# Copyright 2021, The HuggingFace Inc. 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.
"""Testing suite for the PyTorch MVP model."""

import copy
import tempfile
import unittest
from functools import cached_property

import timeout_decorator  # noqa

from transformers import MvpConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    require_torch_fp16,
    slow,
    torch_device,
)

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


if is_torch_available():
    import torch

    from transformers import (
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpTokenizer,
    )
    from transformers.models.mvp.modeling_mvp import MvpDecoder, MvpEncoder, shift_tokens_right


def prepare_mvp_inputs_dict(
    config,
    input_ids,
    decoder_input_ids=None,
    attention_mask=None,
    decoder_attention_mask=None,
):
    if attention_mask is None:
        attention_mask = input_ids.ne(config.pad_token_id)
    if decoder_attention_mask is None:
        decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)

    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class MvpModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
            3,
        )
        input_ids[:, -1] = self.eos_token_id  # Eos Token

        decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        config = self.get_config()
        inputs_dict = prepare_mvp_inputs_dict(config, input_ids, decoder_input_ids)
        return config, inputs_dict

    def get_config(self):
        return MvpConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
        )

    def get_pipeline_config(self):
        config = self.get_config()
        config.max_position_embeddings = 100
        config.vocab_size = 300
        return config

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
        model = MvpModel(config=config).get_decoder().to(torch_device).eval()
        input_ids = inputs_dict["input_ids"]
        attention_mask = inputs_dict["attention_mask"]

        # first forward pass
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = ids_tensor((self.batch_size, 3), 2)

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

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
            "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[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # 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 check_encoder_decoder_model_standalone(self, config, inputs_dict):
        model = MvpModel(config=config).to(torch_device).eval()
        outputs = model(**inputs_dict)

        encoder_last_hidden_state = outputs.encoder_last_hidden_state
        last_hidden_state = outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
            encoder = model.get_encoder()
            encoder.save_pretrained(tmpdirname)
            encoder = MvpEncoder.from_pretrained(tmpdirname).to(torch_device)

        encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
            0
        ]

        self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)

        with tempfile.TemporaryDirectory() as tmpdirname:
            decoder = model.get_decoder()
            decoder.save_pretrained(tmpdirname)
            decoder = MvpDecoder.from_pretrained(tmpdirname).to(torch_device)

        last_hidden_state_2 = decoder(
            input_ids=inputs_dict["decoder_input_ids"],
            attention_mask=inputs_dict["decoder_attention_mask"],
            encoder_hidden_states=encoder_last_hidden_state,
            encoder_attention_mask=inputs_dict["attention_mask"],
        )[0]

        self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)


@require_torch
class MvpHeadTests(unittest.TestCase):
    vocab_size = 99

    def _get_config_and_data(self):
        input_ids = torch.tensor(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ],
            dtype=torch.long,
            device=torch_device,
        )

        batch_size = input_ids.shape[0]
        config = MvpConfig(
            vocab_size=self.vocab_size,
            d_model=24,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=32,
            decoder_ffn_dim=32,
            max_position_embeddings=48,
            eos_token_id=2,
            pad_token_id=1,
            bos_token_id=0,
        )
        return config, input_ids, batch_size

    def test_sequence_classification_forward(self):
        config, input_ids, batch_size = self._get_config_and_data()
        labels = _long_tensor([2] * batch_size).to(torch_device)
        config.num_labels = 3
        model = MvpForSequenceClassification(config)
        model.to(torch_device)
        outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=labels)
        expected_shape = torch.Size((batch_size, config.num_labels))
        self.assertEqual(outputs["logits"].shape, expected_shape)
        self.assertIsInstance(outputs["loss"].item(), float)

    def test_question_answering_forward(self):
        config, input_ids, batch_size = self._get_config_and_data()
        sequence_labels = ids_tensor([batch_size], 2).to(torch_device)
        model = MvpForQuestionAnswering(config)
        model.to(torch_device)
        outputs = model(
            input_ids=input_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )

        self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
        self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
        self.assertIsInstance(outputs["loss"].item(), float)

    @timeout_decorator.timeout(1)
    def test_lm_forward(self):
        config, input_ids, batch_size = self._get_config_and_data()
        lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size).to(torch_device)
        lm_model = MvpForConditionalGeneration(config)
        lm_model.to(torch_device)
        outputs = lm_model(input_ids=input_ids, labels=lm_labels)
        expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape, expected_shape)
        self.assertIsInstance(outputs["loss"].item(), float)

    def test_lm_uneven_forward(self):
        config = MvpConfig(
            vocab_size=self.vocab_size,
            d_model=14,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=8,
            decoder_ffn_dim=8,
            max_position_embeddings=48,
        )
        lm_model = MvpForConditionalGeneration(config).to(torch_device)
        context = torch.tensor(
            [[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
        )
        summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long)
        outputs = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)
        expected_shape = (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape, expected_shape)

    def test_generate_beam_search(self):
        input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], device=torch_device, dtype=torch.long)
        config = MvpConfig(
            vocab_size=self.vocab_size,
            d_model=24,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=32,
            decoder_ffn_dim=32,
            max_position_embeddings=48,
            eos_token_id=2,
            pad_token_id=1,
            bos_token_id=0,
        )
        lm_model = MvpForConditionalGeneration(config).to(torch_device)
        lm_model.eval()

        max_length = 5
        generated_ids = lm_model.generate(
            input_ids.clone(),
            do_sample=True,
            num_return_sequences=1,
            num_beams=2,
            no_repeat_ngram_size=3,
            max_length=max_length,
        )
        self.assertEqual(generated_ids.shape, (input_ids.shape[0], max_length))

    def test_shift_tokens_right(self):
        input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
        shifted = shift_tokens_right(input_ids, 1, 2)
        n_pad_before = input_ids.eq(1).float().sum()
        n_pad_after = shifted.eq(1).float().sum()
        self.assertEqual(shifted.shape, input_ids.shape)
        self.assertEqual(n_pad_after, n_pad_before - 1)
        self.assertTrue(torch.eq(shifted[:, 0], 2).all())

    @slow
    def test_tokenization(self):
        tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
        examples = [" Hello world", " DomDramg"]  # need leading spaces for equality
        fairseq_results = [
            torch.tensor([0, 20920, 232, 2]),
            torch.tensor([0, 11349, 495, 4040, 571, 2]),
        ]
        for ex, desired_result in zip(examples, fairseq_results):
            mvp_toks = tokenizer.encode(ex, return_tensors="pt").squeeze()
            assert_tensors_close(desired_result.long(), mvp_toks, prefix=ex)

    @require_torch_fp16
    def test_generate_fp16(self):
        config, input_ids, batch_size = self._get_config_and_data()
        attention_mask = input_ids.ne(1).to(torch_device)
        model = MvpForConditionalGeneration(config).eval().to(torch_device)
        model.half()
        model.generate(input_ids, attention_mask=attention_mask)
        model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)

    def test_dummy_inputs(self):
        config, *_ = self._get_config_and_data()
        model = MvpForConditionalGeneration(config).eval().to(torch_device)
        model(**model.dummy_inputs)

    def test_resize_tokens_embeddings_more(self):
        config, input_ids, _ = self._get_config_and_data()

        def _get_embs(m):
            return (m.get_input_embeddings().weight.data.clone(), m.get_output_embeddings().weight.data.clone())

        model = MvpForConditionalGeneration(config).eval().to(torch_device)
        input, output = _get_embs(model)
        self.assertTrue(torch.eq(input, output).all())
        new_vocab_size = 45
        model.resize_token_embeddings(new_vocab_size)
        input_new, output_new = _get_embs(model)
        self.assertEqual(input_new.shape, (new_vocab_size, config.d_model))
        self.assertEqual(output_new.shape, (new_vocab_size, config.d_model))
        self.assertTrue(torch.eq(input_new, output_new).all())


@require_torch
class MvpModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (MvpModel, MvpForConditionalGeneration, MvpForSequenceClassification, MvpForQuestionAnswering)
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": MvpModel,
            "fill-mask": MvpForConditionalGeneration,
            "question-answering": MvpForQuestionAnswering,
            "summarization": MvpForConditionalGeneration,
            "text-classification": MvpForSequenceClassification,
            "text-generation": MvpForCausalLM,
            "text2text-generation": MvpForConditionalGeneration,
            "translation": MvpForConditionalGeneration,
            "zero-shot": MvpForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = True

    test_missing_keys = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self,
        pipeline_test_case_name,
        config_class,
        model_architecture,
        tokenizer_name,
        image_processor_name,
        feature_extractor_name,
        processor_name,
    ):
        if (
            pipeline_test_case_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

        return False

    def setUp(self):
        self.model_tester = MvpModelTester(self)
        self.config_tester = ConfigTester(self, config_class=MvpConfig)

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

    def test_save_load_strict(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
            self.assertEqual(info["missing_keys"], [])

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_encoder_decoder_model_standalone(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)

    # MvpForSequenceClassification does not support inputs_embeds
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MvpModel, MvpForConditionalGeneration, MvpForQuestionAnswering):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))

            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

            wte = model.get_input_embeddings()
            if not self.is_encoder_decoder:
                inputs["inputs_embeds"] = wte(input_ids)
            else:
                inputs["inputs_embeds"] = wte(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)

            with torch.no_grad():
                model(**inputs)[0]

    @require_torch_fp16
    def test_generate_fp16(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        model = MvpForConditionalGeneration(config).eval().to(torch_device)
        model.half()
        model.generate(input_ids, attention_mask=attention_mask)
        model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)


def assert_tensors_close(a, b, atol=1e-12, prefix=""):
    """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
    if a is None and b is None:
        return True
    try:
        if torch.allclose(a, b, atol=atol):
            return True
        raise Exception
    except Exception:
        pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
        if a.numel() > 100:
            msg = f"tensor values are {pct_different:.1%} percent different."
        else:
            msg = f"{a} != {b}"
        if prefix:
            msg = prefix + ": " + msg
        raise AssertionError(msg)


def _long_tensor(tok_lst):
    return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)


@require_torch
@require_sentencepiece
@require_tokenizers
class MvpModelIntegrationTests(unittest.TestCase):
    @cached_property
    def default_tokenizer(self):
        return MvpTokenizer.from_pretrained("RUCAIBox/mvp")

    @slow
    def test_inference_no_head(self):
        model = MvpModel.from_pretrained("RUCAIBox/mvp").to(torch_device)
        input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        attention_mask = input_ids.ne(model.config.pad_token_id)
        with torch.no_grad():
            output = model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
        expected_shape = torch.Size((1, 11, 1024))
        self.assertEqual(output.shape, expected_shape)
        expected_slice = torch.tensor(
            [[0.3461, 0.3624, 0.2689], [0.3461, 0.3624, 0.2689], [-0.1562, 1.1637, -0.3784]], device=torch_device
        )
        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-3, atol=1e-3)

    @slow
    def test_summarization_inference(self):
        model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp").to(torch_device)
        tok = self.default_tokenizer
        PGE_ARTICLE = """ Listen to local radio broadcasts for advertisements that reference casinos in your area.\nIf none are in your area, listen to national radio broadcasts for advertisements of casinos in other areas.\nNote the location that is mentioned in each advertisement that involves a casino.\nIf no locations are mentioned, note any additional contact information, such as a website or phone number. Use that information to find out where the casinos are.;\n,\n\nIf you learn about more than 1 casino on the radio, use the Internet to search the distance between your location and each casino. Sites such as maps.google.com or mapquest.com will help you in this search.'"""  # fmt: skip
        EXPECTED_SUMMARY = "Listen to the radio.\nUse the Internet."
        dct = tok.batch_encode_plus(
            [PGE_ARTICLE],
            return_tensors="pt",
        ).to(torch_device)

        hypotheses_batch = model.generate(**dct)

        decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True)
        self.assertEqual(EXPECTED_SUMMARY, decoded[0])


class MvpStandaloneDecoderModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=13,
        d_model=16,
        decoder_seq_length=7,
        is_training=True,
        is_decoder=True,
        use_attention_mask=True,
        use_cache=False,
        use_labels=True,
        decoder_start_token_id=2,
        decoder_ffn_dim=32,
        decoder_layers=2,
        encoder_attention_heads=4,
        decoder_attention_heads=4,
        max_position_embeddings=30,
        is_encoder_decoder=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_labels = use_labels

        self.vocab_size = vocab_size
        self.d_model = d_model
        self.hidden_size = d_model
        self.num_hidden_layers = decoder_layers
        self.decoder_layers = decoder_layers
        self.decoder_ffn_dim = decoder_ffn_dim
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_attention_heads = decoder_attention_heads
        self.num_attention_heads = decoder_attention_heads
        self.eos_token_id = eos_token_id
        self.bos_token_id = bos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.use_cache = use_cache
        self.max_position_embeddings = max_position_embeddings
        self.is_encoder_decoder = is_encoder_decoder

        self.scope = None
        self.decoder_key_length = decoder_seq_length
        self.base_model_out_len = 2
        self.decoder_attention_idx = 1

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        config = MvpConfig(
            vocab_size=self.vocab_size,
            d_model=self.d_model,
            encoder_layers=self.decoder_layers,
            decoder_layers=self.decoder_layers,
            decoder_ffn_dim=self.decoder_ffn_dim,
            encoder_attention_heads=self.encoder_attention_heads,
            decoder_attention_heads=self.decoder_attention_heads,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            use_cache=self.use_cache,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
            max_position_embeddings=self.max_position_embeddings,
            is_encoder_decoder=self.is_encoder_decoder,
        )

        return (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        ) = self.prepare_config_and_inputs()

        encoder_hidden_states = floats_tensor([self.batch_size, self.decoder_seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            lm_labels,
        )

    def create_and_check_decoder_model_past(
        self,
        config,
        input_ids,
        attention_mask,
        lm_labels,
    ):
        config.use_cache = True
        model = MvpDecoder(config=config).to(torch_device).eval()
        # first forward pass
        outputs = model(input_ids, use_cache=True)
        outputs_use_cache_conf = model(input_ids)
        outputs_no_past = model(input_ids, use_cache=False)

        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

        past_key_values = outputs["past_key_values"]

        # 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
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)

        output_from_no_past = model(next_input_ids)["last_hidden_state"]
        output_from_past = model(next_tokens, past_key_values=past_key_values)["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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

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

    def create_and_check_decoder_model_attention_mask_past(
        self,
        config,
        input_ids,
        attention_mask,
        lm_labels,
    ):
        model = MvpDecoder(config=config).to(torch_device).eval()

        # create attention mask
        attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)

        half_seq_length = input_ids.shape[-1] // 2
        attn_mask[:, half_seq_length:] = 0

        # first forward pass
        past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]

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

        # change a random masked slice from input_ids
        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
        random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
        input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens

        # append to next input_ids and attn_mask
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        attn_mask = torch.cat(
            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
            dim=1,
        )

        # get two different outputs
        output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
        output_from_past = model(
            next_tokens, attention_mask=attn_mask, past_key_values=past_key_values, use_cache=True
        )["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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

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

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            attention_mask,
            lm_labels,
        ) = config_and_inputs

        inputs_dict = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


@require_torch
class MvpStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (MvpDecoder, MvpForCausalLM) if is_torch_available() else ()
    is_encoder_decoder = False

    def setUp(
        self,
    ):
        self.model_tester = MvpStandaloneDecoderModelTester(self, is_training=False)
        self.config_tester = ConfigTester(self, config_class=MvpConfig)

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

    def test_decoder_model_past(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)

    def test_decoder_model_attn_mask_past(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)

    @unittest.skip(reason="Decoder cannot keep gradients")
    def test_retain_grad_hidden_states_attentions(self):
        return
