from __future__ import annotations

import os
from pathlib import Path
from typing import Iterable

import numpy as np
import torch

HDR_REFERENCE_WHITE_NITS = 203.0
HDR10_MASTER_DISPLAY = "G(13250,34500)B(7500,3000)R(34000,16000)WP(15635,16450)L(10000000,1)"
HDR10_MAX_CLL = "10000,400"
VIDEO_PROMPT_HDR_OUTPUT_FLAG = "&"


def hdr10_zscale_filter(*, reference_white_nits: float = HDR_REFERENCE_WHITE_NITS) -> str:
    return (
        "zscale=pin=709:tin=linear:min=gbr:rin=full:"
        f"p=2020:t=smpte2084:m=2020_ncl:r=limited:npl={float(reference_white_nits):.12g},"
        "format=yuv420p10le"
    )


def hdr10_x265_params() -> str:
    return f"hdr10=1:repeat-headers=1:master-display={HDR10_MASTER_DISPLAY}:max-cll={HDR10_MAX_CLL}:log-level=none"


class LogC3:
    A = 5.555556
    B = 0.052272
    C = 0.247190
    D = 0.385537
    E = 5.367655
    F = 0.092809
    CUT = 0.010591

    def compress(self, hdr: torch.Tensor) -> torch.Tensor:
        x = torch.clamp(hdr, min=0.0)
        log_part = self.C * torch.log10(self.A * x + self.B) + self.D
        lin_part = self.E * x + self.F
        return torch.where(x >= self.CUT, log_part, lin_part).clamp_(0.0, 1.0)

    def compress_ldr(self, ldr: torch.Tensor) -> torch.Tensor:
        return torch.clamp(ldr, 0.0, 1.0)

    def decompress(self, logc: torch.Tensor) -> torch.Tensor:
        logc = torch.clamp(logc, 0.0, 1.0)
        cut_log = self.E * self.CUT + self.F
        lin_from_log = (torch.pow(10.0, (logc - self.D) / self.C) - self.B) / self.A
        lin_from_lin = (logc - self.F) / self.E
        return torch.where(logc >= cut_log, lin_from_log, lin_from_lin).clamp_(min=0.0)


def hdr_linear_to_vae_range(frames: torch.Tensor, *, transform: str = "logc3") -> torch.Tensor:
    frames = frames.to(dtype=torch.float32)
    if transform != "logc3":
        raise ValueError(f"Unsupported HDR transform: {transform}")
    return LogC3().compress(frames).mul_(2.0).sub_(1.0)


def vae_range_to_hdr_linear(frames: torch.Tensor, *, transform: str = "logc3") -> torch.Tensor:
    frames = frames.to(dtype=torch.float32).add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
    if transform != "logc3":
        raise ValueError(f"Unsupported HDR transform: {transform}")
    return LogC3().decompress(frames)


def linear_to_srgb(linear: torch.Tensor) -> torch.Tensor:
    linear = torch.clamp(linear, 0.0, 1.0)
    low = linear * 12.92
    high = 1.055 * torch.pow(linear, 1.0 / 2.4) - 0.055
    return torch.where(linear <= 0.0031308, low, high).clamp_(0.0, 1.0)


def tonemap_hdr_tensor_to_uint8(video: torch.Tensor, *, exposure: float = 0.0) -> torch.Tensor:
    if video.ndim == 5 and video.shape[0] == 1:
        video = video[0]
    if video.ndim != 4:
        raise ValueError(f"Expected [C,F,H,W] HDR tensor, got {tuple(video.shape)}.")
    scale = float(2.0 ** float(exposure))
    srgb = linear_to_srgb(video.to(dtype=torch.float32).mul(scale))
    return srgb.mul(255.0).round_().clamp_(0.0, 255.0).to(torch.uint8)


def iter_video_chunks(video: torch.Tensor | Iterable[torch.Tensor]):
    if torch.is_tensor(video):
        yield video
        return
    for chunk in video:
        if chunk is not None:
            yield chunk


def iter_hdr_gbrpf32_frames(video: torch.Tensor | Iterable[torch.Tensor]):
    for chunk in iter_video_chunks(video):
        if chunk is None:
            continue
        if chunk.ndim == 5 and chunk.shape[0] == 1:
            chunk = chunk[0]
        if chunk.ndim != 4:
            raise ValueError(f"Expected [C,F,H,W] HDR tensor, got {tuple(chunk.shape)}.")
        frames = chunk.detach().cpu().to(dtype=torch.float32)
        for frame in frames.permute(1, 0, 2, 3):
            yield frame[[1, 2, 0]].contiguous().numpy().astype(np.float32, copy=False).tobytes()


def write_hdr_exr_frames(
    video: torch.Tensor,
    output_dir: str | os.PathLike[str],
    *,
    start_index: int = 0,
    exr_half: bool = True,
) -> int:
    os.environ.setdefault("OPENCV_IO_ENABLE_OPENEXR", "1")
    import cv2

    if video.ndim == 5 and video.shape[0] == 1:
        video = video[0]
    if video.ndim != 4:
        raise ValueError(f"Expected [C,F,H,W] HDR tensor, got {tuple(video.shape)}.")
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    frame_count = int(video.shape[1])
    params: list[int] = []
    if exr_half and hasattr(cv2, "IMWRITE_EXR_TYPE") and hasattr(cv2, "IMWRITE_EXR_TYPE_HALF"):
        params = [int(cv2.IMWRITE_EXR_TYPE), int(cv2.IMWRITE_EXR_TYPE_HALF)]
    frames = video.detach().cpu().to(dtype=torch.float32).permute(1, 2, 3, 0).contiguous()
    for idx, frame in enumerate(frames, start=int(start_index)):
        rgb = frame.numpy().astype(np.float32, copy=False)
        bgr = np.ascontiguousarray(rgb[..., ::-1])
        path = os.path.join(os.fspath(output_dir), f"frame_{idx:06d}.exr")
        if not cv2.imwrite(path, bgr, params):
            raise RuntimeError(f"Failed to write HDR EXR frame: {path}")
    return frame_count


def read_hdr_exr_frames(
    output_dir: str | os.PathLike[str],
    *,
    start_index: int,
    frame_count: int,
) -> torch.Tensor | None:
    os.environ.setdefault("OPENCV_IO_ENABLE_OPENEXR", "1")
    import cv2

    frames = []
    for idx in range(int(start_index), int(start_index) + int(frame_count)):
        path = os.path.join(os.fspath(output_dir), f"frame_{idx:06d}.exr")
        if not os.path.isfile(path):
            return None
        bgr = cv2.imread(path, cv2.IMREAD_UNCHANGED)
        if bgr is None:
            return None
        rgb = np.ascontiguousarray(bgr[..., ::-1]).astype(np.float32, copy=False)
        frames.append(torch.from_numpy(rgb))
    if not frames:
        return None
    return torch.stack(frames, dim=0).permute(3, 0, 1, 2).contiguous()