
	raise RuntimeError(
	"Problem encountered during Horovod import. Please make sure that habana-horovod package is installed.")
	hvd.init()

	host_name = socket.gethostname()
	vmulti = os.environ.get('MULTI_HLS_IPS')
	v1 = os.environ.get('HABANA_INITIAL_WORKSPACE_SIZE_MB')
	v2 = os.environ.get('TF_BF16_CONVERSION')
	print(f"{host_name}: MULTI_HLS_IPS = {vmulti}")
	print(f"{host_name}: HABANA_INITIAL_WORKSPACE_SIZE_MB = {v1}")
	print(f"{host_name}: TF_BF16_CONVERSION = {v2}")
	sys.stdout.flush()
	sys.stderr.flush()

	tf.compat.v1.app.run()