# Copyright 2024, 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 Musicgen Melody model."""

import copy
import inspect
import math
import tempfile
import unittest
from functools import cached_property

import numpy as np
import pytest
from pytest import mark

from transformers import (
    EncodecConfig,
    MusicgenMelodyConfig,
    MusicgenMelodyDecoderConfig,
    PreTrainedConfig,
    T5Config,
)
from transformers.testing_utils import (
    Expectations,
    cleanup,
    get_device_properties,
    is_torch_available,
    is_torchaudio_available,
    require_flash_attn,
    require_torch,
    require_torch_accelerator,
    require_torch_fp16,
    require_torchaudio,
    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, sdpa_kernel
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MusicgenMelodyForCausalLM,
        MusicgenMelodyForConditionalGeneration,
        MusicgenMelodyModel,
        set_seed,
    )

if is_torchaudio_available():
    from transformers import MusicgenMelodyProcessor


def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__:
        if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
            setattr(configs_no_init, key, 1e-10)
        if isinstance(getattr(configs_no_init, key, None), PreTrainedConfig):
            no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
            setattr(configs_no_init, key, no_init_subconfig)
    return configs_no_init


def prepare_musicgen_melody_decoder_inputs_dict(
    config,
    input_ids,
    attention_mask=None,
    encoder_hidden_states=None,
    encoder_attention_mask=None,
):
    if attention_mask is None:
        attention_mask = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])[:, 0, :]
        attention_mask = attention_mask.ne(config.pad_token_id)
    if encoder_attention_mask is None and encoder_hidden_states is not None:
        encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=torch_device)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "encoder_hidden_states": encoder_hidden_states,
        "encoder_attention_mask": encoder_attention_mask,
    }


class MusicgenMelodyDecoderTester:
    def __init__(
        self,
        parent,
        batch_size=3,  # need batch_size != num_hidden_layers because of #29297
        seq_length=7,
        is_training=True,
        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=100,
        pad_token_id=99,
        bos_token_id=99,
        num_codebooks=4,
        conditional_seq_length=4,
        audio_channels=1,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        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.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.num_codebooks = num_codebooks
        self.conditional_seq_length = conditional_seq_length
        self.encoder_seq_length = conditional_seq_length + seq_length
        self.audio_channels = audio_channels

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)
        encoder_hidden_states = floats_tensor([self.batch_size, self.conditional_seq_length, self.hidden_size])

        config = self.get_config()
        inputs_dict = prepare_musicgen_melody_decoder_inputs_dict(
            config,
            input_ids,
            encoder_hidden_states=encoder_hidden_states,
        )
        return config, inputs_dict

    def get_config(self):
        config = MusicgenMelodyDecoderConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            d_ff=self.intermediate_size,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.bos_token_id,
            bos_token_id=self.bos_token_id,
            num_codebooks=self.num_codebooks,
            tie_word_embeddings=False,
            audio_channels=self.audio_channels,
        )
        return config

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


@require_torch
class MusicgenMelodyDecoderTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (MusicgenMelodyModel, MusicgenMelodyForCausalLM) if is_torch_available() else ()
    # Doesn't run generation tests. See `greedy_sample_model_classes` below
    all_generative_model_classes = ()
    greedy_sample_model_classes = (
        (MusicgenMelodyForCausalLM,) if is_torch_available() else ()
    )  # the model uses a custom generation method so we only run a specific subset of the generation tests

    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = MusicgenMelodyDecoderTester(self)
        self.config_tester = ConfigTester(self, config_class=MusicgenMelodyDecoderConfig, hidden_size=16)

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

    # special case for labels
    # Copied from tests.models.musicgen.test_modeling_musicgen.MusicgenDecoderTest._prepare_for_class
    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:
            inputs_dict["labels"] = torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_codebooks),
                dtype=torch.long,
                device=torch_device,
            )
        return inputs_dict

    # Copied from tests.models.musicgen.test_modeling_musicgen.MusicgenDecoderTest.check_training_gradient_checkpointing with Musicgen->MusicgenMelody
    def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
        if not self.model_tester.is_training:
            self.skipTest(reason="model_tester.is_training is set to False")

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.use_cache = False
        config.return_dict = True
        model = MusicgenMelodyForCausalLM(config)

        model.to(torch_device)
        model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
        model.train()

        # Contrarily to the initial method, we don't unfreeze freezed parameters.
        # Indeed, sinusoidal position embeddings have frozen weights that should stay frozen.

        optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

        inputs = self._prepare_for_class(inputs_dict, MusicgenMelodyForCausalLM, return_labels=True)
        loss = model(**inputs).loss
        loss.backward()
        optimizer.step()

        for k, v in model.named_parameters():
            if v.requires_grad:
                self.assertTrue(v.grad is not None, f"{k} in {MusicgenMelodyForCausalLM.__name__} has no gradient!")

    # override since we have to compute the input embeddings over codebooks
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

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

            input_ids = inputs["input_ids"]
            del inputs["input_ids"]

            embed_tokens = model.get_input_embeddings()

            input_ids = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])

            inputs["inputs_embeds"] = sum(
                embed_tokens[codebook](input_ids[:, codebook]) for codebook in range(config.num_codebooks)
            )

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

    # override since we have embeddings / LM heads over multiple codebooks
    def test_model_get_set_embeddings(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            first_embed = model.get_input_embeddings()[0]
            self.assertIsInstance(first_embed, torch.nn.Embedding)
            lm_heads = model.get_output_embeddings()
            self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))

    @unittest.skip(reason="MusicGen melody does not use inputs_embeds")
    def test_inputs_embeds_matches_input_ids(self):
        pass

    @unittest.skip(reason="this model doesn't support all arguments tested")
    def test_model_outputs_equivalence(self):
        pass

    @unittest.skip(reason="this model has multiple inputs embeds and lm heads that should not be tied")
    def test_tied_weights_keys(self):
        pass

    def _get_logits_processor_kwargs(self, do_sample=False, config=None):
        logits_processor_kwargs = {}
        return logits_processor_kwargs

    def test_greedy_generate_stereo_outputs(self):
        original_audio_channels = self.model_tester.audio_channels
        self.model_tester.audio_channels = 2
        super().test_greedy_generate_dict_outputs()
        self.model_tester.audio_channels = original_audio_channels

    @require_flash_attn
    @require_torch_accelerator
    @mark.flash_attn_test
    @slow
    # Copied from tests.models.musicgen.test_modeling_musicgen.MusicgenDecoderTest.test_flash_attn_2_inference_equivalence
    def test_flash_attn_2_inference_equivalence(self):
        for model_class in self.all_model_classes:
            if not model_class._supports_flash_attn:
                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model_fa = model_class.from_pretrained(
                    tmpdirname,
                    dtype=torch.bfloat16,
                    attn_implementation="flash_attention_2",
                )
                model_fa.to(torch_device)

                model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16)
                model.to(torch_device)

                # Ignore copy
                dummy_input = inputs_dict[model.main_input_name]
                if dummy_input.dtype in [torch.float32, torch.float16]:
                    dummy_input = dummy_input.to(torch.bfloat16)

                dummy_attention_mask = inputs_dict.get("attention_mask", None)

                if dummy_attention_mask is not None:
                    # Ignore copy
                    dummy_attention_mask[:, 1:] = 1
                    dummy_attention_mask[:, :1] = 0

                # Ignore copy
                outputs = model(dummy_input, output_hidden_states=True)
                # Ignore copy
                outputs_fa = model_fa(dummy_input, output_hidden_states=True)

                logits = (
                    outputs.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs.decoder_hidden_states[-1]
                )
                logits_fa = (
                    outputs_fa.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs_fa.decoder_hidden_states[-1]
                )

                assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)

                # Ignore copy
                other_inputs = {
                    "output_hidden_states": True,
                }
                if dummy_attention_mask is not None:
                    other_inputs["attention_mask"] = dummy_attention_mask

                outputs = model(dummy_input, **other_inputs)
                outputs_fa = model_fa(dummy_input, **other_inputs)

                logits = (
                    outputs.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs.decoder_hidden_states[-1]
                )
                logits_fa = (
                    outputs_fa.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs_fa.decoder_hidden_states[-1]
                )

                assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2)

                # check with inference + dropout
                model.train()
                _ = model_fa(dummy_input, **other_inputs)

    @require_flash_attn
    @require_torch_accelerator
    @mark.flash_attn_test
    @slow
    # Copied from tests.models.musicgen.test_modeling_musicgen.MusicgenDecoderTest.test_flash_attn_2_inference_equivalence_right_padding
    def test_flash_attn_2_inference_equivalence_right_padding(self):
        for model_class in self.all_model_classes:
            if not model_class._supports_flash_attn:
                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model_fa = model_class.from_pretrained(
                    tmpdirname,
                    dtype=torch.bfloat16,
                    attn_implementation="flash_attention_2",
                )
                model_fa.to(torch_device)

                model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16)
                model.to(torch_device)

                # Ignore copy
                dummy_input = inputs_dict[model.main_input_name]
                if dummy_input.dtype in [torch.float32, torch.float16]:
                    dummy_input = dummy_input.to(torch.bfloat16)

                dummy_attention_mask = inputs_dict.get("attention_mask", None)

                if dummy_attention_mask is not None:
                    # Ignore copy
                    dummy_attention_mask[:, :-1] = 1
                    dummy_attention_mask[:, -1:] = 0

                if model.config.is_encoder_decoder:
                    decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)

                    outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
                    outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True)
                else:
                    outputs = model(dummy_input, output_hidden_states=True)
                    outputs_fa = model_fa(dummy_input, output_hidden_states=True)

                logits = (
                    outputs.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs.decoder_hidden_states[-1]
                )
                logits_fa = (
                    outputs_fa.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs_fa.decoder_hidden_states[-1]
                )

                assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
                # Ignore copy
                other_inputs = {
                    "output_hidden_states": True,
                }
                if dummy_attention_mask is not None:
                    other_inputs["attention_mask"] = dummy_attention_mask

                outputs = model(dummy_input, **other_inputs)
                outputs_fa = model_fa(dummy_input, **other_inputs)

                logits = (
                    outputs.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs.decoder_hidden_states[-1]
                )
                logits_fa = (
                    outputs_fa.hidden_states[-1]
                    if not model.config.is_encoder_decoder
                    else outputs_fa.decoder_hidden_states[-1]
                )

                assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2)

    @unittest.skip(
        reason=(
            "MusicGen has a custom set of generation tests that rely on `GenerationTesterMixin`, controlled by "
            "`greedy_sample_model_classes`"
        )
    )
    def test_generation_tester_mixin_inheritance(self):
        pass


def prepare_musicgen_melody_inputs_dict(
    config,
    input_ids,
    decoder_input_ids,
    attention_mask=None,
    decoder_attention_mask=None,
    labels=None,
):
    if decoder_attention_mask is None:
        decoder_attention_mask = decoder_input_ids.reshape(
            -1, config.decoder.num_codebooks, decoder_input_ids.shape[-1]
        )[:, 0, :]
        decoder_attention_mask = decoder_attention_mask.ne(config.decoder.pad_token_id)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "labels": labels,
    }


class MusicgenMelodyTester:
    def __init__(
        self,
        parent,
        batch_size=3,  # need batch_size != num_hidden_layers because of #29297
        seq_length=7,
        is_training=True,
        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=100,
        pad_token_id=99,
        bos_token_id=99,
        num_codebooks=4,
        num_filters=4,
        codebook_size=128,
        conditional_seq_length=3,
        chroma_length=24,
        audio_channels=1,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        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.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.num_codebooks = num_codebooks
        self.num_filters = num_filters
        self.codebook_size = codebook_size
        self.conditional_seq_length = conditional_seq_length
        self.chroma_length = chroma_length
        self.encoder_seq_length = conditional_seq_length + seq_length
        self.audio_channels = audio_channels

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.conditional_seq_length], self.vocab_size)
        decoder_input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)

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

    def get_config(self):
        text_encoder_config = T5Config(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            d_ff=self.intermediate_size,
            num_layers=self.num_hidden_layers,
            num_heads=self.num_attention_heads,
        )
        audio_encoder_config = EncodecConfig(
            hidden_size=self.vocab_size,
            compress=1,
            num_filters=self.num_filters,
            codebook_size=self.codebook_size,
            codebook_dim=self.vocab_size,
        )
        decoder_config = MusicgenMelodyDecoderConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            ffn_dim=self.intermediate_size,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.bos_token_id,
            bos_token_id=self.bos_token_id,
            num_codebooks=self.num_codebooks,
            tie_word_embeddings=False,
            audio_channels=self.audio_channels,
        )
        config = MusicgenMelodyConfig(
            text_encoder=text_encoder_config,
            audio_encoder=audio_encoder_config,
            decoder=decoder_config,
            chroma_length=self.chroma_length,
        )
        return config

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


@require_torch
# Copied from tests.models.musicgen.test_modeling_musicgen.MusicgenTest with Musicgen->MusicgenMelody, musicgen->musicgen_melody, EncoderDecoder->DecoderOnly, input_values->input_features
class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (MusicgenMelodyForConditionalGeneration,) if is_torch_available() else ()
    # Doesn't run generation tests. See `greedy_sample_model_classes` below
    all_generative_model_classes = ()
    greedy_sample_model_classes = (MusicgenMelodyForConditionalGeneration,) if is_torch_available() else ()
    pipeline_model_mapping = {"text-to-audio": MusicgenMelodyForConditionalGeneration} if is_torch_available() else {}
    # Addition keys that are required for forward. MusicGen isn't encoder-decoder in config so we have to pass decoder ids as additional
    additional_model_inputs = ["decoder_input_ids"]
    # training is not supported yet for MusicGen
    test_resize_embeddings = False
    _is_composite = True

    def setUp(self):
        self.model_tester = MusicgenMelodyTester(self)

    # special case for labels
    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:
            inputs_dict["labels"] = torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_codebooks),
                dtype=torch.long,
                device=torch_device,
            )
        return inputs_dict

    def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
        if not self.model_tester.is_training:
            self.skipTest(reason="model_tester.is_training is set to False")

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.use_cache = False
            config.return_dict = True
            model = model_class(config)

            model.to(torch_device)
            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
            model.train()

            # The audio encoder weights are not used during the forward pass (only during the generate pass)
            # So we need to freeze it to be able to train.
            model.freeze_audio_encoder()

            optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()
            optimizer.step()

            for k, v in model.named_parameters():
                if v.requires_grad:
                    self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!")

    # Ignore copy
    def _check_output_with_attentions(self, outputs, config, input_ids, decoder_input_ids):
        decoder_config = config.decoder

        decoder_attentions = outputs["attentions"]
        num_decoder_layers = decoder_config.num_hidden_layers
        self.assertEqual(len(decoder_attentions), num_decoder_layers)

        output_shape = decoder_input_ids.shape[-1] + input_ids.shape[-1] + self.model_tester.chroma_length
        self.assertEqual(
            decoder_attentions[0].shape[-3:],
            (decoder_config.num_attention_heads, output_shape, output_shape),
        )

    def check_musicgen_melody_model_output_attentions(
        self,
        model_class,
        config,
        input_ids,
        attention_mask,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        model = model_class(config)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            outputs = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
                output_attentions=True,
                **kwargs,
            )
        self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)

    # Ignore copy
    def check_musicgen_melody_model_output_attentions_from_config(
        self,
        model_class,
        config,
        input_ids,
        attention_mask,
        decoder_input_ids,
        decoder_attention_mask,
        **kwargs,
    ):
        # Similar to `check_musicgen_melody_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.
        config.output_attentions = True  # model config -> won't work

        model = model_class(config)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            outputs = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
                **kwargs,
            )
        self.assertTrue(all(key not in outputs for key in ["encoder_attentions", "decoder_attentions"]))
        config.text_encoder.output_attentions = True  # inner model config -> will work
        config.audio_encoder.output_attentions = True
        config.decoder.output_attentions = True

        model = model_class(config)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            outputs = model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                attention_mask=attention_mask,
                decoder_attention_mask=decoder_attention_mask,
                **kwargs,
            )
        self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)

    # override since changing `output_attentions` from the top-level model config won't work
    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            self.check_musicgen_melody_model_output_attentions(model_class, config, **inputs_dict)
            self.check_musicgen_melody_model_output_attentions_from_config(model_class, config, **inputs_dict)

    # override since we have a specific forward signature for musicgen_melody
    # Ignore copy
    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = [
                "input_ids",
                "attention_mask",
                "input_features",
                "decoder_input_ids",
                "decoder_attention_mask",
            ]

            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

    # override since changing `gradient_checkpointing` from the top-level model config won't work
    def test_gradient_checkpointing_backward_compatibility(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            if not model_class.supports_gradient_checkpointing:
                continue

            config.text_encoder.gradient_checkpointing = True
            config.audio_encoder.gradient_checkpointing = True
            config.decoder.gradient_checkpointing = True
            model = model_class(config)
            self.assertTrue(model.is_gradient_checkpointing)

    @unittest.skip(reason="MusicGen has multiple inputs embeds and lm heads that should not be tied")
    def test_tied_weights_keys(self):
        pass

    # override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work
    # Ignore copy
    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.text_encoder.output_hidden_states = True
        config.audio_encoder.output_hidden_states = True
        config.decoder.output_hidden_states = True

        config.text_encoder.output_attentions = True
        config.decoder.output_attentions = True

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

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)

        output = outputs[0]

        encoder_hidden_states = outputs.encoder_hidden_states
        encoder_hidden_states.retain_grad()

        decoder_hidden_states = outputs.hidden_states[0]
        decoder_hidden_states.retain_grad()

        if self.has_attentions:
            decoder_attentions = outputs.attentions[0]
            decoder_attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(decoder_hidden_states.grad)

        if self.has_attentions:
            self.assertIsNotNone(decoder_attentions.grad)

    # override since changing `output_hidden_states` from the top-level model config won't work
    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.encoder_hidden_states

            expected_num_layers = self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(hidden_states), expected_num_layers)

            # Ignore copy
            seq_length = self.model_tester.conditional_seq_length + self.model_tester.chroma_length
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

            # Ignore copy
            seq_length = self.model_tester.encoder_seq_length + self.model_tester.chroma_length
            # Ignore copy
            expected_num_layers = self.model_tester.num_hidden_layers + 1
            # Ignore copy
            hidden_states = outputs.hidden_states
            self.assertIsInstance(hidden_states, (list, tuple))
            self.assertEqual(len(hidden_states), expected_num_layers)

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.text_encoder.output_hidden_states = True
            config.audio_encoder.output_hidden_states = True
            config.decoder.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    # override since we have embeddings / LM heads over multiple codebooks
    def test_model_get_set_embeddings(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), torch.nn.Embedding)
            lm_heads = model.get_output_embeddings()
            self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))

    def _get_logits_processor_kwargs(self, do_sample=False, config=None):
        logits_processor_kwargs = {}
        return logits_processor_kwargs

    @require_torch_fp16
    @require_torch_accelerator  # not all operations are supported in fp16 on CPU
    def test_generate_fp16(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()

        for model_class in self.greedy_sample_model_classes:
            model = model_class(config).eval().to(torch_device)
            model.half()
            # greedy
            model.generate(input_dict["input_ids"], attention_mask=input_dict["attention_mask"], max_new_tokens=10)
            # sampling
            model.generate(
                input_dict["input_ids"], attention_mask=input_dict["attention_mask"], do_sample=True, max_new_tokens=10
            )

    def test_greedy_generate_stereo_outputs(self):
        original_audio_channels = self.model_tester.audio_channels
        self.model_tester.audio_channels = 2
        super().test_greedy_generate_dict_outputs()
        self.model_tester.audio_channels = original_audio_channels

    @require_flash_attn
    @require_torch_accelerator
    @mark.flash_attn_test
    @slow
    def test_flash_attn_2_conversion(self):
        self.skipTest(reason="MusicgenMelody doesn't use the MusicgenMelodyFlashAttention2 class method.")

    @require_torch_accelerator
    @slow
    def test_sdpa_can_dispatch_on_flash(self):
        if not self.has_attentions:
            self.skipTest(reason="Model architecture does not support attentions")

        device_type, major, _ = get_device_properties()
        if device_type == "cuda" and major < 8:
            self.skipTest(reason="This test requires an NVIDIA GPU with compute capability >= 8.0")
        elif device_type == "rocm" and major < 9:
            self.skipTest(reason="This test requires an AMD GPU with compute capability >= 9.0")
        elif device_type not in ["cuda", "rocm", "xpu"]:
            self.skipTest(reason="This test requires a Nvidia or AMD GPU or an Intel XPU")

        torch.compiler.reset()

        for model_class in self.all_model_classes:
            if not model_class._supports_sdpa:
                self.skipTest(f"{model_class.__name__} does not support SDPA")

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            if config.model_type in ["llava", "llava_next", "vipllava", "video_llava"]:
                self.skipTest(
                    reason="Llava-like models currently (transformers==4.39.1) requires an attention_mask input"
                )
            if config.model_type == "paligemma":
                self.skipTest(
                    "PaliGemma-like models currently (transformers==4.41.0) requires an attention_mask input"
                )
            if config.model_type in ["idefics", "idefics2", "idefics3"]:
                self.skipTest(reason="Idefics currently (transformers==4.39.1) requires an image_attention_mask input")
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model = model_class.from_pretrained(
                    tmpdirname,
                    dtype=torch.float16,
                    attn_implementation={"decoder": "sdpa", "audio_encoder": None, "text_encoder": None},
                )
                model.to(torch_device)

                inputs_dict.pop("attention_mask", None)
                inputs_dict.pop("decoder_attention_mask", None)

                for name, inp in inputs_dict.items():
                    if isinstance(inp, torch.Tensor) and inp.dtype in [torch.float32, torch.float16]:
                        inputs_dict[name] = inp.to(torch.float16)

                with sdpa_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
                    _ = model(**inputs_dict)

    def test_sdpa_can_dispatch_composite_models(self):
        if not self.has_attentions:
            self.skipTest(reason="Model architecture does not support attentions")

        if not self._is_composite:
            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

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

                audio_encoder_attn = "sdpa" if model.audio_encoder._supports_sdpa else "eager"
                text_encoder_attn = "sdpa" if model.text_encoder._supports_sdpa else "eager"
                decoder_attn = "sdpa" if model.decoder._supports_sdpa else "eager"

                # `None` as it is the requested one which will be assigned to each sub-config
                # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
                self.assertTrue(model_sdpa.audio_encoder.config._attn_implementation == audio_encoder_attn)
                self.assertTrue(model_sdpa.text_encoder.config._attn_implementation == text_encoder_attn)
                self.assertTrue(model_sdpa.decoder.config._attn_implementation == decoder_attn)
                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
                model_eager = model_eager.eval().to(torch_device)

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

    def test_requires_grad_with_frozen_encoders(self):
        config = self.model_tester.get_config()
        for model_class in self.all_model_classes:
            model = model_class(config)
            model.freeze_audio_encoder()

            audio_encoder_grads = [param.requires_grad for param in model.audio_encoder.parameters()]
            text_encoder_grads = [param.requires_grad for param in model.text_encoder.parameters()]

            self.assertFalse(all(audio_encoder_grads))
            self.assertTrue(all(text_encoder_grads))

            model = model_class(config)
            model.freeze_text_encoder()

            audio_encoder_grads = [param.requires_grad for param in model.audio_encoder.parameters()]
            text_encoder_grads = [param.requires_grad for param in model.text_encoder.parameters()]

            self.assertTrue(all(audio_encoder_grads))
            self.assertFalse(all(text_encoder_grads))

    @unittest.skip(
        reason=(
            "MusicGen has a custom set of generation tests that rely on `GenerationTesterMixin`, controlled by "
            "`greedy_sample_model_classes`"
        )
    )
    def test_generation_tester_mixin_inheritance(self):
        pass

    @unittest.skip(reason=("MusicGen has a set of composite models which might not have SDPA themselves, e.g. T5."))
    @pytest.mark.torch_compile_test
    def test_sdpa_can_compile_dynamic(self):
        pass


# Copied from tests.models.musicgen.test_modeling_musicgen.get_bip_bip
def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
    """Produces a series of 'bip bip' sounds at a given frequency."""
    timesteps = np.arange(int(duration * sample_rate)) / sample_rate
    wav = np.cos(2 * math.pi * 440 * timesteps)
    time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
    envelope = time_period >= 0.5
    return wav * envelope


@require_torch
@require_torchaudio
class MusicgenMelodyIntegrationTests(unittest.TestCase):
    @cached_property
    def model(self):
        return MusicgenMelodyForConditionalGeneration.from_pretrained("ylacombe/musicgen-melody").to(torch_device)

    def setUp(self):
        cleanup(torch_device, gc_collect=True)

    def tearDown(self):
        cleanup(torch_device, gc_collect=True)

    @cached_property
    def processor(self):
        return MusicgenMelodyProcessor.from_pretrained("ylacombe/musicgen-melody")

    @slow
    def test_logits_text_prompt(self):
        model = self.model
        processor = self.processor

        inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")

        # prepare the encoder inputs
        input_ids = inputs.input_ids.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        # prepare the decoder inputs
        pad_token_id = model.generation_config.pad_token_id
        decoder_input_ids = (
            torch.ones((input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long).to(torch_device)
            * pad_token_id
        )

        with torch.no_grad():
            logits = model(
                input_ids,
                attention_mask=attention_mask,
                decoder_input_ids=decoder_input_ids,
            ).logits

        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
            1.1100, -2.1065, -3.7699, -0.7102,  1.3707, -1.7028, -2.6802, -6.0367,
            1.0504, -2.5358, -4.3497,  0.7338,  0.4823, -2.5260,  1.2717,  1.5427
        ])
        # fmt: on
        EXPECTED_OUTPUT_LENGTH = input_ids.shape[1] + 1 + self.model.config.chroma_length

        logits_shape = (
            input_ids.shape[0] * model.decoder.num_codebooks,
            EXPECTED_OUTPUT_LENGTH,
            model.decoder.config.vocab_size,
        )

        self.assertTrue(logits.shape == logits_shape)
        torch.testing.assert_close(logits[0, -1, :16].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4)

    @slow
    def test_logits_text_audio_prompt(self):
        model = self.model
        processor = self.processor

        audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
        text = ["80s music", "Club techno"]

        inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")

        # prepare the text encoder inputs
        input_ids = inputs.input_ids.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        # prepare the audio encoder inputs
        input_features = inputs.input_features.to(torch_device)

        # prepare the decoder inputs
        pad_token_id = model.generation_config.pad_token_id
        decoder_input_ids = (
            torch.ones((input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long).to(torch_device)
            * pad_token_id
        )

        with torch.no_grad():
            logits = model(
                input_ids,
                attention_mask=attention_mask,
                input_features=input_features,
                decoder_input_ids=decoder_input_ids,
            ).logits

        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
        [ 0.7479,  0.3742,  0.6253, -7.9405,  0.7105, -6.9995,  0.7792, -3.0482],
        [-2.7905,  0.7492, -0.2556, -8.1586, -1.6740,  0.5771, -8.3650, -0.0908]
        ])
        # fmt: on

        self.assertTrue(logits.shape == (8, 240, 2048))
        torch.testing.assert_close(logits[1:3, -1, 32:40].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4)

    @slow
    def test_generate_unconditional_greedy(self):
        model = self.model

        # only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same
        unconditional_inputs = self.processor.get_unconditional_inputs(num_samples=1).to(torch_device)

        output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=10, guidance_scale=1.0)

        # fmt: off
        EXPECTED_VALUES = torch.tensor(
            [
                1.2741e-04, -8.0466e-05,  5.5789e-04,  1.0402e-03,  2.6547e-04,
                1.5587e-05, -1.4210e-04, -9.7303e-05,  6.4504e-04,  5.0903e-04,
                9.6474e-04,  1.0498e-03,  3.7210e-05, -5.3652e-04, -3.6579e-04, -2.5678e-04
            ]
        )
        # fmt: on

        self.assertTrue(output_values.shape == (1, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)

    @slow
    def test_generate_unconditional_sampling(self):
        model = self.model

        # for stochastic sampling we can generate multiple outputs
        unconditional_inputs = self.processor.get_unconditional_inputs(num_samples=2).to(torch_device)

        set_seed(42)

        output_values = model.generate(
            **unconditional_inputs, do_sample=True, max_new_tokens=10, guidance_scale=1.0, temperature=1.0, top_k=250
        )

        # fmt: off
        EXPECTED_VALUES = torch.tensor(
            [
                -0.0085, -0.0160,  0.0028,  0.0005, -0.0095,  0.0028, -0.0122, -0.0299,
                -0.0052, -0.0145,  0.0092,  0.0063, -0.0378, -0.0621, -0.0784, -0.0120,
            ]
        )
        # fmt: on

        self.assertTrue(output_values.shape == (2, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)

    @slow
    def test_generate_text_prompt_greedy(self):
        model = self.model
        processor = self.processor

        inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")

        # prepare the encoder inputs
        input_ids = inputs.input_ids.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        output_values = model.generate(
            input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=None, max_new_tokens=10
        )

        # fmt: off
        EXPECTED_VALUES = torch.tensor(
            [
                1.2741e-04, -8.0474e-05,  5.5789e-04,  1.0402e-03,  2.6547e-04,
                1.5597e-05, -1.4210e-04, -9.7309e-05,  6.4504e-04,  5.0903e-04
            ]
        )
        # fmt: on

        self.assertTrue(output_values.shape == (2, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :10].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)

    @slow
    def test_generate_text_prompt_greedy_with_classifier_free_guidance(self):
        model = self.model
        processor = self.processor

        inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")

        # prepare the encoder inputs
        input_ids = inputs.input_ids.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        output_values = model.generate(
            input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=3, max_new_tokens=10
        )

        # fmt: off
        EXPECTED_VALUES = torch.tensor(
            [
                1.2741e-04, -8.0474e-05,  5.5789e-04,  1.0402e-03,  2.6547e-04,
                1.5597e-05, -1.4210e-04, -9.7309e-05,  6.4504e-04,  5.0903e-04,
                9.6475e-04,  1.0499e-03,  3.7215e-05, -5.3651e-04, -3.6578e-04, -2.5678e-04
            ]
        )
        # fmt: on

        self.assertTrue(output_values.shape == (2, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)

    @slow
    def test_generate_text_prompt_sampling(self):
        model = self.model
        processor = self.processor

        inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")

        # prepare the encoder inputs
        input_ids = inputs.input_ids.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        set_seed(42)
        output_values = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=True,
            guidance_scale=None,
            max_new_tokens=10,
            temperature=1.0,
            top_k=250,
        )

        # fmt: off
        expectations = Expectations(
            {
                (None, None): [-0.0165, -0.0222, -0.0041, -0.0058, -0.0145, -0.0023, -0.0160, -0.0310, -0.0055, -0.0127,  0.0104,  0.0105, -0.0326, -0.0611, -0.0744, -0.0083],
                ("cuda", 8): [-0.0165, -0.0221, -0.0040, -0.0058, -0.0145, -0.0024, -0.0160, -0.0310, -0.0055, -0.0127,  0.0104,  0.0105, -0.0326, -0.0612, -0.0744, -0.0082],
            }
        )
        EXPECTED_VALUES = torch.tensor(expectations.get_expectation()).to(torch_device)
        # fmt: on

        self.assertTrue(output_values.shape == (2, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :16], EXPECTED_VALUES, rtol=2e-4, atol=2e-4)

    @slow
    def test_generate_text_audio_prompt(self):
        model = self.model
        processor = self.processor

        audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
        text = ["80s music", "Club techno"]

        inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt").to(torch_device)

        output_values = model.generate(**inputs, do_sample=False, guidance_scale=None, max_new_tokens=10)

        # fmt: off
        EXPECTED_VALUES = torch.tensor(
            [
                -1.1999e-04, -2.2303e-04,  4.6296e-04,  1.0524e-03,  2.4827e-04,
                -4.0294e-05, -1.2468e-04,  4.9846e-05,  7.1484e-04,  4.4198e-04,
                7.9063e-04,  8.8141e-04, -6.1807e-05, -6.1856e-04, -3.6235e-04, -2.7226e-04
            ]
        )
        # fmt: on

        self.assertTrue(output_values.shape == (2, 1, 4480))
        torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)


@require_torch
@require_torchaudio
class MusicgenMelodyStereoIntegrationTests(unittest.TestCase):
    @cached_property
    def model(self):
        return MusicgenMelodyForConditionalGeneration.from_pretrained("ylacombe/musicgen-stereo-melody").to(
            torch_device
        )

    @cached_property
    def processor(self):
        return MusicgenMelodyProcessor.from_pretrained("ylacombe/musicgen-stereo-melody")

    @slow
    def test_generate_unconditional_greedy(self):
        model = self.model

        # only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same
        unconditional_inputs = self.processor.get_unconditional_inputs(num_samples=1).to(torch_device)

        output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=12, guidance_scale=1.0)

        # fmt: off
        EXPECTED_VALUES_LEFT = torch.tensor(
            [
                1.2742e-04, -8.0480e-05,  5.5788e-04,  1.0401e-03,  2.6547e-04,
                1.5587e-05, -1.4211e-04, -9.7308e-05,  6.4503e-04,  5.0903e-04,
                9.6475e-04,  1.0499e-03,  3.7205e-05, -5.3652e-04, -3.6579e-04, 2.5679e-04
            ]
        )
        # fmt: on

        # (bsz, channels, seq_len)
        self.assertTrue(output_values.shape == (1, 2, 5760))
        torch.testing.assert_close(output_values[0, 0, :16].cpu(), EXPECTED_VALUES_LEFT, rtol=6e-4, atol=6e-4)
        torch.testing.assert_close(output_values[0, 1, :16].cpu(), EXPECTED_VALUES_LEFT, rtol=6e-4, atol=6e-4)

    @slow
    def test_generate_text_audio_prompt(self):
        model = self.model
        processor = self.processor

        audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
        text = ["80s music", "Club techno"]

        inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt").to(torch_device)

        output_values = model.generate(**inputs, do_sample=False, guidance_scale=3.0, max_new_tokens=12)

        # fmt: off
        EXPECTED_VALUES_LEFT_FIRST_SAMPLE = torch.tensor(
            [
                -0.0862, -0.1021, -0.0936, -0.0754, -0.0616, -0.0456, -0.0354, -0.0298,
                -0.0036,  0.0222,  0.0523,  0.0660,  0.0496,  0.0356,  0.0457,  0.0769
            ]
        )
        EXPECTED_VALUES_RIGHT_SECOND_SAMPLE = torch.tensor(
            [
                -0.0327, -0.0450, -0.0264, -0.0278, -0.0365, -0.0272, -0.0401, -0.0574,
                -0.0413, -0.0508, -0.0269, -0.0323, -0.0762, -0.1115, -0.1390, -0.0790
            ]
        )
        # fmt: on

        # (bsz, channels, seq_len)
        self.assertTrue(output_values.shape == (2, 2, 5760))
        torch.testing.assert_close(
            output_values[0, 0, :16].cpu(), EXPECTED_VALUES_LEFT_FIRST_SAMPLE, rtol=1e-4, atol=1e-4
        )
        torch.testing.assert_close(
            output_values[1, 1, :16].cpu(), EXPECTED_VALUES_RIGHT_SECOND_SAMPLE, rtol=1e-4, atol=1e-4
        )
