"""Profile TaoNet and TaoNet-SSM component costs on synthetic token batches.

The real-token benchmark tells us end-to-end quality and throughput. This
script is the companion microscope: it times forward components such as the
SSM core, gates, projections, FFN, embeddings, and output head so hardware work
targets the largest measured costs.
"""

from __future__ import annotations

import argparse
from collections import defaultdict
from contextlib import nullcontext
from contextlib import redirect_stdout
import io
import json
import os
from pathlib import Path
import platform
import sys
import time
from typing import Any

import torch

REPO_ROOT = Path(__file__).resolve().parents[1]
SRC_ROOT = REPO_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
    sys.path.insert(0, str(SRC_ROOT))

from taoTrain.config import ModelConfig
from taoTrain.models import get_model


DTYPES = {
    "float32": torch.float32,
    "fp32": torch.float32,
    "float16": torch.float16,
    "fp16": torch.float16,
    "bfloat16": torch.bfloat16,
    "bf16": torch.bfloat16,
}


def synchronize(device: torch.device) -> None:
    if device.type == "cuda":
        torch.cuda.synchronize(device)


def reset_memory(device: torch.device) -> None:
    if device.type == "cuda":
        torch.cuda.reset_peak_memory_stats(device)


def memory_stats(device: torch.device) -> dict[str, float | None]:
    if device.type != "cuda":
        return {"peak_allocated_mb": None, "peak_reserved_mb": None}
    return {
        "peak_allocated_mb": torch.cuda.max_memory_allocated(device) / (1024**2),
        "peak_reserved_mb": torch.cuda.max_memory_reserved(device) / (1024**2),
    }


class ComponentTimer:
    def __init__(self, device: torch.device) -> None:
        self.device = device
        self.records: dict[str, list[float]] = defaultdict(list)
        self._starts: dict[int, Any] = {}
        self._handles = []

    def _record_ms(self, name: str, start: Any) -> None:
        if self.device.type == "cuda":
            end = torch.cuda.Event(enable_timing=True)
            end.record()
            end.synchronize()
            self.records[name].append(float(start.elapsed_time(end)))
        else:
            self.records[name].append((time.perf_counter() - start) * 1000.0)

    def add(self, module: torch.nn.Module, name: str) -> None:
        def pre_hook(mod, inputs):
            del inputs
            if self.device.type == "cuda":
                start = torch.cuda.Event(enable_timing=True)
                start.record()
            else:
                start = time.perf_counter()
            self._starts[id(mod)] = start

        def post_hook(mod, inputs, output):
            del inputs, output
            start = self._starts.pop(id(mod), None)
            if start is not None:
                self._record_ms(name, start)

        self._handles.append(module.register_forward_pre_hook(pre_hook))
        self._handles.append(module.register_forward_hook(post_hook))

    def close(self) -> None:
        for handle in self._handles:
            handle.remove()
        self._handles.clear()

    def summary(self) -> list[dict[str, float | str | int]]:
        rows = []
        for name, values in sorted(self.records.items()):
            if not values:
                continue
            rows.append(
                {
                    "component": name,
                    "calls": len(values),
                    "mean_ms": sum(values) / len(values),
                    "total_ms": sum(values),
                    "min_ms": min(values),
                    "max_ms": max(values),
                }
            )
        rows.sort(key=lambda row: float(row["total_ms"]), reverse=True)
        return rows


def build_config(args: argparse.Namespace, architecture: str) -> ModelConfig:
    d_latent_kv = args.d_latent_kv if args.d_latent_kv is not None else int(args.hidden_dim * 0.75)
    d_rope = args.d_rope if args.d_rope is not None else args.hidden_dim // args.num_heads
    hidden_dim_ff = args.hidden_dim_ff if args.hidden_dim_ff is not None else args.hidden_dim * 4
    return ModelConfig(
        architecture_type=architecture,
        vocab_size=args.vocab_size,
        hidden_dim=args.hidden_dim,
        num_layers=args.num_layers,
        num_heads=args.num_heads,
        max_seq_length=args.seq_len,
        d_latent_kv=d_latent_kv,
        d_rope=d_rope,
        hidden_dim_ff=hidden_dim_ff,
        dropout=args.dropout,
        gqa_groups=args.gqa_groups,
        rope_scale=args.rope_scale,
        yarn_alpha=args.yarn_alpha,
        init_std=args.init_std,
        ssm_core=args.ssm_core,
        ssm_hidden_dim=args.ssm_hidden_dim,
        ssm_mixer_dim=args.ssm_mixer_dim,
        ssm_rank=args.ssm_rank,
        ssm_max_low_rank_scale=args.ssm_max_low_rank_scale,
        ssm_kernel_mode=args.ssm_kernel_mode,
        ssm_kernel_threshold=args.ssm_kernel_threshold,
        ssm_dt_min=args.ssm_dt_min,
        ssm_dt_max=args.ssm_dt_max,
        ssm_dt_init=args.ssm_dt_init,
        ssm_use_padding_mask=False,
        ssm_activation=args.ssm_activation,
        ssm_gate=args.ssm_gate,
        ssm_input_gate=args.ssm_input_gate,
        ssm_layer_scale_init=args.ssm_layer_scale_init,
        ssm_local_shift=args.ssm_local_shift,
        ssm_local_shift_init=args.ssm_local_shift_init,
        ssm_local_shift_per_channel=args.ssm_local_shift_per_channel,
    )


def add_component_hooks(model: torch.nn.Module, architecture: str, timer: ComponentTimer) -> None:
    timer.add(model.token_embedding, "embedding")
    timer.add(model.final_norm, "final_norm")
    timer.add(model.output_head, "output_head")
    for layer_index, block in enumerate(model.blocks):
        prefix = f"block{layer_index}"
        if architecture == "taonet_ssm":
            mixer = block.mixer
            timer.add(mixer.norm, f"{prefix}.mixer.norm")
            if mixer.input_gate is not None:
                timer.add(mixer.input_gate, f"{prefix}.mixer.input_gate")
            timer.add(mixer.input_proj, f"{prefix}.mixer.input_proj")
            timer.add(mixer.ssm, f"{prefix}.mixer.ssm_core")
            timer.add(mixer.activation, f"{prefix}.mixer.activation")
            timer.add(mixer.out_proj, f"{prefix}.mixer.out_proj")
            if mixer.output_gate is not None:
                timer.add(mixer.output_gate, f"{prefix}.mixer.output_gate")
            timer.add(mixer.proj_dropout, f"{prefix}.mixer.dropout")
        else:
            mla = block.mla
            timer.add(mla.norm, f"{prefix}.attention.norm")
            timer.add(mla.q_proj, f"{prefix}.attention.q_proj")
            timer.add(mla.k_proj, f"{prefix}.attention.k_proj")
            timer.add(mla.v_proj, f"{prefix}.attention.v_proj")
            timer.add(mla.out_proj, f"{prefix}.attention.out_proj")
            timer.add(mla.attn_dropout, f"{prefix}.attention.attn_dropout")
            timer.add(mla.proj_dropout, f"{prefix}.attention.proj_dropout")
        timer.add(block.ff_norm, f"{prefix}.ff.norm")
        timer.add(block.ff_gate, f"{prefix}.ff.gate")
        timer.add(block.ff_value, f"{prefix}.ff.value")
        timer.add(block.ff_out, f"{prefix}.ff.out")


def time_repeats(fn, *, device: torch.device, warmup: int, repeats: int) -> dict[str, float]:
    for _ in range(warmup):
        fn()
    synchronize(device)

    latencies = []
    for _ in range(repeats):
        reset_memory(device)
        synchronize(device)
        start = time.perf_counter()
        fn()
        synchronize(device)
        latencies.append(time.perf_counter() - start)
    mean_s = sum(latencies) / len(latencies)
    return {
        "mean_ms": mean_s * 1000.0,
        "min_ms": min(latencies) * 1000.0,
        "max_ms": max(latencies) * 1000.0,
    }


def profile_architecture(
    args: argparse.Namespace,
    *,
    architecture: str,
    device: torch.device,
    dtype: torch.dtype,
) -> dict[str, Any]:
    torch.manual_seed(args.seed)
    if device.type == "cuda":
        torch.cuda.manual_seed_all(args.seed)

    config = build_config(args, architecture)
    with redirect_stdout(io.StringIO()):
        model = get_model(config, device=device)
    model.train()

    input_ids = torch.randint(
        low=0,
        high=args.vocab_size,
        size=(args.batch_size, args.seq_len),
        device=device,
    )
    labels = torch.randint(
        low=0,
        high=args.vocab_size,
        size=(args.batch_size, args.seq_len),
        device=device,
    )
    attention_mask = torch.ones_like(input_ids)

    autocast_enabled = device.type == "cuda" and dtype in {torch.float16, torch.bfloat16}

    def autocast_context():
        if not autocast_enabled:
            return nullcontext()
        return torch.autocast(device_type=device.type, dtype=dtype, enabled=True)

    def forward_only() -> torch.Tensor:
        with torch.no_grad():
            with autocast_context():
                outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
            return outputs["loss"]

    def forward_backward() -> torch.Tensor:
        model.zero_grad(set_to_none=True)
        with autocast_context():
            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
            loss = outputs["loss"]
        loss.backward()
        return loss

    no_timer_forward = time_repeats(
        forward_only,
        device=device,
        warmup=args.warmup,
        repeats=args.repeats,
    )
    no_timer_backward = time_repeats(
        forward_backward,
        device=device,
        warmup=args.warmup,
        repeats=args.repeats,
    )

    timer = ComponentTimer(device)
    add_component_hooks(model, architecture, timer)
    try:
        for _ in range(args.component_warmup):
            forward_only()
        synchronize(device)
        for _ in range(args.component_repeats):
            forward_only()
        synchronize(device)
    finally:
        timer.close()

    tokens = args.batch_size * args.seq_len
    component_rows = timer.summary()
    return {
        "architecture": architecture,
        "total_params": sum(param.numel() for param in model.parameters()),
        "trainable_params": sum(param.numel() for param in model.parameters() if param.requires_grad),
        "forward": {
            **no_timer_forward,
            "tokens_per_s": tokens / max(no_timer_forward["mean_ms"] / 1000.0, 1e-12),
        },
        "forward_backward": {
            **no_timer_backward,
            "tokens_per_s": tokens / max(no_timer_backward["mean_ms"] / 1000.0, 1e-12),
            **memory_stats(device),
        },
        "components_forward": component_rows,
    }


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--architectures", default="taonet,taonet_ssm")
    parser.add_argument("--vocab-size", type=int, default=8192)
    parser.add_argument("--batch-size", type=int, default=32)
    parser.add_argument("--seq-len", type=int, default=512)
    parser.add_argument("--hidden-dim", type=int, default=256)
    parser.add_argument("--num-layers", type=int, default=4)
    parser.add_argument("--num-heads", type=int, default=4)
    parser.add_argument("--d-latent-kv", type=int, default=None)
    parser.add_argument("--d-rope", type=int, default=None)
    parser.add_argument("--hidden-dim-ff", type=int, default=None)
    parser.add_argument("--dropout", type=float, default=0.0)
    parser.add_argument("--gqa-groups", type=int, default=1)
    parser.add_argument("--rope-scale", type=float, default=40.0)
    parser.add_argument("--yarn-alpha", type=float, default=1.0)
    parser.add_argument("--init-std", type=float, default=0.02)
    parser.add_argument("--ssm-core", choices=["gamma_s4", "dplr"], default="dplr")
    parser.add_argument("--ssm-hidden-dim", type=int, default=16)
    parser.add_argument("--ssm-mixer-dim", type=int, default=128)
    parser.add_argument("--ssm-rank", type=int, default=1)
    parser.add_argument("--ssm-max-low-rank-scale", type=float, default=0.1)
    parser.add_argument("--ssm-kernel-mode", choices=["auto", "conv", "conv_transfer", "recurrent"], default="conv")
    parser.add_argument("--ssm-kernel-threshold", type=int, default=1)
    parser.add_argument("--ssm-dt-min", type=float, default=1e-3)
    parser.add_argument("--ssm-dt-max", type=float, default=1e-1)
    parser.add_argument("--ssm-dt-init", type=float, default=1e-2)
    parser.add_argument("--ssm-activation", choices=["gelu", "silu", "identity", "linear"], default="gelu")
    parser.add_argument("--ssm-gate", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--ssm-input-gate", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--ssm-layer-scale-init", type=float, default=0.1)
    parser.add_argument("--ssm-local-shift", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--ssm-local-shift-init", type=float, default=0.1)
    parser.add_argument("--ssm-local-shift-per-channel", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--dtype", choices=sorted(DTYPES), default="bf16")
    parser.add_argument("--device", default="auto")
    parser.add_argument("--warmup", type=int, default=2)
    parser.add_argument("--repeats", type=int, default=5)
    parser.add_argument("--component-warmup", type=int, default=1)
    parser.add_argument("--component-repeats", type=int, default=3)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--output", type=Path, default=None)
    args = parser.parse_args()

    if args.device == "auto":
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    else:
        device = torch.device(args.device)
    dtype = DTYPES[args.dtype]
    if device.type == "cuda":
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True

    results = [
        profile_architecture(args, architecture=architecture.strip(), device=device, dtype=dtype)
        for architecture in args.architectures.split(",")
        if architecture.strip()
    ]
    report = {
        "metadata": {
            "python": platform.python_version(),
            "platform": platform.platform(),
            "torch": torch.__version__,
            "cuda_available": torch.cuda.is_available(),
            "cuda_device": torch.cuda.get_device_name(device) if device.type == "cuda" else None,
            "device": str(device),
            "dtype": str(dtype).replace("torch.", ""),
            "args": vars(args) | {"output": str(args.output) if args.output else None},
        },
        "results": results,
    }

    text = json.dumps(report, indent=2, sort_keys=True, default=str)
    print(text)
    if args.output is not None:
        args.output.parent.mkdir(parents=True, exist_ok=True)
        args.output.write_text(text, encoding="utf-8")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())