"""
Router Model Architecture for Smart ASR Routing.

Regression-based approach: predicts WER for each backend model.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Optional, Dict

from transformers import PreTrainedModel, PretrainedConfig, WhisperModel, WhisperFeatureExtractor
from transformers.modeling_outputs import ModelOutput


class AttentionPooling(nn.Module):
    """Learnable attention pooling for variable-length sequences."""

    def __init__(self, input_dim: int):
        super().__init__()
        self.attention = nn.Sequential(
            nn.Linear(input_dim, 1),
            nn.Tanh()
        )

    def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
        """
        Args:
            x: [Batch, Time, Dim]
            mask: [Batch, Time] (1 for valid, 0 for pad)
        Returns:
            pooled: [Batch, Dim]
        """
        scores = self.attention(x)  # [Batch, Time, 1]

        if mask is not None:
            scores = scores.masked_fill(mask.unsqueeze(-1) == 0, -1e9)

        weights = F.softmax(scores, dim=1)  # [Batch, Time, 1]
        return torch.sum(x * weights, dim=1)  # [Batch, Dim]


class ASRRouterConfig(PretrainedConfig):
    """Configuration for ASRRouter model."""
    model_type = "asr_router"

    def __init__(
        self,
        input_dim: int = 384,  # whisper-tiny encoder dim
        hidden_dim: int = 128,
        intermediate_dim: int = 64,
        dropout: float = 0.1,  # Lower dropout for regression
        num_models: int = 3,   # Number of backends to predict scores for
        **kwargs
    ):
        super().__init__(**kwargs)
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.intermediate_dim = intermediate_dim
        self.dropout = dropout
        self.num_models = num_models


@dataclass
class RouterOutput(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    pred_wers: torch.FloatTensor = None  # Predicted WER for each model


class ASRRouterModel(PreTrainedModel):
    """
    Regression Router.
    Input: 384-dimensional Whisper encoder embeddings
    Output: Estimated WER (0.0+, unbounded) for each backend model.
    Uses Softplus activation to ensure non-negative outputs while allowing WER > 1.0.
    """
    config_class = ASRRouterConfig

    MODEL_ID_MAP = {0: "kyutai", 1: "granite", 2: "tiny_audio"}

    def __init__(self, config: ASRRouterConfig):
        super().__init__(config)

        self.network = nn.Sequential(
            nn.Linear(config.input_dim, config.hidden_dim),
            nn.GELU(),
            nn.LayerNorm(config.hidden_dim),  # Better for batch_size=1
            nn.Dropout(config.dropout),

            nn.Linear(config.hidden_dim, config.intermediate_dim),
            nn.GELU(),
            nn.LayerNorm(config.intermediate_dim),

            nn.Linear(config.intermediate_dim, config.num_models)
        )

        self.post_init()

    def forward(
        self,
        embeddings: torch.Tensor,
        labels: Optional[torch.Tensor] = None,  # Actual WERs from ground truth
    ) -> RouterOutput:

        # Softplus for unbounded positive WER (WER can exceed 1.0)
        pred_wers = F.softplus(self.network(embeddings))

        loss = None
        if labels is not None:
            loss = F.mse_loss(pred_wers, labels)

        return RouterOutput(loss=loss, pred_wers=pred_wers)

    def predict_proba(self, embeddings: torch.Tensor) -> torch.Tensor:
        """Get predicted WERs for each model."""
        with torch.no_grad():
            return F.softplus(self.network(embeddings))


class RouterWithFeatureExtractor:
    """
    Production-ready router with attention pooling and memory optimizations.
    """
    def __init__(self, router: ASRRouterModel, device: str = "cpu"):
        self.device = device
        self.router = router.to(device)
        self.router.eval()

        # Attention pooling for variable-length sequences
        self.attention_pooling = AttentionPooling(input_dim=384).to(device)
        self.attention_pooling.eval()

        # Memory Optimization: Load full model, extract encoder, delete rest
        print("Loading Whisper Encoder...")
        full_whisper = WhisperModel.from_pretrained("openai/whisper-tiny")
        self.whisper_encoder = full_whisper.encoder.to(device)
        self.whisper_encoder.eval()

        del full_whisper.decoder
        del full_whisper
        torch.cuda.empty_cache() if torch.cuda.is_available() else None

        self.feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")

    def extract_features(self, waveform: torch.Tensor) -> torch.Tensor:
        """Extract embeddings using Attention Pooling for variable lengths."""
        if waveform.dim() == 1:
            waveform = waveform.unsqueeze(0)

        # Convert batch tensor to list of 1D numpy arrays (required by WhisperFeatureExtractor)
        audio_np = [w.cpu().numpy() for w in waveform]

        inputs = self.feature_extractor(
            audio_np,
            sampling_rate=16000,
            return_tensors="pt",
            return_attention_mask=True
        )

        input_features = inputs.input_features.to(self.device)
        attention_mask = inputs.attention_mask.to(self.device)

        with torch.no_grad():
            last_hidden_state = self.whisper_encoder(input_features).last_hidden_state

            # Resize mask to match encoder output temporal dimension
            mask_resized = F.interpolate(
                attention_mask.unsqueeze(1).float(),
                size=last_hidden_state.shape[1],
                mode='nearest'
            ).squeeze(1)

            # Attention Pooling
            return self.attention_pooling(last_hidden_state, mask_resized)

    def predict(self, waveform: torch.Tensor) -> Dict:
        """Select the model with the lowest predicted WER."""
        embeddings = self.extract_features(waveform)

        with torch.no_grad():
            output = self.router(embeddings)
            pred_wers = output.pred_wers[0].cpu().numpy()

        scores = {
            "kyutai": float(pred_wers[0]),
            "granite": float(pred_wers[1]),
            "tiny_audio": float(pred_wers[2])
        }

        best_model = min(scores.items(), key=lambda x: x[1])

        return {
            "selected_model": best_model[0],
            "predicted_wers": scores,
            "confidence": max(0.0, 1.0 - best_model[1])  # Clamp since WER can exceed 1.0
        }


# Register for auto classes
ASRRouterConfig.register_for_auto_class()
ASRRouterModel.register_for_auto_class("AutoModel")