"""HuggingFace Qwen3 backbone served through the project's denoiser forward contract."""

from __future__ import annotations

import os

import torch
from torch import Tensor, nn

from diffusion_lm.config import ModelConfig

# The flex template's default 128x128 tiles need ~112 KiB of shared memory at head_dim 128,
# over Ada's 100 KiB per-block ceiling; halved tiles fit. The backward kernel budgets its
# tiles separately, hence the M1/N1/M2/N2 entries. Larger tiles are valid on Hopper.
_FLEX_KERNEL_OPTIONS = {
    'BLOCK_M': 64,
    'BLOCK_N': 64,
    'BLOCK_M1': 32,
    'BLOCK_N1': 64,
    'BLOCK_M2': 64,
    'BLOCK_N2': 32,
}


class Qwen3Denoiser(nn.Module):
    """Wrap ``Qwen3ForCausalLM`` behind the DiffusionTransformer forward contract.

    The backbone always receives a 4D attention mask so its stock causal masking never
    engages: block-diffusion objectives need bidirectional attention inside denoising
    windows, and an omitted mask must mean "attend everything", not "causal".
    ``use_flex_attention`` selects how the boolean blocking matrix reaches the backbone — a
    ``BlockMask`` for the flex kernel, which skips fully-masked blocks, or a materialized
    additive mask for SDPA. Hidden states are gathered at ``output_positions`` before the LM
    head so full-vocabulary logits are never materialized for visible tokens.
    """

    def __init__(
        self,
        config: ModelConfig,
        *,
        load_pretrained: bool = True,
        dtype: torch.dtype | None = None,
    ) -> None:
        super().__init__()
        try:
            from transformers import Qwen3Config, Qwen3ForCausalLM
        except ImportError as exc:
            raise RuntimeError(
                'the hf-qwen3 backbone requires transformers; install the [hf] extra'
            ) from exc
        if config.pretrained_path is None:
            raise ValueError('the hf-qwen3 backbone requires pretrained_path')
        self.config = config
        # Generation on Ada can hit the Triton shared-memory ceiling inside transformers' own
        # compiled flex kernel, where our kernel_options do not reach. MDLM_SDPA=1 sidesteps it
        # for serving and evaluation; flex earns its keep in training, not at q_len 1.
        use_flex = config.use_flex_attention and os.environ.get('MDLM_SDPA') != '1'
        attn_implementation = 'flex_attention' if use_flex else 'sdpa'
        if load_pretrained:
            kwargs = {} if dtype is None else {'dtype': dtype}
            self.backbone = Qwen3ForCausalLM.from_pretrained(
                config.pretrained_path, attn_implementation=attn_implementation, **kwargs
            )
        else:
            # Architecture-only construction: weights come from a later load_state_dict,
            # so checkpoint restore never re-reads the base model files.
            backbone_config = Qwen3Config.from_pretrained(
                config.pretrained_path, attn_implementation=attn_implementation
            )
            self.backbone = Qwen3ForCausalLM(backbone_config)
            if dtype is not None:
                self.backbone.to(dtype)
        self._use_flex = use_flex
        self._validate_backbone()
        self.backbone.config.use_cache = False
        if config.activation_checkpointing:
            self.backbone.gradient_checkpointing_enable(
                gradient_checkpointing_kwargs={'use_reentrant': False}
            )
        # A plain attribute keeps the compiled callable out of the module tree, so checkpoint
        # keys are identical whether or not compilation is on. Shapes reaching the backbone are
        # static; the varying count of output positions is gathered after it returns.
        # Serving generates at ever-changing lengths, which makes a compiled backbone
        # recompile per shape; MDLM_NO_COMPILE=1 turns it off without touching the checkpoint.
        compiling = config.compile_backbone and os.environ.get('MDLM_NO_COMPILE') != '1'
        self._compiled_backbone = (
            torch.compile(self.backbone.model.forward, dynamic=False) if compiling else None
        )
        self.register_buffer(
            '_forbidden_output_token_ids',
            torch.tensor(config.forbidden_output_token_ids, dtype=torch.long),
            persistent=False,
        )

    def _validate_backbone(self) -> None:
        backbone = self.backbone.config
        pairs = (
            ('vocab_size', self.config.vocab_size, backbone.vocab_size),
            ('d_model', self.config.d_model, backbone.hidden_size),
            ('n_layers', self.config.n_layers, backbone.num_hidden_layers),
            ('n_heads', self.config.n_heads, backbone.num_attention_heads),
            ('d_ff', self.config.d_ff, backbone.intermediate_size),
        )
        for name, expected, actual in pairs:
            if expected != actual:
                raise ValueError(f'config {name}={expected} but the backbone has {actual}')
        if self.config.max_seq_len > backbone.max_position_embeddings:
            raise ValueError(
                f'max_seq_len {self.config.max_seq_len} exceeds the backbone context '
                f'{backbone.max_position_embeddings}'
            )

    def _blocked_matrix(
        self,
        input_ids: Tensor,
        attention_mask: Tensor | None,
        attn_mask: Tensor | None,
    ) -> Tensor:
        """Boolean ``[batch, L, L]`` blocking matrix, ``True`` marking a key to suppress."""

        batch_size, seq_len = input_ids.shape
        device = input_ids.device
        if attn_mask is None:
            blocked = torch.zeros(
                batch_size, seq_len, seq_len, dtype=torch.bool, device=device
            )
        else:
            if attn_mask.dtype != torch.bool:
                raise ValueError('attn_mask must be boolean with True marking blocked positions')
            if attn_mask.shape == (seq_len, seq_len):
                blocked = attn_mask.unsqueeze(0).expand(batch_size, -1, -1)
            elif attn_mask.shape == (batch_size, seq_len, seq_len):
                blocked = attn_mask
            else:
                raise ValueError('attn_mask must have shape [L, L] or [batch, L, L]')
        if attention_mask is not None:
            if attention_mask.shape != input_ids.shape:
                raise ValueError('attention_mask must match input_ids')
            blocked = blocked | ~attention_mask.bool()[:, None, :]
        return blocked

    def _additive_mask(self, blocked: Tensor) -> Tensor:
        mask_dtype = self.backbone.model.embed_tokens.weight.dtype
        additive = torch.zeros(blocked.shape, dtype=mask_dtype, device=blocked.device)
        additive = additive.masked_fill(blocked, torch.finfo(mask_dtype).min)
        return additive.unsqueeze(1)

    def forward_cached(
        self,
        input_ids: Tensor,
        *,
        attn_mask: Tensor,
        past_key_values,
        output_positions: Tensor | None = None,
    ) -> tuple[Tensor, object]:
        """Run only the trailing ``input_ids`` against a populated key/value cache.

        ``attn_mask`` holds one row per NEW query and one column per position the query may
        see, cache included: ``[batch, query, cached + query]``. Block-causal masking is what
        makes caching sound here — prefix positions never attend forward into a block, so
        their keys and values stay valid while the block's own tokens keep changing.
        """

        cached = past_key_values.get_seq_length()
        query_len = input_ids.shape[1]
        blocked = attn_mask if attn_mask.dim() == 3 else attn_mask.unsqueeze(0)
        if blocked.shape[-2] != query_len or blocked.shape[-1] != cached + query_len:
            raise ValueError(
                f'attn_mask must be [batch, {query_len}, {cached + query_len}] for a cache of '
                f'{cached} positions, got {tuple(blocked.shape)}'
            )
        rows = blocked.expand(input_ids.shape[0], -1, -1)
        outputs = self.backbone.model(
            input_ids=input_ids,
            attention_mask=self._additive_mask(rows),
            past_key_values=past_key_values,
            use_cache=True,
            **self._backbone_kwargs(),
        )
        hidden = outputs.last_hidden_state
        if output_positions is not None:
            hidden = hidden[output_positions.bool()]
        logits = self.backbone.lm_head(hidden)
        self._forbid(logits)
        return logits, outputs.past_key_values

    def _backbone_kwargs(self) -> dict:
        """Extra backbone arguments the attention implementation needs.

        Every path into the backbone has to carry these, cached or not: without the reduced
        tiles the flex template asks Ada for more shared memory than it has and the kernel
        fails to compile at all.
        """

        if not self._use_flex:
            return {}
        return {'kernel_options': _FLEX_KERNEL_OPTIONS}

    def new_cache(self):
        """Empty key/value cache for :meth:`forward_cached`, kept here so callers stay
        independent of the transformers cache class."""

        from transformers import DynamicCache

        return DynamicCache()

    def _forbid(self, logits: Tensor) -> None:
        if self._forbidden_output_token_ids.numel():
            logits.index_fill_(
                -1, self._forbidden_output_token_ids, torch.finfo(logits.dtype).min
            )
        if self.config.forbidden_output_from is not None:
            logits[..., self.config.forbidden_output_from :] = torch.finfo(logits.dtype).min

    def _flex_mask(self, blocked: Tensor):
        """Compress the blocking matrix into a ``BlockMask`` the flex kernel can skip over.

        ``masking_utils`` forwards any mask reporting a 4D shape untouched, so a ``BlockMask``
        reaches ``flex_attention_forward`` as ``block_mask`` and the backbone never rebuilds a
        causal mask of its own.
        """

        from diffusion_lm.flexattn import build_block_mask

        batch_size, seq_len, _ = blocked.shape
        return build_block_mask(blocked, None, batch_size, seq_len, blocked.device)

    def forward(
        self,
        input_ids: Tensor,
        attention_mask: Tensor | None = None,
        output_positions: Tensor | None = None,
        attn_mask: Tensor | None = None,
    ) -> Tensor:
        if input_ids.ndim != 2:
            raise ValueError('input_ids must have shape [batch, sequence]')
        sequence_length = input_ids.shape[1]
        if sequence_length > self.config.max_seq_len:
            raise ValueError(
                f'sequence length {sequence_length} exceeds max_seq_len '
                f'{self.config.max_seq_len}'
            )
        blocked = self._blocked_matrix(input_ids, attention_mask, attn_mask)
        mask = (
            self._flex_mask(blocked) if self._use_flex else self._additive_mask(blocked)
        )
        call = self._compiled_backbone or self.backbone.model
        hidden = call(
            input_ids=input_ids, attention_mask=mask, use_cache=False,
            **self._backbone_kwargs(),
        ).last_hidden_state
        if output_positions is not None:
            if output_positions.shape != input_ids.shape:
                raise ValueError('output_positions must match input_ids')
            hidden = hidden[output_positions.bool()]
        logits = self.backbone.lm_head(hidden)
        self._forbid(logits)
        return logits

    @property
    def token_embedding(self) -> nn.Embedding:
        """Embedding module surfaced for the optimizer's 32-bit override (tied to the head)."""

        return self.backbone.model.embed_tokens

    @property
    def num_parameters(self) -> int:
        """Count unique trainable parameters (shared embeddings count once)."""

        return sum(parameter.numel() for parameter in self.parameters() if parameter.requires_grad)