# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from torch import Generator

from vllm.platforms import current_platform
from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p

DEVICE = current_platform.device_type

BATCH_SIZE = 1024
VOCAB_SIZE = 128 * 1024


@pytest.fixture(autouse=True)
def reset_default_device():
    """
    Explicitly set the default device, which can affect subsequent tests.
    Adding this fixture helps avoid this problem.
    """
    original_device = torch.get_default_device()
    yield
    torch.set_default_device(original_device)


def test_topk_impl_equivalence():
    torch.set_default_device(DEVICE)
    generator = Generator(device=DEVICE).manual_seed(33)

    logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)

    # Random top-k values between 1 and 9.
    k = torch.randint(1, 10, (BATCH_SIZE,), generator=generator)

    # Set k=vocab_size for ~50% of requests in the batch (top-k disabled).
    k.masked_fill_(
        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=bool), VOCAB_SIZE
    )

    # Top-k only implementation
    result1 = apply_top_k_top_p(logits=logits.clone(), k=k, p=None)

    # Top-p + top-k
    no_op_top_p = torch.tensor([1.0])
    result2 = apply_top_k_top_p(logits=logits.clone(), k=k, p=no_op_top_p)

    assert torch.allclose(result1, result2)


@pytest.mark.skip(
    reason="FlashInfer top-k/top-p renorm comparison fails; "
    "needs investigation of tolerance threshold or "
    "interface differences between Python and FlashInfer implementations"
)
def test_flashinfer_sampler():
    """
    This test verifies that the FlashInfer top-k and top-p sampling
    implementation produces the same results as the Python implementation.

    NOTE: FlashInfer did not directly expose an interface for fused top-k and
    top-p prob renorm (it did provide fused sampling but we cannot compare
    sampling results due to randomness), so we will compare the probability
    renormed consequently by top-k and then top-p of FlashInfer implementation.
    """
    try:
        from flashinfer.sampling import top_k_renorm_probs, top_p_renorm_probs

        is_flashinfer_available = True
    except ImportError:
        is_flashinfer_available = False

    FLASHINFER_ENABLED = current_platform.is_cuda() and is_flashinfer_available

    if not FLASHINFER_ENABLED:
        pytest.skip("FlashInfer not installed or not available on this platform.")

    torch.set_default_device(DEVICE)
    generator = Generator(device=DEVICE).manual_seed(42)

    # Generate random logits
    logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)

    # Generate various top-k and top-p values
    k_values = torch.randint(1, 1000, (BATCH_SIZE,), generator=generator)
    p_values = (
        torch.rand((BATCH_SIZE,), generator=generator) * 0.5 + 0.5
    )  # range in [0.5, 1.0]

    # Sometimes disable top-k (k=vocab_size)
    k_values.masked_fill_(
        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=torch.bool),
        VOCAB_SIZE,
    )

    # Sometimes disable top-p (p=1.0)
    p_values.masked_fill_(
        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=torch.bool), 1.0
    )

    python_logits = apply_top_k_top_p(
        logits=logits.clone(),
        k=k_values,
        p=p_values,
    )
    python_probs = torch.softmax(python_logits, dim=-1)

    # FlashInfer only exposed renorm interfaces for probs so convert first
    flashinfer_probs = torch.softmax(logits.clone(), dim=-1)
    flashinfer_probs = top_k_renorm_probs(
        probs=flashinfer_probs,
        top_k=k_values,
    )
    flashinfer_probs = top_p_renorm_probs(
        probs=flashinfer_probs,
        top_p=p_values,
    )

    # Compare the results
    assert torch.allclose(python_probs, flashinfer_probs, atol=2e-2), (
        "FlashInfer and Python sampling implementations do not match!"
    )
