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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        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 = self.get_config()

        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 get_config(self):
        return 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,
        )

    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, PipelineTesterMixin, 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
    pipeline_model_mapping = (
        {
            "feature-extraction": XLNetModel,
            "question-answering": XLNetForQuestionAnsweringSimple,
            "text-classification": XLNetForSequenceClassification,
            "text-generation": XLNetLMHeadModel,
            "token-classification": XLNetForTokenClassification,
            "zero-shot": XLNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_pruning = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
            return True

        return 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, `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

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)

        for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
            if hasattr(module, param) and getattr(module, param) is not None:
                weight = getattr(module, param)
                weight.data.fill_(3)

    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)
        # fmt: off
        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,
        )
        # fmt: on
        #  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. """

        # fmt: off
        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,
        ]
        # fmt: on
        #  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)
