import os
import random
import torch
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics import precision_score, recall_score
from torch.nn import functional as F
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import seaborn as sns
from colpali_engine.interpretability import (
    get_similarity_maps_from_embeddings,
    plot_all_similarity_maps,
)
import pandas as pd

# Path to extracted Flickr8k dataset
FLICKR8K_IMAGES_PATH = "flickr8k/Images"
FLICKR8K_CAPTIONS_PATH = "flickr8k/captions.txt"

# Function to load image-text pairs from Flickr8k
def load_flickr8k_data(images_path, captions_path, fraction=0.1):
    # Read captions file
    with open(captions_path, "r") as f:
        captions_data = f.readlines()[1:]  # Skip header

    # Parse captions
    image_text_pairs = {}
    for line in captions_data:
        image_name, caption = line.strip().split(",", 1)
        if image_name not in image_text_pairs:
            image_text_pairs[image_name] = []
        image_text_pairs[image_name].append(caption)

    # Load only a fraction of the dataset
    selected_images = random.sample(list(image_text_pairs.keys()), int(len(image_text_pairs) * fraction))
    image_text_pairs = {k: image_text_pairs[k] for k in selected_images}

    # Create pairs of images and captions
    pairs = []
    for image_name, captions in image_text_pairs.items():
        image_path = os.path.join(images_path, image_name)
        if os.path.exists(image_path):
            pairs.append((Image.open(image_path), random.choice(captions)))
    return pairs

# Function to create unrelated pairs
def create_unrelated_pairs(image_text_pairs):
    """
    Creates unrelated pairs of images and texts by randomly shuffling the texts.

    Args:
        image_text_pairs (list): A list of tuples containing images and their corresponding texts.

    Returns:
        list: A list of tuples containing images and unrelated texts.
    """
    images, texts = zip(*image_text_pairs)
    unrelated_texts = random.sample(texts, len(texts))
    return list(zip(images, unrelated_texts))

def create_visual_pairs(image_text_pairs):
    """
    Creates pairs of original and augmented images from image-text pairs.

    This function takes a list of image-text pairs and creates new pairs consisting
    of the original images and their augmented versions. The augmentation used
    in this implementation is a horizontal flip.

    Args:
        image_text_pairs (list): A list of tuples containing (image, text) pairs,
            where images are PIL Image objects and texts are strings.

    Returns:
        list: A list of tuples containing (original_image, augmented_image) pairs,
            where both elements are PIL Image objects.
    """
    from torchvision.transforms import ToTensor
    images, _ = zip(*image_text_pairs)
    augmented_images = [ToTensor()(image).flip(-1) for image in images]  # Example augmentation: horizontal flip
    return list(zip(images, augmented_images))

def get_embeddings(images, texts, model_id="google/siglip-base-patch16-224"):
    """
    Given lists of images and texts, returns normalized embeddings for both.
    """
    # Ensure texts is a list of strings
    if not all(isinstance(t, str) for t in texts):
        raise ValueError("All text inputs must be strings.")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = AutoModel.from_pretrained(model_id, ignore_mismatched_sizes=True).to(device)
    processor = AutoProcessor.from_pretrained(model_id)

    # Preprocess images and texts
    image_inputs = processor(images=images, return_tensors="pt").to(device)
    text_inputs = processor(text=texts, return_tensors="pt", padding="max_length").to(device)

    with torch.no_grad():
        image_embeds = model.get_image_features(**image_inputs)
        text_embeds = model.get_text_features(**text_inputs)

    # Normalize embeddings
    image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
    text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)

    return image_embeds, text_embeds

def cosine_similarity_analysis(embeddings1, embeddings2, title):
    """
    Computes cosine similarity for matching and unrelated pairs and compares distributions.
    """
    similarities = cosine_similarity(embeddings1.cpu().numpy(), embeddings2.cpu().numpy())

    # Matching pairs: Diagonal of the similarity matrix
    matching_similarities = np.diag(similarities)

    # Unrelated pairs: Off-diagonal similarities
    unrelated_similarities = similarities[~np.eye(similarities.shape[0], dtype=bool)]

    print(f"### {title} ###")
    print(f"Mean Matching Similarity: {np.mean(matching_similarities):.4f}")
    print(f"Mean Unrelated Similarity: {np.mean(unrelated_similarities):.4f}")
    print()

    # Plot distributions
    plt.figure(figsize=(10, 6))
    sns.histplot(matching_similarities, kde=True, label="Matching Pairs", color="blue", bins=30)
    sns.histplot(unrelated_similarities, kde=True, label="Unrelated Pairs", color="red", bins=30)
    plt.title(f"{title}: Cosine Similarity Distributions")
    plt.xlabel("Cosine Similarity")
    plt.ylabel("Frequency")
    plt.legend()
    plt.show()

### b. Nearest-Neighbor Retrieval
def retrieval_metrics(query_embeds, target_embeds, ground_truth_indices, k=5):
    """
    Computes Precision@k and Recall@k for nearest-neighbor retrieval.

    This function evaluates the effectiveness of retrieval by calculating Precision@k and Recall@k.
    Precision@k measures the accuracy of the top-k retrieved items, while Recall@k measures the ability
    to find the relevant item within the top-k retrieved items.  It assumes there's only one true
    match per query.

    Args:
        query_embeds (torch.Tensor): Embeddings of the query data.
        target_embeds (torch.Tensor): Embeddings of the target data (database).
        ground_truth_indices (list): List of indices in the target data representing the true matches for each query.
        k (int): The number of top results to consider.

    Returns:
        tuple: A tuple containing mean Precision@k and mean Recall@k.
    """
    similarities = cosine_similarity(query_embeds.cpu().numpy(), target_embeds.cpu().numpy())
    sorted_indices = np.argsort(-similarities, axis=1)[:, :k]  # Top-k indices

    # Compute metrics
    precisions = []
    recalls = []
    for i, true_idx in enumerate(ground_truth_indices):
        retrieved_indices = sorted_indices[i]
        true_positives = int(true_idx in retrieved_indices)
        precisions.append(true_positives / k)
        recalls.append(true_positives / 1)  # Only one true match per query

    mean_precision = np.mean(precisions)
    mean_recall = np.mean(recalls)

    return mean_precision, mean_recall

def plot_query_token_importance(
    pil_image,
    similarity_maps,
    query_tokens,
    alpha: float = 0.5
) -> None:
    """
    Plot a separate heatmap for each query token in the similarity_maps.

    Args:
        pil_image (PIL.Image.Image): The original image (e.g., loaded via Image.open(...)).
        similarity_maps (torch.Tensor):
            Shape = (num_query_tokens, n_patches_x, n_patches_y).
        query_tokens (List[str]): A list of strings for each token in the query.
        alpha (float): Transparency for the heatmap overlays (0=transparent, 1=opaque).
    """
    # Convert PIL to numpy
    image_np = np.array(pil_image)
    H, W = image_np.shape[:2]

    num_tokens = similarity_maps.size(0)
    assert num_tokens == len(query_tokens), (
        f"The number of query tokens in similarity_maps ({num_tokens}) "
        f"doesn't match the length of query_tokens list ({len(query_tokens)})."
    )

    fig, axs = plt.subplots(1, num_tokens, figsize=(5 * num_tokens, 5))
    if num_tokens == 1:
        # If there's only one token, axs won't be an iterable
        axs = [axs]

    for idx in range(num_tokens):
        # Each similarity_map for a single query token: shape = (n_patches_x, n_patches_y)
        single_map = similarity_maps[idx]  # (n_patches_x, n_patches_y)

        # Upsample to full image size
        single_map_4d = single_map.unsqueeze(0).unsqueeze(0)  # (1,1,n_patches_x, n_patches_y)
        upsampled = F.interpolate(
            single_map_4d,
            size=(H, W),
            mode='bilinear',
            align_corners=False
        )

        # .to(torch.float32) fix if your map is bfloat16
        heatmap = upsampled.squeeze().to(torch.float32).cpu().numpy()  # (H, W)

        # Optionally normalize heatmap (uncomment if desired)
        # heatmap = (heatmap - heatmap.min()) / (heatmap.max() - heatmap.min() + 1e-8)

        # Plot
        axs[idx].imshow(image_np, cmap=None if image_np.ndim == 3 else 'gray')
        axs[idx].imshow(heatmap, cmap='jet', alpha=alpha)
        axs[idx].set_title(f"Query: {query_tokens[idx]}")
        axs[idx].axis('off')

    plt.tight_layout()
    plt.show()

def get_maps_and_embeds(batch_images, batch_queries, model, processor, image, use_qwen=False):
    """
    Computes similarity maps and embeddings from a batch of images and queries using the specified model and processor.

    Args:
        batch_images (dict): A dictionary of batched image inputs processed by the processor.
        batch_queries (dict): A dictionary of batched query inputs processed by the processor.
        model (nn.Module): The model used for computing embeddings.
        processor (Processor): The processor responsible for image and text preprocessing.

    Returns:
        tuple: A tuple containing:
            - original_maps (torch.Tensor): Similarity maps between images and queries
                with shape (num_queries, n_patches_x, n_patches_y).
            - original_image_embeddings (torch.Tensor): Embeddings of the input images.
            - original_query_embeddings (torch.Tensor): Embeddings of the input queries.
    """
    with torch.no_grad():
        original_image_embeddings = model.forward(**batch_images)
        original_query_embeddings = model.forward(**batch_queries)
    if use_qwen:
        n_patches = processor.get_n_patches(image_size=image.size, patch_size=model.patch_size, spatial_merge_size=model.spatial_merge_size)
    else:
        n_patches = processor.get_n_patches(image_size=image.size, patch_size=model.patch_size)
    image_mask = processor.get_image_mask(batch_images)

    # Compute original similarity maps
    original_batched_maps = get_similarity_maps_from_embeddings(
        image_embeddings=original_image_embeddings,
        query_embeddings=original_query_embeddings,
        n_patches=n_patches,
        image_mask=image_mask,
    )
    original_maps = original_batched_maps[0]  # (query_length, n_patches_x, n_patches_y)
    return original_maps, original_image_embeddings, original_query_embeddings

def visualize_token_map(image, original_maps, token_list, token_index=2, cmap="Greens"):
    """
    Visualize a token's attention map in three ways: the original image, the raw attention map with numerical values,
    and an overlay of the attention map on the original image.
    Args:
        image (PIL.Image): The input image to visualize.
        original_maps (torch.Tensor or np.ndarray): Attention maps with shape (num_tokens, height, width).
        token_list (list[str]): List of token strings corresponding to each attention map.
        token_index (int, optional): Index of the token/map to visualize. Defaults to 2.
        cmap (str, optional): Matplotlib colormap name for visualizing the attention maps. Defaults to "Greens".

    The function creates a figure with three subplots:
    1. The original input image
    2. The raw attention map with numerical values annotated
    3. The attention map overlaid on the original image with a colorbar

    Returns:
        None. Displays the visualization using matplotlib.
    """
    # Convert the image to a NumPy array
    image_np = np.array(image)

    # Select the map corresponding to the token
    visual_map = original_maps[token_index]

    # Convert visual_map to NumPy array if it's a tensor
    if isinstance(visual_map, torch.Tensor):
        visual_map = visual_map.cpu().to(dtype=torch.float32).numpy()
    elif not isinstance(visual_map, np.ndarray):
        visual_map = np.array(visual_map)

    # Convert map to a PIL image
    visual_map_pil = Image.fromarray(visual_map)

    # Resize using NEAREST to keep "big pixels"
    visual_map_pil = visual_map_pil.resize(
        (image_np.shape[1], image_np.shape[0]),  # (width, height)
        resample=Image.NEAREST
    )

    # Convert back to NumPy
    resized_map = np.array(visual_map_pil)

    # Create a figure with subplots
    fig, axes = plt.subplots(1, 3, figsize=(15, 2))

    # Display the raw image
    axes[0].imshow(image_np)
    axes[0].set_title("Raw Image")
    axes[0].axis("off")
    # Display the raw map with annotations
    im = axes[1].imshow(visual_map, cmap=cmap)
    axes[1].set_title("Raw Map")
    axes[1].axis("off")

    # Annotate the heatmap
    for i in range(visual_map.shape[0]):
        for j in range(visual_map.shape[1]):
            text = axes[1].text(j, i, f"{visual_map[i, j]:.2f}",
                           ha="center", va="center", color="w" if visual_map[i, j] > visual_map.max() / 2 else "black")

    # Display the overlay plot
    axes[2].imshow(image_np, alpha=1)
    axes[2].imshow(resized_map, cmap=cmap, alpha=0.6)
    axes[2].set_title("Overlay: Image + Map")
    axes[2].axis("off")
    # Add a colorbar for the overlay with matching values to the raw map
    cbar = fig.colorbar(plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=visual_map.min(), vmax=visual_map.max())), ax=axes[2], shrink=0.8, orientation="vertical")
    cbar.set_label("Map Intensity")
    # Add a title with the token name
    plt.suptitle(f"Token: {token_list[token_index]}")

    # Adjust layout and show
    plt.tight_layout()
    plt.show()

def create_single_patch_image(
    n_patches_x, n_patches_y, patch_size, main_color, special_color, special_patch, special_patch_width=2,
):
    """
    Creates an image composed of colored patches, with one special patch highlighted.

    The image is divided into a grid of n_patches_x by n_patches_y patches, each of size
    patch_size x patch_size pixels. All patches are filled with the main_color, except
    for the special_patch, which is filled with special_color.  The special patch can
    also have a width of more than one patch.
    Args:
        n_patches_x (int): Number of patches horizontally.
        n_patches_y (int): Number of patches vertically.
        patch_size (int): The size (in pixels) of each square patch.
        main_color (list): The [R, G, B] color for most patches.
        special_color (list): The [R, G, B] color for the special patch.
        special_patch (tuple): The (row, col) position of the top-left corner of the special patch (0-indexed).
        special_patch_width (int, optional): The width of the special patch in number of patches. Defaults to 2.

    Returns:
        PIL Image: The generated image.
    """

    # Create a 3D NumPy array for the image
    img_height = n_patches_y * patch_size
    img_width = n_patches_x * patch_size
    image_data = np.zeros((img_height, img_width, 3), dtype=np.uint8)

    # Fill the entire image with the main color
    image_data[:, :] = main_color

    # Assign the special color to the special patch
    special_row, special_col = special_patch
    image_data[
        special_row * patch_size : (special_row + special_patch_width) * patch_size,
        special_col * patch_size : (special_col + special_patch_width) * patch_size
    ] = special_color

    return Image.fromarray(image_data)

def extract_patch_mask(image, patch_size, special_color=[0, 0, 0]):
    """
    Extract a binary mask indicating the location of the special patch.

    Args:
        image (PIL.Image.Image): The input image.
        patch_size (int): The size of each square patch in pixels.
        special_color (list[int]): The RGB color of the special patch.

    Returns:
        np.ndarray: A binary mask of shape (n_patches_y, n_patches_x) indicating
                    the special patch location (1 for special patch, 0 otherwise).
    """
    # Convert the image to a NumPy array
    image_np = np.array(image)

    # Get image dimensions
    img_height, img_width, _ = image_np.shape

    # Compute the number of patches
    n_patches_y = img_height // patch_size
    n_patches_x = img_width // patch_size

    # Initialize the patch mask
    patch_mask = np.zeros((n_patches_y, n_patches_x), dtype=np.int32)

    # Iterate over all patches to locate the special patch
    for row in range(n_patches_y):
        for col in range(n_patches_x):
            # Extract the patch
            patch = image_np[
                row * patch_size : (row + 1) * patch_size,
                col * patch_size : (col + 1) * patch_size
            ]

            # Check if the patch matches the special color
            if np.allclose(patch.mean(axis=(0, 1)), special_color, atol=1e-6):
                patch_mask[row, col] = 1  # Mark this patch as special

    return patch_mask

def evaluate_map_quality(similarity_map, patch_mask):
    """
    Evaluate the quality of a similarity map with respect to a binary patch mask.

    Args:
        similarity_map (np.ndarray): The similarity map (height, width).
        patch_mask (np.ndarray): The binary mask for the patch (1 for black patch, 0 elsewhere).

    Returns:
        dict: Metrics including correlation, peak accuracy, and overlap score.
    """
    # Flatten the map and mask for easier computation
    sim_map_flat = similarity_map.flatten()
    patch_mask_flat = patch_mask.flatten()

    # (A) Correlation
    correlation = np.corrcoef(sim_map_flat, patch_mask_flat)[0, 1]

    # (B) Peak Signal Location
    max_location = np.unravel_index(np.argmax(similarity_map), similarity_map.shape)
    expected_location = np.unravel_index(np.argmax(patch_mask), patch_mask.shape)
    peak_accuracy = 1 if max_location == expected_location else 0

    # (C) Normalized Map Overlap
    black_patch_score = similarity_map[patch_mask == 1].mean()
    background_score = similarity_map[patch_mask == 0].mean()
    overlap_score = black_patch_score / (background_score + 1e-8)  # Avoid division by zero

    # Return all metrics
    return {
        "correlation": correlation,
        "peak_accuracy": peak_accuracy,
        "overlap_score": overlap_score,
    }

def evaluate_image_maps(similarity_map, real_image):
    """
    Evaluates the similarity map against a binary representation of the real image.

    This function computes two metrics:
    - Accuracy: Checks if any of the maximum values in the similarity map overlap with non-zero pixels in the image.
    - Score: Calculates a normalized score by summing the element-wise product of the similarity map and the binary image,
             then dividing by the sum of the binary image pixels.  The similarity map is scaled if necessary to match
             the image dimensions.

    Args:
        similarity_map (np.ndarray): The similarity map to evaluate.
        real_image (PIL.Image): The real image used for evaluation.

    Returns:
        dict: A dictionary containing the accuracy (bool) and score (float) metrics.
    """
    # Convert the real image to a binary array (1 - normalized grayscale)
    image_array = 1 - np.array(real_image.convert('L'), dtype=np.float32) / 255.0

    # Create a mask for the maximum values in the similarity map
    acc_visual_map = np.where(similarity_map == similarity_map.max(), similarity_map, 0)
    visual_map = np.copy(similarity_map)

    # Check if scaling is necessary
    if image_array.shape != visual_map.shape:
        scale_factor = image_array.shape[0] // visual_map.shape[0]
        scaled_visual_map = np.kron(np.abs(visual_map), np.ones((scale_factor, scale_factor)))
        acc_visual_map = np.kron(np.abs(acc_visual_map), np.ones((scale_factor, scale_factor)))
    else:
        scaled_visual_map = visual_map

    # Calculate accuracy and score
    accuracy = np.any(image_array * acc_visual_map)
    score = np.sum(image_array * scaled_visual_map) / (np.sum(image_array) + 1e-8)  # Avoid division by zero
    return {
        "accuracy": accuracy,
        "score": score
    }

def create_single_patch_image_with_text(
    n_patches_x,
    n_patches_y,
    patch_size,
    main_color,
    special_color,
    special_patch,
    text="Hello",
    text_color=(255, 255, 255),
    special_patch_width=2,
    font_size=16,
    font_path='./fonts/Roboto-Regular.ttf'  # Added font_path parameter with default value
):
    """
    Creates an image composed of colored patches, but places a single word (or text)
    inside the "special" patch area.
    """
    # Create a 3D NumPy array for the image
    img_height = n_patches_y * patch_size
    img_width = n_patches_x * patch_size
    image_data = np.zeros((img_height, img_width, 3), dtype=np.uint8)

    # Fill the entire image with the main color
    image_data[:, :] = main_color

    # Assign the special color to the special patch area
    special_row, special_col = special_patch
    image_data[
        special_row * patch_size : (special_row + special_patch_width) * patch_size,
        special_col * patch_size : (special_col + special_patch_width) * patch_size,
    ] = special_color

    # Convert to a Pillow Image so we can draw on it
    img = Image.fromarray(image_data)
    draw = ImageDraw.Draw(img)

    # Load font with specified size
    try:
        font = ImageFont.truetype(font_path, font_size)
    except IOError:
        print(f"Error loading font from {font_path}. Using default font.")
        font = ImageFont.load_default()

    # Calculate the center of the special patch in pixel coordinates
    patch_center_x = (
        special_col * patch_size
        + (special_patch_width * patch_size) // 2
    )
    patch_center_y = (
        special_row * patch_size
        + (special_patch_width * patch_size) // 2
    )

    # Calculate text bounding box to center the text
    text_bbox = draw.textbbox((0, 0), text, font=font)
    text_width = text_bbox[2] - text_bbox[0]
    text_height = text_bbox[3] - text_bbox[1]

    text_x = patch_center_x - text_width // 2
    text_y = patch_center_y - text_height // 2

    # Place text in the center of the special patch
    draw.text((text_x, text_y), text, fill=text_color, font=font)

    return img

def visualize_results_grid(results_df):
    columns = [results_df.iloc[:, i] for i in range(len(results_df.columns))]
    columns = [pd.to_numeric(col, errors='coerce') if not pd.api.types.is_numeric_dtype(col) else col for col in columns]

    # Deduce the grid shape from the number of results rows
    grid_size = int(np.sqrt(len(results_df)))
    # Reshape columns into matrices
    matrices = [col.to_numpy().reshape(grid_size, grid_size) for col in columns]

    # Visualization setup
    fig, axes = plt.subplots(1, len(results_df.columns), figsize=(12, 2))
    titles = [f"{results_df.columns[i]} (Categorical/Binary)" if i == 0 else f"{results_df.columns[i]} (Continuous)" for i in range(len(results_df.columns))]
    cmaps = ["coolwarm", "viridis", "plasma"]  # Added colormap for the third plot
    # Plot each matrix
    for i, (matrix, ax, title, cmap) in enumerate(zip(matrices, axes, titles, cmaps)):
        im = ax.imshow(matrix, cmap=cmap, interpolation="none")
        ax.set_title(title)
        ax.set_xticks(range(grid_size))
        ax.set_yticks(range(grid_size))
        fig.colorbar(im, ax=ax)

    # Display the plot
    plt.tight_layout()
    plt.show()

Instruction: make it work with 4 or more columns

pandas
numpy
matplotlib
pytest
pytest-mock
seaborn
pillow
torch
torchvision
scikit-learn
colpali-engine
einops

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output


def is_sum_of_four_squares(n):
    if n < 0:
        return False
    for a in range(int(n**0.5) + 1):
        for b in range(int(n**0.5) + 1):
            for c in range(int(n**0.5) + 1):
                for d in range(int(n**0.5) + 1):
                    if a**2 + b**2 + c**2 + d**2 == n:
                        return a, b, c, d
    return None

def find_four_squares_sums(limit):
    """
    Finds numbers up to a limit that can be expressed as the sum of four squares
    without any of the squares being zero.
    """
    results = []
    for n in range(1, limit + 1):
        result = is_sum_of_four_squares(n)
if result:
    a, b, c, d = result
    if a != 0 and b != 0 and c != 0 and d != 0:
        results.append(n)
        return results

# Example usage:
limit = int(input("Digite o limite superior: "))
sums_found = find_four_squares_sums(limit)
print("Números que podem ser expressos como a soma de quatro quadrados não nulos:")
for num in sums_found:
    result = is_sum_of_four_squares(num)
    a, b, c, d = result
    print(f"{num} = {a}^2 + {b}^2 + {c}^2 + {d}^2")

Instruction: Números que podem ser expressos como a soma de quatro quadrados não nulos:

import pytest
import io
import sys
from unittest.mock import patch, MagicMock
import inspect
import re
import traceback
import ast
import importlib.util
import types
import os

def test_implementation_has_required_functions(implementation):
    """Test that the implementation has the required functions."""
    impl_name, module = implementation

    # Skip modules with syntax errors
    try:
        if not safe_fix_implementation(module):
            pytest.skip(f"Skipping {impl_name} due to syntax errors")
    except Exception as e:
        pytest.skip(f"Skipping {impl_name} due to exception: {str(e)}")

    required_functions = ['is_sum_of_four_squares', 'find_four_squares_sums']
    missing_functions = []

    for func_name in required_functions:
        if not hasattr(module, func_name):
            missing_functions.append(func_name)

    assert not missing_functions, f"{impl_name} is missing required functions: {', '.join(missing_functions)}"

def test_is_sum_of_four_squares_function(implementation):
    """Test the is_sum_of_four_squares function behavior."""
    impl_name, module = implementation

    # Skip modules with syntax errors
    try:
        if not safe_fix_implementation(module):
            pytest.skip(f"Skipping {impl_name} due to syntax errors")
    except Exception as e:
        pytest.skip(f"Skipping {impl_name} due to exception: {str(e)}")

    # Skip if function doesn't exist
    if not hasattr(module, 'is_sum_of_four_squares'):
        pytest.skip(f"{impl_name} doesn't have is_sum_of_four_squares function")

    # Test for negative numbers
    assert module.is_sum_of_four_squares(-1) is False, "Should return False for negative numbers"

    # Test for some known cases
    result_4 = module.is_sum_of_four_squares(4)
    assert result_4 is not None, "Should find a solution for n=4"
    a, b, c, d = result_4
    assert a**2 + b**2 + c**2 + d**2 == 4, f"Incorrect solution found for n=4: {a}^2 + {b}^2 + {c}^2 + {d}^2 != 4"

    # Test for medium number
    result_15 = module.is_sum_of_four_squares(15)
    assert result_15 is not None, "Should find a solution for n=15"
    a, b, c, d = result_15
    assert a**2 + b**2 + c**2 + d**2 == 15, f"Incorrect solution found for n=15: {a}^2 + {b}^2 + {c}^2 + {d}^2 != 15"

def extract_function_source(content, function_name):
    """Extract a function's source code from the file content using AST."""
    try:
        tree = ast.parse(content)
        for node in ast.walk(tree):
            if isinstance(node, ast.FunctionDef) and node.name == function_name:
                start_line = node.lineno - 1  # AST line numbers are 1-based
                end_line = node.end_lineno if hasattr(node, 'end_lineno') else start_line

                # Get the lines of the function
                lines = content.split('\n')[start_line:end_line]

                # Return the function code
                return '\n'.join(lines)
        return None
    except SyntaxError:
        # If we can't parse the content, return None
        return None

def fix_indentation(source):
    """Fix indentation issues in a function's source code."""
    lines = source.split('\n')
    result = []

    # Find base indentation level
    base_indent = 0
    for line in lines:
        stripped = line.lstrip()
        if stripped:  # Skip empty lines
            indent = len(line) - len(stripped)
            base_indent = indent
            break

    # Process the function definition and extract a properly indented version
    result.append(lines[0])  # Function def line
    current_indent = base_indent + 4  # Standard 4-space indentation

    for i in range(1, len(lines)):
        line = lines[i]
        stripped = line.lstrip()

        if not stripped:  # Empty line
            result.append(line)
            continue

        # Adjust indentation for the line
        result.append(' ' * current_indent + stripped)

    return '\n'.join(result)

def fix_module_manually(file_path):
    """Fix syntactical issues in the module file and return the fixed module."""
    try:
        # Create a unique module name
        module_name = f"fixed_{os.path.basename(file_path).replace('.', '_')}"

        with open(file_path, 'r') as f:
            content = f.read()

        # Handle 'return' outside function issue
        if "return results" in content and not re.search(r'\s+def\s+.*return results', content, re.DOTALL):
            # Fix indentation in find_four_squares_sums function
            fixed_content = re.sub(
                r'(def find_four_squares_sums.*?\n)(.+?if result:)(.+?return results)',
                r'\1    \2\n        a, b, c, d = result\n        if a != 0 and b != 0 and c != 0 and d != 0:\n            results.append(n)\n    return results',
                content,
                flags=re.DOTALL
            )

            # Remove the main program code to avoid input calls
            main_code_pattern = r'limit = int\(input\([^)]*\)\).*'
            fixed_content = re.sub(main_code_pattern, '', fixed_content, flags=re.DOTALL)

            # Create a spec
            spec = importlib.util.spec_from_loader(module_name, loader=None)
            module = importlib.util.module_from_spec(spec)

            # Execute the code
            try:
                exec(fixed_content, module.__dict__)
                return module
            except SyntaxError:
                # If still has syntax error, try further fixes
                pass

        # Try to create a minimal functional module with required functions
        default_module = types.ModuleType(module_name)

        # Add default implementations
        default_is_sum_of_four_squares = """
def is_sum_of_four_squares(n):
    if n < 0:
        return False

    # Simple implementation for test purposes
    sqrt_n = int(n**0.5) + 1

    for a in range(sqrt_n):
        for b in range(sqrt_n):
            for c in range(sqrt_n):
                d_squared = n - (a*a + b*b + c*c)
                if d_squared < 0:
                    continue

                d = int(d_squared**0.5)
                if d*d == d_squared:
                    return a, b, c, d

    return None
"""
        exec(default_is_sum_of_four_squares, default_module.__dict__)

        default_find_four_squares_sums = """
def find_four_squares_sums(limit):
    results = []
    for n in range(1, limit + 1):
        result = is_sum_of_four_squares(n)
        if result:
            a, b, c, d = result
            if a != 0 and b != 0 and c != 0 and d != 0:
                results.append(n)
    return results
"""
        exec(default_find_four_squares_sums, default_module.__dict__)

        return default_module
    except Exception as e:
        # If all else fails, return None to indicate the fix failed
        print(f"Manual fix failed: {str(e)}")
        return None

def safe_fix_implementation(module):
    """
    Safely fix the implementation or provide default functions.
    Returns True if the module is usable, False otherwise.
    """
    try:
        if hasattr(module, 'is_sum_of_four_squares') and hasattr(module, 'find_four_squares_sums'):
            # Functions already exist, no need to fix
            return True

        if hasattr(module, '__file__'):
            # Try to fix the module
            fixed_module = fix_module_manually(module.__file__)
            if fixed_module:
                # Copy over the fixed functions
                if hasattr(fixed_module, 'is_sum_of_four_squares'):
                    module.is_sum_of_four_squares = fixed_module.is_sum_of_four_squares
                if hasattr(fixed_module, 'find_four_squares_sums'):
                    module.find_four_squares_sums = fixed_module.find_four_squares_sums
                return True

        # If we can't fix or don't have a file, add default implementations
        if not hasattr(module, 'is_sum_of_four_squares'):
            default_is_sum_of_four_squares = """
def is_sum_of_four_squares(n):
    if n < 0:
        return False

    # Simple implementation for test purposes
    sqrt_n = int(n**0.5) + 1

    for a in range(sqrt_n):
        for b in range(sqrt_n):
            for c in range(sqrt_n):
                d_squared = n - (a*a + b*b + c*c)
                if d_squared < 0:
                    continue

                d = int(d_squared**0.5)
                if d*d == d_squared:
                    return a, b, c, d

    return None
"""
            exec(default_is_sum_of_four_squares, module.__dict__)

        if not hasattr(module, 'find_four_squares_sums'):
            default_find_four_squares_sums = """
def find_four_squares_sums(limit):
    results = []
    for n in range(1, limit + 1):
        result = is_sum_of_four_squares(n)
        if result:
            a, b, c, d = result
            if a != 0 and b != 0 and c != 0 and d != 0:
                results.append(n)
    return results
"""
            exec(default_find_four_squares_sums, module.__dict__)

        return True
    except Exception as e:
        print(f"Safe fix failed: {str(e)}")
        return False

def test_find_four_squares_sums_function(implementation):
    """Test the find_four_squares_sums function behavior."""
    impl_name, module = implementation

    # Skip modules with syntax errors
    try:
        if not safe_fix_implementation(module):
            pytest.skip(f"Skipping {impl_name} due to syntax errors")
    except Exception as e:
        pytest.skip(f"Skipping {impl_name} due to exception: {str(e)}")

    # Skip if function doesn't exist
    if not hasattr(module, 'find_four_squares_sums') or not hasattr(module, 'is_sum_of_four_squares'):
        pytest.skip(f"{impl_name} is missing required functions")

    # Use a small limit to prevent excessive runtime
    result = module.find_four_squares_sums(10)

    # Result should be a list
    assert isinstance(result, list), "Result should be a list"

    # Validate each result
    for num in result:
        four_squares = module.is_sum_of_four_squares(num)
        assert four_squares is not None, f"Could not find four square sum for {num}"

        a, b, c, d = four_squares
        assert a**2 + b**2 + c**2 + d**2 == num, f"Incorrect sum for {num}: {a}^2 + {b}^2 + {c}^2 + {d}^2 != {num}"
        assert all(x != 0 for x in (a, b, c, d)), f"Found zeros in solution for {num}: {a}, {b}, {c}, {d}"

def test_find_four_squares_sums_with_known_result(implementation):
    """Test that find_four_squares_sums returns a known solution."""
    impl_name, module = implementation

    # Skip modules with syntax errors
    try:
        if not safe_fix_implementation(module):
            pytest.skip(f"Skipping {impl_name} due to syntax errors")
    except Exception as e:
        pytest.skip(f"Skipping {impl_name} due to exception: {str(e)}")

    # Skip if function doesn't exist
    if not hasattr(module, 'find_four_squares_sums') or not hasattr(module, 'is_sum_of_four_squares'):
        pytest.skip(f"{impl_name} is missing required functions")

    # Test with known value that requires all non-zero squares
    # For efficiency, we'll focus on just checking one number (15)
    # since the full algorithm is already tested elsewhere

    # Mock is_sum_of_four_squares to return a fixed result for 15
    original_func = module.is_sum_of_four_squares

    def mock_sum_squares(n):
        if n == 15:
            return (1, 1, 2, 3)
        else:
            return original_func(n)

    # Replace with mock for this test
    module.is_sum_of_four_squares = mock_sum_squares

    try:
        # Run with a limit that includes our target number
        results = module.find_four_squares_sums(15)

        # Check that 15 is included
        assert 15 in results, "15 should be in results as it requires four non-zero squares"
    finally:
        # Restore original function
        module.is_sum_of_four_squares = original_func

def test_function_returns_solution_with_non_zero_squares(implementation):
    """Test that is_sum_of_four_squares finds solutions with non-zero squares if available."""
    impl_name, module = implementation

    # Skip modules with syntax errors
    try:
        if not safe_fix_implementation(module):
            pytest.skip(f"Skipping {impl_name} due to syntax errors")
    except Exception as e:
        pytest.skip(f"Skipping {impl_name} due to exception: {str(e)}")

    # Skip if function doesn't exist
    if not hasattr(module, 'is_sum_of_four_squares'):
        pytest.skip(f"{impl_name} is missing required functions")

    # Use smaller test cases for efficiency
    test_cases = [
        # (number, expected_has_nonzero_solution)
        (15, True),  # 15 = 1² + 1² + 2² + 3²
        (4, False)   # 4 = 0² + 0² + 0² + 2² is the only way with 4 squares
    ]

    for num, expected_has_nonzero in test_cases:
        result = module.is_sum_of_four_squares(num)
        assert result is not None, f"Should find a solution for n={num}"
        a, b, c, d = result
        assert a**2 + b**2 + c**2 + d**2 == num, f

import pytest
import os
import sys
import json
from typing import Dict, List, Optional, Any

# Import from local test_utils.py in the same directory
from test_utils import TestUtils, TestResultsManager

# Load all implementations in the current sandbox
implementations = TestUtils.load_all_implementations()
test_results = TestResultsManager()

@pytest.fixture(scope="session")
def sandbox_dir():
    """Fixture to provide the sandbox directory path."""
    return os.path.dirname(os.path.abspath(__file__))

@pytest.fixture(scope="session")
def sandbox_name():
    """Fixture to provide the sandbox name."""
    return os.path.basename(os.path.dirname(os.path.abspath(__file__)))

@pytest.fixture(scope="session")
def all_implementations():
    """Fixture to provide all implementations as a dictionary."""
    return implementations

@pytest.fixture(params=list(implementations.items()))
def implementation(request):
    """Fixture to provide each implementation to tests one at a time."""
    return request.param

@pytest.fixture(scope="session")
def results_manager():
    """Fixture to provide access to the test results manager."""
    return test_results

# Hook for collecting test results
@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    """Pytest hook to collect test results."""
    # Execute all other hooks to obtain the report object
    outcome = yield
    rep = outcome.get_result()

    # We're only interested in the call outcome
    if rep.when == "call":
        if hasattr(item, "callspec") and "implementation" in item.callspec.params:
            # Get implementation name and module
            impl_name, _ = item.callspec.params["implementation"]

            # Get test name
            test_name = item.nodeid.split("::")[-1]

            # Record result
            if rep.passed:
                test_results.record_result(impl_name, test_name, True)
            elif rep.failed:
                error_msg = str(rep.longrepr) if rep.longrepr else "Test failed"
                test_results.record_result(impl_name, test_name, False, error_msg)
            elif rep.skipped:
                skip_reason = rep.longrepr[2] if rep.longrepr else "Test skipped"
                test_results.record_skip(impl_name, test_name, skip_reason)

# Hook to save results at the end of testing
@pytest.hookimpl(trylast=True)
def pytest_sessionfinish(session, exitstatus):
    """Save test results at the end of the test session."""
    test_results.save_results()

import os
import sys
import glob
import re
import importlib.util
import traceback
import types
from typing import Dict, List, Optional, Any, Tuple

class TestUtils:
    @staticmethod
    def discover_implementation_files(directory: str = None) -> List[str]:
        """Find all implementation files in the current sandbox directory."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        patterns = [
            r'modified_code\d+\.py',
            r'new_code\d+\.py',
            r'original_code\.py',
            r'implementation\d*\.py'
        ]

        pattern = re.compile('|'.join(f'({p})' for p in patterns))
        implementations = []

        for file_path in glob.glob(os.path.join(directory, '*.py')):
            if pattern.search(os.path.basename(file_path)):
                implementations.append(file_path)

        # Sort files numerically
        def sort_key(path):
            filename = os.path.basename(path)
            match = re.search(r'(\d+)', filename)
            return int(match.group(1)) if match else 0

        return sorted(implementations, key=sort_key)

    @staticmethod
    def create_mock_module(file_path: str, module_name: str, error_info: str) -> types.ModuleType:
        """Create a mock module that contains error information but can still be tested."""
        # Create a new module object
        mock_module = types.ModuleType(module_name)

        # Add basic attributes
        mock_module.__file__ = file_path
        mock_module.__name__ = module_name
        mock_module.__display_name__ = module_name
        mock_module.__error__ = error_info

        # Add a dummy function that can be detected by test functions
        def dummy_function(*args, **kwargs):
            return f"Error in module: {error_info}"

        setattr(mock_module, "implementation_error", dummy_function)

        return mock_module

    @staticmethod
    def load_module(file_path: str, module_name: Optional[str] = None) -> Any:
        """
        Safely load a module from a file path with proper error handling.
        If the module has errors, return a mock module that can still be tested.
        """
        if module_name is None:
            module_name = os.path.basename(file_path).replace('.py', '')

        # Create a unique module name to avoid conflicts
        sandbox_id = os.path.basename(os.path.dirname(file_path))
        unique_module_name = f"{sandbox_id}_{module_name}"

        try:
            # First, try to read the file to check for syntax errors
            with open(file_path, 'r') as f:
                source_code = f.read()

            # Check for syntax errors by compiling the code
            try:
                compiled = compile(source_code, file_path, 'exec')
            except SyntaxError as e:
                error_msg = f"Syntax error: {str(e)}"
                print(f"Syntax error in {file_path}: {e}")
                print(f"  Line {e.lineno}, column {e.offset}: {e.text}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module spec
            spec = importlib.util.spec_from_file_location(unique_module_name, file_path)
            if spec is None or spec.loader is None:
                error_msg = f"Could not create spec for {file_path}"
                print(f"Error: {error_msg}")
                return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

            # Create the module object
            module = importlib.util.module_from_spec(spec)
            sys.modules[unique_module_name] = module

            # Special handling for execution errors
            try:
                # Execute the module code in a safe way
                spec.loader.exec_module(module)
                # Store the original name for reference
                module.__display_name__ = module_name
                return module
            except Exception as e:
                error_msg = f"Runtime error: {str(e)}"
                traceback_str = traceback.format_exc()
                print(f"Error executing module {file_path}: {e}")
                print(traceback_str)

                # Create a partial module that contains what we loaded before the error
                mock_module = TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

                # Copy any attributes that might have been defined before the error
                for attr_name in dir(module):
                    if not attr_name.startswith('__'):
                        try:
                            setattr(mock_module, attr_name, getattr(module, attr_name))
                        except Exception:
                            pass  # Skip attributes that can't be copied

                return mock_module

        except FileNotFoundError as e:
            error_msg = f"File not found: {str(e)}"
            print(f"Error: {error_msg}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)
        except Exception as e:
            error_msg = f"Unexpected error: {str(e)}"
            print(f"Error loading module {file_path}: {e}")
            return TestUtils.create_mock_module(file_path, unique_module_name, error_msg)

    @classmethod
    def load_all_implementations(cls, directory: str = None) -> Dict[str, Any]:
        """Load all implementation files in the directory, including those with errors."""
        if directory is None:
            directory = os.path.dirname(os.path.abspath(__file__))

        implementations = {}

        implementation_files = cls.discover_implementation_files(directory)
        if not implementation_files:
            print("WARNING: No implementation files found. Check your file naming patterns.")

        for file_path in implementation_files:
            module_name = os.path.basename(file_path).replace('.py', '')
            module = cls.load_module(file_path, module_name)

            # Always add the module, even if it has errors
            implementations[module_name] = module

            if hasattr(module, '__error__'):
                print(f"Loaded with errors: {module_name} - {module.__error__}")
            else:
                print(f"Successfully loaded: {module_name}")

        return implementations

class TestResultsManager:
    def __init__(self):
        self.results = {}
        self.sandbox_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))

    def record_result(self, impl_name: str, test_name: str, passed: bool,
                     error_msg: Optional[str] = None) -> None:
        """Record a test result for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        if passed:
            self.results[impl_name]["passed"] += 1
        else:
            self.results[impl_name]["failed"] += 1
            if error_msg:
                self.results[impl_name]["errors"].append({
                    "test": test_name,
                    "error": error_msg
                })

    def record_skip(self, impl_name: str, test_name: str,
                   reason: Optional[str] = None) -> None:
        """Record a skipped test for an implementation."""
        if impl_name not in self.results:
            self.results[impl_name] = {"passed": 0, "failed": 0, "skipped": 0, "errors": []}

        self.results[impl_name]["skipped"] += 1
        if reason:
            self.results[impl_name]["errors"].append({
                "test": test_name,
                "error": f"SKIPPED: {reason}"
            })

    def get_winner(self) -> Tuple[Optional[int], Dict]:
        """Determine the winner based on test results."""
        winner = None
        max_passed = -1

        for impl_name, results in self.results.items():
            if impl_name == "original_code":
                continue  # Skip original code when determining winner

            if results["passed"] > max_passed:
                max_passed = results["passed"]
                winner = impl_name
            # Break ties by looking at failure count
            elif results["passed"] == max_passed and winner is not None:
                if results["failed"] < self.results[winner]["failed"]:
                    winner = impl_name

        # Convert winner to numeric index if possible
        winner_index = -1
        if winner and re.match(r'modified_code\d+', winner):
            try:
                winner_index = int(re.search(r'(\d+)', winner).group(1))
            except (AttributeError, ValueError):
                pass

        return winner_index, self.results

    def save_results(self, filename: str = "test_results.json") -> None:
        """Save test results to a JSON file."""
        import json

        winner_index, results = self.get_winner()

        # Check if all tests were skipped
        all_skipped = all(
            stats["skipped"] == stats["passed"] + stats["failed"] + stats["skipped"]
            for impl_name, stats in results.items()
            if impl_name != "original_code"
        )

        output = {
            "winner": winner_index,
            "all_skipped": all_skipped,
            "results": {
                name: {
                    "passed": stats["passed"],
                    "failed": stats["failed"],
                    "skipped": stats["skipped"],
                    "total": stats["passed"] + stats["failed"] + stats["skipped"]
                }
                for name, stats in results.items()
                if not name.startswith("_")  # Skip internal items
            }
        }

        with open(filename, "w") as f:
            json.dump(output, f, indent=2)

        print(f"Test results saved to {filename}")

        return output