test_modeling_xlnet.py
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test_modeling_xlnet.py
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	# coding=utf-8
	# Copyright 2020 The HuggingFace Team. All rights reserved.
	#
	# 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.


	import random
	import unittest

	from transformers import is_torch_available
	from transformers.testing_utils import require_torch, slow, torch_device

	from .test_configuration_common import ConfigTester
	from .test_generation_utils import GenerationTesterMixin
	from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import (
	XLNetConfig,
	XLNetForMultipleChoice,
	XLNetForQuestionAnswering,
	XLNetForQuestionAnsweringSimple,
	XLNetForSequenceClassification,
	XLNetForTokenClassification,
	XLNetLMHeadModel,
	XLNetModel,
	)
	from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST


	class XLNetModelTester:
	def __init__(
	self,
	parent,
	batch_size=14,
	seq_length=7,
	mem_len=10,
	clamp_len=-1,
	reuse_len=15,
	is_training=True,
	use_labels=True,
	vocab_size=99,
	cutoffs=[10, 50, 80],
	hidden_size=32,
	num_attention_heads=4,
	d_inner=128,
	num_hidden_layers=5,
	type_sequence_label_size=2,
	untie_r=True,
	bi_data=False,
	same_length=False,
	initializer_range=0.05,
	seed=1,
	type_vocab_size=2,
	bos_token_id=1,
	eos_token_id=2,
	pad_token_id=5,
	num_choices=4,
	):
	self.parent = parent
	self.batch_size = 14
	self.seq_length = 7
	self.mem_len = 10
	# self.key_len = seq_length + mem_len
	self.clamp_len = -1
	self.reuse_len = 15
	self.is_training = True
	self.use_labels = True
	self.vocab_size = 99
	self.cutoffs = [10, 50, 80]
	self.hidden_size = 32
	self.num_attention_heads = 4
	self.d_inner = 128
	self.num_hidden_layers = 5
	self.type_sequence_label_size = 2
	self.untie_r = True
	self.bi_data = False
	self.same_length = False
	self.initializer_range = 0.05
	self.seed = 1
	self.type_vocab_size = 2
	self.bos_token_id = 1
	self.eos_token_id = 2
	self.pad_token_id = 5
	self.num_choices = 4

	def prepare_config_and_inputs(self):
	input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
	input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
	segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
	input_mask = random_attention_mask([self.batch_size, self.seq_length])

	input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
	perm_mask = torch.zeros(
	self.batch_size,
	self.seq_length + 1,
	self.seq_length + 1,
	dtype=torch.float,
	device=torch_device,
	)
	perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
	target_mapping = torch.zeros(
	self.batch_size,
	1,
	self.seq_length + 1,
	dtype=torch.float,
	device=torch_device,
	)
	target_mapping[:, 0, -1] = 1.0 # predict last token

	sequence_labels = None
	lm_labels = None
	is_impossible_labels = None
	token_labels = None
	if self.use_labels:
	lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
	sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
	is_impossible_labels = ids_tensor([self.batch_size], 2).float()
	token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

	config = XLNetConfig(
	vocab_size=self.vocab_size,
	d_model=self.hidden_size,
	n_head=self.num_attention_heads,
	d_inner=self.d_inner,
	n_layer=self.num_hidden_layers,
	untie_r=self.untie_r,
	mem_len=self.mem_len,
	clamp_len=self.clamp_len,
	same_length=self.same_length,
	reuse_len=self.reuse_len,
	bi_data=self.bi_data,
	initializer_range=self.initializer_range,
	num_labels=self.type_sequence_label_size,
	bos_token_id=self.bos_token_id,
	pad_token_id=self.pad_token_id,
	eos_token_id=self.eos_token_id,
	)

	return (
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	)

	def set_seed(self):
	random.seed(self.seed)
	torch.manual_seed(self.seed)

	def create_and_check_xlnet_base_model(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetModel(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids_1, input_mask=input_mask)
	result = model(input_ids_1, attention_mask=input_mask)
	result = model(input_ids_1, token_type_ids=segment_ids)
	result = model(input_ids_1)

	config.mem_len = 0
	model = XLNetModel(config)
	model.to(torch_device)
	model.eval()
	base_model_output = model(input_ids_1)
	self.parent.assertEqual(len(base_model_output), 2)

	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
	self.parent.assertListEqual(
	[mem.shape for mem in result.mems],
	[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	def create_and_check_use_mems_train(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetForSequenceClassification(config)
	model.to(torch_device)
	model.train()

	train_size = input_ids_1.shape[0]

	batch_size = 4
	for i in range(train_size // batch_size + 1):
	input_ids = input_ids_1[i : (i + 1) * batch_size]
	labels = sequence_labels[i : (i + 1) * batch_size]
	outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
	self.parent.assertIsNone(outputs.mems)
	self.parent.assertIsNotNone(outputs.loss)

	def create_and_check_xlnet_model_use_mems(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetModel(config=config)
	model.to(torch_device)
	model.eval()

	# first forward pass
	causal_mask = torch.ones(
	input_ids_1.shape[0],
	input_ids_1.shape[1],
	input_ids_1.shape[1],
	dtype=torch.float,
	device=torch_device,
	)
	causal_mask = torch.triu(causal_mask, diagonal=0)
	outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
	outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
	outputs_conf = model(input_ids_1)

	self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
	self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)

	output, mems = outputs_cache.to_tuple()

	# create hypothetical next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

	# append to next input_ids and token_type_ids
	next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)

	# causal mask
	causal_mask = torch.ones(
	input_ids_1.shape[0],
	input_ids_1.shape[1] + 1,
	input_ids_1.shape[1] + 1,
	dtype=torch.float,
	device=torch_device,
	)
	causal_mask = torch.triu(causal_mask, diagonal=0)
	single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)

	# second forward pass
	output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
	output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

	# test that outputs are equal for slice
	self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

	def create_and_check_xlnet_base_model_with_att_output(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetModel(config)
	model.to(torch_device)
	model.eval()

	attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]

	self.parent.assertEqual(len(attentions), config.n_layer)
	self.parent.assertIsInstance(attentions[0], tuple)
	self.parent.assertEqual(len(attentions[0]), 2)
	self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)

	def create_and_check_xlnet_lm_head(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetLMHeadModel(config)
	model.to(torch_device)
	model.eval()

	result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)

	result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)

	_ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)

	self.parent.assertEqual(result1.loss.shape, ())
	self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
	self.parent.assertListEqual(
	[mem.shape for mem in result1.mems],
	[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	self.parent.assertEqual(result2.loss.shape, ())
	self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
	self.parent.assertListEqual(
	[mem.shape for mem in result2.mems],
	[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	def create_and_check_xlnet_qa(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetForQuestionAnswering(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids_1)

	result_with_labels = model(
	input_ids_1,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	cls_index=sequence_labels,
	is_impossible=is_impossible_labels,
	p_mask=input_mask,
	)

	result_with_labels = model(
	input_ids_1,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	cls_index=sequence_labels,
	is_impossible=is_impossible_labels,
	)

	total_loss, mems = result_with_labels.to_tuple()

	result_with_labels = model(
	input_ids_1,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	)

	total_loss, mems = result_with_labels.to_tuple()

	self.parent.assertEqual(result_with_labels.loss.shape, ())
	self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
	self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
	self.parent.assertEqual(
	result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
	)
	self.parent.assertEqual(
	result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
	)
	self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
	self.parent.assertListEqual(
	[mem.shape for mem in result.mems],
	[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	def create_and_check_xlnet_token_classif(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetForTokenClassification(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids_1)
	result = model(input_ids_1, labels=token_labels)

	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
	self.parent.assertListEqual(
	[mem.shape for mem in result.mems],
	[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	def create_and_check_xlnet_sequence_classif(
	self,
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	):
	model = XLNetForSequenceClassification(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids_1)
	result = model(input_ids_1, labels=sequence_labels)

	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
	self.parent.assertListEqual(
	[mem.shape for mem in result.mems],
	[(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
	)

	def prepare_config_and_inputs_for_common(self):
	config_and_inputs = self.prepare_config_and_inputs()
	(
	config,
	input_ids_1,
	input_ids_2,
	input_ids_q,
	perm_mask,
	input_mask,
	target_mapping,
	segment_ids,
	lm_labels,
	sequence_labels,
	is_impossible_labels,
	token_labels,
	) = config_and_inputs
	inputs_dict = {"input_ids": input_ids_1}
	return config, inputs_dict


	@require_torch
	class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
	all_model_classes = (
	(
	XLNetModel,
	XLNetLMHeadModel,
	XLNetForTokenClassification,
	XLNetForSequenceClassification,
	XLNetForQuestionAnswering,
	XLNetForQuestionAnsweringSimple,
	XLNetForMultipleChoice,
	)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (
	(XLNetLMHeadModel,) if is_torch_available() else ()
	) # TODO (PVP): Check other models whether language generation is also applicable
	test_pruning = False

	# XLNet has 2 QA models -> need to manually set the correct labels for one of them here
	def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
	inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

	if return_labels:
	if model_class.__name__ == "XLNetForQuestionAnswering":
	inputs_dict["start_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	inputs_dict["end_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)

	return inputs_dict

	def setUp(self):
	self.model_tester = XLNetModelTester(self)
	self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)

	def test_config(self):
	self.config_tester.run_common_tests()

	def test_xlnet_base_model(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)

	def test_xlnet_base_model_use_mems(self):
	# checking that in auto-regressive mode, :obj:`use_mems` gives the same results
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)

	def test_seq_classification_use_mems_train(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_use_mems_train(*config_and_inputs)

	def test_xlnet_base_model_with_att_output(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)

	def test_xlnet_lm_head(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)

	def test_xlnet_sequence_classif(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)

	def test_xlnet_token_classif(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)

	def test_xlnet_qa(self):
	self.model_tester.set_seed()
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)

	def test_retain_grad_hidden_states_attentions(self):
	# xlnet cannot keep gradients in attentions or hidden states
	return

	def _check_hidden_states_for_generate(
	self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
	):
	self.assertIsInstance(hidden_states, tuple)
	self.assertListEqual(
	[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
	[True] * len(hidden_states),
	)
	self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

	for idx, iter_hidden_states in enumerate(hidden_states):
	# check hidden size
	for i, layer_hidden_states in enumerate(iter_hidden_states):
	# every 2nd tensor is from extra stream
	if i % 2 != 0:
	seq_len = 1
	else:
	# for first item dummy PAD token is appended so need one more
	seq_len = (min_length + 1) if idx == 0 else min_length

	expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
	self.assertEqual(layer_hidden_states.shape, expected_shape)

	def _check_attentions_for_generate(
	self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
	):
	self.assertIsInstance(attentions, tuple)
	self.assertListEqual(
	[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
	)
	self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

	for idx, attentions_item in enumerate(attentions):
	for iter_attentions in attentions_item:
	tgt_len = min_length

	# for first item dummy PAD token is appended so need one more
	if idx == 0:
	tgt_len += 1

	src_len = min_length + idx + 1

	expected_shape = (
	batch_size * num_beam_groups,
	config.num_attention_heads,
	tgt_len,
	src_len,
	)
	# check attn size
	self.assertListEqual(
	[layer_attention.shape for layer_attention in iter_attentions],
	[expected_shape] * len(iter_attentions),
	)

	@slow
	def test_model_from_pretrained(self):
	for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = XLNetModel.from_pretrained(model_name)
	self.assertIsNotNone(model)


	@require_torch
	class XLNetModelLanguageGenerationTest(unittest.TestCase):
	@slow
	def test_lm_generate_xlnet_base_cased(self):
	model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
	model.to(torch_device)
	input_ids = torch.tensor(
	[
	[
	67,
	2840,
	19,
	18,
	1484,
	20,
	965,
	29077,
	8719,
	1273,
	21,
	45,
	273,
	17,
	10,
	15048,
	28,
	27511,
	21,
	4185,
	11,
	41,
	2444,
	9,
	32,
	1025,
	20,
	8719,
	26,
	23,
	673,
	966,
	19,
	29077,
	20643,
	27511,
	20822,
	20643,
	19,
	17,
	6616,
	17511,
	18,
	8978,
	20,
	18,
	777,
	9,
	19233,
	1527,
	17669,
	19,
	24,
	673,
	17,
	28756,
	150,
	12943,
	4354,
	153,
	27,
	442,
	37,
	45,
	668,
	21,
	24,
	256,
	20,
	416,
	22,
	2771,
	4901,
	9,
	12943,
	4354,
	153,
	51,
	24,
	3004,
	21,
	28142,
	23,
	65,
	20,
	18,
	416,
	34,
	24,
	2958,
	22947,
	9,
	1177,
	45,
	668,
	3097,
	13768,
	23,
	103,
	28,
	441,
	148,
	48,
	20522,
	19,
	12943,
	4354,
	153,
	12860,
	34,
	18,
	326,
	27,
	17492,
	684,
	21,
	6709,
	9,
	8585,
	123,
	266,
	19,
	12943,
	4354,
	153,
	6872,
	24,
	3004,
	20,
	18,
	9225,
	2198,
	19,
	12717,
	103,
	22,
	401,
	24,
	6348,
	9,
	12943,
	4354,
	153,
	1068,
	2768,
	2286,
	19,
	33,
	104,
	19,
	176,
	24,
	9313,
	19,
	20086,
	28,
	45,
	10292,
	9,
	4,
	3,
	]
	],
	dtype=torch.long,
	device=torch_device,
	)
	# In 1991, the remains of Russian Tsar Nicholas II and his family
	# (except for Alexei and Maria) are discovered.
	# The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
	# remainder of the story. 1883 Western Siberia,
	# a young Grigori Rasputin is asked by his father and a group of men to perform magic.
	# Rasputin has a vision and denounces one of the men as a horse thief. Although his
	# father initially slaps him for making such an accusation, Rasputin watches as the
	# man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
	# the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
	# with people, even a bishop, begging for his blessing. """

	expected_output_ids = [
	67,
	2840,
	19,
	18,
	1484,
	20,
	965,
	29077,
	8719,
	1273,
	21,
	45,
	273,
	17,
	10,
	15048,
	28,
	27511,
	21,
	4185,
	11,
	41,
	2444,
	9,
	32,
	1025,
	20,
	8719,
	26,
	23,
	673,
	966,
	19,
	29077,
	20643,
	27511,
	20822,
	20643,
	19,
	17,
	6616,
	17511,
	18,
	8978,
	20,
	18,
	777,
	9,
	19233,
	1527,
	17669,
	19,
	24,
	673,
	17,
	28756,
	150,
	12943,
	4354,
	153,
	27,
	442,
	37,
	45,
	668,
	21,
	24,
	256,
	20,
	416,
	22,
	2771,
	4901,
	9,
	12943,
	4354,
	153,
	51,
	24,
	3004,
	21,
	28142,
	23,
	65,
	20,
	18,
	416,
	34,
	24,
	2958,
	22947,
	9,
	1177,
	45,
	668,
	3097,
	13768,
	23,
	103,
	28,
	441,
	148,
	48,
	20522,
	19,
	12943,
	4354,
	153,
	12860,
	34,
	18,
	326,
	27,
	17492,
	684,
	21,
	6709,
	9,
	8585,
	123,
	266,
	19,
	12943,
	4354,
	153,
	6872,
	24,
	3004,
	20,
	18,
	9225,
	2198,
	19,
	12717,
	103,
	22,
	401,
	24,
	6348,
	9,
	12943,
	4354,
	153,
	1068,
	2768,
	2286,
	19,
	33,
	104,
	19,
	176,
	24,
	9313,
	19,
	20086,
	28,
	45,
	10292,
	9,
	4,
	3,
	19,
	12943,
	4354,
	153,
	27,
	442,
	22,
	2771,
	4901,
	9,
	69,
	27,
	442,
	22,
	2771,
	24,
	11335,
	20,
	18,
	9225,
	2198,
	9,
	69,
	27,
	442,
	22,
	2771,
	24,
	11335,
	20,
	18,
	9225,
	2198,
	9,
	69,
	27,
	442,
	22,
	2771,
	]
	# In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
	# are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
	# narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
	# is asked by his father and a group of men to perform magic. Rasputin has a vision and
	# denounces one of the men as a horse thief. Although his father initially slaps
	# him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
	# Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
	# Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
	# <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
	# He is asked to perform a ritual of the Virgin Mary. He is asked to perform

	output_ids = model.generate(input_ids, max_length=200, do_sample=False)
	self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

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