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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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# Unless required by applicable law or agreed to in writing, software
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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 AdaptiveProjectedGuidance(BaseGuidance):
    """
    Adaptive Projected Guidance (APG): https://huggingface.co/papers/2410.02416

    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.
        adaptive_projected_guidance_momentum (`float`, defaults to `None`):
            The momentum parameter for the adaptive projected guidance. Disabled if set to `None`.
        adaptive_projected_guidance_rescale (`float`, defaults to `15.0`):
            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
        adaptive_projected_guidance_norm_dim (`int` or `tuple[int]`, *optional*):
            Dimension(s) over which to compute the APG norm and projection. If omitted, all non-batch dimensions are
            used, preserving the original behavior.
        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,
        adaptive_projected_guidance_momentum: float | None = None,
        adaptive_projected_guidance_rescale: float = 15.0,
        adaptive_projected_guidance_norm_dim: int | tuple[int, ...] | None = None,
        eta: float = 1.0,
        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.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
        self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale
        self.adaptive_projected_guidance_norm_dim = adaptive_projected_guidance_norm_dim
        self.eta = eta
        self.guidance_rescale = guidance_rescale
        self.use_original_formulation = use_original_formulation
        self.momentum_buffer = None

    def prepare_inputs(self, data: dict[str, tuple[torch.Tensor, torch.Tensor]]) -> list["BlockState"]:
        if self._step == 0:
            if self.adaptive_projected_guidance_momentum is not None:
                self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
        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"]:
        if self._step == 0:
            if self.adaptive_projected_guidance_momentum is not None:
                self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
        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_apg_enabled():
            pred = pred_cond
        else:
            pred = normalized_guidance(
                pred_cond,
                pred_uncond,
                self.guidance_scale,
                self.momentum_buffer,
                self.eta,
                self.adaptive_projected_guidance_rescale,
                self.use_original_formulation,
                self.adaptive_projected_guidance_norm_dim,
            )

        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._count_prepared == 1

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

    def _is_apg_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


class MomentumBuffer:
    def __init__(self, momentum: float):
        self.momentum = momentum
        self.running_average = 0

    def update(self, update_value: torch.Tensor):
        new_average = self.momentum * self.running_average
        self.running_average = update_value + new_average

    def __repr__(self) -> str:
        """
        Returns a string representation showing momentum, shape, statistics, and a slice of the running_average.
        """
        if isinstance(self.running_average, torch.Tensor):
            shape = tuple(self.running_average.shape)

            # Calculate statistics
            with torch.no_grad():
                stats = {
                    "mean": self.running_average.mean().item(),
                    "std": self.running_average.std().item(),
                    "min": self.running_average.min().item(),
                    "max": self.running_average.max().item(),
                }

            # Get a slice (max 3 elements per dimension)
            slice_indices = tuple(slice(None, min(3, dim)) for dim in shape)
            sliced_data = self.running_average[slice_indices]

            # Format the slice for display (convert to float32 for numpy compatibility with bfloat16)
            slice_str = str(sliced_data.detach().float().cpu().numpy())
            if len(slice_str) > 200:  # Truncate if too long
                slice_str = slice_str[:200] + "..."

            stats_str = ", ".join([f"{k}={v:.4f}" for k, v in stats.items()])

            return (
                f"MomentumBuffer(\n"
                f"  momentum={self.momentum},\n"
                f"  shape={shape},\n"
                f"  stats=[{stats_str}],\n"
                f"  slice={slice_str}\n"
                f")"
            )
        else:
            return f"MomentumBuffer(momentum={self.momentum}, running_average={self.running_average})"


def normalized_guidance(
    pred_cond: torch.Tensor,
    pred_uncond: torch.Tensor,
    guidance_scale: float,
    momentum_buffer: MomentumBuffer | None = None,
    eta: float = 1.0,
    norm_threshold: float = 0.0,
    use_original_formulation: bool = False,
    norm_dim: int | tuple[int, ...] | None = None,
):
    diff = pred_cond - pred_uncond
    if norm_dim is None:
        dim = [-i for i in range(1, len(diff.shape))]
    elif isinstance(norm_dim, int):
        dim = [norm_dim]
    else:
        dim = list(norm_dim)

    if momentum_buffer is not None:
        momentum_buffer.update(diff)
        diff = momentum_buffer.running_average

    if norm_threshold > 0:
        ones = torch.ones_like(diff)
        diff_norm = diff.norm(p=2, dim=dim, keepdim=True)
        scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
        diff = diff * scale_factor

    if diff.device.type in {"mps", "npu"}:
        v0, v1 = diff.cpu().double(), pred_cond.cpu().double()
    else:
        v0, v1 = diff.double(), pred_cond.double()
    v1 = torch.nn.functional.normalize(v1, dim=dim)
    v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1
    v0_orthogonal = v0 - v0_parallel
    diff_parallel = v0_parallel.to(device=diff.device, dtype=diff.dtype)
    diff_orthogonal = v0_orthogonal.to(device=diff.device, dtype=diff.dtype)
    normalized_update = diff_orthogonal + eta * diff_parallel

    pred = pred_cond if use_original_formulation else pred_uncond
    pred = pred + guidance_scale * normalized_update

    return pred
