"""
Klarity HF Space - Core Processing Module
Extracted and adapted from klarity.py for headless (server) use.
Lite mode only, CPU device.
"""

import os
import sys
import shutil
import subprocess
import logging
from pathlib import Path

log = logging.getLogger(__name__)

import torch
import cv2
import numpy as np

IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif', '.webp'}
VIDEO_EXTENSIONS = {'.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.wmv',
                 '.m4v', '.mpeg', '.mpg', '.3gp', '.ts', '.mts', '.m2ts', '.ogv'}

NAFNET_CONFIGS_LITE = {
    'deblur': {
        'width': 32,
        'middle_blk_num': 1,
        'enc_blk_nums': [1, 1, 1, 28],
        'dec_blk_nums': [1, 1, 1, 1],
    },
    'denoise': {
        'width': 32,
        'middle_blk_num': 12,
        'enc_blk_nums': [2, 2, 4, 8],
        'dec_blk_nums': [2, 2, 2, 2],
    },
}


class ModelManager:
    """Loads and caches all lite AI models on CPU."""

    def __init__(self, models_dir: str):
        self.models_dir = models_dir
        self.device = torch.device('cpu')
        self._denoise = None
        self._deblur = None
        self._upscale = None
        self._framegen = None

    # --- public lazy loaders ---
    def load_denoise(self):
        if self._denoise is not None:
            return self._denoise
        from nafnet_arch import NAFNet
        cfg = NAFNET_CONFIGS_LITE['denoise']
        model = NAFNet(img_channel=3, **cfg)
        self._load_nafnet_weights(model, os.path.join(self.models_dir, 'denoise-lite.pth'))
        self._denoise = model.to(self.device).eval()
        return self._denoise

    def load_deblur(self):
        if self._deblur is not None:
            return self._deblur
        from nafnet_arch import NAFNetLocal
        cfg = NAFNET_CONFIGS_LITE['deblur']
        model = NAFNetLocal(img_channel=3, **cfg)
        self._load_nafnet_weights(model, os.path.join(self.models_dir, 'deblur-lite.pth'))
        self._deblur = model.to(self.device).eval()
        return self._deblur

    def load_upscale(self):
        if self._upscale is not None:
            return self._upscale
        from sr_arch import SRVGGNetCompact
        model = SRVGGNetCompact(
            num_in_ch=3, num_out_ch=3, num_feat=64,
            num_conv=32, upscale=4, act_type='prelu',
        )
        path = os.path.join(self.models_dir, 'upscale-lite.pth')
        ckpt = torch.load(path, map_location='cpu', weights_only=False)
        # upscale checkpoints use 'params_ema' or 'params' or raw state_dict
        sd = ckpt.get('params_ema', ckpt.get('params', ckpt))
        sd = self._strip_state_dict({'params': sd})
        model.load_state_dict(sd)
        self._upscale = model.to(self.device).eval()
        return self._upscale

    def load_framegen(self):
        if self._framegen is not None:
            return self._framegen
        from rife_arch import RIFE
        model = RIFE(mode='lite')
        model.load_model(self.models_dir, mode='lite')
        model.eval()
        model.device()
        self._framegen = model
        return self._framegen

    # --- helpers ---
    @staticmethod
    def _strip_state_dict(ckpt):
        sd = ckpt.get('params', ckpt.get('state_dict', ckpt))
        for k in list(sd.keys()):
            if k.startswith('module.'):
                sd[k[7:]] = sd.pop(k)
        return sd

    def _load_nafnet_weights(self, model, path):
        ckpt = torch.load(path, map_location='cpu', weights_only=False)
        model.load_state_dict(self._strip_state_dict(ckpt))


# ------------------------------------------------------------------ #
#  Low-level tensor / image helpers                                   #
# ------------------------------------------------------------------ #
def img2tensor(img, device):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
    return torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(device)


def tensor2img(tensor):
    arr = tensor.squeeze(0).permute(1, 2, 0).cpu().numpy()
    arr = np.clip(arr * 255, 0, 255).astype(np.uint8)
    return cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)


def pad_image(img, modulo=32):
    h, w = img.shape[2], img.shape[3]
    new_h = ((h - 1) // modulo + 1) * modulo
    new_w = ((w - 1) // modulo + 1) * modulo
    if new_h > h or new_w > w:
        img = torch.nn.functional.pad(img, (0, new_w - w, 0, new_h - h))
    return img, (h, w)


def run_nafnet(model, img_tensor):
    with torch.no_grad():
        padded, (h, w) = pad_image(img_tensor)
        out = model(padded)
        return out[:, :, :h, :w]


def run_upscale(model, img_tensor):
    with torch.no_grad():
        padded, (h, w) = pad_image(img_tensor, modulo=4)
        out = model(padded)
        return out[:, :, : h * 4, : w * 4]


# ------------------------------------------------------------------ #
#  Image processing functions                                        #
# ------------------------------------------------------------------ #
def img_denoise(img, mm, cb=None):
    if cb: cb("Denoising...")
    m = mm.load_denoise()
    return tensor2img(run_nafnet(m, img2tensor(img, mm.device)))


def img_deblur(img, mm, cb=None):
    if cb: cb("Deblurring...")
    m = mm.load_deblur()
    return tensor2img(run_nafnet(m, img2tensor(img, mm.device)))


def img_upscale(img, mm, factor=4, cb=None):
    if cb: cb(f"Upscaling x{factor}...")
    m = mm.load_upscale()
    out = tensor2img(run_upscale(m, img2tensor(img, mm.device)))
    if factor == 2:
        h, w = out.shape[:2]
        out = cv2.resize(out, (w // 2, h // 2), interpolation=cv2.INTER_LANCZOS4)
    return out


def img_clean(img, mm, cb=None):
    img = img_denoise(img, mm, cb)
    img = img_deblur(img, mm, cb)
    return img


def img_full(img, mm, factor=4, cb=None):
    img = img_denoise(img, mm, cb)
    img = img_deblur(img, mm, cb)
    img = img_upscale(img, mm, factor, cb)
    return img


IMAGE_FUNCS = {
    'denoise': lambda img, mm, cb, f: img_denoise(img, mm, cb),
    'deblur':  lambda img, mm, cb, f: img_deblur(img, mm, cb),
    'upscale': lambda img, mm, cb, f: img_upscale(img, mm, f, cb),
    'clean':   lambda img, mm, cb, f: img_clean(img, mm, cb),
    'full':    lambda img, mm, cb, f: img_full(img, mm, f, cb),
}


# ------------------------------------------------------------------ #
#  Video helpers                                                     #
# ------------------------------------------------------------------ #
def _run_ffmpeg(cmd, label="ffmpeg"):
    """Run an ffmpeg command and return (returncode, stderr_text).

    Logs stderr on failure and returns details so callers can raise
    meaningful errors instead of a bare CalledProcessError.
    """
    log.info("Running: %s", " ".join(cmd))
    r = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
    if r.returncode != 0:
        stderr_snip = (r.stderr or "").strip().splitlines()[-3:]  # last 3 lines
        log.error("%s failed (rc=%d):\n%s", label, r.returncode, r.stderr or "")
        raise RuntimeError(
            f"{label} failed (exit code {r.returncode}). "
            f"Last ffmpeg output:\n" + "\n".join(stderr_snip)
        )
    return r


def ensure_ffmpeg():
    if shutil.which('ffmpeg') is None:
        raise RuntimeError("ffmpeg is not installed on this Space.")


def video_info(path):
    cap = cv2.VideoCapture(path)
    if not cap.isOpened():
        cap.release()
        raise RuntimeError(f"Cannot open video: {path}")
    fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
    count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    cap.release()
    return fps, count, w, h


def extract_frames(video_path, out_dir):
    os.makedirs(out_dir, exist_ok=True)
    cmd = ['ffmpeg', '-y', '-i', video_path, '-vsync', '0',
           os.path.join(out_dir, '%08d.png')]
    _run_ffmpeg(cmd, label="Frame extraction")
    frames = sorted(f for f in os.listdir(out_dir) if f.endswith('.png'))
    if not frames:
        raise RuntimeError(
            f"Frame extraction produced 0 frames. The video may be empty or unsupported."
        )
    log.info("Extracted %d frames", len(frames))
    return frames


def extract_audio(video_path, audio_path):
    cmd = ['ffmpeg', '-y', '-i', video_path, '-vn', '-acodec', 'copy', audio_path]
    try:
        r = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
        if r.returncode == 0 and os.path.isfile(audio_path):
            log.info("Audio extracted OK")
            return True
    except Exception as e:
        log.warning("Audio extraction failed (non-fatal): %s", e)
    # Audio extraction is non-fatal — video processing continues without audio
    return False


def frames_to_video(frames_dir, out_path, fps, audio_path=None):
    tmp = out_path + '_temp.mp4'
    cmd = ['ffmpeg', '-y', '-framerate', str(fps),
           '-i', os.path.join(frames_dir, '%08d.png'),
           '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '18', tmp]
    _run_ffmpeg(cmd, label="Video compilation")
    if audio_path and os.path.isfile(audio_path):
        cmd2 = ['ffmpeg', '-y', '-i', tmp, '-i', audio_path,
                '-c:v', 'copy', '-c:a', 'aac',
                '-map', '0:v:0', '-map', '1:a:0?', out_path]
        r2 = subprocess.run(cmd2, capture_output=True, text=True, timeout=300)
        if r2.returncode == 0 and os.path.isfile(out_path):
            os.remove(tmp)
            return
        log.warning("Audio merge failed (rc=%d), using video without audio", r2.returncode)
    # Fallback: use video-only
    if os.path.exists(tmp):
        if os.path.exists(out_path):
            os.remove(out_path)
        os.rename(tmp, out_path)


def process_video_frames(src_dir, dst_dir, frames, label, func, cb=None, cancel_event=None):
    os.makedirs(dst_dir, exist_ok=True)
    total = len(frames)
    for i, fname in enumerate(frames):
        if cancel_event and cancel_event.is_set():
            raise RuntimeError("Processing cancelled")
        if cb:
            cb(f"{label} — frame {i + 1}/{total}")
        img = cv2.imread(os.path.join(src_dir, fname))
        if img is None:
            raise RuntimeError(
                f"Failed to read frame {fname} from {src_dir}. "
                f"The frame file may be corrupted."
            )
        img = func(img)
        cv2.imwrite(os.path.join(dst_dir, fname), img)


# ------------------------------------------------------------------ #
#  RIFE frame generation                                              #
# ------------------------------------------------------------------ #
def pad_for_rife(img, scale=1.0):
    div = max(64, int(64 / scale))
    h, w = img.shape[:2]
    nh = ((h - 1) // div + 1) * div
    nw = ((w - 1) // div + 1) * div
    if nh > h or nw > w:
        img = np.pad(img, ((0, nh - h), (0, nw - w), (0, 0)), mode='edge')
    return img, (h, w)


def generate_frames(src_dir, dst_dir, multi, mm, cb=None, cancel_event=None):
    model = mm.load_framegen()
    frames = sorted(f for f in os.listdir(src_dir) if f.endswith('.png'))
    if len(frames) < 2:
        raise ValueError("Need at least 2 frames for interpolation.")
    os.makedirs(dst_dir, exist_ok=True)
    idx = 0
    for i in range(len(frames) - 1):
        if cancel_event and cancel_event.is_set():
            raise RuntimeError("Processing cancelled")
        if cb:
            cb(f"Interpolating — pair {i + 1}/{len(frames) - 1}")
        img0 = cv2.imread(os.path.join(src_dir, frames[i]))
        img1 = cv2.imread(os.path.join(src_dir, frames[i + 1]))
        img0, (oh, ow) = pad_for_rife(img0)
        img1, _ = pad_for_rife(img1)
        t0 = img2tensor(img0, mm.device)
        t1 = img2tensor(img1, mm.device)
        cv2.imwrite(os.path.join(dst_dir, f'{idx:08d}.png'), img0[:oh, :ow])
        idx += 1
        for j in range(multi - 1):
            ts = (j + 1) / multi
            with torch.no_grad():
                mid = model.inference(t0, t1, ts, 1.0)
            cv2.imwrite(os.path.join(dst_dir, f'{idx:08d}.png'), tensor2img(mid)[:oh, :ow])
            idx += 1
    last = cv2.imread(os.path.join(src_dir, frames[-1]))
    cv2.imwrite(os.path.join(dst_dir, f'{idx:08d}.png'), last)
    return idx + 1


def blend_frames_for_fps(frames_dir, target_fps, generated_fps, cb=None):
    """Blend generated frames down to a lower target FPS using ffmpeg minterpolate.

    Matches the desktop Klarity behaviour: when the user requests a target FPS
    that is below the generated (max) FPS, frames are blended to produce a
    smooth video at the requested rate.
    """
    if generated_fps <= 0:
        return frames_dir
    ratio = target_fps / generated_fps
    if ratio >= 0.99:
        # No meaningful difference, skip blending
        return frames_dir
    blended_dir = frames_dir + '_blended'
    os.makedirs(blended_dir, exist_ok=True)
    if cb:
        cb(f"Blending frames to {target_fps:.1f} FPS...")
    cmd = [
        'ffmpeg', '-y',
        '-framerate', str(generated_fps),
        '-i', os.path.join(frames_dir, '%08d.png'),
        '-vf', f'minterpolate=fps={target_fps:.2f}:mi_mode=blend',
        '-vsync', '0',
        os.path.join(blended_dir, '%08d.png'),
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)
    if result.returncode == 0 and os.path.exists(blended_dir):
        blended_frames = sorted(f for f in os.listdir(blended_dir) if f.endswith('.png'))
        if len(blended_frames) > 0:
            return blended_dir
    # Fallback: if blending failed, return original
    log.warning("Frame blending failed, using generated frames as-is")
    if os.path.exists(blended_dir):
        shutil.rmtree(blended_dir)
    return frames_dir


def clamp_fps(fps, orig_fps, multi):
    """Clamp user-provided FPS to valid range [min, max] matching desktop Klarity.

    - fps is None  -> use max_fps (default)
    - fps < min_fps -> warning + use max_fps
    - fps > max_fps -> warning + use max_fps
    - otherwise      -> use fps as-is

    Returns the clamped FPS value.
    """
    min_fps = orig_fps
    max_fps = orig_fps * multi
    if fps is None:
        return max_fps
    if fps < min_fps:
        log.warning(
            "Target FPS %.2f below minimum (%.2f). Using max: %.2f",
            fps, min_fps, max_fps,
        )
        return max_fps
    if fps > max_fps:
        log.warning(
            "Target FPS %.2f exceeds maximum (%.2f). Using max: %.2f",
            fps, max_fps, max_fps,
        )
        return max_fps
    return fps


# ------------------------------------------------------------------ #
#  High-level process dispatcher                                      #
# ------------------------------------------------------------------ #
def get_supported_modes(path):
    ext = Path(path).suffix.lower()
    if ext in IMAGE_EXTENSIONS:
        return ['denoise', 'deblur', 'upscale', 'clean', 'full']
    if ext in VIDEO_EXTENSIONS:
        return ['denoise', 'deblur', 'upscale', 'clean', 'full',
                'frame-gen', 'clean-frame-gen', 'full-frame-gen']
    return []


MODE_SETTINGS = {
    'denoise':          {'upscale': False, 'multi': False, 'fps': False},
    'deblur':           {'upscale': False, 'multi': False, 'fps': False},
    'upscale':          {'upscale': True,  'multi': False, 'fps': False},
    'clean':            {'upscale': False, 'multi': False, 'fps': False},
    'full':             {'upscale': True,  'multi': False, 'fps': False},
    'frame-gen':        {'upscale': False, 'multi': True,  'fps': True},
    'clean-frame-gen':  {'upscale': False, 'multi': True,  'fps': True},
    'full-frame-gen':   {'upscale': True,  'multi': True,  'fps': True},
}

MODE_SUFFIXES = {
    'denoise': '_denoised', 'deblur': '_deblurred',
    'upscale': '_upscaled', 'clean': '_cleaned', 'full': '_enhanced',
    'frame-gen': '_generated',
    'clean-frame-gen': '_clean_generated', 'full-frame-gen': '_full_enhanced',
}


def process_file(input_path, mode, mm, out_dir, *,
                 upscale_factor=4, multi=2, fps=None, cb=None, cancel_event=None):
    ext = Path(input_path).suffix.lower()
    # If extension not recognized, try MIME-based detection
    if ext not in IMAGE_EXTENSIONS and ext not in VIDEO_EXTENSIONS:
        import mimetypes
        mime, _ = mimetypes.guess_type(input_path)
        log.warning("Unrecognized extension '%s', MIME: %s", ext, mime)
        if mime:
            if mime.startswith('image/'):
                return _proc_image(input_path, mode, mm, out_dir, upscale_factor, cb, cancel_event)
            if mime.startswith('video/'):
                return _proc_video(input_path, mode, mm, out_dir, upscale_factor, multi, fps, cb, cancel_event)
        raise ValueError(f"Unsupported format: {ext} (MIME: {mime})")
    if ext in IMAGE_EXTENSIONS:
        return _proc_image(input_path, mode, mm, out_dir, upscale_factor, cb, cancel_event)
    return _proc_video(input_path, mode, mm, out_dir, upscale_factor, multi, fps, cb, cancel_event)


def _proc_image(path, mode, mm, out_dir, uf, cb, cancel_event=None):
    if cb: cb("Reading image…")
    img = cv2.imread(path)
    if img is None:
        raise ValueError(f"Cannot read image: {path}")
    if cb: cb(f"Processing ({mode})…")
    result = IMAGE_FUNCS[mode](img, mm, cb, uf)
    name = Path(path).stem + MODE_SUFFIXES.get(mode, '_processed') + Path(path).suffix
    out = os.path.join(out_dir, name)
    cv2.imwrite(out, result)
    return {'type': 'image', 'before': path, 'after': out}


def _proc_video(path, mode, mm, out_dir, uf, multi, fps, cb, cancel_event=None):
    log.info("_proc_video: path=%s mode=%s uf=%s multi=%s fps=%s",
             os.path.basename(path), mode, uf, multi, fps)
    ensure_ffmpeg()
    orig_fps, frame_count, w, h = video_info(path)
    log.info("Video info: %.2f FPS, %d frames, %dx%d", orig_fps, frame_count, w, h)
    tmp = os.path.join(out_dir, 'tmp')
    if os.path.exists(tmp):
        shutil.rmtree(tmp)
    os.makedirs(tmp)

    orig_exc = None
    try:
        frames_dir = os.path.join(tmp, 'frames')
        audio_path = os.path.join(tmp, 'audio.aac')

        log.info("Step 1: Extracting frames from %s", os.path.basename(path))
        if cancel_event and cancel_event.is_set():
            raise RuntimeError("Processing cancelled")
        if cb: cb("Extracting frames…")
        frames = extract_frames(path, frames_dir)
        log.info("Step 2: Extracting audio")
        extract_audio(path, audio_path)
        if cancel_event and cancel_event.is_set():
            raise RuntimeError("Processing cancelled")

        fg_modes = {'frame-gen', 'clean-frame-gen', 'full-frame-gen'}

        if mode in fg_modes:
            cur = frames_dir
            if mode in ('clean-frame-gen', 'full-frame-gen'):
                log.info("Step 3a: Denoising %d frames", len(frames))
                d = os.path.join(tmp, 'denoised')
                process_video_frames(cur, d, frames, "Denoising",
                                     lambda img: img_denoise(img, mm), cancel_event=cancel_event)
                cur = d
                log.info("Step 3b: Deblurring %d frames", len(frames))
                d = os.path.join(tmp, 'cleaned')
                process_video_frames(cur, d, frames, "Deblurring",
                                     lambda img: img_deblur(img, mm), cancel_event=cancel_event)
                cur = d
            if mode == 'full-frame-gen':
                log.info("Step 3c: Upscaling x%d, %d frames", uf, len(frames))
                d = os.path.join(tmp, 'upscaled')
                process_video_frames(cur, d, frames, f"Upscaling x{uf}",
                    lambda img: img_upscale(img, mm, uf), cancel_event=cancel_event)
                cur = d
                frames = sorted(f for f in os.listdir(cur) if f.endswith('.png'))
            max_fps = orig_fps * multi
            target_fps = clamp_fps(fps, orig_fps, multi)
            log.info("Step 4: Frame generation (multi=%d, target_fps=%.1f)", multi, target_fps)
            if cb: cb("Generating frames…")
            gen = os.path.join(tmp, 'generated')
            generate_frames(cur, gen, multi, mm, cancel_event=cancel_event)
            final_frames = gen
            if target_fps < max_fps:
                log.info("Step 5: Blending to %.1f FPS", target_fps)
                final_frames = blend_frames_for_fps(gen, target_fps, max_fps, cb)
            log.info("Step 6: Compiling video at %.1f FPS", target_fps)
            if cb: cb("Compiling video…")
            name = Path(path).stem + MODE_SUFFIXES.get(mode, '_processed') + '.mp4'
            out = os.path.join(out_dir, name)
            frames_to_video(final_frames, out, target_fps,
                            audio_path if os.path.exists(audio_path) else None)
        else:
            steps = []
            if mode in ('denoise', 'clean', 'full'):
                steps.append(("Denoising", lambda img, c=None: img_denoise(img, mm, c)))
            if mode in ('deblur', 'clean', 'full'):
                steps.append(("Deblurring", lambda img, c=None: img_deblur(img, mm, c)))
            if mode in ('upscale', 'full'):
                steps.append((f"Upscaling x{uf}", lambda img, c=None: img_upscale(img, mm, uf, c)))
            cur = frames_dir
            for step_i, (label, fn) in enumerate(steps):
                d = os.path.join(tmp, f'step_{step_i}')
                log.info("Step 3.%d: %s (%d frames)", step_i, label, len(frames))
                process_video_frames(cur, d, frames, label, fn, cancel_event=cancel_event)
                cur = d
            log.info("Step 4: Compiling video at %.2f FPS", orig_fps)
            if cb: cb("Compiling video…")
            name = Path(path).stem + MODE_SUFFIXES.get(mode, '_processed') + '.mp4'
            out = os.path.join(out_dir, name)
            frames_to_video(cur, out, orig_fps,
                            audio_path if os.path.exists(audio_path) else None)
        log.info("Video processing complete: %s", out)
        return {'type': 'video', 'before': path, 'after': out}
    except Exception as e:
        orig_exc = e
        raise
    finally:
        # Cleanup tmp — but never let cleanup errors mask the real error
        try:
            if os.path.exists(tmp):
                shutil.rmtree(tmp)
        except Exception as cleanup_err:
            log.error("Cleanup failed (non-fatal): %s", cleanup_err)
            if orig_exc is None:
                # Only re-raise cleanup error if there was no original error
                raise


def is_image(path):
    return Path(path).suffix.lower() in IMAGE_EXTENSIONS

def is_video(path):
    return Path(path).suffix.lower() in VIDEO_EXTENSIONS