import os 
from dotenv import load_dotenv

from evoagentx.benchmark import MBPPPLUS, AFlowMBPPPLUS
from evoagentx.optimizers import AFlowOptimizer
from evoagentx.models import LiteLLMConfig, LiteLLM, OpenAILLMConfig, OpenAILLM 
import os

EXPERIMENTAL_CONFIG = {
    "humaneval": {
        "question_type": "code", 
        "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
    }, 
    "mbpp": {
        "question_type": "code", 
        "operators": ["Custom", "CustomCodeGenerate", "Test", "ScEnsemble"] 
    },
    "hotpotqa": {
        "question_type": "qa", 
        "operators": ["Custom", "AnswerGenerate", "QAScEnsemble"]
    },
    "gsm8k": {
        "question_type": "math", 
        "operators": ["Custom", "ScEnsemble", "Programmer"]
    },
    "math": {
        "question_type": "math", 
        "operators": ["Custom", "ScEnsemble", "Programmer"]
    }
    
}


class MBPPSplits(AFlowMBPPPLUS):

    def _load_data(self):

        # load the original MBPP data 
        mbpp_test_data = AFlowMBPPPLUS().get_dev_data()
        # split the data into dev and test
        import numpy as np 
        np.random.seed(42)
        permutation = np.random.permutation(len(mbpp_test_data))
        # radnomly select 50 samples for dev and 100 samples for test (be consistent with other models)
        dev_data_task_ids = [mbpp_test_data[idx]["task_id"] for idx in permutation[:30]]
        super()._load_data() 
        full_data = self._dev_data
        self._dev_data = [example for example in full_data if example["task_id"] in dev_data_task_ids]
    

def main():

    from evoagentx.models import OpenAILLMConfig, OpenAILLM,AzureOpenAIConfig,LiteLLMConfig,LiteLLM
    from evoagentx.workflow import SEWWorkFlowGraph 
    from evoagentx.agents import AgentManager
    from evoagentx.benchmark import ClassEval
    from evoagentx.evaluators import Evaluator 
    from evoagentx.optimizers import SEWOptimizer 
    from evoagentx.core.callbacks import suppress_logger_info

    os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"] = "gpt-4o-mini"
    os.environ["AZURE_OPENAI_ENDPOINT"] = "https://75244-mfztkr7x-eastus2.cognitiveservices.azure.com/"
    os.environ["AZURE_OPENAI_KEY"] = "8PNMdsUYGdMPsCfl0baO0hjtnGE2m40zJTrUGC3vKnHdpjnkOgeQJQQJ99BIACHYHv6XJ3w3AAAAACOG7VZI"
    os.environ["AZURE_OPENAI_API_VERSION"] = "2024-12-01-preview"

    # llm_config = OpenAILLMConfig(model="gpt-4o-mini-2024-07-18", openai_key=OPENAI_API_KEY, top_p=0.85, temperature=0.2, frequency_penalty=0.0, presence_penalty=0.0)

    llm_config = LiteLLMConfig(model="azure/" + os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"),  # Azure model format
        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
        azure_key=os.getenv("AZURE_OPENAI_KEY"),
        api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-12-01-preview"), top_p=0.85, temperature=0.2, frequency_penalty=0.0, presence_penalty=0.0)

    executor_llm = LiteLLM(config=llm_config)
    optimizer_llm = LiteLLM(config=llm_config)

    # load benchmark
    mbpp_input = MBPPSplits()
    mbpp = AFlowMBPPPLUS()
    mbpp._dev_data = mbpp_input._dev_data
    mbpp.error_list = {}
    # create optimizer
    optimizer = AFlowOptimizer(
        graph_path = "examples/aflow/code_generation",
        optimized_path = "examples/aflow/mbppplus_full_new/optimized",
        optimizer_llm=optimizer_llm,
        executor_llm=executor_llm,
        validation_rounds=5,
        eval_rounds=1,
        max_rounds=10,
        **EXPERIMENTAL_CONFIG["mbpp"]
    )

    # run optimization
    optimizer.optimize(mbpp)

    # run test 
    optimizer.test(mbpp) # use `test_rounds: List[int]` to specify the rounds to test 


if __name__ == "__main__":
    main()