# coding=utf-8
# Copyright 2022 Stanford University Team and 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.

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from diffusers.configuration_utils import register_to_config
from diffusers.schedulers import DDIMScheduler
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput


class GaudiDDIMScheduler(DDIMScheduler):
    """
    Extends [Diffusers' DDIMScheduler](https://huggingface.co/docs/diffusers/api/schedulers#diffusers.DDIMScheduler) to run optimally on Gaudi:
    - All time-dependent parameters are generated at the beginning
    - At each time step, tensors are rolled to update the values of the time-dependent parameters

    Args:
        num_train_timesteps (`int`): number of diffusion steps used to train the model.
        beta_start (`float`): the starting `beta` value of inference.
        beta_end (`float`): the final `beta` value.
        beta_schedule (`str`):
            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
        trained_betas (`np.ndarray`, optional):
            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
        clip_sample (`bool`, default `True`):
            option to clip predicted sample between -1 and 1 for numerical stability.
        clip_sample_range (`float`, default `1.0`):
            the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
        set_alpha_to_one (`bool`, default `True`):
            each diffusion step uses the value of alphas product at that step and at the previous one. For the final
            step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
            otherwise it uses the value of alpha at step 0.
        steps_offset (`int`, default `0`):
            an offset added to the inference steps. You can use a combination of `offset=1` and
            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
            stable diffusion.
        prediction_type (`str`, default `epsilon`, optional):
            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
            https://imagen.research.google/video/paper.pdf)
        thresholding (`bool`, default `False`):
            whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
            Note that the thresholding method is unsuitable for latent-space diffusion models (such as
            stable-diffusion).
        dynamic_thresholding_ratio (`float`, default `0.995`):
            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
            (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.
        sample_max_value (`float`, default `1.0`):
            the threshold value for dynamic thresholding. Valid only when `thresholding=True`.
        timestep_spacing (`str`, default `"leading"`):
            The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
            Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.
        rescale_betas_zero_snr (`bool`, default `False`):
            whether to rescale the betas to have zero terminal SNR (proposed by https://arxiv.org/pdf/2305.08891.pdf).
            This can enable the model to generate very bright and dark samples instead of limiting it to samples with
            medium brightness. Loosely related to
            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
    """

    @register_to_config
    def __init__(
        self,
        num_train_timesteps: int = 1000,
        beta_start: float = 0.0001,
        beta_end: float = 0.02,
        beta_schedule: str = "linear",
        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
        clip_sample: bool = True,
        set_alpha_to_one: bool = True,
        steps_offset: int = 0,
        prediction_type: str = "epsilon",
        thresholding: bool = False,
        dynamic_thresholding_ratio: float = 0.995,
        clip_sample_range: float = 1.0,
        sample_max_value: float = 1.0,
        timestep_spacing: str = "leading",
        rescale_betas_zero_snr: bool = False,
    ):
        super().__init__(
            num_train_timesteps,
            beta_start,
            beta_end,
            beta_schedule,
            trained_betas,
            clip_sample,
            set_alpha_to_one,
            steps_offset,
            prediction_type,
        )

        self.reset_timestep_dependent_params()

    def reset_timestep_dependent_params(self):
        self.are_timestep_dependent_params_set = False
        self.alpha_prod_t_list = []
        self.alpha_prod_t_prev_list = []
        self.variance_list = []

    def get_params(self):
        if not self.are_timestep_dependent_params_set:
            prev_timesteps = self.timesteps - self.config.num_train_timesteps // self.num_inference_steps
            for timestep, prev_timestep in zip(self.timesteps, prev_timesteps):
                alpha_prod_t = self.alphas_cumprod[timestep]
                alpha_prod_t_prev = (
                    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
                )

                self.alpha_prod_t_list.append(alpha_prod_t)
                self.alpha_prod_t_prev_list.append(alpha_prod_t_prev)
                self.variance_list.append(self._get_variance(alpha_prod_t, alpha_prod_t_prev))

            self.alpha_prod_t_list = torch.stack(self.alpha_prod_t_list)
            self.alpha_prod_t_prev_list = torch.stack(self.alpha_prod_t_prev_list)
            self.variance_list = torch.stack(self.variance_list)
            self.are_timestep_dependent_params_set = True

        alpha_prod_t = self.alpha_prod_t_list[0]
        self.alpha_prod_t_list = torch.roll(self.alpha_prod_t_list, shifts=-1, dims=0)
        alpha_prod_t_prev = self.alpha_prod_t_prev_list[0]
        self.alpha_prod_t_prev_list = torch.roll(self.alpha_prod_t_prev_list, shifts=-1, dims=0)
        variance = self.variance_list[0]
        self.variance_list = torch.roll(self.variance_list, shifts=-1, dims=0)

        return alpha_prod_t, alpha_prod_t_prev, variance

    # def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
    #     """
    #     Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
    #     current timestep.
    #     Args:
    #         sample (`torch.FloatTensor`): input sample
    #         timestep (`int`, optional): current timestep
    #     Returns:
    #         `torch.FloatTensor`: scaled input sample
    #     """
    #     return sample

    def _get_variance(self, alpha_prod_t, alpha_prod_t_prev):
        beta_prod_t = 1 - alpha_prod_t
        beta_prod_t_prev = 1 - alpha_prod_t_prev

        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

    def step(
        self,
        model_output: torch.FloatTensor,
        sample: torch.FloatTensor,
        eta: float = 0.0,
        use_clipped_model_output: bool = False,
        generator=None,
        variance_noise: Optional[torch.FloatTensor] = None,
        return_dict: bool = True,
    ) -> Union[DDIMSchedulerOutput, Tuple]:
        """
        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
        process from the learned model outputs (most often the predicted noise).

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sample (`torch.FloatTensor`):
                current instance of sample being created by diffusion process.
            eta (`float`): weight of noise for added noise in diffusion step.
            use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
                predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
                `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
                coincide with the one provided as input and `use_clipped_model_output` will have not effect.
            generator: random number generator.
            variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
                can directly provide the noise for the variance itself. This is useful for methods such as
                CycleDiffusion. (https://arxiv.org/abs/2210.05559)
            return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class

        Returns:
            [`diffusers.schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
            [`diffusers.schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.
        """
        if self.num_inference_steps is None:
            raise ValueError(
                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
            )

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        # Done in self.get_params() below

        # 2. compute alphas, betas
        alpha_prod_t, alpha_prod_t_prev, variance = self.get_params()
        beta_prod_t = 1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
            pred_epsilon = model_output
        elif self.config.prediction_type == "sample":
            pred_original_sample = model_output
            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
        elif self.config.prediction_type == "v_prediction":
            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                " `v_prediction`"
            )

        # 4. Clip or threshold "predicted x_0"
        if self.config.thresholding:
            pred_original_sample = self._threshold_sample(pred_original_sample)
        elif self.config.clip_sample:
            pred_original_sample = pred_original_sample.clamp(
                -self.config.clip_sample_range, self.config.clip_sample_range
            )

        # 5. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        std_dev_t = eta * variance ** (0.5)

        if use_clipped_model_output:
            # the pred_epsilon is always re-derived from the clipped x_0 in Glide
            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)

        # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon

        # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction

        if eta > 0:
            device = model_output.device
            if variance_noise is not None and generator is not None:
                raise ValueError(
                    "Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
                    " `variance_noise` stays `None`."
                )

            if variance_noise is None:
                # torch.randn is broken on HPU so running it on CPU
                variance_noise = torch.randn(
                    model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator
                )
                if device.type == "hpu":
                    variance_noise = variance_noise.to(device)

            prev_sample = prev_sample + std_dev_t * variance_noise

        if not return_dict:
            return (prev_sample,)

        return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)

    # def add_noise(
    #     self,
    #     original_samples: torch.FloatTensor,
    #     noise: torch.FloatTensor,
    #     timesteps: torch.IntTensor,
    # ) -> torch.FloatTensor:
    #     # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
    #     self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
    #     timesteps = timesteps.to(original_samples.device)

    #     sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
    #     sqrt_alpha_prod = sqrt_alpha_prod.flatten()
    #     while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
    #         sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)

    #     sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
    #     sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
    #     while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
    #         sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

    #     noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    #     return noisy_samples
