# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.

"""Regression tests for full finetuning precision on no-bf16 GPUs (V100/T4).

Full finetuning upcasts trainable weights to float32, so the model dtype is
float32 (not bfloat16). The SFTTrainer mixed-precision template in
unsloth/models/rl.py must then:
  - run the forward pass under float16 autocast for normal models,
  - keep FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) in pure float32,
  - never select bf16 on hardware without bf16.

We execute the REAL template block extracted from rl.py source (no heavy unsloth
import) against mocked inputs. See issue #4082.
"""
from __future__ import annotations

import os
import sys
import types
from pathlib import Path

import pytest

torch = pytest.importorskip("torch")

RL_PY = Path(__file__).resolve().parents[2] / "unsloth" / "models" / "rl.py"


def _extract_mixed_precision_code() -> str:
    lines = RL_PY.read_text().split("\n")
    try:
        start = next(i for i, l in enumerate(lines) if "mixed_precision = (" in l)
    except StopIteration:
        pytest.skip("mixed_precision template not found in rl.py")
    body, k = [], start + 1
    while lines[k].strip() != ")":
        body.append(lines[k]); k += 1
    return eval("(\n" + "\n".join(body) + "\n)")  # only string literals + comments


CODE = _extract_mixed_precision_code()


def _decide(dtype, *, bf16_supported, force_float32, full_finetuning,
            mixed_precision, fp16, bf16):
    """Run the template block; return (args.fp16, args.bf16, ACCELERATE_MP, raised)."""
    uz = types.ModuleType("unsloth_zoo"); uzu = types.ModuleType("unsloth_zoo.utils")
    uzu._get_dtype = lambda x: x
    sys.modules.setdefault("unsloth_zoo", uz); sys.modules["unsloth_zoo.utils"] = uzu
    for k in ("UNSLOTH_FORCE_FLOAT32", "UNSLOTH_ENABLE_FULL_FINETUNING",
              "UNSLOTH_MIXED_PRECISION", "ACCELERATE_MIXED_PRECISION"):
        os.environ.pop(k, None)
    os.environ["UNSLOTH_FORCE_FLOAT32"] = "1" if force_float32 else "0"
    os.environ["UNSLOTH_ENABLE_FULL_FINETUNING"] = "1" if full_finetuning else "0"
    os.environ["UNSLOTH_MIXED_PRECISION"] = mixed_precision
    orig = torch.cuda.is_bf16_supported
    torch.cuda.is_bf16_supported = lambda *a, **k: bf16_supported
    args = types.SimpleNamespace(fp16=fp16, bf16=bf16, mixed_precision=None)
    emb = types.SimpleNamespace(weight=types.SimpleNamespace(dtype=dtype))
    model = types.SimpleNamespace(
        config=types.SimpleNamespace(dtype=dtype, torch_dtype=dtype),
        get_input_embeddings=lambda: emb)
    raised = None
    try:
        exec(CODE, {"torch": torch, "os": os}, {"args": args, "model": model})
    except TypeError:
        raised = "TypeError"
    finally:
        torch.cuda.is_bf16_supported = orig
    return args.fp16, args.bf16, os.environ.get("ACCELERATE_MIXED_PRECISION"), raised


def test_v100_normal_fullft_fp16_explicit():
    # Normal model, full FT (weights upcast to float32), V100, fp16=True.
    fp16, bf16, amp, raised = _decide(
        torch.float32, bf16_supported=False, force_float32=False,
        full_finetuning=True, mixed_precision="float32", fp16=True, bf16=False)
    assert raised is None
    assert (fp16, bf16) == (True, False)   # float32 weights + fp16 forward


def test_v100_normal_fullft_precision_unset():
    # Same, but user left precision unset -> must pick fp16, never bf16.
    fp16, bf16, amp, raised = _decide(
        torch.float32, bf16_supported=False, force_float32=False,
        full_finetuning=True, mixed_precision="float32", fp16=False, bf16=False)
    assert raised is None
    assert (fp16, bf16) == (True, False)
    assert amp == "fp16"


def test_force_float32_model_fullft_is_pure_float32():
    # FORCE_FLOAT32 model (Gemma3, gpt_oss, ...) in full FT -> pure float32, no autocast.
    fp16, bf16, amp, raised = _decide(
        torch.float32, bf16_supported=False, force_float32=True,
        full_finetuning=True, mixed_precision="float32", fp16=True, bf16=False)
    assert raised is None
    assert (fp16, bf16) == (False, False)
    assert amp in (None, "no")


def test_no_bf16_on_volta_in_auto_branch():
    # bf16 model dtype but no bf16 HW, precision unset -> fp16, never bf16.
    fp16, bf16, amp, raised = _decide(
        torch.bfloat16, bf16_supported=False, force_float32=False,
        full_finetuning=False, mixed_precision="float32", fp16=False, bf16=False)
    assert bf16 is False


def test_bf16_gpu_unchanged_auto_branch():
    # Regression guard: on a bf16 GPU, a float32 model with unset precision
    # still selects bf16 autocast (behavior must not change for bf16 hardware).
    fp16, bf16, amp, raised = _decide(
        torch.float32, bf16_supported=True, force_float32=False,
        full_finetuning=True, mixed_precision="float32", fp16=False, bf16=False)
    assert raised is None
    assert (fp16, bf16) == (False, True)


def test_genuine_bf16_model_with_fp16_still_raises():
    # A real bfloat16 model on bf16 HW with fp16 requested is a genuine mismatch.
    _, _, _, raised = _decide(
        torch.bfloat16, bf16_supported=True, force_float32=False,
        full_finetuning=False, mixed_precision="float32", fp16=True, bf16=False)
    assert raised == "TypeError"