"""
P3 Integration Test: Verify 6-warp multi-head decode fast path
produces identical results to a PyTorch reference.

Tests MHA, MQA, GQA at HD = 64, 128, 256.
Cosine similarity >= 0.999998 between kernel output and reference.
"""
import torch
import math
import sys
import os

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from dsv4.kernels.attention.fmha_multihead_op import fmha_multihead_decode_raw


def cosine_sim(a, b):
    a = a.flatten().float()
    b = b.flatten().float()
    return (a @ b) / (a.norm() * b.norm() + 1e-30)


def test_fast_path():
    """Test kernel vs PyTorch reference for MHA, MQA, GQA at various HD."""
    torch.manual_seed(42)

    configs = [
        # (n_q, n_kv, N, hd, desc)
        (4, 4, 64, 64, "MHA hd=64"),
        (4, 4, 128, 64, "MHA hd=64 N=128"),
        (4, 4, 64, 128, "MHA hd=128"),
        (4, 4, 64, 256, "MHA hd=256"),
        (4, 1, 64, 64, "MQA hd=64"),
        (4, 1, 128, 64, "MQA hd=64 N=128"),
        (4, 1, 64, 128, "MQA hd=128"),
        (4, 1, 64, 256, "MQA hd=256"),
        (128, 1, 64, 64, "MQA Pro hd=64"),
        (128, 1, 64, 128, "MQA Pro hd=128"),
        (8, 2, 64, 64, "GQA hd=64"),
        (8, 4, 64, 128, "GQA hd=128"),
    ]

    all_pass = True
    for n_q, n_kv, N, hd, desc in configs:
        scale = 1.0 / math.sqrt(hd)
        q_per_kv = n_q // n_kv

        try:
            # ---- Create data in KERNEL layout ----
            # Q: (1, n_q, 1, hd) — each head has 1 row of hd elements
            q_4d = torch.randn(1, n_q, 1, hd, dtype=torch.bfloat16, device='cuda').contiguous()

            # K: (1, n_kv, N, hd)
            k_4d = torch.randn(1, n_kv, N, hd, dtype=torch.bfloat16, device='cuda').contiguous()

            # V: (1, n_kv, hd, N) — the KERNEL expects V transposed (hd, N) per head
            v_4d = torch.randn(1, n_kv, hd, N, dtype=torch.bfloat16, device='cuda').contiguous()

            # ---- Kernel output ----
            sb = torch.zeros(1, n_q, dtype=torch.float32, device='cuda')
            o_4d, lse_4d = fmha_multihead_decode_raw(
                q_4d, k_4d, v_4d, scale, 0, 0, False, sb
            )
            o_kernel = o_4d  # (1, n_q, 1, hd)

            # ---- PyTorch reference using the SAME data ----
            # Q: (n_q, 1, hd), K: (n_kv, N, hd), V: (n_kv, N, hd) — V is TRANSPOSED from kernel layout
            q_ref = q_4d[0]  # (n_q, 1, hd)
            k_ref = k_4d[0]  # (n_kv, N, hd)
            v_ref = v_4d[0].transpose(-1, -2)  # (n_kv, N, hd) — transpose (hd,N) -> (N,hd)

            o_ref = torch.zeros(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
            for kv_idx in range(n_kv):
                k_h = k_ref[kv_idx]  # (N, hd)
                v_h = v_ref[kv_idx]  # (N, hd)
                for qi in range(q_per_kv):
                    q_idx = kv_idx * q_per_kv + qi
                    q_h = q_ref[q_idx]  # (1, hd)
                    s = torch.matmul(q_h.float(), k_h.float().T) * scale  # (1, N)
                    s = torch.softmax(s, dim=-1)
                    o = torch.matmul(s, v_h.float())  # (1, hd)
                    o_ref[q_idx] = o.bfloat16()

            # ---- Compare per-head ----
            worst_cos = 1.0
            for h in range(n_q):
                cos = torch.nn.functional.cosine_similarity(
                    o_kernel[0, h].float().flatten().unsqueeze(0),
                    o_ref[h].float().flatten().unsqueeze(0),
                ).item()
                worst_cos = min(worst_cos, cos)

            status = "PASS" if worst_cos >= 0.999990 else "FAIL"
            if status == "FAIL":
                all_pass = False
            print(f"  {status} {desc}: worst_cos={worst_cos:.6f}")

        except Exception as e:
            import traceback
            print(f"  FAIL {desc}: {e}")
            traceback.print_exc()
            all_pass = False

    return all_pass


def test_full_api():
    """Test the full dsv4_attention API (goes through fast path for T=1, N<=128)."""
    from dsv4.kernels.attention.production import dsv4_attention

    torch.manual_seed(99)

    configs = [
        (8, 8, 128, 64, "MHA hd=64"),
        (8, 8, 128, 128, "MHA hd=128"),
        (8, 1, 128, 64, "MQA hd=64"),
        (8, 1, 128, 128, "MQA hd=128"),
        (8, 2, 128, 64, "GQA hd=64"),
    ]

    all_pass = True
    for n_q, n_kv, N, hd, desc in configs:
        scale = 1.0 / math.sqrt(hd)
        try:
            q = torch.randn(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
            if n_kv == 1:
                k = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
                v = torch.randn(N, hd, dtype=torch.bfloat16, device='cuda')
            else:
                k = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')
                v = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')

            # Full API call (should use fast path)
            o_fast = dsv4_attention(q, k, v, scale=scale)

            # Reference
            o_ref = reference_attention_api(q, k, v, scale, n_q, n_kv, N, hd)

            cos = cosine_sim(o_ref, o_fast).item()
            status = "PASS" if cos >= 0.999990 else "FAIL"
            if status == "FAIL":
                all_pass = False
            print(f"  {status} [full API] {desc}: cos={cos:.6f}")
        except Exception as e:
            import traceback
            print(f"  FAIL [full API] {desc}: {e}")
            traceback.print_exc()
            all_pass = False

    return all_pass


def reference_attention_api(q, k, v, scale, n_q, n_kv, N, hd):
    """Reference that matches dsv4_attention input format."""
    q_per_kv = n_q // n_kv
    if k.dim() == 2:
        k = k.unsqueeze(0)
    if v.dim() == 2:
        v = v.unsqueeze(0)
    output = torch.zeros(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
    for kv_idx in range(n_kv):
        k_h = k[kv_idx]  # (N, hd)
        v_h = v[kv_idx]  # (N, hd)
        for qi in range(q_per_kv):
            q_idx = kv_idx * q_per_kv + qi
            q_h = q[q_idx]  # (1, hd)
            s = torch.matmul(q_h.float(), k_h.float().T) * scale
            s = torch.softmax(s, dim=-1)
            o = torch.matmul(s, v_h.float())
            output[q_idx] = o.bfloat16()
    return output


if __name__ == "__main__":
    print("P3 Integration Test: 6-warp decode fast path")
    print("=" * 60)
    ok1 = test_fast_path()
    print()
    ok2 = test_full_api()
    print("=" * 60)
    ok = ok1 and ok2
    print("ALL PASS" if ok else "SOME FAILED")
    sys.exit(0 if ok else 1)