"""Self-contained Hugging Face implementation of the chess policy model."""

from __future__ import annotations

import math
from dataclasses import dataclass

import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.attention import SDPBackend, sdpa_kernel
from torch.utils.checkpoint import checkpoint
from transformers import PreTrainedModel
from transformers.utils import ModelOutput

try:
    from .configuration_chess_policy import ChessPolicyConfig
except ImportError:  # Allows convert_checkpoint.py to run from this folder.
    from configuration_chess_policy import ChessPolicyConfig


class SwiGLU(nn.Module):
    def __init__(self, d_model: int, hidden: int, dropout: float) -> None:
        super().__init__()
        self.gate = nn.Linear(d_model, hidden, bias=False)
        self.up = nn.Linear(d_model, hidden, bias=False)
        self.down = nn.Linear(hidden, d_model, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down(self.dropout(F.silu(self.gate(x)) * self.up(x)))


class SDPASelfAttention(nn.Module):
    """Bias-free bidirectional attention using PyTorch fused SDPA."""

    def __init__(self, d_model: int, n_heads: int, dropout: float) -> None:
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.dropout = dropout
        self.in_proj_weight = nn.Parameter(torch.empty(3 * d_model, d_model))
        self.out_proj = nn.Linear(d_model, d_model, bias=False)

    def forward(
        self,
        x: torch.Tensor,
        key_padding_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        batch, tokens, _ = x.shape
        qkv = F.linear(x, self.in_proj_weight)
        qkv = qkv.view(batch, tokens, 3, self.n_heads, self.head_dim)
        query, key, value = qkv.unbind(dim=2)
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        attention_mask = None
        if key_padding_mask is not None:
            attention_mask = (~key_padding_mask)[:, None, None, :]

        sdpa_args = {
            "attn_mask": attention_mask,
            "dropout_p": self.dropout if self.training else 0.0,
            "is_causal": False,
        }
        if query.is_cuda:
            with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION):
                attended = F.scaled_dot_product_attention(
                    query, key, value, **sdpa_args
                )
        else:
            attended = F.scaled_dot_product_attention(
                query, key, value, **sdpa_args
            )
        attended = attended.transpose(1, 2).contiguous()
        return self.out_proj(attended.view(batch, tokens, self.d_model))


class BidirectionalTransformerBlock(nn.Module):
    def __init__(
        self,
        d_model: int,
        n_heads: int,
        swiglu_hidden: int,
        dropout: float,
        stack_depth: int,
    ) -> None:
        super().__init__()
        self.attention_norm = nn.LayerNorm(d_model)
        self.attention = SDPASelfAttention(d_model, n_heads, dropout)
        self.attention_dropout = nn.Dropout(dropout)
        self.ffn_norm = nn.LayerNorm(d_model)
        self.ffn = SwiGLU(d_model, swiglu_hidden, dropout)
        self.ffn_dropout = nn.Dropout(dropout)
        self.reset_parameters(stack_depth)

    def reset_parameters(self, stack_depth: int) -> None:
        residual_std = 0.02 / math.sqrt(2.0 * stack_depth)
        nn.init.normal_(self.attention.in_proj_weight, mean=0.0, std=0.02)
        nn.init.normal_(self.attention.out_proj.weight, mean=0.0, std=residual_std)
        nn.init.normal_(self.ffn.gate.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.ffn.up.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.ffn.down.weight, mean=0.0, std=residual_std)

    def forward(
        self,
        x: torch.Tensor,
        key_padding_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        attended = self.attention(self.attention_norm(x), key_padding_mask)
        x = x + self.attention_dropout(attended)
        x = x + self.ffn_dropout(self.ffn(self.ffn_norm(x)))
        return x


class BidirectionalTransformerStack(nn.Module):
    def __init__(self, *, layers: int, config: ChessPolicyConfig) -> None:
        super().__init__()
        self.activation_checkpointing = config.activation_checkpointing
        self.layers = nn.ModuleList(
            [
                BidirectionalTransformerBlock(
                    config.d_model,
                    config.n_heads,
                    config.swiglu_hidden,
                    config.dropout,
                    stack_depth=layers,
                )
                for _ in range(layers)
            ]
        )
        self.final_norm = nn.LayerNorm(config.d_model)

    def forward(
        self,
        x: torch.Tensor,
        key_padding_mask: torch.Tensor | None = None,
    ) -> torch.Tensor:
        for layer in self.layers:
            if self.activation_checkpointing and self.training:
                x = checkpoint(layer, x, key_padding_mask, use_reentrant=False)
            else:
                x = layer(x, key_padding_mask)
        return self.final_norm(x)


class BoardEncoder(nn.Module):
    def __init__(self, config: ChessPolicyConfig) -> None:
        super().__init__()
        self.piece_embedding = nn.Embedding(config.piece_states, config.d_model)
        self.square_embedding = nn.Embedding(config.board_squares, config.d_model)
        self.summary_token = nn.Parameter(torch.empty(1, 1, config.d_model))
        self.transformer = BidirectionalTransformerStack(
            layers=config.board_layers, config=config
        )
        nn.init.normal_(self.piece_embedding.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.square_embedding.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.summary_token, mean=0.0, std=0.02)

    def forward(self, boards: torch.Tensor) -> torch.Tensor:
        if boards.ndim != 2 or boards.shape[1] != 64:
            raise ValueError(f"Expected boards shaped [N, 64], got {boards.shape}")
        boards = boards.to(torch.int64)
        square_ids = torch.arange(64, device=boards.device)
        squares = self.piece_embedding(boards) + self.square_embedding(square_ids)
        summary = self.summary_token.expand(boards.shape[0], -1, -1)
        encoded = self.transformer(torch.cat((summary, squares), dim=1))
        return encoded[:, 0]


@dataclass
class ChessPolicyOutput(ModelOutput):
    """Hugging Face output with per-candidate move scores."""

    loss: torch.Tensor | None = None
    logits: torch.Tensor | None = None
    candidate_mask: torch.Tensor | None = None
    distill_loss: torch.Tensor | None = None


class ChessTransitionPolicy(PreTrainedModel):
    """Score legal chess moves as contextualized latent transitions."""

    config_class = ChessPolicyConfig
    base_model_prefix = "chess_policy"
    main_input_name = "current_boards"

    def _init_weights(self, module: nn.Module) -> None:
        """Keep the project's explicit initialization scheme unchanged."""

        # The submodules are initialized explicitly below.  This no-op lets
        # Hugging Face's post_init register loader metadata without replacing
        # the original initialization scheme.
        del module

    def __init__(self, config: ChessPolicyConfig) -> None:
        super().__init__(config)
        self.board_encoder = BoardEncoder(config)
        self.candidate_type_embedding = nn.Embedding(2, config.d_model)
        self.candidate_transformer = BidirectionalTransformerStack(
            layers=config.candidate_layers, config=config
        )
        self.query = nn.Linear(config.d_model, config.d_model, bias=False)
        self.key = nn.Linear(config.d_model, config.d_model, bias=False)
        self.query_norm = nn.RMSNorm(config.d_model, elementwise_affine=False)
        self.key_norm = nn.RMSNorm(config.d_model, elementwise_affine=False)
        nn.init.normal_(self.candidate_type_embedding.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.query.weight, mean=0.0, std=0.02)
        nn.init.normal_(self.key.weight, mean=0.0, std=0.02)
        self.post_init()

    def forward(
        self,
        *,
        current_boards: torch.Tensor,
        successor_boards: torch.Tensor,
        candidate_owner: torch.Tensor,
        candidate_offsets: torch.Tensor,
        candidate_mask: torch.Tensor,
        target_indices: torch.Tensor | None = None,
        teacher_logits: torch.Tensor | None = None,
        distill_temperature: float = 1.0,
        teacher_temperature: float = 120.0,
        return_dict: bool | None = None,
    ) -> ChessPolicyOutput | tuple[torch.Tensor, ...]:
        batch_size = current_boards.shape[0]
        all_boards = torch.cat((current_boards, successor_boards), dim=0)
        all_states = self.board_encoder(all_boards)
        current_states = all_states[:batch_size]
        successor_states = all_states[batch_size:]

        candidate_owner = candidate_owner.to(torch.int64)
        transitions = successor_states - current_states[candidate_owner]
        local_indices = (
            torch.arange(transitions.shape[0], device=transitions.device)
            - candidate_offsets[candidate_owner]
        )

        max_candidates = candidate_mask.shape[1]
        candidate_sequence = transitions.new_zeros(
            batch_size, max_candidates + 1, self.config.d_model
        )
        candidate_sequence[:, 0] = current_states
        candidate_sequence[candidate_owner, local_indices + 1] = transitions

        type_ids = torch.ones(
            batch_size,
            max_candidates + 1,
            dtype=torch.int64,
            device=transitions.device,
        )
        type_ids[:, 0] = 0
        candidate_sequence = candidate_sequence + self.candidate_type_embedding(type_ids)

        valid_mask = torch.cat(
            (
                torch.ones(
                    batch_size,
                    1,
                    dtype=torch.bool,
                    device=candidate_mask.device,
                ),
                candidate_mask,
            ),
            dim=1,
        )
        contextualized = self.candidate_transformer(
            candidate_sequence, key_padding_mask=~valid_mask
        )
        move_states = contextualized[:, 1:]

        query = self.query_norm(self.query(current_states))
        keys = self.key_norm(self.key(move_states))
        logits = torch.einsum("bd,bnd->bn", query, keys)
        logits = logits / math.sqrt(self.config.d_model)
        logits = logits.masked_fill(~candidate_mask, float("-inf"))

        loss = None
        distill_loss = None
        if teacher_logits is not None:
            if distill_temperature <= 0 or teacher_temperature <= 0:
                raise ValueError("distillation temperatures must be positive")
            teacher_logits = teacher_logits.to(logits.dtype).masked_fill(
                ~candidate_mask, float("-inf")
            )
            teacher_log_probs = F.log_softmax(
                teacher_logits / teacher_temperature, dim=1
            )
            teacher_probs = teacher_log_probs.exp()
            student_log_probs = F.log_softmax(logits / distill_temperature, dim=1)
            safe_student_log_probs = torch.where(
                candidate_mask, student_log_probs, torch.zeros_like(student_log_probs)
            )
            safe_teacher_log_probs = torch.where(
                candidate_mask, teacher_log_probs, torch.zeros_like(teacher_log_probs)
            )
            distill_loss = (
                teacher_probs * (safe_teacher_log_probs - safe_student_log_probs)
            ).sum(dim=1).mean() * (distill_temperature**2)
        if target_indices is not None:
            loss = F.cross_entropy(logits, target_indices)
        elif distill_loss is not None:
            loss = distill_loss

        if return_dict is False:
            return tuple(
                value
                for value in (loss, logits, candidate_mask, distill_loss)
                if value is not None
            )
        return ChessPolicyOutput(
            loss=loss,
            logits=logits,
            candidate_mask=candidate_mask,
            distill_loss=distill_loss,
        )

    def parameter_breakdown(self) -> dict[str, int]:
        board = sum(p.numel() for p in self.board_encoder.parameters())
        candidates = sum(p.numel() for p in self.candidate_transformer.parameters())
        candidate_types = sum(p.numel() for p in self.candidate_type_embedding.parameters())
        scorer = sum(p.numel() for p in self.query.parameters()) + sum(
            p.numel() for p in self.key.parameters()
        )
        return {
            "board_encoder": board,
            "candidate_transformer": candidates,
            "candidate_type_embedding": candidate_types,
            "scorer": scorer,
            "total": sum(p.numel() for p in self.parameters()),
        }