# Copyright 2022 The HuggingFace 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.


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

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


class AudioDiffusionPipeline(DiffusionPipeline):
    """
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Parameters:
        vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None
        unet ([`UNet2DConditionModel`]): UNET model
        mel ([`Mel`]): transform audio <-> spectrogram
        scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler
    """

    _optional_components = ["vqvae"]

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

    def get_input_dims(self) -> Tuple:
        """Returns dimension of input image

        Returns:
            `Tuple`: (height, width)
        """
        input_module = self.vqvae if self.vqvae is not None else self.unet
        # For backwards compatibility
        sample_size = (
            (input_module.sample_size, input_module.sample_size)
            if type(input_module.sample_size) == int
            else input_module.sample_size
        )
        return sample_size

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

        Returns:
            `int`: 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,
    ) -> Union[
        Union[AudioPipelineOutput, ImagePipelineOutput],
        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
    ]:
        """Generate random mel spectrogram from audio input and convert to audio.

        Args:
            batch_size (`int`): number of samples to generate
            audio_file (`str`): must be a file on disk due to Librosa limitation or
            raw_audio (`np.ndarray`): audio as numpy array
            slice (`int`): slice number of audio to convert
            start_step (int): step to start from
            steps (`int`): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
            generator (`torch.Generator`): random number generator or None
            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`): random number generator used to de-noise or None
            eta (`float`): parameter between 0 and 1 used with DDIM scheduler
            noise (`torch.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None
            encoding (`torch.Tensor`): for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim)
            return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple

        Returns:
            `List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios
        """

        steps = steps or self.get_default_steps()
        self.scheduler.set_timesteps(steps)
        step_generator = step_generator or generator
        # For backwards compatibility
        if type(self.unet.sample_size) == int:
            self.unet.sample_size = (self.unet.sample_size, self.unet.sample_size)
        input_dims = self.get_input_dims()
        self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0])
        if noise is None:
            noise = randn_tensor(
                (
                    batch_size,
                    self.unet.in_channels,
                    self.unet.sample_size[0],
                    self.unet.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.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(
            map(lambda _: Image.fromarray(_[:, :, 0]), images)
            if images.shape[3] == 1
            else map(lambda _: Image.fromarray(_, mode="RGB").convert("L"), images)
        )

        audios = list(map(lambda _: self.mel.image_to_audio(_), 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 step process: recover noisy image from 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`: 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.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`): first tensor to interpolate between
            x1 (`torch.Tensor`): seconds tensor to interpolate between
            alpha (`float`): interpolation between 0 and 1

        Returns:
            `torch.Tensor`: 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)
