"""PSM: Phoneme-Synchrony Manifold.

Hypothesis
----------
Phonemes are physical instructions on the vocal tract. Articulation of /b/
requires bilabial closure, /f/ requires labiodental contact, etc. The
mapping { phoneme -> lip shape } is largely *speaker-independent*; the same
phoneme by different speakers yields similar lip shapes up to a small
identity-style variation.

If we learn an embedding space where (phoneme, lip_shape) pairs from REAL
videos lie on a compact manifold, then fake videos — whose generators
memorize audio->lip correlations but may violate the physical constraint
in subtle ways — will lie farther off-manifold on average.

Advantages over 'one-class real distribution learning'
------------------------------------------------------
We are NOT learning the full real joint. We are learning only the
*phoneme-conditional* distribution. Quality / domain differences across
CelebV-HQ / DFDC / HDTF mostly leak through identity and texture, not
through the phoneme-lip relation, so the conditional is far more robust.

Implementation
--------------
  phoneme feats: wav2vec2-lv-60-espeak-cv-ft  (CTC phoneme model, frozen)
  lip feats:     MediaPipe lip crop -> DINO ViT-S  (half trainable)
  projection head (MLP) -> 256-D manifold

Contrastive loss: anchor = phoneme token; positive = temporally-aligned
lip token; negatives = lip tokens from the same batch from other clips
AND a momentum memory bank.

Classifier: at inference we score the average per-frame (phoneme, lip)
compatibility. High compatibility => real; low => fake.
"""
from __future__ import annotations

from typing import Any, Dict, List, Optional

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from .base import BaseMethod


# -----------------------------------------------------------------------------
# phoneme encoder (wav2vec2 frozen)
# -----------------------------------------------------------------------------
class PhonemeEncoder(nn.Module):
    def __init__(self, hf_id: str, freeze: bool = True):
        super().__init__()
        from transformers import Wav2Vec2Model
        self.model = Wav2Vec2Model.from_pretrained(hf_id)
        self.feature_dim = self.model.config.hidden_size
        if freeze:
            for p in self.model.parameters():
                p.requires_grad_(False)
            self.model.eval()

    def forward(self, wav: torch.Tensor) -> torch.Tensor:
        with torch.no_grad() if not any(p.requires_grad for p in self.model.parameters()) else torch.enable_grad():
            out = self.model(wav)
        return out.last_hidden_state       # (B, T_a, D)


# -----------------------------------------------------------------------------
# lip crop (MediaPipe lazily, CPU) + ViT image encoder
# -----------------------------------------------------------------------------
class MediaPipeLipCropper:
    """Run MediaPipe once per forward to get lip bbox per frame.

    Falls back to center crop if MediaPipe unavailable or no face found.
    Lazy-initialized per worker to be DataLoader-safe.
    """
    _LIP_LANDMARKS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291,
                      308, 324, 318, 402, 317, 14, 87, 178, 88, 95]

    def __init__(self, output_size: int = 96):
        self.output_size = output_size
        self._mp = None

    def _ensure(self):
        if self._mp is None:
            try:
                import mediapipe as mp
                self._mp = mp.solutions.face_mesh.FaceMesh(
                    static_image_mode=False, max_num_faces=1, refine_landmarks=False,
                )
            except Exception:
                self._mp = False  # mark as unavailable

    def crop_frame(self, frame: np.ndarray) -> np.ndarray:
        """frame: (H, W, 3) uint8 RGB. Returns (out, out, 3) uint8 RGB."""
        self._ensure()
        H, W = frame.shape[:2]
        if self._mp is False:
            return self._center(frame)
        res = self._mp.process(frame)
        if not res.multi_face_landmarks:
            return self._center(frame)
        lm = res.multi_face_landmarks[0].landmark
        xs = np.array([lm[i].x for i in self._LIP_LANDMARKS]) * W
        ys = np.array([lm[i].y for i in self._LIP_LANDMARKS]) * H
        x0, x1 = xs.min(), xs.max(); y0, y1 = ys.min(), ys.max()
        cx, cy = (x0 + x1) / 2, (y0 + y1) / 2
        # square, with some margin
        side = max(x1 - x0, y1 - y0) * 1.8
        x0 = int(max(0, cx - side / 2)); x1 = int(min(W, cx + side / 2))
        y0 = int(max(0, cy - side / 2)); y1 = int(min(H, cy + side / 2))
        crop = frame[y0:y1, x0:x1]
        if crop.size == 0:
            return self._center(frame)
        import cv2
        return cv2.resize(crop, (self.output_size, self.output_size))

    def _center(self, frame: np.ndarray) -> np.ndarray:
        import cv2
        H, W = frame.shape[:2]
        s = min(H, W) // 2
        cy, cx = H // 2, W // 2
        crop = frame[cy - s:cy + s, cx - s:cx + s]
        return cv2.resize(crop, (self.output_size, self.output_size))


class LipEncoder(nn.Module):
    """MediaPipe lip crop -> DINO ViT-S. Trainable top half."""

    def __init__(self, vit_hf_id: str, freeze_ratio: float = 0.5, lip_size: int = 96):
        super().__init__()
        from transformers import AutoModel
        self.vit = AutoModel.from_pretrained(vit_hf_id)
        self.feature_dim = self.vit.config.hidden_size
        self.lip_size = lip_size
        if freeze_ratio > 0:
            layers = self.vit.encoder.layer
            n = int(len(layers) * freeze_ratio)
            for layer in layers[:n]:
                for p in layer.parameters():
                    p.requires_grad_(False)

    def forward(self, lip_imgs: torch.Tensor) -> torch.Tensor:
        """lip_imgs: (B, T, 3, lip_size, lip_size) already normalized.
        Returns (B, T, D)."""
        B, T, C, H, W = lip_imgs.shape
        x = lip_imgs.reshape(B * T, C, H, W)
        # ViT expects 224; upsample if needed
        if H != 224:
            x = F.interpolate(x, size=(224, 224), mode="bilinear", align_corners=False)
        out = self.vit(pixel_values=x)
        tok = out.last_hidden_state[:, 0]   # CLS token
        return tok.view(B, T, -1)


# -----------------------------------------------------------------------------
# manifold projection + classifier
# -----------------------------------------------------------------------------
class ProjectionHead(nn.Module):
    def __init__(self, in_dim: int, out_dim: int):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(in_dim, in_dim), nn.GELU(), nn.Linear(in_dim, out_dim),
        )

    def forward(self, x): return F.normalize(self.net(x), dim=-1)


# -----------------------------------------------------------------------------
# Lightning module
# -----------------------------------------------------------------------------
class PSMLitModule(BaseMethod):
    def __init__(self, method_cfg, backbone_cfg, data_cfg):
        super().__init__(method_cfg=method_cfg, backbone_cfg=backbone_cfg, data_cfg=data_cfg)

        self.phoneme = PhonemeEncoder(
            hf_id=method_cfg.phoneme_encoder.hf_id,
            freeze=method_cfg.phoneme_encoder.freeze,
        )
        self.lip = LipEncoder(
            vit_hf_id=method_cfg.lip_encoder.vit_hf_id,
            freeze_ratio=method_cfg.lip_encoder.freeze_ratio,
        )

        pd = method_cfg.manifold.projection_dim
        self.proj_p = ProjectionHead(self.phoneme.feature_dim, pd)
        self.proj_l = ProjectionHead(self.lip.feature_dim, pd)

        # classifier on [compat, lip_pool, phon_pool]
        self.cls = nn.Sequential(
            nn.Linear(pd * 2 + 1, method_cfg.classifier.hidden),
            nn.GELU(),
            nn.Dropout(method_cfg.classifier.dropout),
            nn.Linear(method_cfg.classifier.hidden, 1),
        )

        self.register_buffer(
            "mem_bank",
            F.normalize(torch.randn(method_cfg.manifold.num_negatives, pd), dim=-1),
        )
        self.register_buffer("mem_ptr", torch.zeros(1, dtype=torch.long))

        # MediaPipe cropper runs on tensors in _prepare_lip_tensor below;
        # for simplicity we use center-crop in this initial version and
        # expose a hook to plug in MediaPipe at dataloader level.

    # ------------------------------------------------------------------
    @staticmethod
    def _resample_to_match(src: torch.Tensor, target_T: int) -> torch.Tensor:
        """Linear interpolation of token sequence along T dim."""
        B, T, D = src.shape
        x = src.transpose(1, 2)   # (B, D, T)
        x = F.interpolate(x, size=target_T, mode="linear", align_corners=False)
        return x.transpose(1, 2)  # (B, target_T, D)

    def _lip_from_video(self, video: torch.Tensor) -> torch.Tensor:
        """Produce lip crops from a normalized video tensor.

        Initial version: center crop the bottom-half of the frame (lip area
        heuristic). A more accurate version using MediaPipe runs in the
        dataloader; see TODO below.
        """
        B, T, C, H, W = video.shape
        # crop bottom half then rightmost 80% of width
        crop = video[..., H // 2:, :]
        # resize to 96 for lip encoder-friendly size
        crop = F.interpolate(
            crop.reshape(B * T, C, crop.shape[-2], crop.shape[-1]),
            size=(96, 96), mode="bilinear", align_corners=False,
        ).view(B, T, C, 96, 96)
        return crop

    # ------------------------------------------------------------------
    @torch.no_grad()
    def _enqueue(self, feats: torch.Tensor):
        K = self.mem_bank.size(0)
        n = feats.size(0)
        ptr = int(self.mem_ptr.item())
        if ptr + n > K:
            first = K - ptr
            self.mem_bank[ptr:] = feats[:first]
            self.mem_bank[:n - first] = feats[first:]
            self.mem_ptr[0] = (n - first) % K
        else:
            self.mem_bank[ptr:ptr + n] = feats
            self.mem_ptr[0] = (ptr + n) % K

    # ------------------------------------------------------------------
    def forward_feats(self, video, audio):
        ph = self.phoneme(audio)                 # (B, T_a, Dp)
        lip_imgs = self._lip_from_video(video)   # (B, T_v, 3, 96, 96)
        li = self.lip(lip_imgs)                  # (B, T_v, Dl)

        # resample phoneme to video's T
        T_v = li.shape[1]
        ph_r = self._resample_to_match(ph, T_v)

        zp = self.proj_p(ph_r)                   # (B, T_v, pd) unit
        zl = self.proj_l(li)
        return zp, zl

    # ------------------------------------------------------------------
    def _compatibility(self, zp, zl) -> torch.Tensor:
        """Per-sample mean cosine similarity between aligned phoneme & lip
        embeddings. Higher => more on-manifold => more real."""
        return (zp * zl).sum(dim=-1).mean(dim=1)   # (B,)

    def training_step(self, batch, batch_idx):
        if batch is None:
            return None
        video = batch["video"]; audio = batch["audio"]
        labels = batch["label"].long()

        zp, zl = self.forward_feats(video, audio)
        B, T, D = zp.shape
        zp_flat = zp.reshape(B * T, D)
        zl_flat = zl.reshape(B * T, D)

        # contrastive on REAL only
        is_real = (labels == 0)
        if is_real.any():
            rmask = is_real.unsqueeze(1).expand(-1, T).reshape(-1)
            zp_r = zp_flat[rmask]; zl_r = zl_flat[rmask]
            neg_bank = self.mem_bank                # (K, D)
            logits = torch.cat([
                (zp_r * zl_r).sum(-1, keepdim=True),        # positives
                zp_r @ neg_bank.t(),                         # negatives
            ], dim=1)                                         # (N, 1+K)
            logits = logits / self.method_cfg.manifold.temperature
            target = torch.zeros(logits.size(0), dtype=torch.long, device=logits.device)
            loss_con = F.cross_entropy(logits, target)
            with torch.no_grad():
                self._enqueue(zl_r.detach())
        else:
            loss_con = zp.new_zeros([])

        # consistency: neighbouring frames' compatibility should be smooth
        compat = (zp * zl).sum(dim=-1)            # (B, T)
        loss_cons = ((compat[:, 1:] - compat[:, :-1]) ** 2).mean()

        # classifier
        feat_cls = torch.cat([
            zp.mean(1), zl.mean(1), compat.mean(1, keepdim=True),
        ], dim=-1)
        logits_cls = self.cls(feat_cls)
        loss_cls = F.binary_cross_entropy_with_logits(
            logits_cls.squeeze(-1), labels.float(),
        )

        loss = (
            self.method_cfg.loss.contrastive_weight * loss_con
            + self.method_cfg.loss.consistency_weight * loss_cons
            + self.method_cfg.loss.cls_weight * loss_cls
        )

        self.log_dict({
            "train/loss": loss,
            "train/loss_con": loss_con,
            "train/loss_cons": loss_cons,
            "train/loss_cls": loss_cls,
        }, on_step=True, on_epoch=True, sync_dist=True)
        return loss

    @torch.no_grad()
    def score(self, batch: Dict[str, Any]) -> torch.Tensor:
        zp, zl = self.forward_feats(batch["video"], batch["audio"])
        compat = (zp * zl).sum(dim=-1)
        feat_cls = torch.cat([
            zp.mean(1), zl.mean(1), compat.mean(1, keepdim=True),
        ], dim=-1)
        logits = self.cls(feat_cls)
        return torch.sigmoid(logits.squeeze(-1))