"""
Combined training loss with Human-Pattern Term:

L_total = L_CE + λ₁ · L_style + λ₂ · L_semantic + λ₃ · L_human_pattern

Where:
  L_CE       = cross-entropy language model loss (standard token prediction)
  L_style    = style consistency loss (cosine distance between output and target style vectors)
  L_semantic = semantic similarity loss (cosine distance between sentence embeddings)
  L_human_pattern = 1 - HumanPatternClassifier.score(output_text)
  λ₁         = style loss weight (default 0.3)
  λ₂         = semantic loss weight (default 0.5)
  λ₃         = human pattern weight (default 0.4)
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
from typing import Optional, List, Dict
from loguru import logger


class CombinedCorrectionLoss(nn.Module):
    """V1 combined loss: L_CE + λ₁·L_style + λ₂·L_semantic."""

    def __init__(
        self,
        lambda_style: float = 0.3,
        lambda_semantic: float = 0.5,
        sem_model_name: str = "all-mpnet-base-v2",
        device: str = "cpu",
    ):
        super().__init__()
        self.lambda_style = lambda_style
        self.lambda_semantic = lambda_semantic
        self.device = device

        # Cross-entropy loss
        self.ce_loss = nn.CrossEntropyLoss(ignore_index=-100)

        # Frozen sentence transformer for semantic similarity
        logger.info(f"Loading sentence transformer for loss: {sem_model_name}")
        self.sem_model = SentenceTransformer(sem_model_name, device=device)
        self.sem_model.eval()
        # Freeze sentence transformer weights
        for param in self.sem_model.parameters():
            param.requires_grad = False

    def _style_loss(
        self,
        output_style_vec: torch.Tensor,
        target_style_vec: torch.Tensor,
    ) -> torch.Tensor:
        """1 - cosine_similarity(output_style, target_style)."""
        if output_style_vec.dim() == 1:
            output_style_vec = output_style_vec.unsqueeze(0)
        if target_style_vec.dim() == 1:
            target_style_vec = target_style_vec.unsqueeze(0)
        cos_sim = F.cosine_similarity(output_style_vec, target_style_vec, dim=-1)
        return (1.0 - cos_sim).mean()

    def _semantic_loss(
        self,
        input_texts: List[str],
        output_texts: List[str],
    ) -> torch.Tensor:
        """Penalises meaning change between input and output."""
        with torch.no_grad():
            input_embeddings = self.sem_model.encode(input_texts, convert_to_tensor=True)
            output_embeddings = self.sem_model.encode(output_texts, convert_to_tensor=True)

        cos_sim = F.cosine_similarity(input_embeddings, output_embeddings, dim=-1)
        # Loss = 1 - similarity (we want high similarity = low loss)
        return (1.0 - cos_sim).mean()

    def forward(
        self,
        logits: torch.Tensor,
        labels: torch.Tensor,
        output_style_vec: Optional[torch.Tensor] = None,
        target_style_vec: Optional[torch.Tensor] = None,
        input_texts: Optional[List[str]] = None,
        output_texts: Optional[List[str]] = None,
    ) -> Dict[str, torch.Tensor]:
        """Compute combined loss."""
        losses = {}

        # L_CE: cross-entropy
        # logits: [batch, seq_len, vocab_size]
        # labels: [batch, seq_len]
        if logits.dim() == 3:
            ce_logits = logits.view(-1, logits.size(-1))
            ce_labels = labels.view(-1)
        else:
            ce_logits = logits
            ce_labels = labels
        l_ce = self.ce_loss(ce_logits, ce_labels)
        losses["ce_loss"] = l_ce

        total = l_ce

        # L_style
        if output_style_vec is not None and target_style_vec is not None:
            l_style = self._style_loss(output_style_vec, target_style_vec)
            losses["style_loss"] = l_style
            total = total + self.lambda_style * l_style

        # L_semantic
        if input_texts is not None and output_texts is not None:
            l_semantic = self._semantic_loss(input_texts, output_texts)
            losses["semantic_loss"] = l_semantic
            total = total + self.lambda_semantic * l_semantic

        losses["total_loss"] = total
        return losses


class CombinedCorrectionLossV2(nn.Module):
    """V2 combined loss with human-pattern term: L_CE + λ₁·L_style + λ₂·L_semantic + λ₃·L_human_pattern."""

    def __init__(
        self,
        lambda_style: float = 0.3,
        lambda_semantic: float = 0.5,
        lambda_human_pattern: float = 0.4,
        classifier_path: str = "checkpoints/human_pattern_classifier.pt",
        sem_model_name: str = "all-mpnet-base-v2",
        device: str = "cpu",
    ):
        super().__init__()
        self.lambda_style = lambda_style
        self.lambda_semantic = lambda_semantic
        self.lambda_human_pattern = lambda_human_pattern
        self.device = device

        # V1 components
        self.ce_loss = nn.CrossEntropyLoss(ignore_index=-100)

        # Sentence transformer on CPU to save GPU VRAM for main model
        logger.info(f"Loading sentence transformer for loss: {sem_model_name} (on CPU)")
        self.sem_model = SentenceTransformer(sem_model_name, device="cpu")
        self.sem_model.eval()

        # Load frozen human pattern classifier
        from .human_pattern_extractor import HumanPatternClassifier, HumanPatternFeatureExtractor
        self.hp_classifier = HumanPatternClassifier()
        try:
            state_dict = torch.load(classifier_path, map_location=device, weights_only=True)
            self.hp_classifier.load_state_dict(state_dict)
            logger.info(f"Loaded human pattern classifier from {classifier_path}")
        except FileNotFoundError:
            logger.warning(f"Human pattern classifier not found at {classifier_path}, using random weights")

        self.hp_classifier.eval()
        for param in self.hp_classifier.parameters():
            param.requires_grad = False

        # Feature extractor on CPU to save GPU VRAM for main model
        self.hp_extractor = HumanPatternFeatureExtractor(device="cpu")

    def _human_pattern_loss(self, output_texts: List[str], compute_device: torch.device = None) -> torch.Tensor:
        """Loss = 1 - human_score. Penalise AI-like outputs."""
        scores = []
        for text in output_texts:
            score = self.hp_classifier.score(text, self.hp_extractor)
            scores.append(score)
        device = compute_device if compute_device is not None else self.device
        human_scores = torch.tensor(scores, dtype=torch.float32, device=device)
        return (1.0 - human_scores).mean()

    def forward(
        self,
        logits: torch.Tensor,
        labels: torch.Tensor,
        output_style_vec: Optional[torch.Tensor] = None,
        target_style_vec: Optional[torch.Tensor] = None,
        input_texts: Optional[List[str]] = None,
        output_texts: Optional[List[str]] = None,
    ) -> Dict[str, torch.Tensor]:
        """Compute combined loss with human pattern term."""
        losses = {}

        # L_CE
        if logits.dim() == 3:
            ce_logits = logits.view(-1, logits.size(-1))
            ce_labels = labels.view(-1)
        else:
            ce_logits = logits
            ce_labels = labels
        l_ce = self.ce_loss(ce_logits, ce_labels)
        losses["ce_loss"] = l_ce
        total = l_ce

        # L_style
        if output_style_vec is not None and target_style_vec is not None:
            # Ensure both vectors are on the same device (style vecs may come from CPU fingerprinter)
            compute_device = logits.device
            output_style_vec = output_style_vec.to(compute_device)
            target_style_vec = target_style_vec.to(compute_device)
            if output_style_vec.dim() == 1:
                output_style_vec = output_style_vec.unsqueeze(0)
            if target_style_vec.dim() == 1:
                target_style_vec = target_style_vec.unsqueeze(0)
            cos_sim = F.cosine_similarity(output_style_vec, target_style_vec, dim=-1)
            l_style = (1.0 - cos_sim).mean()
            losses["style_loss"] = l_style
            total = total + self.lambda_style * l_style

        # L_semantic
        if input_texts is not None and output_texts is not None:
            with torch.no_grad():
                input_emb = self.sem_model.encode(input_texts, convert_to_tensor=True)
                output_emb = self.sem_model.encode(output_texts, convert_to_tensor=True)
            # sem_model is on CPU, move embeddings to compute device
            input_emb = input_emb.to(logits.device)
            output_emb = output_emb.to(logits.device)
            cos_sim = F.cosine_similarity(input_emb, output_emb, dim=-1)
            l_semantic = (1.0 - cos_sim).mean()
            losses["semantic_loss"] = l_semantic
            total = total + self.lambda_semantic * l_semantic

        # L_human_pattern
        if output_texts is not None:
            l_human = self._human_pattern_loss(output_texts, compute_device=logits.device)
            losses["human_pattern_loss"] = l_human
            total = total + self.lambda_human_pattern * l_human

        losses["total_loss"] = total
        return losses