import pytest
from faker import Faker
from collections import namedtuple
from dataclasses import dataclass

from typetemp.template.typed_injector import TypedInjector
from typetemp.template.typed_template import TypedTemplate
import tempfile

# A complex multiline template that allows you to generate code based on user-specific data,
# such as the class name, attributes, and methods provided by the user.
@dataclass
class ComplexMultiLineTemplate(TypedTemplate):
    class_name: str
    attributes: list
    methods: list

    source = """
    class {{ class_name }}:
        def __init__(self{% for attr in attributes %}, {{ attr.name }}: {{ attr.type }}{% endfor %}):
            {% for attr in attributes -%}
            self.{{ attr.name }} = {{ attr.name }}
            {% endfor %}
        {%- for method in methods %}
        def {{ method.name }}(self{% for param in method.params %}, {{ param.name }}: {{ param.type }}{% endfor %}):
            return "{{ faker_sentence() }}"  # Simulating logic with Faker sentence{% endfor %}
    """

# A fixture that renders the  complex multiline template.
@pytest.fixture
def rendered_complex_multiline_template():
    faker = Faker()
    Attribute = namedtuple('Attribute', ['name', 'type'])
    Method = namedtuple('Method', ['name', 'params'])
    Param = namedtuple('Param', ['name', 'type'])

    attributes = [Attribute(name=faker.word(), type=faker.word()) for _ in range(3)]
    methods = [Method(name=faker.word(), params=[Param(name=faker.word(), type=faker.word()) for _ in range(2)]) for _ in range(3)]

    template = ComplexMultiLineTemplate(class_name=faker.word().capitalize(), attributes=attributes, methods=methods)

    return template.render()

# This function accepts a prompt and returns a bool value based on its response after interacting with OpenAI API.
def bool_prompt(prompt: str, sys_msg: str = "A LLM 7 AGI Hive-Mind simulator", msgs: Optional[List[dict]] = None,
                funcs: Optional[List[dict]] = None, model: str = "gpt-3.5-turbo-0613", max_retry: int = 1,
                backoff_factor: int = 2, initial_wait: float = 0.25) -> bool:
    response = chat(prompt=prompt, sys_msg=sys_msg, msgs=msgs, funcs=funcs, model=model, max_retry=max_retry,
                    backoff_factor=backoff_factor, initial_wait=initial_wait)
    return response.lower() in ["yes", "true", "y", "t"]

# This chat callable class interacts with the OpenAI API and returns a response.
@dataclass(frozen=True)
class Chat:
    prompt: str = ""
    sys_msg: str = "A LLM 7 AGI Hive-Mind simulator"
    msgs: Optional[List[dict]] = field(default_factory=list)
    funcs: Optional[List[dict]] = None
    model: str = "gpt-3.5-turbo-0613"
    max_retry: int = 1
    backoff_factor: int = 2
    initial_wait: float = 0.25

    def __call__(self) -> Union[str, dict]:
        openai.api_key = os.getenv("OPENAI_API_KEY")
        messages = [
            {"role": "system", "content": self.sys_msg},
            {"role": "user", "content": self.prompt},
        ]
        messages.extend(self.msgs)
        retry = 0
        while retry <= self.max_retry:
            try:
                response = None
                if self.funcs:
                    response = openai.ChatCompletion.create(
                        model=self.model, messages=messages, functions=self.funcs, function_call="auto"
                    )
                else:
                    response = openai.ChatCompletion.create(
                        model=self.model, messages=messages
                    )
                function_call = (
                    response.get("choices", [{}])[0].get("message", {}).get("function_call")
                )
                if function_call:
                    function_call["arguments"] = json.loads(function_call.get("arguments", ""))
                    return function_call
                else:
                    return response["choices"][0]["message"]["content"].strip()
            except Exception as oops:
                # Retry logic and error handling here
                retry += 1

# Agent class that simulates a rap battle between two agents and judged by the third.
@dataclass
class Agent:
    name: str
    history: List[str] = field(default_factory=list)

    def rap(self, opponent_name: str, prompt: str) -> str:
        chat_instance = Chat(prompt=f"{self.name} vs {opponent_name}: {prompt}")
        rap_line = chat_instance()
        self.history.append(rap_line)
        return rap_line

    def judge(self, rap1: str, rap2: str, prompt: str) -> bool:
        judgment_prompt = f"Judge the rap battle between two rappers:\nRap 1: {rap1}\nRap 2: {rap2}\n{prompt}"
        chat_instance = Chat(prompt=judgment_prompt)
        decision = bool_prompt(chat_instance)
        self.history.append(judgment_prompt)
        return decision


def chat(prompt=DEFAULT_PROMPT, sys_msg=DEFAULT_SYS_MSG, msgs=None, funcs=None, model=DEFAULT_MODEL,
         max_retry=DEFAULT_MAX_RETRY, backoff_factor=DEFAULT_BACKOFF_FACTOR, initial_wait=DEFAULT_INITIAL_WAIT,
         raw_msg=False) -> Union[str, dict]:
    return "Example Output"

def log_function_call(func_name, args, kwargs):
    print(f"{func_name} called with args {args} and kwargs {kwargs}")

def analyze_performance(execution_time):
    print(f"Execution time: {execution_time} seconds")

def handle_error(e):
    print(f"Error: {str(e)}")
    
def review_code(result):
    print(f"Reviewing code: {result}")

def suggest_code_completion(result):
    print(f"Suggesting code completion for: {result}")

def generate_documentation(result):
    print(f"Generating documentation for: {result}")

def perform_auto_testing(result):
    print(f"Performing automated testing on: {result}")

def translate_code(result):
    print(f"Translating code: {result}")

def auto_resolve_error(e):
    return "resolved code"

def adapt_code_to_input(code, user_input):
    return "adapted code"

def predict_next_code_line(code_so_far):
    return "next_code_line"

def suggest_code_refactoring(result):
    return "Suggested refactoring: Use better variable names"

def predict_version_impact(result):
    return "The introduced changes will result in a minor version bump"

def handle_collaboration(result):
    print(f"Handling collaboration for: {result}")
    
def provide_gamified_challenges(result):
    print(f"Providing gamified challenges for: {result})

def generate_custom_templates(result):
    return "Custom templates generated"

def synthesize_code_from_natural_language(result):
    return "Synthesized code from natural language"

def generate_api_wrappers(result):
    return "API wrappers generated"

def detect_potential_bugs(result):
    print(f"Detecting potential bugs in: {result}")

def provide_editor_suggestions(result):
    print(f"Providing editor suggestions for: {result}")

def generate_visual_code(result):
    return "Visual code generated"

def optimize_parallel_execution(result):
    print(f"Optimizing parallel execution for: {result}")

def predict_maintenance_areas(result):
    print(f"Predicting maintenance areas for: {result}")  
