# Copyright 2025 The RES4LYF Team (Clybius) 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.

from typing import ClassVar, List, Literal, Optional, Tuple, Union

import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

from diffusers.utils import logging

logger = logging.get_logger(__name__)


class RiemannianFlowScheduler(SchedulerMixin, ConfigMixin):
    """
    Riemannian Flow scheduler using Exponential Integrator step.
    """

    _compatibles: ClassVar[List[str]] = [e.name for e in KarrasDiffusionSchedulers]
    order: ClassVar[int] = 1

    @register_to_config
    def __init__(
        self,
        num_train_timesteps: int = 1000,
        beta_start: float = 0.00085,
        beta_end: float = 0.012,
        beta_schedule: str = "linear",
        metric_type: Literal["euclidean", "hyperbolic", "spherical", "lorentzian"] = "hyperbolic",
        curvature: float = 1.0,
        prediction_type: str = "epsilon",
        timestep_spacing: str = "linspace",
        rescale_betas_zero_snr: bool = False,
        use_karras_sigmas: bool = False,
        use_exponential_sigmas: bool = False,
        use_beta_sigmas: bool = False,
        use_flow_sigmas: bool = False,
        shift: float = 1.0,
        use_dynamic_shifting: bool = False,
        base_shift: float = 0.5,
        max_shift: float = 1.15,
        base_image_seq_len: int = 256,
        max_image_seq_len: int = 4096,
    ):
        from .scheduler_utils import betas_for_alpha_bar, rescale_zero_terminal_snr

        if beta_schedule == "linear":
            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
        elif beta_schedule == "scaled_linear":
            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
        elif beta_schedule == "squaredcos_cap_v2":
            self.betas = betas_for_alpha_bar(num_train_timesteps)
        else:
            raise NotImplementedError(f"{beta_schedule} does not exist.")

        if rescale_betas_zero_snr:
            self.betas = rescale_zero_terminal_snr(self.betas)

        self.alphas = 1.0 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

        # Standard deviation of the initial noise distribution
        self.init_noise_sigma = 1.0

        # Setable values
        self.num_inference_steps = None
        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
        self.sigmas = torch.zeros((num_train_timesteps,), dtype=torch.float32)

        self._step_index = None
        self._begin_index = None

    @property
    def step_index(self) -> Optional[int]:
        return self._step_index

    @property
    def begin_index(self) -> Optional[int]:
        return self._begin_index

    def set_begin_index(self, begin_index: int = 0) -> None:
        self._begin_index = begin_index

    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, mu: Optional[float] = None, dtype: torch.dtype = torch.float32):
        from .scheduler_utils import (
            apply_shift,
            get_dynamic_shift,
            get_sigmas_beta,
            get_sigmas_exponential,
            get_sigmas_flow,
            get_sigmas_karras,
        )

        self.num_inference_steps = num_inference_steps
        timestep_spacing = getattr(self.config, "timestep_spacing", "linspace")
        steps_offset = getattr(self.config, "steps_offset", 0)

        if timestep_spacing == "linspace":
            timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
        elif timestep_spacing == "leading":
            step_ratio = self.config.num_train_timesteps // self.num_inference_steps
            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy()
            timesteps += steps_offset
        elif timestep_spacing == "trailing":
            step_ratio = self.config.num_train_timesteps / self.num_inference_steps
            timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy()
            timesteps -= 1
        else:
            raise ValueError(f"timestep_spacing {timestep_spacing} is not supported.")

        # Derived sigma range from alphas_cumprod
        # In FM, we usually go from sigma_max to sigma_min
        base_sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        # Note: alphas_cumprod[0] is ~0.999 (small sigma), alphas_cumprod[-1] is ~0.0001 (large sigma)
        start_sigma = base_sigmas[-1]
        end_sigma = base_sigmas[0]

        t = torch.linspace(0, 1, num_inference_steps, device=device)
        metric_type = self.config.metric_type
        curvature = self.config.curvature

        if metric_type == "euclidean":
            result = start_sigma * (1 - t) + end_sigma * t
        elif metric_type == "hyperbolic":
            x_start = torch.tanh(torch.tensor(start_sigma / 2, device=device))
            x_end = torch.tanh(torch.tensor(end_sigma / 2, device=device))
            d = torch.acosh(torch.clamp(1 + 2 * ((x_start - x_end)**2) / ((1 - x_start**2) * (1 - x_end**2) + 1e-9), min=1.0))
            lambda_t = torch.sinh(t * d) / (torch.sinh(d) + 1e-9)
            result = 2 * torch.atanh(torch.clamp((1 - lambda_t) * x_start + lambda_t * x_end, -0.999, 0.999))
        elif metric_type == "spherical":
            k = torch.tensor(curvature, device=device)
            theta_start = start_sigma * torch.sqrt(k)
            theta_end = end_sigma * torch.sqrt(k)
            result = torch.sin((1 - t) * theta_start + t * theta_end) / torch.sqrt(k)
        elif metric_type == "lorentzian":
            gamma = 1 / torch.sqrt(torch.clamp(1 - curvature * t**2, min=1e-9))
            result = (start_sigma * (1 - t) + end_sigma * t) * gamma
        else:
            result = start_sigma * (1 - t) + end_sigma * t

        result = torch.clamp(result, min=min(start_sigma, end_sigma), max=max(start_sigma, end_sigma))

        if start_sigma > end_sigma:
            result, _ = torch.sort(result, descending=True)

        sigmas = result.cpu().numpy()

        if getattr(self.config, "use_karras_sigmas", False):
            sigmas = get_sigmas_karras(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif getattr(self.config, "use_exponential_sigmas", False):
            sigmas = get_sigmas_exponential(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif getattr(self.config, "use_beta_sigmas", False):
            sigmas = get_sigmas_beta(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif getattr(self.config, "use_flow_sigmas", False):
            sigmas = get_sigmas_flow(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()

        shift = getattr(self.config, "shift", 1.0)
        use_dynamic_shifting = getattr(self.config, "use_dynamic_shifting", False)
        if shift != 1.0 or use_dynamic_shifting:
            if use_dynamic_shifting and mu is not None:
                shift = get_dynamic_shift(
                    mu,
                    getattr(self.config, "base_shift", 0.5),
                    getattr(self.config, "max_shift", 1.5),
                    getattr(self.config, "base_image_seq_len", 256),
                    getattr(self.config, "max_image_seq_len", 4096),
                )
            sigmas = apply_shift(torch.from_numpy(sigmas), shift).numpy()

        self.sigmas = torch.from_numpy(np.concatenate([sigmas, [0.0]])).to(device=device, dtype=dtype)
        self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=dtype)
        self.init_noise_sigma = self.sigmas.max().item() if self.sigmas.numel() > 0 else 1.0

        self._step_index = None
        self._begin_index = None

    def index_for_timestep(self, timestep, schedule_timesteps=None):
        from .scheduler_utils import index_for_timestep
        if schedule_timesteps is None:
            schedule_timesteps = self.timesteps
        return index_for_timestep(timestep, schedule_timesteps)

    def add_noise(
        self,
        original_samples: torch.Tensor,
        noise: torch.Tensor,
        timesteps: torch.Tensor,
    ) -> torch.Tensor:
        from .scheduler_utils import add_noise_to_sample
        return add_noise_to_sample(original_samples, noise, self.sigmas, timesteps, self.timesteps)

    def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
        if self._step_index is None:
            self._init_step_index(timestep)
        if self.config.prediction_type == "flow_prediction":
            return sample
        sigma = self.sigmas[self._step_index]
        sample = sample / ((sigma**2 + 1) ** 0.5)
        return sample

    def step(
        self,
        model_output: torch.Tensor,
        timestep: Union[float, torch.Tensor],
        sample: torch.Tensor,
        return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        if self._step_index is None:
            self._init_step_index(timestep)

        step_index = self._step_index
        sigma = self.sigmas[step_index]
        sigma_next = self.sigmas[step_index + 1]

        # Determine denoised (x_0 prediction)
        if self.config.prediction_type == "epsilon":
            x0 = sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
            alpha_t = 1.0 / (sigma**2 + 1)**0.5
            sigma_t = sigma * alpha_t
            x0 = alpha_t * sample - sigma_t * model_output
        elif self.config.prediction_type == "sample":
            x0 = model_output
        elif self.config.prediction_type == "flow_prediction":
            x0 = sample - sigma * model_output
        else:
            x0 = model_output

        # Exponential Integrator Update (1st order)
        if sigma_next == 0:
            x_next = x0
        else:
            h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
            x_next = torch.exp(-h) * sample + (1 - torch.exp(-h)) * x0

        self._step_index += 1

        if not return_dict:
            return (x_next,)
        return SchedulerOutput(prev_sample=x_next)

    def _init_step_index(self, timestep):
        if self.begin_index is None:
            if isinstance(timestep, torch.Tensor):
                timestep = timestep.to(self.timesteps.device)
            self._step_index = self.index_for_timestep(timestep)
        else:
            self._step_index = self._begin_index

    def __len__(self):
        return self.config.num_train_timesteps
