import torch
import torch.nn as nn
from transformers import PreTrainedModel

from .configuration_emcoder import EmCoderConfig


class EmCoderCore(nn.Module):
    """The core encoder architecture of EmCoder, without the classification head."""

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

        self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
        self.pos_embedding = nn.Embedding(config.max_seq_len, config.d_model)
        self.embed_norm = nn.LayerNorm(config.d_model)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=config.d_model,
            nhead=config.n_head,
            dim_feedforward=config.d_ffn,
            dropout=config.dropout,
            activation="gelu",
            norm_first=True,
            batch_first=True,
        )
        self.encoder = nn.TransformerEncoder(
            encoder_layer=encoder_layer, num_layers=config.n_layers
        )

        self.final_norm = nn.LayerNorm(config.d_model)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
        """Standard forward pass through the encoder."""
        seq_len = x.size(1)
        pos_ids = torch.arange(seq_len, device=x.device).unsqueeze(0)

        x = self.token_embedding(x) + self.pos_embedding(pos_ids)

        x = self.embed_norm(x)
        x = self.dropout(x)

        padding_mask = mask == 0

        encoded = self.encoder(x, src_key_padding_mask=padding_mask)
        return self.final_norm(encoded)


class EmCoder(PreTrainedModel):
    """The full EmCoder model, including the classification head."""

    config_class = EmCoderConfig

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

        self.encoder = EmCoderCore(config)
        self.classifier = nn.Sequential(
            nn.Linear(config.d_model, config.d_model),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.d_model, config.num_labels),
        )

        self.post_init()


    def _set_mc_dropout(self, active: bool = True):
        for m in self.modules():
            if isinstance(m, nn.Dropout) or isinstance(m, nn.MultiheadAttention):
                m.train(active)

    @staticmethod
    def _masked_mean_pooling(
        features: torch.Tensor, mask: torch.Tensor
    ) -> torch.Tensor:
        mask = mask.unsqueeze(-1)  # (B, S, 1)
        masked_features = features * mask  # (B, S, D)
        sum_masked_features = masked_features.sum(dim=1)  # (B, D)
        count_tokens = torch.clamp(mask.sum(dim=1), min=1e-9)  # (B, 1)
        return sum_masked_features / count_tokens  # (B, D)


    def mc_forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        n_samples: int,
        max_batch_size: int | None = None,
    ) -> torch.Tensor:
        """
        Performs Monte Carlo Dropout inference to quantify epistemic uncertainty.

        Args:
            x: Input token IDs of shape (B, S).
            mask: Attention mask of shape (B, S).
            n_samples: Total number of Monte Carlo samples.
            max_batch_size: Maximum number of samples in one forward pass.

        Returns:
            Logits of shape (n_samples, B, num_labels).
        """
        if max_batch_size is None:
            max_batch_size = n_samples

        B, S = x.shape
        num_labels = self.classifier[-1].out_features

        all_logits = torch.empty((n_samples, B, num_labels), device=x.device)

        is_training = self.training
        self._set_mc_dropout(active=True)
        try:
            for i in range(0, n_samples, max_batch_size):
                batch_samples = min(max_batch_size, n_samples - i)

                x_stacked = x.repeat(batch_samples, 1)  # (batch_samples * B, S)
                mask_stacked = mask.repeat(batch_samples, 1)  # (batch_samples * B, S)

                features = self.encoder(
                    x_stacked, mask_stacked
                )  # (batch_samples * B, S, D)

                pooled = self._masked_mean_pooling(features, mask_stacked)
                logits = self.classifier(pooled)  # (n_samples * B, num_labels)

                all_logits[i : i + batch_samples] = logits.view(batch_samples, B, -1)
        finally:
            self._set_mc_dropout(active=is_training)

        return all_logits




    def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
        """Standard forward pass without MC Dropout."""
        features = self.encoder(x, mask)

        pooled = self._masked_mean_pooling(features, mask)
        return self.classifier(pooled)