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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

import math
from typing import TYPE_CHECKING

import torch

from ..configuration_utils import register_to_config
from .guider_utils import BaseGuidance, GuiderOutput, rescale_noise_cfg


if TYPE_CHECKING:
    from ..modular_pipelines.modular_pipeline import BlockState


class TangentialClassifierFreeGuidance(BaseGuidance):
    """
    Tangential Classifier Free Guidance (TCFG): https://huggingface.co/papers/2503.18137

    Args:
        guidance_scale (`float`, defaults to `7.5`):
            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
            deterioration of image quality.
        guidance_rescale (`float`, defaults to `0.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
            Flawed](https://huggingface.co/papers/2305.08891).
        use_original_formulation (`bool`, defaults to `False`):
            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
            we use the diffusers-native implementation that has been in the codebase for a long time. See
            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
        start (`float`, defaults to `0.0`):
            The fraction of the total number of denoising steps after which guidance starts.
        stop (`float`, defaults to `1.0`):
            The fraction of the total number of denoising steps after which guidance stops.
    """

    _input_predictions = ["pred_cond", "pred_uncond"]

    @register_to_config
    def __init__(
        self,
        guidance_scale: float = 7.5,
        guidance_rescale: float = 0.0,
        use_original_formulation: bool = False,
        start: float = 0.0,
        stop: float = 1.0,
        enabled: bool = True,
    ):
        super().__init__(start, stop, enabled)

        self.guidance_scale = guidance_scale
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation

    def prepare_inputs(self, data: dict[str, tuple[torch.Tensor, torch.Tensor]]) -> list["BlockState"]:
        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
            data_batch = self._prepare_batch(data, tuple_idx, input_prediction)
            data_batches.append(data_batch)
        return data_batches

    def prepare_inputs_from_block_state(
        self, data: "BlockState", input_fields: dict[str, str | tuple[str, str]]
    ) -> list["BlockState"]:
        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
        data_batches = []
        for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):
            data_batch = self._prepare_batch_from_block_state(input_fields, data, tuple_idx, input_prediction)
            data_batches.append(data_batch)
        return data_batches

    def forward(self, pred_cond: torch.Tensor, pred_uncond: torch.Tensor | None = None) -> GuiderOutput:
        pred = None

        if not self._is_tcfg_enabled():
            pred = pred_cond
        else:
            pred = normalized_guidance(pred_cond, pred_uncond, self.guidance_scale, self.use_original_formulation)

        if self.guidance_rescale > 0.0:
            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)

        return GuiderOutput(pred=pred, pred_cond=pred_cond, pred_uncond=pred_uncond)

    @property
    def is_conditional(self) -> bool:
        return self._num_outputs_prepared == 1

    @property
    def num_conditions(self) -> int:
        num_conditions = 1
        if self._is_tcfg_enabled():
            num_conditions += 1
        return num_conditions

    def _is_tcfg_enabled(self) -> bool:
        if not self._enabled:
            return False

        is_within_range = True
        if self._num_inference_steps is not None:
            skip_start_step = int(self._start * self._num_inference_steps)
            skip_stop_step = int(self._stop * self._num_inference_steps)
            is_within_range = skip_start_step <= self._step < skip_stop_step

        is_close = False
        if self.use_original_formulation:
            is_close = math.isclose(self.guidance_scale, 0.0)
        else:
            is_close = math.isclose(self.guidance_scale, 1.0)

        return is_within_range and not is_close


def normalized_guidance(
    pred_cond: torch.Tensor, pred_uncond: torch.Tensor, guidance_scale: float, use_original_formulation: bool = False
) -> torch.Tensor:
    cond_dtype = pred_cond.dtype
    preds = torch.stack([pred_cond, pred_uncond], dim=1).float()
    preds = preds.flatten(2)
    U, S, Vh = torch.linalg.svd(preds, full_matrices=False)
    Vh_modified = Vh.clone()
    Vh_modified[:, 1] = 0

    uncond_flat = pred_uncond.reshape(pred_uncond.size(0), 1, -1).float()
    x_Vh = torch.matmul(uncond_flat, Vh.transpose(-2, -1))
    x_Vh_V = torch.matmul(x_Vh, Vh_modified)
    pred_uncond = x_Vh_V.reshape(pred_uncond.shape).to(cond_dtype)

    pred = pred_cond if use_original_formulation else pred_uncond
    shift = pred_cond - pred_uncond
    pred = pred + guidance_scale * shift

    return pred
