# Copyright 2024 SLAPaper
#
# 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
#
#     https://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 torch
from tqdm.auto import trange


@torch.no_grad()
def sample_dpmpp_2m_alt(model, x, sigmas, extra_args=None, callback=None, disable=None):
    """DPM-Solver++(2M) alt"""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    sigma_fn = lambda t: t.neg().exp()
    t_fn = lambda sigma: sigma.log().neg()
    old_denoised = None

    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigmas[i],
                    "denoised": denoised,
                }
            )
        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
        h = t_next - t
        if old_denoised is None or sigmas[i + 1] == 0:
            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
        else:
            h_last = t - t_fn(sigmas[i - 1])
            r = h_last / h
            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
        sigma_progress = i / len(sigmas)
        adjustment_factor = 1 + (0.15 * (sigma_progress * sigma_progress))
        old_denoised = denoised * adjustment_factor
    return x


def add_sample_dpmpp_2m_alt_comfy() -> None:
    try:
        from comfy.samplers import KSampler, k_diffusion_sampling  # type: ignore
    except ImportError:
        return

    if "dpmpp_2m_alt" not in KSampler.SAMPLERS:
        try:
            idx = KSampler.SAMPLERS.index("dpmpp_2m")
            KSampler.SAMPLERS.insert(idx + 1, "dpmpp_2m_alt")
            setattr(k_diffusion_sampling, "sample_dpmpp_2m_alt", sample_dpmpp_2m_alt)
            import importlib

            importlib.reload(k_diffusion_sampling)
        except ValueError:
            pass


def add_custom_samplers():
    samplers = [
        add_sample_dpmpp_2m_alt_comfy,
    ]
    for add_sampler in samplers:
        add_sampler()