# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# 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 gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechT5HifiGan,
    SpeechT5HifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import backend_empty_cache, enable_full_determinism, nightly, torch_device

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = AudioLDMPipeline
    params = TEXT_TO_AUDIO_PARAMS
    batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "num_waveforms_per_prompt",
            "generator",
            "latents",
            "output_type",
            "return_dict",
            "callback",
            "callback_steps",
        ]
    )

    supports_dduf = False

    def get_dummy_components(self):
        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            block_out_channels=(8, 16),
            layers_per_block=1,
            norm_num_groups=8,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=(8, 16),
            class_embed_type="simple_projection",
            projection_class_embeddings_input_dim=8,
            class_embeddings_concat=True,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=[8, 16],
            in_channels=1,
            out_channels=1,
            norm_num_groups=8,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=4,
        )
        torch.manual_seed(0)
        text_encoder_config = ClapTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=8,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=1,
            num_hidden_layers=1,
            pad_token_id=1,
            vocab_size=1000,
            projection_dim=8,
        )
        text_encoder = ClapTextModelWithProjection(text_encoder_config)
        tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)

        vocoder_config = SpeechT5HifiGanConfig(
            model_in_dim=8,
            sampling_rate=16000,
            upsample_initial_channel=16,
            upsample_rates=[2, 2],
            upsample_kernel_sizes=[4, 4],
            resblock_kernel_sizes=[3, 7],
            resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
            normalize_before=False,
        )

        vocoder = SpeechT5HifiGan(vocoder_config)

        components = {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "vocoder": vocoder,
        }
        return components

    def get_dummy_inputs(self, device, seed=0):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "A hammer hitting a wooden surface",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
        }
        return inputs

    def test_audioldm_ddim(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components()
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        output = audioldm_pipe(**inputs)
        audio = output.audios[0]

        assert audio.ndim == 1
        assert len(audio) == 256

        audio_slice = audio[:10]
        expected_slice = np.array(
            [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]
        )

        assert np.abs(audio_slice - expected_slice).max() < 1e-2

    def test_audioldm_prompt_embeds(self):
        components = self.get_dummy_components()
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)
        inputs["prompt"] = 3 * [inputs["prompt"]]

        # forward
        output = audioldm_pipe(**inputs)
        audio_1 = output.audios[0]

        inputs = self.get_dummy_inputs(torch_device)
        prompt = 3 * [inputs.pop("prompt")]

        text_inputs = audioldm_pipe.tokenizer(
            prompt,
            padding="max_length",
            max_length=audioldm_pipe.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_inputs = text_inputs["input_ids"].to(torch_device)

        prompt_embeds = audioldm_pipe.text_encoder(
            text_inputs,
        )
        prompt_embeds = prompt_embeds.text_embeds
        # additional L_2 normalization over each hidden-state
        prompt_embeds = F.normalize(prompt_embeds, dim=-1)

        inputs["prompt_embeds"] = prompt_embeds

        # forward
        output = audioldm_pipe(**inputs)
        audio_2 = output.audios[0]

        assert np.abs(audio_1 - audio_2).max() < 1e-2

    def test_audioldm_negative_prompt_embeds(self):
        components = self.get_dummy_components()
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)
        negative_prompt = 3 * ["this is a negative prompt"]
        inputs["negative_prompt"] = negative_prompt
        inputs["prompt"] = 3 * [inputs["prompt"]]

        # forward
        output = audioldm_pipe(**inputs)
        audio_1 = output.audios[0]

        inputs = self.get_dummy_inputs(torch_device)
        prompt = 3 * [inputs.pop("prompt")]

        embeds = []
        for p in [prompt, negative_prompt]:
            text_inputs = audioldm_pipe.tokenizer(
                p,
                padding="max_length",
                max_length=audioldm_pipe.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_inputs = text_inputs["input_ids"].to(torch_device)

            text_embeds = audioldm_pipe.text_encoder(
                text_inputs,
            )
            text_embeds = text_embeds.text_embeds
            # additional L_2 normalization over each hidden-state
            text_embeds = F.normalize(text_embeds, dim=-1)

            embeds.append(text_embeds)

        inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds

        # forward
        output = audioldm_pipe(**inputs)
        audio_2 = output.audios[0]

        assert np.abs(audio_1 - audio_2).max() < 1e-2

    def test_audioldm_negative_prompt(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        negative_prompt = "egg cracking"
        output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
        audio = output.audios[0]

        assert audio.ndim == 1
        assert len(audio) == 256

        audio_slice = audio[:10]
        expected_slice = np.array(
            [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]
        )

        assert np.abs(audio_slice - expected_slice).max() < 1e-2

    def test_audioldm_num_waveforms_per_prompt(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        prompt = "A hammer hitting a wooden surface"

        # test num_waveforms_per_prompt=1 (default)
        audios = audioldm_pipe(prompt, num_inference_steps=2).audios

        assert audios.shape == (1, 256)

        # test num_waveforms_per_prompt=1 (default) for batch of prompts
        batch_size = 2
        audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios

        assert audios.shape == (batch_size, 256)

        # test num_waveforms_per_prompt for single prompt
        num_waveforms_per_prompt = 2
        audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios

        assert audios.shape == (num_waveforms_per_prompt, 256)

        # test num_waveforms_per_prompt for batch of prompts
        batch_size = 2
        audios = audioldm_pipe(
            [prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
        ).audios

        assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)

    def test_audioldm_audio_length_in_s(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)
        vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate

        inputs = self.get_dummy_inputs(device)
        output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
        audio = output.audios[0]

        assert audio.ndim == 1
        assert len(audio) / vocoder_sampling_rate == 0.016

        output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
        audio = output.audios[0]

        assert audio.ndim == 1
        assert len(audio) / vocoder_sampling_rate == 0.032

    def test_audioldm_vocoder_model_in_dim(self):
        components = self.get_dummy_components()
        audioldm_pipe = AudioLDMPipeline(**components)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        prompt = ["hey"]

        output = audioldm_pipe(prompt, num_inference_steps=1)
        audio_shape = output.audios.shape
        assert audio_shape == (1, 256)

        config = audioldm_pipe.vocoder.config
        config.model_in_dim *= 2
        audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
        output = audioldm_pipe(prompt, num_inference_steps=1)
        audio_shape = output.audios.shape
        # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
        assert audio_shape == (1, 256)

    def test_attention_slicing_forward_pass(self):
        self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical()

    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
    )
    def test_xformers_attention_forwardGenerator_pass(self):
        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)


@nightly
class AudioLDMPipelineSlowTests(unittest.TestCase):
    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
        generator = torch.Generator(device=generator_device).manual_seed(seed)
        latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
        latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
        inputs = {
            "prompt": "A hammer hitting a wooden surface",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 2.5,
        }
        return inputs

    def test_audioldm(self):
        audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 25
        audio = audioldm_pipe(**inputs).audios[0]

        assert audio.ndim == 1
        assert len(audio) == 81920

        audio_slice = audio[77230:77240]
        expected_slice = np.array(
            [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]
        )
        max_diff = np.abs(expected_slice - audio_slice).max()
        assert max_diff < 1e-2


@nightly
class AudioLDMPipelineNightlyTests(unittest.TestCase):
    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
        generator = torch.Generator(device=generator_device).manual_seed(seed)
        latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
        latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
        inputs = {
            "prompt": "A hammer hitting a wooden surface",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 2.5,
        }
        return inputs

    def test_audioldm_lms(self):
        audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
        audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
        audioldm_pipe = audioldm_pipe.to(torch_device)
        audioldm_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_inputs(torch_device)
        audio = audioldm_pipe(**inputs).audios[0]

        assert audio.ndim == 1
        assert len(audio) == 81920

        audio_slice = audio[27780:27790]
        expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
        max_diff = np.abs(expected_slice - audio_slice).max()
        assert max_diff < 3e-2
