"""Robust evaluation framework for PallasBench.

Adapts SakanaAI robust-kbench CUDA verification filters to the JAX/Pallas
kernel ecosystem and adds tiling analysis, IR capture, and hardware-aware
throughput metrics targeting A100 GPUs.
"""

from __future__ import annotations

import difflib
import inspect
import math
import re
import subprocess
import time
from dataclasses import dataclass, field, asdict
from datetime import datetime, timezone
from typing import Any, Callable, Sequence

import jax
import jax.numpy as jnp
import numpy as np

from pallasbench.utils import generate_inputs, check_correctness, time_fn


# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------

A100_BANDWIDTH_GB_S = 2039.0
A100_SM_COUNT = 108
TRITON_BLOCK_ELEMENT_LIMIT = 1_048_576  # 1M elements


# ===================================================================
# 1. Robustness Filters (adapted from robust-kbench forward filters)
# ===================================================================

def filter_output_range(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    num_seeds: int = 10,
    dtype: str = "float32",
) -> bool:
    """Return True if ALL outputs across seeds fall within (-0.01, 0.01).

    A True result is suspicious: the kernel may be producing near-zero
    constant output regardless of input, indicating it ignores its inputs
    or has a hardcoded return value.
    """
    outputs = []
    for seed in range(num_seeds):
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=seed)
        try:
            out = pallas_fn(*inputs)
            jax.block_until_ready(out)
            outputs.append(np.asarray(out, dtype=np.float32))
        except Exception:
            return False
    if not outputs:
        return False
    stacked = np.stack(outputs)
    return bool(np.all((stacked > -0.01) & (stacked < 0.01)))


def filter_output_std(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    num_seeds: int = 10,
    dtype: str = "float32",
) -> bool:
    """Return True if the per-element std across seeds is < 0.01 everywhere.

    A True result is suspicious: the kernel produces nearly identical outputs
    no matter which random seed generated the inputs.
    """
    outputs = []
    for seed in range(num_seeds):
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=seed)
        try:
            out = pallas_fn(*inputs)
            jax.block_until_ready(out)
            outputs.append(np.asarray(out, dtype=np.float32))
        except Exception:
            return False
    if not outputs:
        return False
    stacked = np.stack(outputs)
    stds = np.std(stacked, axis=0)
    return bool(np.all(stds < 0.01))


def filter_output_axes(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    num_seeds: int = 10,
    dtype: str = "float32",
) -> bool:
    """Return True if the output has low variance along any axis.

    Checks each axis of the stacked output tensor; if ANY axis has all
    standard deviations below 0.01 that axis carries no information, which
    is suspicious for a legitimate computation.
    """
    outputs = []
    for seed in range(num_seeds):
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=seed)
        try:
            out = pallas_fn(*inputs)
            jax.block_until_ready(out)
            outputs.append(np.asarray(out, dtype=np.float32))
        except Exception:
            return False
    if not outputs:
        return False
    stacked = np.stack(outputs)
    for axis in range(stacked.ndim):
        axis_std = np.std(stacked, axis=axis)
        if np.all(axis_std < 0.01):
            return True
    return False


def filter_input_impact(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    num_seeds: int = 5,
    dtype: str = "float32",
) -> bool:
    """Return True if different inputs produce the same output.

    Runs the kernel with varying input seeds.  If the per-element std
    across runs is < 0.01 everywhere the kernel does not actually depend
    on its inputs.
    """
    outputs = []
    for seed in range(num_seeds):
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=seed)
        try:
            out = pallas_fn(*inputs)
            jax.block_until_ready(out)
            outputs.append(np.asarray(out, dtype=np.float32))
        except Exception:
            return False
    if not outputs:
        return False
    stacked = np.stack(outputs)
    stds = np.std(stacked, axis=0)
    return bool(np.all(stds < 0.01))


def filter_llm_sanity(kernel_source: str) -> dict[str, bool]:
    """Static-analysis heuristics for suspicious Pallas kernel source.

    Returns a dict with boolean flags:
      - is_redundant:  source appears to hardcode outputs or ignore BlockRef
                       inputs entirely
      - is_trivial:    kernel body is trivially short or calls the JAX baseline
                       function directly (defeating the purpose of a custom kernel)
    """
    is_redundant = False
    is_trivial = False

    src = kernel_source.strip()

    lines = [
        l.strip()
        for l in src.splitlines()
        if l.strip() and not l.strip().startswith("#")
    ]
    body_lines = [
        l
        for l in lines
        if not l.startswith("def ")
        and not l.startswith("import ")
        and not l.startswith("from ")
    ]

    # Hardcoded constant stores: writing a literal scalar to the output ref
    hardcoded_patterns = [
        r"o_ref\[.*\]\s*=\s*[\d.]+",
        r"o_ref\[.*\]\s*=\s*jnp\.(zeros|ones|full)\b",
        r"output_ref\[.*\]\s*=\s*[\d.]+",
    ]
    for pat in hardcoded_patterns:
        if re.search(pat, src):
            is_redundant = True
            break

    # Check if kernel never reads from input refs
    ref_read_pattern = r"[a-zA-Z_]+_ref\[.*\]"
    ref_reads = re.findall(ref_read_pattern, src)
    write_refs = re.findall(r"(o_ref|out_ref|output_ref)\[", src)
    if write_refs and not any(
        r
        for r in ref_reads
        if not re.match(r"(o_ref|out_ref|output_ref)\[", r)
    ):
        is_redundant = True

    # Trivially short body (e.g. single-line pass-through)
    if len(body_lines) <= 2:
        is_trivial = True

    # Calls baseline JAX functions internally
    baseline_markers = [
        "jax_baseline",
        "jax.nn.relu",
        "jax.nn.gelu",
        "jax.nn.softmax",
        "jax.nn.log_softmax",
        "jnp.dot(",
        "jnp.matmul(",
    ]
    for marker in baseline_markers:
        if marker in src:
            is_trivial = True
            break

    return {"is_redundant": is_redundant, "is_trivial": is_trivial}


# ===================================================================
# 2. Tiling Analysis
# ===================================================================

def analyze_tiling(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str = "float32",
) -> dict[str, Any]:
    """Extract tiling metadata from a Pallas kernel via Jaxpr tracing.

    Returns a dict with:
      - grid: tuple of grid dimensions (or None if not extractable)
      - block_shape: tuple of block dimensions (or None)
      - block_elements: total elements per block
      - triton_limit: the 1M element ceiling
      - tiling_efficiency_pct: block_elements / triton_limit * 100
      - sm_coverage: grid product vs A100 SM count
    """
    result: dict[str, Any] = {
        "grid": None,
        "block_shape": None,
        "block_elements": 0,
        "triton_limit": TRITON_BLOCK_ELEMENT_LIMIT,
        "tiling_efficiency_pct": 0.0,
        "sm_coverage": 0.0,
    }

    try:
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=0)
        abs_inputs = [jax.ShapeDtypeStruct(x.shape, x.dtype) for x in inputs]
        jaxpr = jax.make_jaxpr(pallas_fn)(*abs_inputs)
        jaxpr_str = str(jaxpr)

        grid_match = re.search(r"grid=\(([^)]+)\)", jaxpr_str)
        if grid_match:
            dims = tuple(
                int(x.strip())
                for x in grid_match.group(1).split(",")
                if x.strip()
            )
            result["grid"] = dims
            grid_product = math.prod(dims)
            result["sm_coverage"] = grid_product / A100_SM_COUNT

        block_match = re.search(r"block_shape=\(([^)]+)\)", jaxpr_str)
        if block_match:
            bshape = tuple(
                int(x.strip())
                for x in block_match.group(1).split(",")
                if x.strip()
            )
            result["block_shape"] = bshape
            block_elems = math.prod(bshape)
            result["block_elements"] = block_elems
            result["tiling_efficiency_pct"] = (
                block_elems / TRITON_BLOCK_ELEMENT_LIMIT * 100.0
            )
    except Exception:
        pass

    return result


# ===================================================================
# 3. IR Capture
# ===================================================================

def capture_jaxpr(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str = "float32",
) -> str:
    """Return the Jaxpr text for the given Pallas function."""
    try:
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=0)
        abs_inputs = [jax.ShapeDtypeStruct(x.shape, x.dtype) for x in inputs]
        jaxpr = jax.make_jaxpr(pallas_fn)(*abs_inputs)
        return str(jaxpr)
    except Exception as exc:
        return f"<jaxpr capture failed: {exc}>"


def capture_stablehlo(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str = "float32",
) -> str:
    """Return the StableHLO text for the given Pallas function."""
    try:
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=0)
        lowered = jax.jit(pallas_fn).lower(*inputs)
        return lowered.as_text(dialect="stablehlo")
    except Exception as exc:
        return f"<stablehlo capture failed: {exc}>"


def capture_all_ir(
    pallas_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str = "float32",
) -> dict[str, Any]:
    """Capture all available IR representations.

    Returns a dict with:
      - jaxpr: Jaxpr text
      - stablehlo: StableHLO text
      - triton_ir_size_bytes: approximate byte-size of the Triton-IR
        (estimated from the StableHLO text length as a proxy since
        direct Triton-IR capture requires lowering through the GPU
        compiler which is not always available)
    """
    jaxpr_text = capture_jaxpr(pallas_fn, input_shapes, dtype=dtype)
    stablehlo_text = capture_stablehlo(pallas_fn, input_shapes, dtype=dtype)
    triton_ir_size = len(stablehlo_text.encode("utf-8"))
    return {
        "jaxpr": jaxpr_text,
        "stablehlo": stablehlo_text,
        "triton_ir_size_bytes": triton_ir_size,
    }


# ===================================================================
# 4. Hardware Metrics Helpers
# ===================================================================

def _gpu_memory_used_mb() -> float:
    """Query current GPU memory usage via nvidia-smi."""
    try:
        out = subprocess.check_output(
            [
                "nvidia-smi",
                "--query-gpu=memory.used",
                "--format=csv,noheader,nounits",
            ],
            text=True,
            timeout=5,
        )
        return float(out.strip().splitlines()[0])
    except Exception:
        return 0.0


def _compute_throughput(
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str,
    kernel_time_ms: float,
) -> tuple[float, float]:
    """Return (throughput_gb_s, hw_bw_util_pct) for read+write traffic."""
    bytes_per_elem = jnp.dtype(dtype).itemsize
    total_elements = sum(math.prod(s) for s in input_shapes)
    # Assume read all inputs + write one output the size of the first input
    read_bytes = total_elements * bytes_per_elem
    write_bytes = math.prod(input_shapes[0]) * bytes_per_elem
    total_bytes = read_bytes + write_bytes
    if kernel_time_ms <= 0:
        return 0.0, 0.0
    throughput_gb_s = (total_bytes / 1e9) / (kernel_time_ms / 1e3)
    hw_bw_util_pct = (throughput_gb_s / A100_BANDWIDTH_GB_S) * 100.0
    return throughput_gb_s, hw_bw_util_pct


# ===================================================================
# 5. RobustPallasResult
# ===================================================================

@dataclass
class RobustPallasResult:
    """Complete evaluation record for a single PallasBench kernel."""

    # Identity
    task_name: str = ""
    level: int = 0
    category: str = ""
    input_shapes: list[tuple[int, ...]] = field(default_factory=list)
    dtype: str = "float32"

    # Correctness
    correct: bool = False
    errors: list[str] = field(default_factory=list)
    multi_seed_correct: bool = False
    multi_dtype_correct: bool = False

    # Performance
    baseline_time_ms: float = 0.0
    kernel_time_ms: float = 0.0
    speedup: float = 0.0

    # Hardware metrics
    throughput_gb_s: float = 0.0
    hw_bw_util_pct: float = 0.0
    gpu_mem_delta_mb: float = 0.0

    # Robustness filters
    filter_output_range: bool = False
    filter_output_std: bool = False
    filter_output_axes: bool = False
    filter_input_impact: bool = False
    filter_llm_sanity: dict[str, bool] = field(default_factory=dict)

    # Tiling
    grid_dims: tuple | None = None
    block_shape: tuple | None = None
    block_elements: int = 0
    tiling_efficiency: float = 0.0

    # IR
    jaxpr: str = ""
    stablehlo: str = ""

    # Source tracking
    original_source: str = ""
    fixed_source: str = ""
    diff: str = ""

    # Metadata
    timestamp: str = ""

    def to_dict(self) -> dict[str, Any]:
        """Serialize to a plain dictionary."""
        return asdict(self)

    @property
    def is_robust(self) -> bool:
        """True when the kernel passes all robustness filters."""
        sanity = self.filter_llm_sanity or {}
        return (
            self.correct
            and not self.filter_output_range
            and not self.filter_output_std
            and not self.filter_output_axes
            and not self.filter_input_impact
            and not sanity.get("is_redundant", False)
            and not sanity.get("is_trivial", False)
        )

    @property
    def summary_line(self) -> str:
        """One-line human-readable status string."""
        status = "ROBUST" if self.is_robust else "FLAGGED"
        return (
            f"[{status}] {self.task_name}: "
            f"correct={self.correct} speedup={self.speedup:.2f}x "
            f"throughput={self.throughput_gb_s:.1f} GB/s "
            f"bw_util={self.hw_bw_util_pct:.1f}%"
        )


# ===================================================================
# 6. Main Evaluation Entry Point
# ===================================================================

def _check_multi_seed(
    pallas_fn: Callable,
    baseline_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtype: str,
    n_seeds: int = 10,
    atol: float = 1e-3,
    rtol: float = 1e-3,
) -> bool:
    """Correctness check across many random seeds."""
    for seed in range(n_seeds):
        inputs = generate_inputs(input_shapes, dtype=dtype, seed=seed)
        try:
            out_k = pallas_fn(*inputs)
            out_b = baseline_fn(*inputs)
            jax.block_until_ready(out_k)
            jax.block_until_ready(out_b)
            if out_k.shape != out_b.shape:
                return False
            if not jnp.allclose(out_k, out_b, atol=atol, rtol=rtol):
                return False
        except Exception:
            return False
    return True


def _check_multi_dtype(
    pallas_fn: Callable,
    baseline_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    dtypes_to_test: Sequence[str] = ("float32", "float16", "bfloat16"),
    atol: float = 1e-2,
    rtol: float = 1e-2,
) -> bool:
    """Correctness check across multiple dtypes."""
    for dt in dtypes_to_test:
        try:
            inputs = generate_inputs(input_shapes, dtype=dt, seed=0)
            out_k = pallas_fn(*inputs)
            out_b = baseline_fn(*inputs)
            jax.block_until_ready(out_k)
            jax.block_until_ready(out_b)
            if out_k.shape != out_b.shape:
                return False
            out_k_f32 = out_k.astype(jnp.float32)
            out_b_f32 = out_b.astype(jnp.float32)
            if not jnp.allclose(out_k_f32, out_b_f32, atol=atol, rtol=rtol):
                return False
        except Exception:
            return False
    return True


def _compute_diff(original: str, fixed: str) -> str:
    """Unified diff between original and fixed source."""
    if not original and not fixed:
        return ""
    orig_lines = original.splitlines(keepends=True)
    fixed_lines = fixed.splitlines(keepends=True)
    return "".join(
        difflib.unified_diff(
            orig_lines,
            fixed_lines,
            fromfile="original",
            tofile="fixed",
        )
    )


def evaluate_kernel_robust(
    pallas_fn: Callable,
    baseline_fn: Callable,
    input_shapes: Sequence[tuple[int, ...]],
    task_name: str = "unnamed",
    level: int = 0,
    category: str = "",
    dtype: str = "float32",
    n_correctness: int = 5,
    n_warmup: int = 10,
    n_trials: int = 100,
    atol: float = 1e-3,
    rtol: float = 1e-3,
    filter_seeds: int = 10,
    original_source: str = "",
    fixed_source: str = "",
) -> RobustPallasResult:
    """Run the full robust evaluation pipeline for a single Pallas kernel.

    This is the main entry point that orchestrates:
      1. Standard correctness and timing from PallasBench
      2. Multi-seed and multi-dtype correctness
      3. All four robustness filters (range, std, axes, input_impact)
      4. Static LLM sanity analysis on kernel source
      5. Tiling analysis via Jaxpr inspection
      6. IR capture (Jaxpr + StableHLO)
      7. Hardware throughput and memory delta
      8. Source diff tracking
    """
    result = RobustPallasResult(
        task_name=task_name,
        level=level,
        category=category,
        input_shapes=list(input_shapes),
        dtype=dtype,
        timestamp=datetime.now(timezone.utc).isoformat(),
    )

    # --- Correctness -------------------------------------------------------
    correct, errors = check_correctness(
        pallas_fn=pallas_fn,
        baseline_fn=baseline_fn,
        input_shapes=input_shapes,
        dtype=dtype,
        n_checks=n_correctness,
        atol=atol,
        rtol=rtol,
    )
    result.correct = correct
    result.errors = errors

    result.multi_seed_correct = _check_multi_seed(
        pallas_fn,
        baseline_fn,
        input_shapes,
        dtype,
        n_seeds=filter_seeds,
        atol=atol,
        rtol=rtol,
    )
    result.multi_dtype_correct = _check_multi_dtype(
        pallas_fn,
        baseline_fn,
        input_shapes,
    )

    # --- Timing ------------------------------------------------------------
    inputs = generate_inputs(input_shapes, dtype=dtype, seed=42)
    result.baseline_time_ms = time_fn(
        baseline_fn,
        inputs,
        n_warmup=n_warmup,
        n_trials=n_trials,
    )
    result.kernel_time_ms = time_fn(
        pallas_fn,
        inputs,
        n_warmup=n_warmup,
        n_trials=n_trials,
    )
    result.speedup = (
        result.baseline_time_ms / result.kernel_time_ms
        if result.kernel_time_ms > 0
        else 0.0
    )

    # --- Hardware metrics --------------------------------------------------
    mem_before = _gpu_memory_used_mb()
    warmup_out = pallas_fn(*inputs)
    jax.block_until_ready(warmup_out)
    mem_after = _gpu_memory_used_mb()
    result.gpu_mem_delta_mb = mem_after - mem_before

    tp, bw = _compute_throughput(input_shapes, dtype, result.kernel_time_ms)
    result.throughput_gb_s = tp
    result.hw_bw_util_pct = bw

    # --- Robustness filters ------------------------------------------------
    result.filter_output_range = filter_output_range(
        pallas_fn,
        input_shapes,
        num_seeds=filter_seeds,
        dtype=dtype,
    )
    result.filter_output_std = filter_output_std(
        pallas_fn,
        input_shapes,
        num_seeds=filter_seeds,
        dtype=dtype,
    )
    result.filter_output_axes = filter_output_axes(
        pallas_fn,
        input_shapes,
        num_seeds=filter_seeds,
        dtype=dtype,
    )
    result.filter_input_impact = filter_input_impact(
        pallas_fn,
        input_shapes,
        num_seeds=min(filter_seeds, 5),
        dtype=dtype,
    )

    # Source-level sanity (use fixed_source if available, else try inspect)
    source_text = fixed_source or original_source
    if not source_text:
        try:
            source_text = inspect.getsource(pallas_fn)
        except (TypeError, OSError):
            source_text = ""
    result.filter_llm_sanity = (
        filter_llm_sanity(source_text) if source_text else {}
    )

    # --- Tiling analysis ---------------------------------------------------
    tiling = analyze_tiling(pallas_fn, input_shapes, dtype=dtype)
    result.grid_dims = tiling["grid"]
    result.block_shape = tiling["block_shape"]
    result.block_elements = tiling["block_elements"]
    result.tiling_efficiency = tiling["tiling_efficiency_pct"]

    # --- IR capture --------------------------------------------------------
    result.jaxpr = capture_jaxpr(pallas_fn, input_shapes, dtype=dtype)
    result.stablehlo = capture_stablehlo(pallas_fn, input_shapes, dtype=dtype)

    # --- Source tracking ---------------------------------------------------
    result.original_source = original_source
    result.fixed_source = fixed_source
    result.diff = _compute_diff(original_source, fixed_source)

    return result