import json
import numpy as np
from typing import Optional

def make_id(config: dict, keys_to_ignore: list[str]) -> str:
    keys = sorted(set(config.keys()))
    return "_".join(str(config[k]) for k in keys if k not in keys_to_ignore)

class ModelBenchmarkData:

    def __init__(self, json_path: str) -> None:
        with open(json_path, "r") as f:
            self.data = json.load(f)

    def compute_e2e_latency(self, measures: dict) -> tuple[float, Optional[float]]:
        return measures["e2e_latency"]

    def compute_ttft(self, measures: dict) -> float:
        return measures["t_tokens"][0] - measures["wall_time_start"]

    def compute_itl(self, measures: dict) -> Optional[float]:
        if len(measures["t_tokens"]) < 2:
            return None
        delta_t = measures["t_tokens"][-1] - measures["t_tokens"][0]
        num_tokens = len(measures["t_tokens"]) - 1
        return delta_t / num_tokens

    def ensure_coherence(self) -> tuple[int, int, int]:
        all_hyperparams = set()
        for cfg_name, data in self.data.items():
            config = data["metadata"]["config"]
            hyperparams = (config["batch_size"], config["sequence_length"], config["num_tokens_to_generate"])
            all_hyperparams.add(hyperparams)
        if len(all_hyperparams) > 1:
            raise ValueError(
                f"Different batch size, sequence length or nb of tokens to generate between configs: {all_hyperparams}"
            )
        return all_hyperparams.pop()

    def get_bar_plot_data(self, collapse_on_cache: bool = True, collapse_on_compile_mode: bool = True) -> dict:
        # Gather data for each scenario
        per_scenario_data = {}
        for i, (cfg_name, data) in enumerate(self.data.items()):
            per_scenario_data[cfg_name] = {
                "ttft": [self.compute_ttft(d) for d in data["measures"]],
                "itl": [self.compute_itl(d) for d in data["measures"]],
                "e2e": [self.compute_e2e_latency(d) for d in data["measures"]],
                "config": data["metadata"]["config"],
            }
        # Eventually collapse on cache
        if collapse_on_cache:
            collapsed_keys = {}
            for cfg_name, data in per_scenario_data.items():
                keys_to_ignore = ["name"]
                keys_to_ignore += (["use_cache"] if collapse_on_cache else [])
                keys_to_ignore += (["compile_mode"] if collapse_on_compile_mode else [])
                cfg_id = make_id(data["config"], keys_to_ignore)
                cfg_e2e = np.mean(data["e2e"])
                other_name, other_e2e = collapsed_keys.get(cfg_id, (None, 1e16))
                if cfg_e2e < other_e2e:
                    collapsed_keys[cfg_id] = (cfg_name, cfg_e2e)
            per_scenario_data = {k: per_scenario_data[k] for k, _ in collapsed_keys.values()}

        return per_scenario_data


def load_data(keep_common_scenarios_only: bool = False) -> dict[str, ModelBenchmarkData]:
    data = {
        "MI325": ModelBenchmarkData("mi325_data.json"),
        "H100": ModelBenchmarkData("h100_data.json"),
    }
    if keep_common_scenarios_only:
        common_scenarios = set(data["MI325"].data.keys()) & set(data["H100"].data.keys())
        for device_name, device_data in data.items():
            device_data.data = {k: v for k, v in device_data.data.items() if k in common_scenarios}
    return data