"""Coarse VRAM-fit estimation for one-consumer-GPU LoRA jobs.

Used by the open-model policy (``model_policy = "allow"``) to sanity-check that an
unlisted HF model can plausibly run on the requested GPU before provisioning it.

These are deliberately coarse heuristics (documented ±20%): they exist to catch
*provably impossible* configurations (70B bf16 on a 24 GB card) and to warn on tight
fits — not to guarantee success. Calibrated against the measured catalog entries
(Qwen3-0.6B/4B/8B, Qwen3.5 dense).
"""

from __future__ import annotations

import math
import os
import re
from dataclasses import dataclass


def _gpu_vram_table() -> dict[str, int]:
    try:
        from flash.providers.base import GPU_INFO

        return {name: info.vram_gb for name, info in GPU_INFO.items()}
    except Exception:
        return {"RTX 4090": 24, "RTX 5090": 32}


GPU_VRAM_GB = _gpu_vram_table()

_BYTES_PER_PARAM = {
    "bf16": 2.0,
    "fp16": 2.0,
    "4bit-qlora": 0.55,  # NF4 weights + quantization constants
}

# Fixed overheads (GB): CUDA context + activations w/ gradient checkpointing +
# LoRA params/grads/Adam states (tiny at rank<=64) + fragmentation headroom.
_BASE_OVERHEAD_GB = 4.0
# Activations with gradient checkpointing scale ~linearly with tokens-in-flight
# (batch x seq) and model width (~sqrt of params). Coef calibrated so 4.7B SFT at
# seq 32k / batch 1 lands ~22 GB (measured: fits a 32 GB 5090).
_ACT_COEF = 0.12
# SFT activations peak on the worker's PER-DEVICE micro-batch, not [train].batch_size
# (which is the global/effective batch realized via gradient accumulation). The worker
# caps the micro-batch at 4: per_device = min(batch_size, 4). Mirror that here so an
# unset/long-context SFT run (batch defaults low) still reserves the micro-batch activation
# peak, and a large effective batch isn't mis-counted as resident VRAM (it's grad-accum,
# not in-flight activations).
_SFT_PER_DEVICE_BS_DEFAULT = 4


def _sft_per_device_bs() -> int:
    """The worker's per-device SFT micro-batch — the activation-peak driver to size against.

    SFT micro-batch is a MANAGED default: the control plane no longer forwards
    ``SFT_PER_DEVICE_BS`` to the worker (build_worker_env dropped the tuning allowlist), and the
    worker's own process env never carries it, so the worker always runs the fixed default. The
    allocator must size against that SAME fixed value — reading the control-plane process env here
    would size a card for a micro-batch the worker never uses, under-routing an
    ``SFT_PER_DEVICE_BS=1`` operator env to a too-small GPU that then OOMs at the default
    micro-batch 4 (the asymmetry the env-knobs cleanup removed everywhere else)."""
    return _SFT_PER_DEVICE_BS_DEFAULT
# Colocated-GRPO vLLM KV pool: grows with the engine's max context (seq) and model
# width, but vLLM bounds the pool to a fraction of the card and PAGES rather than OOMs,
# so it's capped (_KV_CAP) instead of growing without bound at long context.
_KV_COEF = 2.0
_KV_CAP = 8.0
# GRPO backward (activations + fp32 logits over the completion micro-batch) per unit
# context x model width. Grad checkpointing makes this MILD in seq -- calibrated to
# measured boundaries: 0.8B GRPO fits 24 GB up to seq 32k (seq ~free), while 4.7B GRPO
# steps off a 32 GB card between seq 16k and 32k. group size scales it sublinearly.
_TRAIN_COEF = 0.27
# Fixed floor for colocated-vLLM GRPO: the vLLM engine's CUDA context + KV pool (sized to the
# CARD's VRAM via gpu_util, not the model) + the 2nd resident weight copy is ~model-independent
# for small models and dominates their param estimate, so tiny/mid models all need the 32 GB tier.
# MEASURED at the default group_size=8: 0.8B GRPO OOMs a 20 GB card; 2B GRPO OOMs a 24 GB card
# (-> both need 32); 4B GRPO fits 32 (param est ~31 already clears this floor, so it's untouched).
_VLLM_COLOCATE_FLOOR_GB = 28.0
_VOCAB_DEFAULT = 152_000  # Qwen3.x tokenizer vocab (drives the fp32-logits GRPO term)
# Matches the worker's logits budget (6 GB): the per-device fp32 logits are capped to this
# (rl_per_device_comps spills the rest into grad-accum), so the estimator never reserves above it.
_LOGITS_BUDGET_GB = 6.0


def grpo_seq_escalation_gb(params_b: float | None, seq_len: int) -> int:
    """Extra GB a long-context GRPO run needs beyond its base footprint.

    Big-model GRPO is tight: colocate holds 2 weight copies + a KV pool, so headroom shrinks
    with model size and long context overflows it. Calibrated on a bf16 9.7B GRPO run (RunPod):
    fits 80 GB to seq 4096 but OOMs at 8192. Safe headroom ~ 48500/params_b tokens; past that
    escalate, STEEPER for bigger models. Applies to both catalog and open-model GRPO so neither
    under-provisions.
    """
    coef = 0.9
    if not params_b:
        return 0
    seq_thresh = 48_500.0 / params_b
    if seq_len <= seq_thresh:
        return 0
    return math.ceil(coef * params_b * (seq_len / seq_thresh - 1))


def params_b_from_str(s: str | None) -> float | None:
    """Leading param count (billions) from a catalog ``params`` string, e.g.
    "4.7B (text-only fine-tune)" -> 4.7, "9.7B (text-only fine-tune)" -> 9.7."""
    if not s:
        return None
    m = re.search(r"([0-9]+(?:\.[0-9]+)?)\s*B", s)
    return float(m.group(1)) if m else None


@dataclass(frozen=True)
class VramEstimate:
    params_b: float | None
    algorithm: str
    quant: str
    est_gb: float | None
    gpu: str
    gpu_gb: int
    verdict: str  # "fits" | "tight" | "too_big" | "unknown"

    def describe(self) -> str:
        if self.est_gb is None:
            return f"{self.gpu}: VRAM need unknown (could not read model size)"
        return (
            f"{self.gpu} ({self.gpu_gb} GB): estimated ~{self.est_gb:.0f} GB needed "
            f"({self.params_b:.1f}B params, {self.quant}, {self.algorithm}) -> {self.verdict}"
        )


def estimate_vram_gb(
    params_b: float,
    algorithm: str,
    quant: str = "bf16",
    *,
    seq_len: int = 1024,
    max_tokens: int | None = None,
    lora_rank: int = 32,
    batch_size: int = 1,
    group_size: int = 8,
    thinking: bool = False,
    use_vllm: bool = True,
) -> float:
    """Estimated peak VRAM (GB) for a LoRA job on one GPU, over the full knob matrix.

    Terms (all in GB):
      weights      params x bytes/param (bf16=2, 4bit-qlora=0.55)
      base         CUDA context + framework + fragmentation headroom
      lora_opt     LoRA adapter + grads + Adam states (rank-linear, model-scaled)
      activations  grad-checkpointed activations ~ batch x seq x sqrt(params)
      grpo only:
        +weights   colocated vLLM holds a 2nd resident weight copy at the rollout peak
                   (sleep mode offloads it BETWEEN steps, not during) -- skipped when
                   use_vllm is False (transformers generation, single copy)
        kv         vLLM KV pool ~ seq x sqrt(params)
        logits     fp32 logits [per_device_comps, completion, vocab]
    """
    bpp = _BYTES_PER_PARAM.get(quant, 2.0)
    weights = params_b * bpp
    algo = "grpo" if (algorithm or "").lower() in ("grpo", "rl") else "sft"
    width = math.sqrt(max(params_b, 0.1))
    lora_opt = (lora_rank / 16.0) * (0.3 + 0.04 * params_b)
    base = weights + _BASE_OVERHEAD_GB + lora_opt
    if algo == "grpo":
        # GRPO alternates two phases that DON'T peak together (sleep mode offloads the
        # vLLM engine during the backward), so peak = max(rollout, train), not the sum:
        #   rollout: colocated vLLM 2nd weight copy + KV pool (skipped if use_vllm=False)
        #   train:   backward activations + fp32 logits -- MILD in seq (grad ckpt)
        rollout = 0.0
        if use_vllm:
            rollout = weights + min(_KV_COEF * (seq_len / 1024.0) * width, _KV_CAP)
        group_factor = max(1.0, (max(1, group_size) / 4.0) ** 0.5)
        think_factor = 1.3 if thinking else 1.0
        activations = _TRAIN_COEF * (seq_len / 1024.0) * width * group_factor * think_factor
        # fp32 logits [per_device, completion, vocab] are the documented GRPO OOM driver. The
        # worker MEMORY-CAPS per_device (rl_per_device_comps) so the live logits never exceed the
        # logits budget (6 GB) and the rest spills into grad-accum -- so the IRREDUCIBLE floor the
        # card must hold is the per_device=1 logits for the completion length: it scales with
        # max_tokens (NOT seq_len) and is capped at the budget. completion defaults to the recipe
        # budget (~min(seq_len, 1024)) when max_tokens is unset.
        completion = max_tokens if max_tokens else min(seq_len, 1024)
        logits = min(completion * _VOCAB_DEFAULT * 4 / 1e9, _LOGITS_BUDGET_GB)
        train = activations + logits
        return base + max(rollout, train)
    # SFT: the activation peak is the worker's per-device micro-batch (capped at 4),
    # NOT the global/effective batch_size (gradient accumulation realizes
    # that). Size to the micro-batch the worker actually runs: a default/long-context run reserves
    # for the per-device cap (no under-routing to a too-small card), while a large effective
    # batch_size is capped at it (no over-routing, since accum isn't resident VRAM).
    sft_per_device = min(max(1, batch_size), _sft_per_device_bs())
    return base + _ACT_COEF * sft_per_device * (seq_len / 1024.0) * width


def model_required_vram_gb(
    model_id: str,
    algorithm: str,
    *,
    train=None,
    thinking: bool = False,
    headroom: float = 1.1,
) -> int:
    """Cheapest-sufficient VRAM (GB) for a specific run -- the matrix the allocator and
    ``resolve_gpu_policy`` both size against.

    Catalog models size from their known param count + the run's actual knobs (``train``
    may be a TrainSpec, a dict, or None for recipe defaults). Curated GRPO floors
    (``grpo_min_vram_gb``) stay as HARD floors so we never under-provision a validated
    model; the matrix only ever sizes UP from there. Unlisted open models size from HF
    metadata, falling back to the 24 GB tier when the size can't be read.
    """

    # Best-effort knob extraction: this provisional sizing runs at parse time BEFORE the
    # dedicated train validators, so malformed knobs (nan/inf/strings/<=0) must fall back
    # to a default here, never crash -- config_schema raises the proper ConfigError next.
    def _g(obj, key):
        if obj is None:
            return None
        return obj.get(key) if isinstance(obj, dict) else getattr(obj, key, None)

    def _pos_int(v, default):
        try:
            if isinstance(v, bool):
                return default
            f = float(v)
            return int(f) if math.isfinite(f) and f >= 1 else default
        except (TypeError, ValueError):
            return default

    max_tokens = _pos_int(_g(train, "max_tokens"), None)
    # Default sequence length when [train].max_length is unset. For GRPO this must MIRROR what
    # run_rl() actually starts vLLM at — max(1024, RLConfig.max_prompt_len + completion) — not a
    # flat 1024, or the allocator can pick a GPU sized for 1024 tokens while the worker launches a
    # ~2368-token (3584 with thinking) engine and OOMs after provisioning. Completion = the run's
    # [train].max_tokens override, else the recipe's thinking/non-thinking completion default.
    if (algorithm or "").lower() in ("grpo", "rl"):
        from flash.engine.recipe import RECIPE

        _rl = RECIPE.rl
        _completion = max_tokens or (
            _rl.max_completion_len_thinking if thinking else _rl.max_completion_len
        )
        _grpo_default_len = max(1024, _rl.max_prompt_len + int(_completion))
    else:
        _grpo_default_len = 1024
    seq_len = _pos_int(_g(train, "max_length"), _grpo_default_len)
    lora_rank = _pos_int(_g(train, "lora_rank"), 32)
    group_size = _pos_int(_g(train, "group_size"), 8)
    # Default to the worker's per-device SFT micro-batch (4): an unset
    # [train].batch_size still realizes that micro-batch on the worker, so size for it
    # rather than 1 (which would under-route a long-context SFT run to a too-small card).
    batch_size = _pos_int(_g(train, "batch_size"), _sft_per_device_bs())

    def _need(
        params_b: float, algorithm: str, *, quant: str = "bf16", use_vllm: bool = True
    ) -> int:
        # estimate over the run's full knob matrix, then apply the safety headroom. Both the
        # catalog and open-model paths size through here so they stay in sync on the knob set.
        est = estimate_vram_gb(
            params_b,
            algorithm,
            quant,
            seq_len=seq_len,
            max_tokens=max_tokens,
            lora_rank=lora_rank,
            batch_size=batch_size,
            group_size=group_size,
            thinking=thinking,
            use_vllm=use_vllm,
        )
        return math.ceil(est * headroom)

    from flash.catalog import MODELS

    info = MODELS.get(model_id)
    is_grpo = (algorithm or "").lower() in ("grpo", "rl")
    if info is not None:
        params_b = params_b_from_str(info.params)
        quant = getattr(info, "quant", "bf16") or "bf16"
        # GRPO always runs the rollout on a colocated vLLM engine, so sizing must reserve room for
        # the 2nd (rollout) weight copy on the same card.
        use_vllm = True
        need = _need(params_b or 4.0, algorithm, quant=quant, use_vllm=use_vllm)
        # Hard floor the param-based matrix can't see: a curated GRPO floor.
        floor = 0
        if is_grpo and getattr(info, "grpo_min_vram_gb", 0):
            floor = int(info.grpo_min_vram_gb)
        if quant == "4bit-qlora":
            # GRPO needs the curated grpo_min_vram_gb (2 weight copies + KV); SFT is single-copy and
            # fits the smaller min_vram_gb. Don't leak the GRPO floor into SFT allocations or SFT
            # over-provisions.
            _q_floor = (
                int(getattr(info, "grpo_min_vram_gb", 0) or info.min_vram_gb)
                if is_grpo
                else int(info.min_vram_gb)
            )
            floor = max(floor, _q_floor)
        # Big-model GRPO is TIGHT at its floor (2 weight copies + KV pool), so long context
        # overflows it -> escalate to a bigger tier. See grpo_seq_escalation_gb.
        if is_grpo and floor:
            floor += grpo_seq_escalation_gb(params_b, seq_len)
        need = max(need, floor)
        # vLLM-colocate floor: the engine (CUDA context + KV pool sized to the CARD's VRAM +
        # framework) + the 2nd resident weight copy add a ~constant the param estimate misses,
        # so small-model GRPO under-provisions. MEASURED at the default group_size=8: 0.8B GRPO
        # fits a 24 GB card but OOMs 20 (est ~18, ~6 GB headroom on 24); 2B GRPO OOMs a 24 GB
        # card (est ~20 but the colocate cost tips it past 24 -> needs the 32 tier). So sub-~1B
        # models floor at 24, while larger small-models that the param estimate still under-shoots
        # floor at the 32 tier. 4B+ already exceed this via their param estimate, so untouched.
        if is_grpo and use_vllm:
            floor_gb = 24 if (params_b or 0.0) <= 1.0 else int(_VLLM_COLOCATE_FLOOR_GB)
            need = max(need, floor_gb)
        return need
    # Unlisted open model: size from HF metadata (GRPO is the heavier phase).
    params_b = fetch_hf_params_b(model_id)
    if params_b is None:
        return 24
    # Open models size against the heavier GRPO phase regardless of the requested algorithm.
    need = _need(params_b, "grpo")
    # Same long-context GRPO escalation as the catalog path so a big open model isn't
    # under-provisioned at long context either.
    if is_grpo:
        need += grpo_seq_escalation_gb(params_b, seq_len)
    return need


def fetch_hf_params_b(model_id: str) -> float | None:
    """Total params (billions) from the HF API safetensors metadata (no download)."""
    if os.environ.get("FLASH_SKIP_NET"):
        return None
    try:
        from huggingface_hub import HfApi

        info = HfApi(token=os.environ.get("HF_TOKEN")).model_info(
            model_id, expand=["safetensors"]
        )
        total = getattr(getattr(info, "safetensors", None), "total", None)
        if total:
            return float(total) / 1e9
    except Exception:
        # Best-effort size probe (network/HF-metadata may be unavailable); fall through
        # to None so callers report "size unknown" rather than failing.
        pass
    return None


def check_fit(
    model_id: str,
    algorithm: str,
    gpu: str,
    quant: str = "bf16",
    params_b: float | None = None,
) -> VramEstimate:
    """Estimate whether ``model_id`` plausibly trains on ``gpu``; never raises."""
    gpu_gb = GPU_VRAM_GB.get(gpu, 32)
    if params_b is None:
        params_b = fetch_hf_params_b(model_id)
    if params_b is None:
        return VramEstimate(None, algorithm, quant, None, gpu, gpu_gb, "unknown")
    est = estimate_vram_gb(params_b, algorithm, quant)
    if est > gpu_gb * 1.15:
        verdict = "too_big"
    elif est > gpu_gb * 0.85:
        verdict = "tight"
    else:
        verdict = "fits"
    return VramEstimate(params_b, algorithm, quant, est, gpu, gpu_gb, verdict)