import copy
import re
from typing import List, Tuple

from jinja2 import Environment, StrictUndefined

from pr_agent.algo.ai_handler import AiHandler
from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models
from pr_agent.algo.token_handler import TokenHandler
from pr_agent.algo.utils import load_yaml, set_custom_labels
from pr_agent.config_loader import get_settings
from pr_agent.git_providers import get_git_provider
from pr_agent.git_providers.git_provider import get_main_pr_language
from pr_agent.log import get_logger


class PRGenerateLabels:
    def __init__(self, pr_url: str, args: list = None):
        """
        Initialize the PRGenerateLabels object with the necessary attributes and objects for generating labels
        corresponding to the PR using an AI model.
        Args:
            pr_url (str): The URL of the pull request.
            args (list, optional): List of arguments passed to the PRGenerateLabels class. Defaults to None.
        """
        # Initialize the git provider and main PR language
        self.git_provider = get_git_provider()(pr_url)
        self.main_pr_language = get_main_pr_language(
            self.git_provider.get_languages(), self.git_provider.get_files()
        )
        self.pr_id = self.git_provider.get_pr_id()

        # Initialize the AI handler
        self.ai_handler = AiHandler()
    
        # Initialize the variables dictionary
        self.vars = {
            "title": self.git_provider.pr.title,
            "branch": self.git_provider.get_pr_branch(),
            "description": self.git_provider.get_pr_description(full=False),
            "language": self.main_pr_language,
            "diff": "",  # empty diff for initial calculation
            "use_bullet_points": get_settings().pr_description.use_bullet_points,
            "extra_instructions": get_settings().pr_description.extra_instructions,
            "commit_messages_str": self.git_provider.get_commit_messages(),
            "custom_labels": "",
            "custom_labels_examples": "",
            "enable_custom_labels": get_settings().config.enable_custom_labels,
        }

        # Initialize the token handler
        self.token_handler = TokenHandler(
            self.git_provider.pr,
            self.vars,
            get_settings().pr_custom_labels_prompt.system,
            get_settings().pr_custom_labels_prompt.user,
        )
    
        # Initialize patches_diff and prediction attributes
        self.patches_diff = None
        self.prediction = None

    async def run(self):
        """
        Generates a PR labels using an AI model and publishes it to the PR.
        """

        try:
            get_logger().info(f"Generating a PR labels {self.pr_id}")
            if get_settings().config.publish_output:
                self.git_provider.publish_comment("Preparing PR labels...", is_temporary=True)

            await retry_with_fallback_models(self._prepare_prediction)

            get_logger().info(f"Preparing answer {self.pr_id}")
            if self.prediction:
                self._prepare_data()
            else:
                return None

            pr_labels = self._prepare_labels()

            if get_settings().config.publish_output:
                get_logger().info(f"Pushing labels {self.pr_id}")
                if self.git_provider.is_supported("get_labels"):
                    current_labels = self.git_provider.get_labels()
                    if current_labels is None:
                        current_labels = []
                    self.git_provider.publish_labels(pr_labels + current_labels)
                self.git_provider.remove_initial_comment()
        except Exception as e:
            get_logger().error(f"Error generating PR labels {self.pr_id}: {e}")
        
        return ""

    async def _prepare_prediction(self, model: str) -> None:
        """
        Prepare the AI prediction for the PR labels based on the provided model.

        Args:
            model (str): The name of the model to be used for generating the prediction.

        Returns:
            None

        Raises:
            Any exceptions raised by the 'get_pr_diff' and '_get_prediction' functions.

        """

        get_logger().info(f"Getting PR diff {self.pr_id}")
        self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
        get_logger().info(f"Getting AI prediction {self.pr_id}")
        self.prediction = await self._get_prediction(model)

    async def _get_prediction(self, model: str) -> str:
        """
        Generate an AI prediction for the PR labels based on the provided model.

        Args:
            model (str): The name of the model to be used for generating the prediction.

        Returns:
            str: The generated AI prediction.
        """
        variables = copy.deepcopy(self.vars)
        variables["diff"] = self.patches_diff  # update diff

        environment = Environment(undefined=StrictUndefined)
        set_custom_labels(variables)
        system_prompt = environment.from_string(get_settings().pr_custom_labels_prompt.system).render(variables)
        user_prompt = environment.from_string(get_settings().pr_custom_labels_prompt.user).render(variables)

        if get_settings().config.verbosity_level >= 2:
            get_logger().info(f"\nSystem prompt:\n{system_prompt}")
            get_logger().info(f"\nUser prompt:\n{user_prompt}")

        response, finish_reason = await self.ai_handler.chat_completion(
            model=model,
            temperature=0.2,
            system=system_prompt,
            user=user_prompt
        )

        return response

    def _prepare_data(self):
        # Load the AI prediction data into a dictionary
        self.data = load_yaml(self.prediction.strip())



    def _prepare_labels(self) -> List[str]:
        pr_types = []

        # If the 'PR Type' key is present in the dictionary, split its value by comma and assign it to 'pr_types'
        if 'PR Type' in self.data:
            if type(self.data['PR Type']) == list:
                pr_types = self.data['PR Type']
            elif type(self.data['PR Type']) == str:
                pr_types = self.data['PR Type'].split(',')

        return pr_types
