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from __future__ import annotations

import dataclasses

import torch


@dataclasses.dataclass(frozen=True)
class DpSamplingSupport:
    requested: bool
    enabled: bool
    infra_supports: bool
    drafter_available: bool
    backend_supports_verify: bool
    tp_size: int
    tp_group_set: bool


@dataclasses.dataclass(frozen=True)
class DpSamplingTopology:
    tp_rank: int
    tp_size: int
    tp_group: tuple[int, ...] | None
    skip_all_gather: bool
    tie_word_embeddings: bool = False

    @property
    def tp_group_set(self) -> bool:
        return self.tp_group is not None


@dataclasses.dataclass(frozen=True)
class DpSamplingRuntimeConfig:
    enabled: bool = False
    vocab_size: int | None = None
    max_bucket_bs: int | None = None
    min_bs: int | None = None
    num_tokens_per_req: int = 1
    topology: DpSamplingTopology | None = None
    device: torch.device | str | None = None


@dataclasses.dataclass(frozen=True)
class DpSamplingRuntimeLimits:
    runtime_vocab_size: int
    max_num_seqs: int
    data_parallel_size: int
    num_tokens_per_req: int
    configured_min_bs: int | None
    device: torch.device | str


def resolve_dp_sampling_support(
    *,
    requested: bool,
    drafter_available: bool,
    backend_supports_verify: bool,
    topology: DpSamplingTopology,
) -> DpSamplingSupport:
    tp_size = int(topology.tp_size)
    tp_group_set = topology.tp_group_set
    infra_supports = (
        drafter_available and backend_supports_verify and tp_size > 1 and tp_group_set
    )
    support = DpSamplingSupport(
        requested=bool(requested),
        enabled=infra_supports and bool(requested),
        infra_supports=infra_supports,
        drafter_available=drafter_available,
        backend_supports_verify=backend_supports_verify,
        tp_size=tp_size,
        tp_group_set=tp_group_set,
    )
    if support.requested and not support.infra_supports:
        raise RuntimeError(
            "--dp-sampling was set but Batch-DP spec-verify "
            "preconditions are not met: "
            f"drafter={support.drafter_available}, "
            f"backend_supports_dp_verify={support.backend_supports_verify}, "
            f"tp_size={support.tp_size}, "
            f"tp_group_set={support.tp_group_set}"
        )
    return support


def resolve_dp_sampling_runtime(
    *,
    support: DpSamplingSupport,
    lm_head_rows: int,
    topology: DpSamplingTopology,
    limits: DpSamplingRuntimeLimits,
) -> DpSamplingRuntimeConfig:
    if not support.enabled:
        return DpSamplingRuntimeConfig(
            num_tokens_per_req=limits.num_tokens_per_req,
            topology=topology,
            device=limits.device,
        )
    validate_dp_sampling_lm_head_vocab(
        lm_head_rows=lm_head_rows,
        vocab_size=limits.runtime_vocab_size,
        tp_size=topology.tp_size,
        skip_all_gather=topology.skip_all_gather,
        tie_word_embeddings=topology.tie_word_embeddings,
    )
    dp_vocab_size = int(lm_head_rows)
    if not topology.skip_all_gather:
        dp_vocab_size *= int(topology.tp_size)
    dp_vocab_size = (
        (dp_vocab_size + int(topology.tp_size) - 1) // int(topology.tp_size)
    ) * int(topology.tp_size)
    max_bs = limits.max_num_seqs // max(limits.data_parallel_size, 1)
    max_bucket_bs = (
        (max_bs + topology.tp_size - 1) // topology.tp_size
    ) * topology.tp_size
    min_bs = (
        int(limits.configured_min_bs)
        if limits.configured_min_bs is not None
        else 2 * int(topology.tp_size)
    )
    if min_bs < 1:
        raise ValueError("dp_sampling_min_bs must be >= 1")
    return DpSamplingRuntimeConfig(
        enabled=True,
        vocab_size=dp_vocab_size,
        max_bucket_bs=max_bucket_bs,
        min_bs=min_bs,
        num_tokens_per_req=limits.num_tokens_per_req,
        topology=topology,
        device=limits.device,
    )


def slice_dp_vocab_mask(
    vocab_mask: torch.Tensor | None,
    *,
    full_bs: int,
    pad_bs: int,
    num_tokens_per_req: int,
    shard: slice,
) -> torch.Tensor | None:
    if vocab_mask is None:
        return None
    n = num_tokens_per_req
    if pad_bs > full_bs:
        vocab_mask = torch.nn.functional.pad(
            vocab_mask,
            (0, 0, 0, (pad_bs - full_bs) * n),
            value=-1,
        )
    return vocab_mask.view(pad_bs, n, -1)[shard].reshape(-1, vocab_mask.shape[-1])


def validate_dp_sampling_lm_head_vocab(
    *,
    lm_head_rows: int,
    vocab_size: int,
    tp_size: int,
    skip_all_gather: bool,
    tie_word_embeddings: bool,
) -> None:
    if skip_all_gather and int(lm_head_rows) < int(vocab_size):
        raise RuntimeError(
            "Batch-DP sampling with skip_all_gather requires a replicated/"
            "full-vocab LM head. Got a sharded LM head with "
            f"lm_head_rows={lm_head_rows}, vocab_size={vocab_size}, "
            f"tp_size={tp_size}, tie_word_embeddings={tie_word_embeddings}. "
            "Disable --dp-sampling or use a model path that resolves a "
            "replicated LM head."
        )