"""
Pytest configuration and fixtures for behavioral tests.
"""
import pytest
import numpy as np
import joblib
from pathlib import Path
from sklearn.feature_extraction.text import TfidfVectorizer

from hopcroft_skill_classification_tool_competition.config import DATA_PATHS
from hopcroft_skill_classification_tool_competition.features import (
    clean_github_text,
    get_label_columns,
    load_data_from_db
)


@pytest.fixture(scope="session")
def trained_model():
    """Load the trained model for testing."""
    model_path = Path(DATA_PATHS["models_dir"]) / "random_forest_tfidf_gridsearch_smote.pkl"
    
    # Fallback to baseline if SMOTE model not found
    if not model_path.exists():
        model_path = Path(DATA_PATHS["models_dir"]) / "random_forest_tfidf_gridsearch.pkl"
    
    if not model_path.exists():
        pytest.skip(f"Model not found at {model_path}. Please train a model first.")
    
    return joblib.load(model_path)


@pytest.fixture(scope="session")
def tfidf_vectorizer(trained_model):
    """
    Extract or reconstruct the TF-IDF vectorizer from the trained model.
    
    Note: In a production setting, you should save and load the vectorizer separately.
    For now, we'll create a new one fitted on the training data with max_features=1000.
    """
    # Load training features to get vocabulary
    features_path = Path(DATA_PATHS["features"])
    
    if not features_path.exists():
        pytest.skip(f"Features not found at {features_path}. Please run feature extraction first.")
    
    # For testing purposes, we need to reconstruct the vectorizer with same params as training
    # The model expects 1000 features based on the error message
    from hopcroft_skill_classification_tool_competition.features import extract_tfidf_features
    
    try:
        df = load_data_from_db()
        # Use max_features=1000 to match the trained model
        _, vectorizer = extract_tfidf_features(df, max_features=1000)
        return vectorizer
    except Exception as e:
        pytest.skip(f"Could not load vectorizer: {e}")


@pytest.fixture(scope="session")
def label_names():
    """Get the list of label names from the database."""
    try:
        df = load_data_from_db()
        return get_label_columns(df)
    except Exception as e:
        pytest.skip(f"Could not load label names: {e}")


@pytest.fixture
def predict_text(trained_model, tfidf_vectorizer):
    """
    Factory fixture that returns a function to predict skills from raw text.
    
    Returns:
        Function that takes text and returns predicted label indices
    """
    def _predict(text: str, return_proba: bool = False):
        """
        Predict skills from raw text.
        
        Args:
            text: Raw GitHub issue text
            return_proba: If True, return probabilities instead of binary predictions
        
        Returns:
            If return_proba=False: indices of predicted labels (numpy array)
            If return_proba=True: probability matrix (n_samples, n_labels)
        """
        # Clean and transform text
        cleaned = clean_github_text(text)
        features = tfidf_vectorizer.transform([cleaned]).toarray()
        
        if return_proba:
            # Get probabilities for multi-output classifier
            # Note: RandomForest returns probabilities per estimator
            try:
                probas = np.array([
                    estimator.predict_proba(features)[0][:, 1]  # Get probability of class 1
                    for estimator in trained_model.estimators_
                ]).T
                return probas
            except Exception:
                # Fallback to binary predictions if probabilities not available
                return trained_model.predict(features)
        
        # Get binary predictions
        predictions = trained_model.predict(features)[0]
        
        # Return indices of positive labels
        return np.where(predictions == 1)[0]
    
    return _predict


@pytest.fixture
def predict_with_labels(predict_text, label_names):
    """
    Factory fixture that returns a function to predict skills with label names.
    
    Returns:
        Function that takes text and returns list of predicted label names
    """
    def _predict(text: str):
        """
        Predict skill labels from raw text.
        
        Args:
            text: Raw GitHub issue text
        
        Returns:
            List of predicted label names
        """
        indices = predict_text(text)
        return [label_names[i] for i in indices]
    
    return _predict


def pytest_configure(config):
    """Register custom markers."""
    config.addinivalue_line(
        "markers", "invariance: Tests for invariance (changes should not affect predictions)"
    )
    config.addinivalue_line(
        "markers", "directional: Tests for directional expectations (changes should affect predictions predictably)"
    )
    config.addinivalue_line(
        "markers", "mft: Minimum Functionality Tests (basic examples with expected outputs)"
    )
    config.addinivalue_line(
        "markers", "training: Tests for model training validation (loss, overfitting, devices)"
    )