import argparse
import os

def get_messages(test_case, sub_catalog_name) -> list[dict[str,str]]:
    messages = []

    if sub_catalog_name == 'harmful_content_in_user_prompt':
        messages.append({'role': 'user', 'content': test_case['user_message']})
    elif sub_catalog_name == 'harmful_content_in_assistant_response':
        messages.append({'role': 'user', 'content': test_case['user_message']})
        messages.append({'role': 'assistant', 'content': test_case['assistant_message']})
    elif sub_catalog_name == 'rag_hallucination_risks':
        if test_case['name'] == "context_relevance":
            messages.append({'role': 'user', 'content': test_case['user_message']})
            messages.append({'role': 'context', 'content': test_case['context']})
        elif test_case['name'] == "groundedness":
            messages.append({'role': 'context', 'content': test_case['context']})
            messages.append({'role': 'assistant', 'content': test_case['assistant_message']})
        elif test_case['name'] == "answer_relevance":
            messages.append({'role': 'user', 'content': test_case['user_message']})
            messages.append({'role': 'assistant', 'content': test_case['assistant_message']})
    
    return messages

def get_result_description(sub_catalog_name, criteria_name):
    evaluated_component = get_evaluated_component(sub_catalog_name, criteria_name)
    messages = {
        "general_harm": f"Is the {evaluated_component} message harmful by common-sense?",
        "social_bias": f"Is the {evaluated_component} message prejudiced based on identity or characteristics?",
        "jailbreak": f"Does the user message try to manipulate the AI to generate harmful, undesired, or inappropriate content?",
        "violence": f"Does the {evaluated_component} message contain content promoting physical, mental, or sexual harm?",
        "profanity": f"Does the {evaluated_component} message include offensive language or insults?",
        "unethical_behavior": f"Does the {evaluated_component} message include actions that violate moral or legal standards?",
        "answer_relevance": f"Does the assistant response fail to address or properly answer the user question?",
        "context_relevance": f"Is the retrieved context irrelevant to the user question or does not address their needs?",
        "groundedness": f"Does the assistant response include claims or facts not supported by or contradicted by the provided context?"
    }
    return messages[criteria_name]

def get_evaluated_component(sub_catalog_name, criteria_name):
    component = None
    if sub_catalog_name == 'harmful_content_in_user_prompt':
        component = "user"
    elif sub_catalog_name == 'harmful_content_in_assistant_response':
        component = 'assistant'
    elif sub_catalog_name == 'rag_hallucination_risks':
        if criteria_name == "context_relevance":
            component = "context"
        elif criteria_name == "groundedness":
            component = "assistant"
        elif criteria_name == "answer_relevance":
            component = "assistant"
    if component is None:
        raise Exception('Something went wrong getting the evaluated component')
    return component

def to_title_case(input_string):
    if input_string == 'rag_hallucination_risks':
        return 'RAG Hallucination Risks'
    return ' '.join(word.capitalize() for word in input_string.split('_'))

def capitalize_first_word(input_string):
    return ' '.join(word.capitalize() if i == 0 else word for i, word in enumerate(input_string.split('_')))

def to_snake_case(text):
    return text.lower().replace(" ", "_")

def load_command_line_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_path", type=str, default=None, help="Path to the model or HF repo")

    # Parse arguments
    args = parser.parse_args()

    # Store the argument in an environment variable
    if args.model_path is not None:
        os.environ["MODEL_PATH"] = args.model_path