# 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

from .phi_functions import Phi, calculate_gamma

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class RESMultistepScheduler(SchedulerMixin, ConfigMixin):
    """
    RESMultistepScheduler (Restartable Exponential Integrator) ported from RES4LYF.

    Supports RES 2M, 3M and DEIS 2M, 3M variants.

    Args:
        num_train_timesteps (`int`, defaults to 1000):
            The number of diffusion steps to train the model.
        beta_start (`float`, defaults to 0.0001):
            The starting `beta` value of inference.
        beta_end (`float`, defaults to 0.02):
            The final `beta` value.
        beta_schedule (`str`, defaults to "linear"):
            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
        prediction_type (`str`, defaults to "epsilon"):
            The prediction type of the scheduler function.
        variant (`str`, defaults to "res_2m"):
            The specific RES/DEIS variant to use. Supported: "res_2m", "res_3m", "deis_2m", "deis_3m".
        use_analytic_solution (`bool`, defaults to True):
            Whether to use high-precision analytic solutions for phi functions.
    """

    _compatibles: ClassVar[List[str]] = [e.name for e in KarrasDiffusionSchedulers]
    order = 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",
        prediction_type: str = "epsilon",
        variant: Literal["res_2m", "res_3m", "deis_2m", "deis_3m"] = "res_2m",
        use_analytic_solution: bool = True,
        timestep_spacing: str = "linspace",
        steps_offset: int = 0,
        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} is not implemented")

        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)

        # Buffer for multistep
        self.model_outputs = []
        self.x0_outputs = []
        self.prev_sigmas = []
        self._step_index = None
        self._begin_index = None
        self.init_noise_sigma = 1.0

    @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 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]
        return sample / ((sigma**2 + 1) ** 0.5)

    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

        if self.config.timestep_spacing == "linspace":
            timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
        elif self.config.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 += self.config.steps_offset
        elif self.config.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 {self.config.timestep_spacing} is not supported.")

        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        # Linear remapping for Flow Matching
        if self.config.use_flow_sigmas:
             # Standardize linear spacing
             sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
        else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)

        if self.config.use_karras_sigmas:
            sigmas = get_sigmas_karras(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif self.config.use_exponential_sigmas:
            sigmas = get_sigmas_exponential(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif self.config.use_beta_sigmas:
            sigmas = get_sigmas_beta(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
        elif self.config.use_flow_sigmas:
             # Already handled above, ensuring variable consistency
             sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)

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

        if self.config.use_flow_sigmas:
             timesteps = sigmas * self.config.num_train_timesteps

        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
        self.model_outputs = []
        self.x0_outputs = []
        self.prev_sigmas = []

        self.lower_order_nums = 0

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

        h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)

        # RECONSTRUCT X0 (Matching PEC pattern)
        if self.config.prediction_type == "epsilon":
            x0 = sample - sigma * model_output
        elif self.config.prediction_type == "sample":
            x0 = 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 == "flow_prediction":
            x0 = sample - sigma * model_output
        else:
            raise ValueError(f"prediction_type {self.config.prediction_type} is not supported.")

        self.model_outputs.append(model_output)
        self.x0_outputs.append(x0)
        self.prev_sigmas.append(sigma)

        # Order logic
        variant = self.config.variant
        order = int(variant[-2]) if variant.endswith("m") else 1

        # Effective order for current step
        curr_order = min(len(self.prev_sigmas), order) if sigma > 0 else 1

        if self.config.prediction_type == "flow_prediction":
            # Variable Step Adams-Bashforth for Flow Matching
            dt = sigma_next - sigma
            v_n = model_output

            if curr_order == 1:
                 x_next = sample + dt * v_n
            elif curr_order == 2:
                 # AB2
                 sigma_prev = self.prev_sigmas[-2]
                 dt_prev = sigma - sigma_prev
                 r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0

                 # Stability check
                 if dt_prev == 0 or r < -0.9 or r > 2.0: # Fallback
                     x_next = sample + dt * v_n
                 else:
                     c0 = 1 + 0.5 * r
                     c1 = -0.5 * r
                     x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])
            elif curr_order >= 3:
                 # Re-implement AB2 logic
                 sigma_prev = self.prev_sigmas[-2]
                 dt_prev = sigma - sigma_prev
                 r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0
                 c0 = 1 + 0.5 * r
                 c1 = -0.5 * r
                 x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])

            self._step_index += 1
            if len(self.model_outputs) > order:
                self.model_outputs.pop(0)
                self.x0_outputs.pop(0)
                self.prev_sigmas.pop(0)

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

        # Exponential Integrator Setup
        phi = Phi(h, [0], getattr(self.config, "use_analytic_solution", True))
        phi_1 = phi(1)

        if variant.startswith("res"):
            # Force Order 1 at the end of schedule
            if self.num_inference_steps is not None and self._step_index >= self.num_inference_steps - 3:
                 curr_order = 1

            if curr_order == 2:
                h_prev = -torch.log(self.prev_sigmas[-1] / self.prev_sigmas[-2])
            elif curr_order == 3:
                pass
            else:
                pass

            # Exponential Integrator Update in x-space
            if curr_order == 1:
                res = phi_1 * x0
            elif curr_order == 2:
                # b2 = -phi_2 / r
                # b2 = -phi_2 / r = -phi(2) / (h_prev/h)
                # Here we use: b2 = phi(2) / ((-h_prev / h) + 1e-9)
                # Since (-h_prev/h) is negative (-r), this gives correct negative sign for b2.

                # Stability check
                r_check = h_prev / (h + 1e-9) # This is effectively -r if using h_prev definition above?
                # Wait, h_prev above is -log(). Positive.
                # h is positive.
                # So h_prev/h is positive. defined as r in other files.
                # But here code uses -h_prev / h in denominator.

                # Stability check
                r_check = h_prev / (h + 1e-9)

                # Hard Restart
                if r_check < 0.5 or r_check > 2.0:
                    res = phi_1 * x0
                else:
                    b2 = phi(2) / ((-h_prev / h) + 1e-9)
                    b1 = phi_1 - b2
                    res = b1 * self.x0_outputs[-1] + b2 * self.x0_outputs[-2]
            elif curr_order == 3:
                # Generalized AB3 for Exponential Integrators
                h_p1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
                h_p2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
                r1 = h_p1 / (h + 1e-9)
                r2 = h_p2 / (h + 1e-9)

                if r1 < 0.5 or r1 > 2.0 or r2 < 0.5 or r2 > 2.0:
                     res = phi_1 * x0
                else:
                    phi_2, phi_3 = phi(2), phi(3)
                    denom = r2 - r1 + 1e-9
                    b3 = (phi_3 + r1 * phi_2) / (r2 * denom)
                    b2 = -(phi_3 + r2 * phi_2) / (r1 * denom)
                    b1 = phi_1 - b2 - b3
                    res = b1 * self.x0_outputs[-1] + b2 * self.x0_outputs[-2] + b3 * self.x0_outputs[-3]
            else:
                res = phi_1 * x0

            if sigma_next == 0:
                x_next = x0
            else:
                x_next = torch.exp(-h) * sample + h * res

        else:
            # DEIS logic (Linear multistep in log-sigma space)
            b = self._get_deis_coefficients(curr_order, sigma, sigma_next)

            # For DEIS, we apply b to the denoised estimates
            res = torch.zeros_like(sample)
            for i, b_val in enumerate(b[0]):
                idx = len(self.x0_outputs) - 1 - i
                if idx >= 0:
                    res += b_val * self.x0_outputs[idx]

            # DEIS update in x-space
            if sigma_next == 0:
                x_next = x0
            else:
                x_next = torch.exp(-h) * sample + h * res

        self._step_index += 1

        if len(self.model_outputs) > order:
            self.model_outputs.pop(0)
            self.x0_outputs.pop(0)
            self.prev_sigmas.pop(0)

        if not return_dict:
            return (x_next,)

        return SchedulerOutput(prev_sample=x_next)

    def _get_res_coefficients(self, rk_type, h, c2, c3):
        ci = [0, c2, c3]
        phi = Phi(h, ci, getattr(self.config, "use_analytic_solution", True))

        if rk_type == "res_2s":
            b2 = phi(2) / (c2 + 1e-9)
            b = [[phi(1) - b2, b2]]
            a = [[0, 0], [c2 * phi(1, 2), 0]]
        elif rk_type == "res_3s":
            gamma_val = calculate_gamma(c2, c3)
            b3 = phi(2) / (gamma_val * c2 + c3 + 1e-9)
            b2 = gamma_val * b3
            b = [[phi(1) - (b2 + b3), b2, b3]]
            a = [] # Simplified
        else:
            b = [[phi(1)]]
            a = [[0]]
        return a, b, ci

    def _get_deis_coefficients(self, order, sigma, sigma_next):
        h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
        phi = Phi(h, [0], getattr(self.config, "use_analytic_solution", True))
        phi_1 = phi(1)

        if order == 1:
            return [[phi_1]]
        elif order == 2:
            h_prev = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
            r = h_prev / (h + 1e-9)

            # Correct Adams-Bashforth-like coefficients for Exponential Integrators

            # Hard Restart for stability
            if r < 0.5 or r > 2.0:
                 return [[phi_1]]

            phi_2 = phi(2)
            b2 = -phi_2 / (r + 1e-9)
            b1 = phi_1 - b2
            return [[b1, b2]]
        elif order == 3:
            h_prev1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
            h_prev2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
            r1 = h_prev1 / (h + 1e-9)
            r2 = h_prev2 / (h + 1e-9)

            if r1 < 0.5 or r1 > 2.0 or r2 < 0.5 or r2 > 2.0:
                return [[phi_1]]

            phi_2 = phi(2)
            phi_3 = phi(3)

            # Generalized AB3 for Exponential Integrators (Varying steps)
            denom = r2 - r1 + 1e-9
            b3 = (phi_3 + r1 * phi_2) / (r2 * denom)
            b2 = -(phi_3 + r2 * phi_2) / (r1 * denom)
            b1 = phi_1 - (b2 + b3)
            return [[b1, b2, b3]]
        else:
            return [[phi_1]]

    def _init_step_index(self, timestep):
        if self.begin_index is None:
            self._step_index = self.index_for_timestep(timestep)
        else:
            self._step_index = self._begin_index

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