import glob
import json
import math
import os
from dataclasses import dataclass

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
from src.submission.check_validity import is_model_on_hub
from src.evaluation.model_trace_eval import compute_model_trace_p_value
from src.evaluation.initialize_models import is_model_allowed

@dataclass
class EvalResult:
    """Represents one perplexity evaluation result."""
    eval_name: str # org_model_precision (uid)
    full_model: str # org/model (path on hub)
    org: str 
    model: str
    revision: str # commit hash, "" if main
    results: dict
    precision: Precision = Precision.Unknown
    model_type: ModelType = ModelType.PT  # Default to pretrained
    weight_type: WeightType = WeightType.Original
    architecture: str = "Unknown"
    still_on_hub: bool = False

    @classmethod
    def init_from_json_file(self, json_filepath):
        """Inits the result from the specific model result file"""
        with open(json_filepath) as fp:
            data = json.load(fp)

        config = data.get("config")

        # Precision
        precision = Precision.from_str(config.get("model_dtype"))

        # Get model and org
        org_and_model = config.get("model_name", config.get("model_args", None))
        org_and_model = org_and_model.split("/", 1)

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            result_key = f"{model}_{precision.value.name}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            result_key = f"{org}_{model}_{precision.value.name}"
        full_model = "/".join(org_and_model)

        still_on_hub, _, model_config = is_model_on_hub(
            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
        )
        architecture = "?"
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)

        # Extract perplexity result
        results = {}
        if "perplexity" in data["results"]:
            results["perplexity"] = data["results"]["perplexity"]["perplexity"]

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

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        import sys
        
        sys.stderr.write(f"\n=== PROCESSING RESULT TO_DICT ===\n")
        sys.stderr.write(f"Processing result for model: {self.full_model}\n")
        sys.stderr.write(f"Raw results: {self.results}\n")
        sys.stderr.write(f"Model precision: {self.precision}\n")
        sys.stderr.write(f"Model type: {self.model_type}\n")
        sys.stderr.write(f"Weight type: {self.weight_type}\n")
        sys.stderr.flush()
        
        # Calculate average, handling perplexity (lower is better)
        scores = []
        perplexity_score = None
        sys.stderr.write(f"Available tasks: {[task.name for task in Tasks]}\n")
        
        for task in Tasks:
            sys.stderr.write(f"Looking for task: {task.value.benchmark} in results\n")
            if task.value.benchmark in self.results:
                score = self.results[task.value.benchmark]
                perplexity_score = score  # Save the raw score
                sys.stderr.write(f"Found score for {task.value.benchmark}: {score}\n")
                # Convert perplexity to a 0-100 scale where lower perplexity = higher score
                # Using a log scale since perplexity can vary widely
                # Cap at 100 for very low perplexity and 0 for very high perplexity
                score = max(0, min(100, 100 * (1 - math.log(score) / 10)))
                scores.append(score)
                sys.stderr.write(f"Converted score: {score}\n")
            else:
                sys.stderr.write(f"Task {task.value.benchmark} not found in results\n")
            sys.stderr.flush()
        
        average = sum(scores) / len(scores) if scores else 0
        sys.stderr.write(f"Calculated average score: {average}\n")
        sys.stderr.flush()
        
        # Create data dictionary with comprehensive debugging
        data_dict = {}
        
        # Add core columns
        data_dict["eval_name"] = self.eval_name
        data_dict[AutoEvalColumn.precision.name] = self.precision.value.name
        data_dict[AutoEvalColumn.model_type.name] = self.model_type.value.name
        data_dict[AutoEvalColumn.model_type_symbol.name] = self.model_type.value.symbol
        data_dict[AutoEvalColumn.weight_type.name] = self.weight_type.value.name
        data_dict[AutoEvalColumn.architecture.name] = self.architecture
        data_dict[AutoEvalColumn.model.name] = make_clickable_model(self.full_model)
        data_dict[AutoEvalColumn.revision.name] = self.revision
        data_dict[AutoEvalColumn.average.name] = average
        data_dict[AutoEvalColumn.still_on_hub.name] = self.still_on_hub
        
        # Add default values for missing model info
        data_dict[AutoEvalColumn.license.name] = "Unknown"
        data_dict[AutoEvalColumn.params.name] = 0
        data_dict[AutoEvalColumn.likes.name] = 0
        
        # Compute model trace p-value
        sys.stderr.write(f"\n🧬 COMPUTING MODEL TRACE P-VALUE FOR: {self.full_model}\n")
        sys.stderr.write(f"   - Revision: {self.revision if self.revision else 'main'}\n")
        sys.stderr.write(f"   - Precision: {self.precision.value.name.lower()}\n")
        sys.stderr.flush()
        
        try:
            model_trace_p_value = compute_model_trace_p_value(
                self.full_model, 
                self.revision if self.revision else "main",
                self.precision.value.name.lower()
            )
            
            if model_trace_p_value is not None:
                sys.stderr.write(f"✅ Model trace p-value computed successfully: {model_trace_p_value}\n")
            else:
                sys.stderr.write(f"⚠️  Model trace p-value is None (computation failed or not available)\n")
                
        except Exception as e:
            sys.stderr.write(f"💥 Exception during model trace p-value computation: {e}\n")
            import traceback
            sys.stderr.write(f"Traceback: {traceback.format_exc()}\n")
            model_trace_p_value = None
            
        data_dict[AutoEvalColumn.model_trace_p_value.name] = model_trace_p_value
        sys.stderr.write(f"📝 Added to data_dict: {AutoEvalColumn.model_trace_p_value.name} = {model_trace_p_value}\n")
        sys.stderr.flush()
        
        sys.stderr.write(f"Created base data_dict with {len(data_dict)} columns\n")
        sys.stderr.flush()

        # Add task-specific scores
        for task in Tasks:
            task_col_name = task.value.col_name
            if task.value.benchmark in self.results:
                task_score = self.results[task.value.benchmark]
                data_dict[task_col_name] = task_score
                sys.stderr.write(f"Added task score: {task_col_name} = {task_score}\n")
            else:
                data_dict[task_col_name] = None
                sys.stderr.write(f"Added None for missing task: {task_col_name}\n")
            sys.stderr.flush()

        sys.stderr.write(f"Final data dict has {len(data_dict)} columns: {list(data_dict.keys())}\n")
        sys.stderr.write(f"=== END PROCESSING RESULT TO_DICT ===\n")
        sys.stderr.flush()
        
        return data_dict

def get_raw_eval_results(results_path: str) -> list[EvalResult]:
    """From the path of the results folder root, extract all perplexity results"""
    import sys
    
    sys.stderr.write(f"\nSearching for result files in: {results_path}\n")
    sys.stderr.flush()
    
    model_result_filepaths = []

    for root, _, files in os.walk(results_path):
        # Process all JSON files, regardless of other files in the directory
        for file in files:
            if file.endswith(".json"):
                model_result_filepaths.append(os.path.join(root, file))

    sys.stderr.write(f"Found {len(model_result_filepaths)} result files\n")
    sys.stderr.flush()

    eval_results = {}
    for model_result_filepath in model_result_filepaths:
        try:
            sys.stderr.write(f"\nProcessing file: {model_result_filepath}\n")
            sys.stderr.flush()
            
            # Quick pre-check: Try to extract model name from file before full processing
            try:
                with open(model_result_filepath, 'r') as f:
                    data = json.load(f)
                config = data.get("config", {})
                model_name = config.get("model_name", "")
                
                if model_name and not is_model_allowed(model_name):
                    sys.stderr.write(f"⏭️ Skipping non-allowed model file: {model_result_filepath} (model: {model_name})\n")
                    sys.stderr.flush()
                    continue
                    
            except Exception as e:
                sys.stderr.write(f"⚠️ Error pre-checking file {model_result_filepath}: {e}\n")
                sys.stderr.flush()
                continue
            
            # Creation of result
            eval_result = EvalResult.init_from_json_file(model_result_filepath)
            sys.stderr.write(f"Created result object for: {eval_result.full_model}\n")
            sys.stderr.flush()

            # 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})
                sys.stderr.write(f"Updated existing result for {eval_name}\n")
                sys.stderr.flush()
            else:
                eval_results[eval_name] = eval_result
                sys.stderr.write(f"Added new result for {eval_name}\n")
                sys.stderr.flush()
        except Exception as e:
            sys.stderr.write(f"Error processing result file {model_result_filepath}: {e}\n")
            import traceback
            sys.stderr.write(f"Traceback: {traceback.format_exc()}\n")
            sys.stderr.flush()
            continue

    results = []
    sys.stderr.write(f"\nProcessing {len(eval_results)} evaluation results\n")
    sys.stderr.flush()
    
    for v in eval_results.values():
        try:
            sys.stderr.write(f"\nConverting result to dict for: {v.full_model}\n")
            sys.stderr.flush()
            
            # Filter to only allowed models
            if not is_model_allowed(v.full_model):
                sys.stderr.write(f"⏭️ Skipping non-allowed model: {v.full_model}\n")
                sys.stderr.flush()
                continue
            
            v.to_dict() # we test if the dict version is complete
            results.append(v)
            sys.stderr.write("Successfully converted and added result\n")
            sys.stderr.flush()
        except KeyError as e:
            sys.stderr.write(f"Error converting result to dict: {e}\n")
            import traceback
            sys.stderr.write(f"Traceback: {traceback.format_exc()}\n")
            sys.stderr.flush()
            continue

    sys.stderr.write(f"\nReturning {len(results)} processed results\n")
    sys.stderr.flush()
    return results