import os
from typing import List, Any, Callable, Dict, Optional
import briefcase_ai as bf

class Experiment:
    """
    Orchestrates high-volume AI evaluations across multiple models and iterations.
    Supports local execution and distributed Ray/io.net clusters.
    """
    def __init__(self, name: str, models: List[str], iterations: int = 1):
        self.name = name
        self.models = models
        self.iterations = iterations

    def run(self, dataset: List[Any], task_fn: Callable):
        """
        Executes the cross-product evaluation matrix.
        """
        if os.getenv("BENCHMARK_DISTRIBUTED") == "1":
            return self._run_distributed(dataset, task_fn)
        return self._run_local(dataset, task_fn)

    def _run_local(self, dataset: List[Any], task_fn: Callable):
        print(f" Starting Local Experiment: {self.name}")
        results = []
        for model_id in self.models:
            model = bf.Model(model_id)
            for i in range(self.iterations):
                for item in dataset:
                    # Inject metadata into current context
                    res = task_fn(item, model)
                    results.append(res)
        return results

    def _run_distributed(self, dataset: List[Any], task_fn: Callable):
        print(f" Dispatching Distributed Experiment: {self.name}")
        import ray
        if not ray.is_initialized():
            ray.init(address="auto")
            
        # Implementation would use the distributed workers defined earlier
        pass
