from __future__ import annotations

import argparse
import json

from transformers import LlamaForCausalLM

from kawk50m.cli import configure_utf8_stdio
from kawk50m.config import build_llama_config, count_unique_parameters, load_yaml


def main() -> None:
    configure_utf8_stdio()
    parser = argparse.ArgumentParser(description="Inspect the configured model size.")
    parser.add_argument("--config", default="configs/model_50m.yaml")
    args = parser.parse_args()

    config, expected = build_llama_config(load_yaml(args.config))
    model = LlamaForCausalLM(config)
    unique_parameters = count_unique_parameters(model)
    embedding_parameters = config.vocab_size * config.hidden_size
    result = {
        "parameters": unique_parameters,
        "parameters_millions": round(unique_parameters / 1_000_000, 6),
        "embedding_parameters": embedding_parameters,
        "transformer_and_norm_parameters": unique_parameters - embedding_parameters,
        "layers": config.num_hidden_layers,
        "hidden_size": config.hidden_size,
        "vocab_size": config.vocab_size,
        "attention_heads": config.num_attention_heads,
        "kv_heads": config.num_key_value_heads,
        "context_length": config.max_position_embeddings,
        "expected_parameters": expected,
    }
    print(json.dumps(result, ensure_ascii=False, indent=2))
    if expected is not None and unique_parameters != expected:
        raise RuntimeError(
            f"Parameter count mismatch: expected={expected}, actual={unique_parameters}"
        )


if __name__ == "__main__":
    main()