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# Licensed under the Apache License, Version 2.0 (the "License");
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from math import acos, sin

import numpy as np
import torch
from PIL import Image

from ....models import AutoencoderKL, UNet2DConditionModel
from ....schedulers import DDIMScheduler, DDPMScheduler
from ....utils.torch_utils import randn_tensor
from ...pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class AudioDiffusionPipeline(DiffusionPipeline):
    """
    Pipeline for audio diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    Parameters:
        vqae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        mel ([`Mel`]):
            Transform audio into a spectrogram.
        scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`] or [`DDPMScheduler`].
    """

    _optional_components = ["vqvae"]

    def __init__(
        self,
        vqvae: AutoencoderKL,
        unet: UNet2DConditionModel,
        mel: Mel,
        scheduler: DDIMScheduler | DDPMScheduler,
    ):
        super().__init__()
        self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)

    def get_default_steps(self) -> int:
        """Returns default number of steps recommended for inference.

        Returns:
            `int`:
                The number of steps.
        """
        return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000

    @torch.no_grad()
    def __call__(
        self,
        batch_size: int = 1,
        audio_file: str = None,
        raw_audio: np.ndarray = None,
        slice: int = 0,
        start_step: int = 0,
        steps: int = None,
        generator: torch.Generator = None,
        mask_start_secs: float = 0,
        mask_end_secs: float = 0,
        step_generator: torch.Generator = None,
        eta: float = 0,
        noise: torch.Tensor = None,
        encoding: torch.Tensor = None,
        return_dict=True,
    ) -> AudioPipelineOutput | ImagePipelineOutput | tuple[list[Image.Image], tuple[int, list[np.ndarray]]]:
        """
        The call function to the pipeline for generation.

        Args:
            batch_size (`int`):
                Number of samples to generate.
            audio_file (`str`):
                An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
            raw_audio (`np.ndarray`):
                The raw audio file as a NumPy array.
            slice (`int`):
                Slice number of audio to convert.
            start_step (int):
                Step to start diffusion from.
            steps (`int`):
                Number of denoising steps (defaults to `50` for DDIM and `1000` for DDPM).
            generator (`torch.Generator`):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            mask_start_secs (`float`):
                Number of seconds of audio to mask (not generate) at start.
            mask_end_secs (`float`):
                Number of seconds of audio to mask (not generate) at end.
            step_generator (`torch.Generator`):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.
                None
            eta (`float`):
                Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
                applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            noise (`torch.Tensor`):
                A noise tensor of shape `(batch_size, 1, height, width)` or `None`.
            encoding (`torch.Tensor`):
                A tensor for [`UNet2DConditionModel`] of shape `(batch_size, seq_length, cross_attention_dim)`.
            return_dict (`bool`):
                Whether or not to return a [`AudioPipelineOutput`], [`ImagePipelineOutput`] or a plain tuple.

        Examples:

        For audio diffusion:

        ```py
        import torch
        from IPython.display import Audio
        from diffusers import DiffusionPipeline

        device = "cuda" if torch.cuda.is_available() else "cpu"
        pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)

        output = pipe()
        display(output.images[0])
        display(Audio(output.audios[0], rate=mel.get_sample_rate()))
        ```

        For latent audio diffusion:

        ```py
        import torch
        from IPython.display import Audio
        from diffusers import DiffusionPipeline

        device = "cuda" if torch.cuda.is_available() else "cpu"
        pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)

        output = pipe()
        display(output.images[0])
        display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
        ```

        For other tasks like variation, inpainting, outpainting, etc:

        ```py
        output = pipe(
            raw_audio=output.audios[0, 0],
            start_step=int(pipe.get_default_steps() / 2),
            mask_start_secs=1,
            mask_end_secs=1,
        )
        display(output.images[0])
        display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
        ```

        Returns:
            `list[PIL Image]`:
                A list of Mel spectrograms (`float`, `list[np.ndarray]`) with the sample rate and raw audio.
        """

        steps = steps or self.get_default_steps()
        self.scheduler.set_timesteps(steps)
        step_generator = step_generator or generator
        # For backwards compatibility
        if isinstance(self.unet.config.sample_size, int):
            self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
            noise = randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                ),
                generator=generator,
                device=self.device,
            )
        images = noise
        mask = None

        if audio_file is not None or raw_audio is not None:
            self.mel.load_audio(audio_file, raw_audio)
            input_image = self.mel.audio_slice_to_image(slice)
            input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
                (input_image.height, input_image.width)
            )
            input_image = (input_image / 255) * 2 - 1
            input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)

            if self.vqvae is not None:
                input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
                    generator=generator
                )[0]
                input_images = self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
                images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])

            pixels_per_second = (
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            mask_start = int(mask_start_secs * pixels_per_second)
            mask_end = int(mask_end_secs * pixels_per_second)
            mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
            if isinstance(self.unet, UNet2DConditionModel):
                model_output = self.unet(images, t, encoding)["sample"]
            else:
                model_output = self.unet(images, t)["sample"]

            if isinstance(self.scheduler, DDIMScheduler):
                images = self.scheduler.step(
                    model_output=model_output,
                    timestep=t,
                    sample=images,
                    eta=eta,
                    generator=step_generator,
                )["prev_sample"]
            else:
                images = self.scheduler.step(
                    model_output=model_output,
                    timestep=t,
                    sample=images,
                    generator=step_generator,
                )["prev_sample"]

            if mask is not None:
                if mask_start > 0:
                    images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
                if mask_end > 0:
                    images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            images = 1 / self.vqvae.config.scaling_factor * images
            images = self.vqvae.decode(images)["sample"]

        images = (images / 2 + 0.5).clamp(0, 1)
        images = images.cpu().permute(0, 2, 3, 1).numpy()
        images = (images * 255).round().astype("uint8")
        images = list(
            (Image.fromarray(_[:, :, 0]) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(_, mode="RGB").convert("L") for _ in images)
        )

        audios = [self.mel.image_to_audio(_) for _ in images]
        if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))

    @torch.no_grad()
    def encode(self, images: list[Image.Image], steps: int = 50) -> np.ndarray:
        """
        Reverse the denoising step process to recover a noisy image from the generated image.

        Args:
            images (`list[PIL Image]`):
                list of images to encode.
            steps (`int`):
                Number of encoding steps to perform (defaults to `50`).

        Returns:
            `np.ndarray`:
                A noise tensor of shape `(batch_size, 1, height, width)`.
        """

        # Only works with DDIM as this method is deterministic
        assert isinstance(self.scheduler, DDIMScheduler)
        self.scheduler.set_timesteps(steps)
        sample = np.array(
            [np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
        )
        sample = (sample / 255) * 2 - 1
        sample = torch.Tensor(sample).to(self.device)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
            prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            alpha_prod_t = self.scheduler.alphas_cumprod[t]
            alpha_prod_t_prev = (
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            beta_prod_t = 1 - alpha_prod_t
            model_output = self.unet(sample, t)["sample"]
            pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
            sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output

        return sample

    @staticmethod
    def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
        """Spherical Linear intERPolation.

        Args:
            x0 (`torch.Tensor`):
                The first tensor to interpolate between.
            x1 (`torch.Tensor`):
                Second tensor to interpolate between.
            alpha (`float`):
                Interpolation between 0 and 1

        Returns:
            `torch.Tensor`:
                The interpolated tensor.
        """

        theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
        return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
