# Copyright 2020 HuggingFace Inc. team.
#
# 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 tempfile
import unittest

from transformers import is_torch_available, logging
from transformers.testing_utils import (
    CaptureLogger,
    Expectations,
    require_deterministic_for_xpu,
    require_torch,
    slow,
    torch_device,
)

from ...test_modeling_common import ids_tensor
from ..bart.test_modeling_bart import BartStandaloneDecoderModelTester
from ..bert.test_modeling_bert import BertModelTester
from ..bert_generation.test_modeling_bert_generation import BertGenerationEncoderTester
from ..gpt2.test_modeling_gpt2 import GPT2ModelTester
from ..prophetnet.test_modeling_prophetnet import ProphetNetStandaloneDecoderModelTester
from ..roberta.test_modeling_roberta import RobertaModelTester


if is_torch_available():
    import numpy as np
    import torch

    from transformers import (
        AutoConfig,
        AutoTokenizer,
        BartForCausalLM,
        BertGenerationDecoder,
        BertGenerationEncoder,
        BertLMHeadModel,
        BertModel,
        BertTokenizer,
        EncoderDecoderConfig,
        EncoderDecoderModel,
        GPT2LMHeadModel,
        ProphetNetForCausalLM,
        RobertaForCausalLM,
        RobertaModel,
    )
    from transformers.modeling_outputs import BaseModelOutput


@require_torch
class EncoderDecoderMixin:
    supports_sdpa = False

    def get_encoder_decoder_model(self, config, decoder_config):
        raise NotImplementedError

    def prepare_config_and_inputs(self):
        raise NotImplementedError

    def get_pretrained_model(self):
        raise NotImplementedError

    def check_encoder_decoder_model_from_pretrained_configs(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        encoder_decoder_config = EncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
        self.assertTrue(encoder_decoder_config.decoder.is_decoder)

        enc_dec_model = EncoderDecoderModel(encoder_decoder_config)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()

        self.assertTrue(enc_dec_model.config.is_encoder_decoder)

        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

    def check_encoder_decoder_model(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        self.assertTrue(enc_dec_model.config.decoder.is_decoder)
        self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
        self.assertTrue(enc_dec_model.config.is_encoder_decoder)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )
        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

        encoder_outputs = BaseModelOutput(last_hidden_state=encoder_hidden_states)
        outputs_encoder_decoder = enc_dec_model(
            encoder_outputs=encoder_outputs,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

        # Test passing encoder_outputs as tuple.
        encoder_outputs = (encoder_hidden_states,)
        outputs_encoder_decoder = enc_dec_model(
            encoder_outputs=encoder_outputs,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

    def check_encoder_decoder_model_from_pretrained_using_model_paths(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        with (
            tempfile.TemporaryDirectory() as encoder_tmp_dirname,
            tempfile.TemporaryDirectory() as decoder_tmp_dirname,
        ):
            encoder_model.save_pretrained(encoder_tmp_dirname)
            decoder_model.save_pretrained(decoder_tmp_dirname)
            model_kwargs = {"encoder_hidden_dropout_prob": 0.0}

            # BartConfig has no hidden_dropout_prob.
            if not hasattr(decoder_config, "hidden_dropout_prob"):
                model_kwargs["decoder_activation_function"] = "gelu"
            else:
                model_kwargs["decoder_hidden_dropout_prob"] = 0.0

            enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(
                encoder_tmp_dirname, decoder_tmp_dirname, **model_kwargs
            )
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            return_dict=True,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

    def check_encoder_decoder_model_from_pretrained(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        return_dict,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
        enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            return_dict=True,
        )

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

    def check_save_and_load(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()
        with torch.no_grad():
            outputs = enc_dec_model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as tmpdirname:
                enc_dec_model.save_pretrained(tmpdirname)
                enc_dec_model = EncoderDecoderModel.from_pretrained(tmpdirname)
                enc_dec_model.to(torch_device)

                after_outputs = enc_dec_model(
                    input_ids=input_ids,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    def check_save_and_load_encoder_decoder_model(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        enc_dec_model.eval()
        with torch.no_grad():
            outputs = enc_dec_model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
            )
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with (
                tempfile.TemporaryDirectory() as encoder_tmp_dirname,
                tempfile.TemporaryDirectory() as decoder_tmp_dirname,
            ):
                enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
                enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
                enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(
                    encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
                    decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
                )
                enc_dec_model.to(torch_device)

                after_outputs = enc_dec_model(
                    input_ids=input_ids,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                    decoder_attention_mask=decoder_attention_mask,
                )
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    def check_encoder_decoder_model_labels(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        labels,
        **kwargs,
    ):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            labels=labels,
        )

        loss = outputs_encoder_decoder["loss"]
        # check that backprop works
        loss.backward()

        self.assertEqual(
            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
        )
        self.assertEqual(
            outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
        )

    def _check_output_with_attentions(
        self, outputs_encoder_decoder, config, input_ids, decoder_config, decoder_input_ids
    ):
        encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
        self.assertEqual(len(encoder_attentions), config.num_hidden_layers)

        self.assertEqual(
            encoder_attentions[0].shape[-3:], (config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])
        )

        decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
        num_decoder_layers = (
            decoder_config.num_decoder_layers
            if hasattr(decoder_config, "num_decoder_layers")
            else decoder_config.num_hidden_layers
        )
        self.assertEqual(len(decoder_attentions), num_decoder_layers)

        self.assertEqual(
            decoder_attentions[0].shape[-3:],
            (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
        )

        cross_attentions = outputs_encoder_decoder["cross_attentions"]
        self.assertEqual(len(cross_attentions), num_decoder_layers)

        cross_attention_input_seq_len = decoder_input_ids.shape[-1] * (
            1 + (decoder_config.ngram if hasattr(decoder_config, "ngram") else 0)
        )
        self.assertEqual(
            cross_attentions[0].shape[-3:],
            (decoder_config.num_attention_heads, cross_attention_input_seq_len, input_ids.shape[-1]),
        )

    def check_encoder_decoder_model_output_attentions(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        labels,
        **kwargs,
    ):
        # force eager attention to support output attentions
        config._attn_implementation = "eager"
        decoder_config._attn_implementation = "eager"

        # make the decoder inputs a different shape from the encoder inputs to harden the test
        decoder_input_ids = decoder_input_ids[:, :-1]
        decoder_attention_mask = decoder_attention_mask[:, :-1]
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
            output_attentions=True,
        )
        self._check_output_with_attentions(
            outputs_encoder_decoder, config, input_ids, decoder_config, decoder_input_ids
        )

    def check_encoder_decoder_model_output_attentions_from_config(
        self,
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        decoder_config,
        decoder_input_ids,
        decoder_attention_mask,
        labels,
        **kwargs,
    ):
        # Similar to `check_encoder_decoder_model_output_attentions`, but with `output_attentions` triggered from the
        # config file. Contrarily to most models, changing the model's config won't work -- the defaults are loaded
        # from the inner models' configurations.

        # force eager attention to support output attentions
        config._attn_implementation = "eager"
        decoder_config._attn_implementation = "eager"

        decoder_input_ids = decoder_input_ids[:, :-1]
        decoder_attention_mask = decoder_attention_mask[:, :-1]
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.config._attn_implementation = "eager"  # model config -> won't work
        enc_dec_model.config.output_attentions = True  # model config -> won't work
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )
        self.assertTrue(
            all(
                key not in outputs_encoder_decoder
                for key in ["encoder_attentions", "decoder_attentions", "cross_attentions"]
            )
        )

        config.output_attentions = True  # inner model config -> will work
        decoder_config.output_attentions = True
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        enc_dec_model.to(torch_device)
        outputs_encoder_decoder = enc_dec_model(
            input_ids=input_ids,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=decoder_attention_mask,
        )
        self._check_output_with_attentions(
            outputs_encoder_decoder, config, input_ids, decoder_config, decoder_input_ids
        )

    def check_encoder_decoder_model_generate(self, input_ids, config, decoder_config, **kwargs):
        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)

        # Generate until max length
        if hasattr(enc_dec_model.config, "eos_token_id"):
            enc_dec_model.config.eos_token_id = None
        if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
            enc_dec_model.config.decoder.eos_token_id = None
        if hasattr(enc_dec_model.generation_config, "eos_token_id"):
            enc_dec_model.generation_config.eos_token_id = None
        enc_dec_model.to(torch_device)
        enc_dec_model.generation_config.max_length = 20

        # Bert does not have a bos token id, so use pad_token_id instead
        generated_output = enc_dec_model.generate(
            input_ids,
            decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id,
            max_length=enc_dec_model.generation_config.max_length,
        )
        self.assertEqual(generated_output.shape, (input_ids.shape[0],) + (enc_dec_model.generation_config.max_length,))

    def test_encoder_decoder_model(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model(**input_ids_dict)

    def test_encoder_decoder_model_from_pretrained_configs(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)

    def test_encoder_decoder_model_from_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)

    def test_encoder_decoder_model_from_pretrained_return_dict(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)

    def test_encoder_decoder_model_from_pretrained_using_model_paths(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_from_pretrained_using_model_paths(**input_ids_dict, return_dict=False)

    def test_save_and_load_from_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_save_and_load(**input_ids_dict)

    def test_save_and_load_from_encoder_decoder_pretrained(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_save_and_load_encoder_decoder_model(**input_ids_dict)

    def test_encoder_decoder_model_labels(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_labels(**input_ids_dict)

    def test_encoder_decoder_model_output_attentions(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_output_attentions(**input_ids_dict)

    def test_encoder_decoder_model_output_attentions_from_config(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_output_attentions_from_config(**input_ids_dict)

    def test_encoder_decoder_model_generate(self):
        input_ids_dict = self.prepare_config_and_inputs()
        self.check_encoder_decoder_model_generate(**input_ids_dict)

    def test_training_gradient_checkpointing(self):
        inputs_dict = self.prepare_config_and_inputs()
        encoder_model, decoder_model = self.get_encoder_decoder_model(
            inputs_dict["config"], inputs_dict["decoder_config"]
        )

        model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
        model.to(torch_device)
        model.gradient_checkpointing_enable()
        model.train()

        model.config.decoder_start_token_id = 0
        model.config.pad_token_id = 0

        model_inputs = {
            "input_ids": inputs_dict["input_ids"],
            "attention_mask": inputs_dict["attention_mask"],
            "labels": inputs_dict["labels"],
            "decoder_input_ids": inputs_dict["decoder_input_ids"],
        }
        model_inputs = {k: v.to(torch_device) for k, v in model_inputs.items()}

        loss = model(**model_inputs).loss
        loss.backward()

    @slow
    @require_deterministic_for_xpu
    def test_real_model_save_load_from_pretrained(self):
        model_2 = self.get_pretrained_model()
        model_2.to(torch_device)
        input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
        decoder_input_ids = ids_tensor([13, 1], model_2.config.encoder.vocab_size)
        attention_mask = ids_tensor([13, 5], vocab_size=2)
        with torch.no_grad():
            outputs = model_2(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
            )
            out_2 = outputs[0].cpu().numpy()
            out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as tmp_dirname:
                model_2.save_pretrained(tmp_dirname)
                model_1 = EncoderDecoderModel.from_pretrained(tmp_dirname)
                model_1.to(torch_device)

                after_outputs = model_1(
                    input_ids=input_ids,
                    decoder_input_ids=decoder_input_ids,
                    attention_mask=attention_mask,
                )
                out_1 = after_outputs[0].cpu().numpy()
                out_1[np.isnan(out_1)] = 0
                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    def test_sdpa_can_dispatch_composite_models(self):
        if not self.supports_sdpa:
            self.skipTest("SDPA is not supported")

        inputs_dict = self.prepare_config_and_inputs()
        encoder_config, decoder_config = inputs_dict["config"], inputs_dict["decoder_config"]
        config = EncoderDecoderConfig.from_encoder_decoder_configs(
            encoder_config=encoder_config, decoder_config=decoder_config
        )
        model = EncoderDecoderModel(config=config)

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname)
            model_sdpa = EncoderDecoderModel.from_pretrained(tmpdirname)
            model_sdpa = model_sdpa.eval().to(torch_device)

            # see https://github.com/huggingface/transformers/pull/32238
            # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
            encoder_attn = "sdpa" if model.encoder._supports_sdpa else "eager"
            decoder_attn = "sdpa" if model.decoder._supports_sdpa else "eager"
            self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
            self.assertTrue(model_sdpa.encoder.config._attn_implementation == encoder_attn)
            self.assertTrue(model_sdpa.decoder.config._attn_implementation == decoder_attn)

            # Also test that nothing break if we request SDPA explicitly, when both sub-parts support it.
            # If the model supports sdpa (i.e. all of sub-models supports it) we'll dispatch safely
            # Otherwise we should raise error that SDPA is not supported, as some of the sub-models doesn't support
            if encoder_attn == "sdpa" and decoder_attn == "sdpa":
                model_sdpa_explicit = EncoderDecoderModel.from_pretrained(tmpdirname, attn_implementation="sdpa")
                model_sdpa_explicit = model_sdpa_explicit.eval().to(torch_device)

                self.assertTrue(model_sdpa_explicit.config._attn_implementation == "sdpa")
            else:
                with self.assertRaises(ValueError):
                    model_sdpa_explicit = EncoderDecoderModel.from_pretrained(tmpdirname, attn_implementation="sdpa")

            model_eager = EncoderDecoderModel.from_pretrained(
                tmpdirname,
                attn_implementation="eager",
            )
            model_eager = model_eager.eval().to(torch_device)

            self.assertTrue(model_eager.config._attn_implementation == "eager")
            self.assertTrue(model_eager.encoder.config._attn_implementation == "eager")
            self.assertTrue(model_eager.decoder.config._attn_implementation == "eager")

            for name, submodule in model_eager.named_modules():
                class_name = submodule.__class__.__name__
                if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
                    raise ValueError("The eager model should not have SDPA attention layers")


@require_torch
class BertEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google-bert/bert-base-cased", "google-bert/bert-base-cased"
        )

    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = BertModel(config)
        decoder_model = BertLMHeadModel(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester = BertModelTester(self)
        encoder_config_and_inputs = model_tester.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_token_type_ids,
            decoder_input_mask,
            decoder_sequence_labels,
            decoder_token_labels,
            decoder_choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_token_type_ids": decoder_token_type_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_sequence_labels": decoder_sequence_labels,
            "decoder_token_labels": decoder_token_labels,
            "decoder_choice_labels": decoder_choice_labels,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": decoder_token_labels,
        }

    @slow
    def test_bert2bert_summarization(self):
        model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
        model.to(torch_device)
        tokenizer = BertTokenizer.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")

        ARTICLE_SIGMA = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David Boren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 1856, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confederate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking full membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on the fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more involved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members allegedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a fraternity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloyd's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing incidents."""

        ARTICLE_AMERICA = """(CNN) -- The 2013 America's Cup will be faster than ever after organizers announced that wingsail catamarans will be the vessels of choice. The race has historically been between yachts with a single hull, however the 34th edition of the contest will be between multi-hull vessels with wings rather than traditional sails. This means the boats will travel faster through the water, with top speeds in excess of 30 knots, almost three times as fast as in the past. The Golden Gate Yacht Club, hosts of the 2013 race and holders of the cup, have also announced a new, shorter race format for the competition. In an attempt to boost interest in one of sailing's showpiece events an annual World Series will also take place, starting in 2011, resulting a world champion team being crowned. In addition, a youth America's Cup will also be introduced, set to begin in 2012. In a statement on the International Sailing Federation (ISAF) website, the CEO of 2010's winning syndicate BMW ORACLE Racing Russell Coutts explained the reasons behind the changes. "We believe this new format and new boat will put the America's Cup back at the pinnacle of our sport," said Coutts. "These changes will give equal opportunity to competitors and long-term economic stability to all teams and all commercial partners. We promised fairness and innovation and this is what we've delivered." The statement also explained how, in addition to generating interest in the contest, the new annual America's Cup World Series will provide increased commercial revenue for the teams and their sponsors. The venue for the 2013 contest is not due to be announced until the end of the year, with San Francisco, Valencia and a location near Rome believed to be under consideration. Vincenzo Onorato, President of the 2013 challengers Mascalzone Latino, supported the changes: "I think that we need to acknowledge that the Defender has kept its word. The America's Cup is going to have fair rules and a truly independent management of the racing."""

        EXPECTED_SUMMARY_SIGMA = """sae was founded in 1856, five years before the civil war. the fraternity has had to work hard to change recently. the university of oklahoma president says the university's affiliation with the fraternity is permanently done. the sae has had a string of members in recent months."""

        EXPECTED_SUMMARY_AMERICA = """the 2013 america's cup will be faster than ever. the 34th edition of the competition will be held in 2011. the 2013 race will be between multi - hull vessels with wings rather than traditional sails. the new america'' cup will provide increased commercial revenue. the event will also be expanded to a youth america'cup."""

        input_dict = tokenizer(
            [ARTICLE_SIGMA, ARTICLE_AMERICA],
            padding="max_length",
            max_length=512,
            return_tensors="pt",
        )
        output_ids = model.generate(
            input_dict["input_ids"].to(torch_device), attention_mask=input_dict["attention_mask"].to(torch_device)
        )
        summary = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        self.assertEqual(summary, [EXPECTED_SUMMARY_SIGMA, EXPECTED_SUMMARY_AMERICA])

    def test_bert2bert_default_decoder_attention_mask(self):
        torch.manual_seed(0)
        test_dict = self.prepare_config_and_inputs()
        encoder_config, decoder_config = test_dict["config"], test_dict["decoder_config"]

        encoder_config.pad_token_id = 5
        encoder_config.decoder_start_token_id = 2
        decoder_config.pad_token_id = 5
        decoder_config.decoder_start_token_id = 2

        config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config)
        config.pad_token_id = 5
        config.decoder_start_token_id = 2

        encoder_model, decoder_model = self.get_encoder_decoder_model(encoder_config, decoder_config)
        model = EncoderDecoderModel(config=config, encoder=encoder_model, decoder=decoder_model)

        input_ids = torch.tensor(
            [
                [10, 55, 89, 11, 57, 32, 36, 78, 46, 28, 5, 5, 5],
                [10, 21, 97, 71, 63, 19, 12, 57, 5, 5, 5, 5, 5],
            ]
        )
        attention_mask = input_ids.new_tensor(input_ids != 5)
        labels = torch.tensor(
            [
                [33, 23, 91, 12, 19, 96, 5, 5],
                [87, 85, 13, 31, 5, 5, 5, 5],
            ]
        )

        logger = logging.get_logger("transformers.modeling_utils")
        logger.warning_once.cache_clear()

        with CaptureLogger(logger) as cl:
            torch.manual_seed(0)
            output = model(input_ids, attention_mask, labels=labels)

        # Assert that the warning does not show up since a default decoder_attention_mask should have been created.
        self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)

        # Create a new attention mask that ignores padding, and test that the loss differs for this new attention mask
        # and the default attention mask.
        attention_mask_ignoring_padding = torch.ones(labels.shape, dtype=torch.long)
        torch.manual_seed(0)
        ignore_pad_tokens_output = model(
            input_ids, attention_mask, labels=labels, decoder_attention_mask=attention_mask_ignoring_padding
        )
        self.assertNotAlmostEqual(output.loss.item(), ignore_pad_tokens_output.loss.item())


@require_torch
class BertGenerationEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google/bert_for_seq_generation_L-24_bbc_encoder", "google/bert_for_seq_generation_L-24_bbc_encoder"
        )

    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = BertGenerationEncoder(config)
        decoder_model = BertGenerationDecoder(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester = BertGenerationEncoderTester(self)
        encoder_config_and_inputs = model_tester.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            input_mask,
            token_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_input_mask,
            decoder_token_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_token_labels": decoder_token_labels,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": decoder_token_labels,
        }

    @slow
    @require_deterministic_for_xpu
    def test_roberta2roberta_summarization(self):
        model = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_bbc")
        model.to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_bbc")

        ARTICLE_PS3 = """The problem is affecting people using the older versions of the PlayStation 3, called the "Fat" model.The problem isn't affecting the newer PS3 Slim systems that have been on sale since September last year.Sony have also said they are aiming to have the problem fixed shortly but is advising some users to avoid using their console for the time being."We hope to resolve this problem within the next 24 hours," a statement reads. "In the meantime, if you have a model other than the new slim PS3, we advise that you do not use your PS3 system, as doing so may result in errors in some functionality, such as recording obtained trophies, and not being able to restore certain data."We believe we have identified that this problem is being caused by a bug in the clock functionality incorporated in the system."The PlayStation Network is used by millions of people around the world.It allows users to play their friends at games like Fifa over the internet and also do things like download software or visit online stores."""

        ARTICLE_TOSHIBA = """An independent panel appointed by Toshiba found institutional accounting irregularities, the firm said in a statement to investors. Toshiba said it "takes the situation it has caused very seriously" and that it "deeply apologised" to shareholders. The overstatement was roughly triple an initial Toshiba estimate. The probe could lead to a restatement of earnings, a board overhaul and potential action by regulators. "Within Toshiba, there was a corporate culture in which one could not go against the wishes of superiors," the report said. "Therefore, when top management presented 'challenges', division presidents, line managers and employees below them continually carried out inappropriate accounting practices to meet targets in line with the wishes of their superiors." The improper accounting practices stretched back to 2008."""

        # fmt: off
        EXPECTED_SUMMARIES_PS3 = Expectations(
            {
                ("xpu", 3): """Sony has said that a bug in its PlayStation 3 console is preventing them from using the machine as a computer .""",
                ("cuda", 7): """Sony has said that a bug in its PlayStation 3 console is preventing them from using the machine as a computer.""",
            }
        ) # fmt: on
        EXPECTED_SUMMARY_PS3 = EXPECTED_SUMMARIES_PS3.get_expectation()

        EXPECTED_SUMMARIES_TOSHIBA = Expectations(
            {
                (
                    "xpu",
                    3,
                ): """Japanese electronics giant Toshiba overstated its annual earnings by more than a third last year , according to a report .""",
                (
                    "cuda",
                    7,
                ): """Japanese electronics giant Toshiba overstated its annual earnings by more than a third last year, according to a report.""",
            }
        )
        EXPECTED_SUMMARY_TOSHIBA = EXPECTED_SUMMARIES_TOSHIBA.get_expectation()

        input_dict = tokenizer(
            [ARTICLE_PS3, ARTICLE_TOSHIBA], max_length=512, padding="max_length", return_tensors="pt"
        )
        output_ids = model.generate(
            input_dict["input_ids"].to(torch_device), attention_mask=input_dict["attention_mask"].to(torch_device)
        )
        summary = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        self.assertEqual(summary, [EXPECTED_SUMMARY_PS3, EXPECTED_SUMMARY_TOSHIBA])


@require_torch
class RoBertaEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = RobertaModel(config)
        decoder_model = RobertaForCausalLM(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester = RobertaModelTester(self)
        encoder_config_and_inputs = model_tester.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_token_type_ids,
            decoder_input_mask,
            decoder_sequence_labels,
            decoder_token_labels,
            decoder_choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_token_type_ids": decoder_token_type_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_sequence_labels": decoder_sequence_labels,
            "decoder_token_labels": decoder_token_labels,
            "decoder_choice_labels": decoder_choice_labels,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": decoder_token_labels,
        }

    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "FacebookAI/roberta-base", "FacebookAI/roberta-base"
        )


@require_torch
class GPT2EncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    supports_sdpa = True

    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = BertModel(config)
        decoder_model = GPT2LMHeadModel(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester_encoder = BertModelTester(self, batch_size=13)
        model_tester_decoder = GPT2ModelTester(self, batch_size=13)
        encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_input_mask,
            decoder_token_type_ids,
            decoder_sequence_labels,
            decoder_token_labels,
            decoder_choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        #  disable cache for now
        decoder_config.use_cache = False
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_token_type_ids": decoder_token_type_ids,
            "decoder_attention_mask": decoder_input_mask,
            "decoder_sequence_labels": decoder_sequence_labels,
            "decoder_token_labels": decoder_token_labels,
            "decoder_choice_labels": decoder_choice_labels,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": decoder_token_labels,
        }

    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google-bert/bert-base-cased", "openai-community/gpt2"
        )

    @unittest.skip
    def test_encoder_decoder_model_shared_weights(self):
        pass

    @slow
    @require_deterministic_for_xpu
    def test_bert2gpt2_summarization(self):
        model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16")

        model.to(torch_device)
        tokenizer_in = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
        tokenizer_out = AutoTokenizer.from_pretrained("openai-community/gpt2")

        ARTICLE_STUDENTS = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David Boren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 1856, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confederate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking full membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on the fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more involved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members allegedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a fraternity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloyd's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing incidents."""

        EXPECTED_SUMMARIES_STUDENTS = Expectations(
            {
                (
                    "xpu",
                    3,
                ): """SAS Alpha Epsilon suspended the students, but university president says it's permanent .\nThe fraternity has had to deal with a string of student deaths since 2010 .\nSAS has more than 200,000 members, many of whom are students .\nA student died while being forced into excessive alcohol consumption .""",
                (
                    "cuda",
                    7,
                ): """SAS Alpha Epsilon suspended the students, but university president says it's permanent.\nThe fraternity has had to deal with a string of student deaths since 2010.\nSAS has more than 200,000 members, many of whom are students.\nA student died while being forced into excessive alcohol consumption.""",
            }
        )
        EXPECTED_SUMMARY_STUDENTS = EXPECTED_SUMMARIES_STUDENTS.get_expectation()

        input_dict = tokenizer_in(ARTICLE_STUDENTS, return_tensors="pt")
        output_ids = model.generate(input_dict["input_ids"].to(torch_device))
        summary = tokenizer_out.batch_decode(output_ids, skip_special_tokens=True)

        self.assertEqual(summary, [EXPECTED_SUMMARY_STUDENTS])


@require_torch
class ProphetNetEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = BertModel(config)
        decoder_model = ProphetNetForCausalLM(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester_encoder = BertModelTester(self, batch_size=13)
        model_tester_decoder = ProphetNetStandaloneDecoderModelTester(
            self, batch_size=13, hidden_size=32, max_position_embeddings=512
        )
        encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            lm_labels,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        #  disable cache for now
        decoder_config.use_cache = False
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": lm_labels,
        }

    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google-bert/bert-large-uncased", "microsoft/prophetnet-large-uncased"
        )

    @unittest.skip
    def test_encoder_decoder_model_shared_weights(self):
        pass


@require_torch
class BartEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
    def get_encoder_decoder_model(self, config, decoder_config):
        encoder_model = BertModel(config)
        decoder_model = BartForCausalLM(decoder_config)
        return encoder_model, decoder_model

    def prepare_config_and_inputs(self):
        model_tester_encoder = BertModelTester(self, batch_size=13)
        model_tester_decoder = BartStandaloneDecoderModelTester(
            self, batch_size=13, d_model=32, max_position_embeddings=512
        )
        encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
        decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = encoder_config_and_inputs
        (
            decoder_config,
            decoder_input_ids,
            decoder_attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            lm_labels,
        ) = decoder_config_and_inputs

        # make sure that cross attention layers are added
        decoder_config.add_cross_attention = True
        #  disable cache for now
        decoder_config.use_cache = False
        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_config": decoder_config,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "encoder_hidden_states": encoder_hidden_states,
            "labels": lm_labels,
        }

    def get_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google-bert/bert-large-uncased", "facebook/bart-large"
        )

    @unittest.skip
    def test_encoder_decoder_model_shared_weights(self):
        pass


@require_torch
class EncoderDecoderModelTest(unittest.TestCase):
    def get_from_encoderdecoder_pretrained_model(self):
        return EncoderDecoderModel.from_encoder_decoder_pretrained(
            "google-bert/bert-base-uncased", "google-bert/bert-base-uncased"
        )

    def get_decoder_config(self):
        config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")
        config.is_decoder = True
        config.add_cross_attention = True
        return config

    def get_encoderdecoder_model(self):
        return EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")

    def get_encoder_decoder_models(self):
        encoder_model = BertModel.from_pretrained("google-bert/bert-base-uncased")
        decoder_model = BertLMHeadModel.from_pretrained(
            "google-bert/bert-base-uncased", config=self.get_decoder_config()
        )
        return {"encoder": encoder_model, "decoder": decoder_model}

    def _check_configuration_tie(self, model):
        assert id(model.decoder.config) == id(model.config.decoder)
        assert id(model.encoder.config) == id(model.config.encoder)

    @slow
    def test_configuration_tie(self):
        model = self.get_from_encoderdecoder_pretrained_model()
        self._check_configuration_tie(model)

        model = EncoderDecoderModel(**self.get_encoder_decoder_models())
        self._check_configuration_tie(model)

        model = self.get_encoderdecoder_model()
        self._check_configuration_tie(model)
