import json
from pathlib import Path
from json import JSONDecodeError
import logging
import math
import os
from dataclasses import dataclass, field
from typing import Optional, Dict, List

import dateutil
import numpy as np

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Precision, Tasks, WeightType

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

@dataclass
class EvalResult:
    # Also see src.display.utils.AutoEvalColumn for what will be displayed.
    eval_name: str # org_model_precision (uid)
    full_model: str # org/model (path on hub)
    org: Optional[str]
    model: str
    revision: str # commit hash, "" if main
    results: Dict[str, float]
    precision: Precision = Precision.Unknown
    model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
    weight_type: WeightType = WeightType.Original
    architecture: str = "Unknown" # From config file
    license: str = "?"
    likes: int = 0
    num_params: int = 0
    date: str = "" # submission date of request file
    still_on_hub: bool = True
    is_merge: bool = False
    flagged: bool = False
    status: str = "FINISHED"
    # List of tags, initialized to a new empty list for each instance to avoid the pitfalls of mutable default arguments.
    tags: List[str] = field(default_factory=list)
 
    
    @classmethod
    def init_from_json_file(cls, json_filepath: str) -> 'EvalResult':
        with open(json_filepath, 'r') as fp:
            data = json.load(fp)

        config = data.get("config_general", {})
        precision = Precision.from_str(config.get("model_dtype", "unknown"))
        org_and_model = config.get("model_name", "").split("/", 1)
        org = org_and_model[0] if len(org_and_model) > 1 else None
        model = org_and_model[-1]
        result_key = "_".join(filter(None, [*org_and_model, precision.value.name]))
        full_model = "/".join(org_and_model)

        results = cls.extract_results(data)  # Properly call the method to extract results

        return cls(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            precision=precision,
            revision=config.get("model_sha", "")
        )

    @staticmethod
    def extract_results(data: Dict) -> Dict[str, float]:
        results = {}
        for task in Tasks:
            task_value = task.value

            if task_value.benchmark == "hendrycksTest":
                if any(data.get("versions", {}).get(mmlu_k, 1) == 0 for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]):
                    continue

            if task_value.benchmark == "truthfulqa:mc":
                task_key = "harness|truthfulqa:mc|0"
                if task_key in data["results"]:
                    task_metric_value = data["results"][task_key][task_value.metric]
                    if math.isnan(float(task_metric_value)):
                        results[task_value.benchmark] = 0.0
                        continue

            accs = [float(v.get(task_value.metric, 0)) for k, v in data["results"].items() if task_value.benchmark in k and v.get(task_value.metric, None) is not None]
            if accs:
                mean_acc = np.mean(accs) * 100.0
                results[task_value.benchmark] = mean_acc

        return results


    def update_with_request_file(self, requests_path):
        """Finds the relevant request file for the current model and updates info with it."""
        try:
            request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
            with open(request_file, "r") as f:
                request = json.load(f)
            
            self.model_type = ModelType.from_str(request.get("model_type", "Unknown"))
            self.weight_type = WeightType[request.get("weight_type", "Original")]
            self.num_params = int(request.get("params", 0))  # Ensuring type safety
            self.date = request.get("submitted_time", "")
            self.architecture = request.get("architectures", "Unknown")
            self.status = request.get("status", "FAILED")
            
        except FileNotFoundError:
            self.status = "FAILED"
            logging.error(f"Request file not found for {self.org}/{self.model}")
        except JSONDecodeError:
            self.status = "FAILED"
            logging.error(f"Error decoding JSON from the request file for {self.org}/{self.model}")
        except KeyError as e:
            self.status = "FAILED"
            logging.error(f"Key error {e} in processing request file for {self.org}/{self.model}")
        except Exception as e:  # Catch-all for any other unexpected exceptions
            self.status = "FAILED"
            logging.error(f"Unexpected error {e} for {self.org}/{self.model}")


    def update_with_dynamic_file_dict(self, file_dict):
        """Update object attributes based on the provided dictionary, with error handling for missing keys and type validation."""
        # Default values set for optional or potentially missing keys.
        self.license = file_dict.get("license", "?")
        self.likes = int(file_dict.get("likes", 0))  # Ensure likes is treated as an integer
        self.still_on_hub = file_dict.get("still_on_hub", False)  # Default to False if key is missing
        self.tags = file_dict.get("tags", [])
        
        # Calculate `flagged` only if 'tags' is not empty and avoid calculating each time
        self.flagged = "flagged" in self.tags


    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            AutoEvalColumn.precision.name: self.precision.value.name,
            AutoEvalColumn.model_type.name: self.model_type.value.name,
            AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
            AutoEvalColumn.architecture.name: self.architecture,
            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            AutoEvalColumn.fullname.name: self.full_model,
            AutoEvalColumn.revision.name: self.revision,
            AutoEvalColumn.average.name: average,
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.likes.name: self.likes,
            AutoEvalColumn.params.name: self.num_params,
            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
            AutoEvalColumn.merged.name: "merge" in self.tags if self.tags else False,
            AutoEvalColumn.moe.name: ("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower(),
            AutoEvalColumn.flagged.name: self.flagged,
        }

        for task in Tasks:
            data_dict[task.value.col_name] = self.results[task.value.benchmark]

        return data_dict
    

def get_request_file_for_model(requests_path, model_name, precision):
    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
    requests_path = Path(requests_path)
    pattern = f"{model_name}_eval_request_*.json"
    
    # Using pathlib to find files matching the pattern
    request_files = list(requests_path.glob(pattern))
    
    # Sort the files by name in descending order to mimic 'reverse=True'
    request_files.sort(reverse=True)

    # Select the correct request file based on 'status' and 'precision'
    for request_file in request_files:
        with request_file.open("r") as f:
            req_content = json.load(f)
            if req_content["status"] == "FINISHED" and req_content["precision"] == precision.split(".")[-1]:
                return str(request_file)
    
    # Return empty string if no file found that matches criteria
    return ""

def get_raw_eval_results(results_path: str, requests_path: str, dynamic_path: str) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []

    for root, _, files in os.walk(results_path):
        # We should only have json files in model results
        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
            continue

        # Sort the files by date
        try:
            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
        except dateutil.parser._parser.ParserError:
            files = [files[-1]]

        for file in files:
            model_result_filepaths.append(os.path.join(root, file))

    with open(dynamic_path) as f:
        dynamic_data = json.load(f)

    eval_results = {}
    for model_result_filepath in model_result_filepaths:
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath)
        eval_result.update_with_request_file(requests_path)
        if eval_result.full_model == "databricks/dbrx-base":
            print("WE HERE")
        if eval_result.full_model in dynamic_data:
            eval_result.update_with_dynamic_file_dict(dynamic_data[eval_result.full_model])
            # Hardcoding because of gating problem
            if any([org in eval_result.full_model for org in ["meta-llama/", "google/", "tiiuae/"]]):
                eval_result.still_on_hub = True

        # Store results of same eval together
        eval_name = eval_result.eval_name
        if eval_name in eval_results.keys():
            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
        else:
            eval_results[eval_name] = eval_result

    results = []
    for v in eval_results.values():
        try:
            if v.status == "FINISHED":
                v.to_dict()  # we test if the dict version is complete
                results.append(v)
        except KeyError:  # not all eval values present
            continue

    return results