import pandas as pd
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,
)

# 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)
    # Example augmentation: horizontal flip
    augmented_images = [ToTensor()(image).flip(-1) for image in images]
    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,
    )
    # (query_length, n_patches_x, n_patches_y)
    original_maps = original_batched_maps[0].permute(0, 2, 1).contiguous()
    return original_maps, original_image_embeddings, original_query_embeddings

def visualize_token_map(image, original_maps, token_list, token_index=2, cmap="Greens", figsize=(15, 2), show_text=True):
    """
    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")

    if(show_text):
        # 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 (torch.Tensor): 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.
    """
    # Ensure similarity_map is in float32 and on the CPU
    similarity_map = similarity_map.to(dtype=torch.float32).cpu()

    # Flatten the map and mask for easier computation
    sim_map_flat = similarity_map.numpy().flatten()
    patch_mask_flat = patch_mask.flatten()

    # (A) Correlation
    correlation = np.corrcoef(sim_map_flat, patch_mask_flat.astype(np.float32))[0, 1]

    # (B) Peak Signal Location
    max_location = np.unravel_index(np.argmax(sim_map_flat), 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().item()
    background_score = similarity_map[patch_mask == 0].mean().item()
    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 quality of similarity maps by comparing them to a real image.

    This function assesses the alignment between a similarity map and a corresponding
    real image. It calculates several metrics:

    - Accuracy: Checks if any of the maximum values in the similarity map overlap with
      non-zero pixels in the real image (converted to grayscale).
    - Score: Computes a normalized score by summing the element-wise product of the
      similarity map and the normalized grayscale image, divided by the sum of the
      grayscale image pixel values.  This measures the weighted overlap, giving more
      importance to brighter regions in the real image.
    - Rank: Determines the rank of the average value within the special patch in the sorted
      list of all values in the similarity map. This indicates how strongly the map
      highlights the special patch compared to other regions.

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

    Returns:
        dict: A dictionary containing the calculated metrics: accuracy, score, and rank.
    """
    # 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)

    # 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))
        )
        rank_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
    bin_image = (image_array != 0).astype(int)
    rank = np.sum(bin_image * rank_map) / np.sum(bin_image)  # Avoid division by zero
    rank = np.where(
        np.isclose(sorted(list(np.abs(similarity_map.ravel())))[::-1], rank)
    )[0][0]

    return {
        "accuracy": accuracy,
        "score": score,
        "rank": rank,
    }

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,
    # Added font_path parameter with default value
    font_path="./fonts/Roboto-Regular.ttf",
):
    """
    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))
    ]
    # Added colormap for the fourth plot
    cmaps = ["coolwarm"] * len(results_df.columns)
    # 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()

def run_expe_word_square(
    word_to_write,
    token,
    n_patches_x,
    n_patches_y,
    patch_size,
    model,
    processor,
    device,
    use_qwen,
    main_color=[255, 255, 255],
    special_color=(0, 0, 0),
):

    all_images_text = [
        create_single_patch_image_with_text(
            n_patches_x=n_patches_x,
            n_patches_y=n_patches_y,
            patch_size=patch_size,
            main_color=main_color,
            special_color=main_color,
            special_patch=(row, col),
            text=word_to_write,
            text_color=(0,0,0),  # text_color,
            font_size=9,
        )
        for row in range(0, n_patches_y, 2)
        for col in range(0, n_patches_x, 2)
    ]

    all_maps = []
    for image in all_images_text:
        batch_images = processor.process_images([image]).to(device)
        batch_queries = processor.process_queries([token]).to(device)
        original_maps, original_image_embeddings, original_query_embeddings = (
            get_maps_and_embeds(
                batch_images, batch_queries, model, processor, image, use_qwen=use_qwen
            )
        )
        original_maps = original_maps.to(dtype=torch.float32).cpu().numpy()
        all_maps.append(original_maps)

    input_ids = batch_queries["input_ids"][0]  # shape: (num_subtokens,)
    token_list = [processor.tokenizer.decode([token_id]) for token_id in input_ids]
    # print(token_list)
    indexes = [i for i, x in enumerate(token_list) if "<" not in x and ">" not in x][2:]
    # print(indexes)
    # print(np.array(token_list)[[indexes]])

    results_df = pd.DataFrame(columns=["accuracy", "score", "rank"])
    for i, (this_map, image) in enumerate(zip(all_maps, all_images_text)):
        visual_map = this_map[token_index]
        metrics = evaluate_image_maps(visual_map, image)
        results_df.loc[i] = metrics.values()
    return results_df

Instruction: --------------------------------------------------------------------------- TypeError                                 Traceback (most recent call last) Cell In[46], line 16       5 for i, (this_map, image) in enumerate(zip(all_maps, all_images_text)):       6 # Evaluate quality       7     # visualize_token_map(    (...)      12     #     cmap="Greens"      13     # )      15     visual_map = this_map[token_index] ---> 16     metrics = evaluate_image_maps(visual_map, image)      17     print(metrics)      18     results_df.loc[i] = metrics.values()  File ~/sky_workdir/test_check.py:547, in evaluate_image_maps(similarity_map, real_image)     544 image_array = 1 - np.array(real_image.convert("L"), dtype=np.float32) / 255.0     546 # Create a mask for the maximum values in the similarity map --> 547 acc_visual_map = np.where(similarity_map == similarity_map.max(), similarity_map, 0)     548 visual_map = np.copy(similarity_map)     550 # Check if scaling is necessary  File /opt/conda/lib/python3.10/site-packages/torch/_tensor.py:1149, in Tensor.__array__(self, dtype)    1147     return handle_torch_function(Tensor.__array__, (self,), self, dtype=dtype)    1148 if dtype is None: -> 1149     return self.numpy()    1150 else:    1151     return self.numpy().astype(dtype, copy=False)  TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

# test_evaluate_image_maps_cpu_and_visual_map.py

import re
import pytest
import inspect

def _get_source(module):
    """Helper to fetch source of evaluate_image_maps or skip."""
    if not hasattr(module, "evaluate_image_maps"):
        pytest.skip(f"{module.__name__} has no evaluate_image_maps function")
    return inspect.getsource(module.evaluate_image_maps)

def test_cpu_to_numpy_somewhere(implementation):
    """
    Must call `.cpu().numpy()` at least once on similarity_map.
    """
    _, module = implementation
    src = _get_source(module)
    assert ".cpu().numpy()" in src, (
        f"{module.__name__}: you must call `.cpu().numpy()` on the tensor before any numpy ops"
    )

def test_max_called_on_numpy_or_tensor_cpu(implementation):
    """
    Must call `.max()` *after* converting to numpy, OR call `.cpu().max()`.
    """
    _, module = implementation
    src = _get_source(module)

    # 1) tensor.cpu().max() pattern
    tensor_cpu_max = re.search(r"similarity_map\.cpu\(\)\.max\(\)", src)

    # 2) numpy‐array‐max pattern: .cpu().numpy().max(
    numpy_max = re.search(r"\.cpu\(\)\.numpy\(\)\.max\(\)", src)

    assert tensor_cpu_max or numpy_max, (
        f"{module.__name__}: you must take the max on the CPU (either "
        "`similarity_map.cpu().max()` or "
        "`similarity_map.cpu().numpy().max()`) not on the raw CUDA tensor"
    )

def test_visual_map_initialization_and_relationship(implementation):
    """
    Test that visual_map (or acc_visual_map) is defined and
    derived from similarity_map or its accumulated version.
    """
    _, module = implementation
    src = _get_source(module)

    # Check definition
    assert "visual_map" in src, (
        f"{module.__name__}: no 'visual_map' defined in evaluate_image_maps"
    )

    # Check spatial relationship
    relations = [
        "visual_map" in src and "similarity_map" in src,
        "visual_map" in src and "acc_visual_map" in src,
        "np.where" in src and "similarity_map.max" in src
    ]
    assert any(relations), (
        f"{module.__name__}: visual_map must be derived from similarity_map "
        "or acc_visual_map (e.g. via np.where(similarity_map==similarity_map.max(), ...))"
    )

def test_mask_creation_with_max(implementation):
    """
    Test that acc_visual_map (or equivalent) uses similarity_map.max()
    to create a mask via np.where or copy.
    """
    _, module = implementation
    src = _get_source(module)

    # look for np.where(...) with similarity_map.max()
    assert "np.where" in src and "similarity_map.max" in src, (
        f"{module.__name__}: mask creation should use np.where(similarity_map==similarity_map.max(), ...)"
    )

numpy
torch
pillow
pytest
pytest-mock
pandas
matplotlib
seaborn
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


Instruction: Функция для метода спряжённых градиентов. Python.

import pytest
import numpy as np
import inspect
from typing import Callable, Union, Tuple, Dict, Any
import warnings
import os

# Helper functions for testing
def is_positive_definite(A):
    """Check if matrix A is positive definite"""
    try:
        np.linalg.cholesky(A)
        return True
    except np.linalg.LinAlgError:
        return False

def generate_test_matrices(size=5, condition_number=None):
    """Generate a positive definite matrix and a right-hand side vector"""
    # Create a random matrix
    np.random.seed(42)  # Ensure reproducibility
    A_random = np.random.rand(size, size)
    # Make it symmetric
    A = A_random.T @ A_random + size * np.eye(size)  # Adding identity ensures positive definiteness
    # Create a random right-hand side vector
    b = np.random.rand(size)
    # Compute the exact solution
    x_exact = np.linalg.solve(A, b)
    return A, b, x_exact

def get_solver_function(module):
    """Get the conjugate gradient solver function from the module"""
    # Find the first function that starts with 'conjugate' in its name
    for name, obj in inspect.getmembers(module):
        if inspect.isfunction(obj) and (
            name.startswith('conjugate') or
            'conjugate' in name.lower() or
            'grad' in name.lower() or
            'cg' == name.lower()
        ):
            return obj

    return None  # Return None instead of raising an exception

def normalize_output(result):
    """Normalize the output from different implementations to a consistent format"""
    if isinstance(result, tuple) and len(result) >= 1:
        # For implementations that return (x, info_dict) or other tuple formats
        return result[0]
    else:
        # For implementations that return just x
        return result

def create_matvec_wrapper(A_matrix):
    """Create a matvec function compatible with numpy's matrix-vector multiplication"""
    def A_callable(v):
        v = np.asarray(v)
        return A_matrix.dot(v)
    return A_callable

def implementation_supports_callable(solver):
    """Check if implementation likely supports callable matrices"""
    if solver is None:
        return False

    try:
        source = inspect.getsource(solver)
        return ('callable' in source and
                ('matvec' in source or 'if callable(A)' in source))
    except (IOError, TypeError):
        return False

def test_solver_implementation_exists(implementation):
    """Test that the implementation contains a conjugate gradient function"""
    impl_name, module = implementation

    # Check if the module contains a function that starts with 'conjugate' or has gradient in name
    found = False
    for name, obj in inspect.getmembers(module):
        if inspect.isfunction(obj) and (
            'conjugate' in name.lower() or
            'grad' in name.lower() or
            'cg' == name.lower()
        ):
            found = True
            break

    assert found, f"Implementation {impl_name} does not contain a conjugate gradient function"

def test_basic_functionality(implementation):
    """Test that the function correctly solves a simple linear system"""
    impl_name, module = implementation

    # Get the solver function
    solver = get_solver_function(module)
    if solver is None:
        pytest.skip(f"Implementation {impl_name} does not contain a conjugate gradient function")

    try:
        # Generate a test problem
        A, b, x_exact = generate_test_matrices(size=5)

        # Solve the system using the implementation
        result = solver(A, b)
        x_computed = normalize_output(result)

        # Check that solution is close to the exact solution
        assert np.allclose(x_computed, x_exact, rtol=1e-5), \
            f"Implementation {impl_name} does not correctly solve the system"
    except Exception as e:
        pytest.fail(f"Implementation {impl_name} failed in basic functionality test: {str(e)}")

def test_convergence_with_zero_initial_guess(implementation):
    """Test that the function converges with a zero initial guess"""
    impl_name, module = implementation

    # Get the solver function
    solver = get_solver_function(module)
    if solver is None:
        pytest.skip(f"Implementation {impl_name} does not contain a conjugate gradient function")

    try:
        # Generate a test problem
        A, b, x_exact = generate_test_matrices(size=5)

        # Solve with explicit zero initial guess
        try:
            result = solver(A, b, x0=np.zeros_like(b))
            x_computed = normalize_output(result)

            # Check that solution is close to the exact solution
            assert np.allclose(x_computed, x_exact, rtol=1e-5), \
                f"Implementation {impl_name} does not converge with zero initial guess"
        except TypeError as e:
            if "x0" in str(e) and "unexpected keyword" in str(e):
                pytest.skip(f"Implementation {impl_name} does not support explicit x0 parameter")
            else:
                raise
    except Exception as e:
        pytest.fail(f"Implementation {impl_name} failed with zero initial guess: {str(e)}")

def test_convergence_with_random_initial_guess(implementation):
    """Test that the function converges with a random initial guess"""
    impl_name, module = implementation

    # Get the solver function
    solver = get_solver_function(module)
    if solver is None:
        pytest.skip(f"Implementation {impl_name} does not contain a conjugate gradient function")

    try:
        # Generate a test problem
        A, b, x_exact = generate_test_matrices(size=5)

        # Set a fixed seed for reproducibility
        np.random.seed(42)

        # Random initial guess
        x0 = np.random.rand(len(b))

        try:
            # Solve with random initial guess
            result = solver(A, b, x0=x0)
            x_computed = normalize_output(result)

            # Check that solution is close to the exact solution
            assert np.allclose(x_computed, x_exact, rtol=1e-5), \
                f"Implementation {impl_name} does not converge with random initial guess"
        except TypeError as e:
            if "x0" in str(e) and "unexpected keyword" in str(e):
                pytest.skip(f"Implementation {impl_name} does not support explicit x0 parameter")
            else:
                raise
    except Exception as e:
        pytest.fail(f"Implementation {impl_name} failed with random initial guess: {str(e)}")

def test_tolerance_parameter(implementation):
    """Test that the function respects the tolerance parameter"""
    impl_name, module = implementation

    # Get the solver function
    solver = get_solver_function(module)
    if solver is None:
        pytest.skip(f"Implementation {impl_name} does not contain a conjugate gradient function")

    try:
        # Generate a test problem
        A, b, x_exact = generate_test_matrices(size=5)

        # Store the exact solution for comparison
        x_exact_copy = x_exact.copy()

        try:
            # Solve with loose tolerance (should converge quickly)
            result = solver(A, b, tol=1e-3)
            x_computed_loose = normalize_output(result)

            # Solve with tight tolerance (should be more accurate)
            result = solver(A, b, tol=1e-10)
            x_computed_tight = normalize_output(result)

            # Check both solutions are reasonable
            assert np.allclose(x_computed_loose, x_exact_copy, rtol=1e-2, atol=1e-2), \
                f"Implementation {impl_name} solution with loose tolerance is too inaccurate"

            assert np.allclose(x_computed_tight, x_exact_copy, rtol=1e-5), \
                f"Implementation {impl_name} solution with tight tolerance is inaccurate"

        except TypeError as e:
            if "tol" in str(e) and "unexpected keyword" in str(e):
                pytest.skip(f"Implementation {impl_name} does not support explicit tol parameter")
            else:
                raise

    except Exception as e:
        pytest.fail(f"Implementation {impl_name} failed in tolerance test: {str(e)}")

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


dataloader = DataLoader(
            dataset,
            batch_size=10,
            shuffle=False,
            collate_fn=default_data_collator
        )

for batch in dataloader:
    batch = {k: v.to(device) for k, v in batch.items() if k in ["input_ids", "attention_mask", "labels"]}

    with torch.no_grad():
        outputs = model.generate(
            input_ids=batch["input_ids"],
            attention_mask=batch["attention_mask"],
            max_new_tokens=max_new_tokens,
            pad_token_id=tokenizer.pad_token_id
        )

Instruction: --------------------------------------------------------------------------- ValueError                                Traceback (most recent call last) <ipython-input-11-eb20bf643b6e> in <cell line: 8>()       6         )       7  ----> 8 for batch in dataloader:       9     batch = {k: v.to(device) for k, v in batch.items() if k in ["input_ids", "attention_mask", "labels"]}      10   ~/Documents/labs2/venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py in __next__(self)     629                 # TODO(https://github.com/pytorch/pytorch/issues/76750)     630                 self._reset()  # type: ignore[call-arg] --> 631             data = self._next_data()     632             self._num_yielded += 1     633             if self._dataset_kind == _DatasetKind.Iterable and \  ~/Documents/labs2/venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py in _next_data(self)     673     def _next_data(self):     674         index = self._next_index()  # may raise StopIteration --> 675         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration     676         if self._pin_memory:     677             data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)  ~/Documents/labs2/venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)      49                 data = self.dataset.__getitems__(possibly_batched_index)      50             else: ---> 51                 data = [self.dataset[idx] for idx in possibly_batched_index]      52         else:      53             data = self.dataset[possibly_batched_index]  ~/Documents/labs2/venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)      49                 data = self.dataset.__getitems__(possibly_batched_index)      50             else: ---> 51                 data = [self.dataset[idx] for idx in possibly_batched_index]      52         else:      53             data = self.dataset[possibly_batched_index]  <ipython-input-5-c26cf61ce212> in __getitem__(self, idx)     141      142     def __getitem__(self, idx): --> 143         return self.tokenizer(self.texts[idx], return_tensors="pt", truncation=True, padding="max_length", max_length=16)     144      145 # Load tokenizer and model  ~/Documents/labs2/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, text_target, text_pair_target, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)    2856             if not self._in_target_context_manager:    2857                 self._switch_to_input_mode() -> 2858             encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)    2859         if text_target is not None:    2860             self._switch_to_target_mode()  ~/Documents/labs2/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py in _call_one(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)    2962             )    2963         else: -> 2964             return self.encode_plus(    2965                 text=text,    2966                 text_pair=text_pair,  ~/Documents/labs2/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py in encode_plus(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)    3026     3027         # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' -> 3028         padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(    3029             padding=padding,    3030             truncation=truncation,  ~/Documents/labs2/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py in _get_padding_truncation_strategies(self, padding, truncation, max_length, pad_to_multiple_of, verbose, **kwargs)    2761         # Test if we have a padding token    2762         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.pad_token is None or self.pad_token_id < 0): -> 2763             raise ValueError(    2764                 "Asking to pad but the tokenizer does not have a padding token. "    2765                 "Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "  ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.

import os
import re
import pytest
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import PreTrainedTokenizer, PreTrainedModel, default_data_collator
from unittest.mock import MagicMock

# === Mocks & Helpers ===

class MockTokenizer(MagicMock):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # start with no pad_token so we trigger your pad-token logic
        self.pad_token = None
        self.eos_token = "<eos>"
        self.pad_token_id = None
        self.eos_token_id = 2

class MockModel(MagicMock):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.config = MagicMock()
        self.config.pad_token_id = None

class MockDataset(Dataset):
    def __init__(self):
        self.data = [{
            "input_ids":      torch.tensor([1,2,3]),
            "attention_mask": torch.tensor([1,1,1]),
            "labels":         torch.tensor([0,1,0]),
        }]
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx]

def setup_mock_env():
    tokenizer = MockTokenizer(spec=PreTrainedTokenizer)
    model     = MockModel(spec=PreTrainedModel)
    # safe_generate never raises; it just records calls in the MagicMock
    def safe_generate(**kwargs):
        return torch.tensor([[1,2,3]])
    model.generate = MagicMock(side_effect=safe_generate)
    dataset = MockDataset()
    device  = torch.device("cpu")
    max_new_tokens = 16
    # re‑use Transformers’ default_data_collator
    def collator(batch):
        return default_data_collator(batch)
    return tokenizer, model, dataset, device, max_new_tokens, collator

def find_implementation_file(impl_name: str):
    """
    Look for <impl_name>.py in cwd or this test’s folder.
    """
    roots = [".", os.path.dirname(os.path.abspath(__file__))]
    for root in roots:
        fn = os.path.join(root, f"{impl_name}.py")
        if os.path.exists(fn):
            return fn, open(fn, "r").read()
    return None, None

def check_pad_token_setting(src: str) -> bool:
    """
    True if we see either:
     - an explicit `if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token`
     - or a pad_token_id fallback when calling generate
    """
    explicit = re.search(
        r"if\s+tokenizer\.pad_token\s+is\s+None\s*:\s*tokenizer\.pad_token\s*=\s*tokenizer\.eos_token",
        src
    )
    fallback = re.search(
        r"pad_token_id\s*=\s*(?:tokenizer\.pad_token_id\s*\|\|\s*tokenizer\.eos_token_id|tokenizer\.eos_token_id)",
        src
    )
    ternary = re.search(
        r"pad_token_id\s*=\s*tokenizer\.eos_token_id\s+if\s+tokenizer\.pad_token\s+is\s+None\s+else\s+tokenizer\.pad_token_id",
        src
    )
    return bool(explicit or fallback or ternary)

# === Tests ===

def test_tokenizer_pad_token_set(implementation):
    """
    Fail if the file never sets tokenizer.pad_token when it’s None.
    """
    impl_name, _ = implementation
    path, src = find_implementation_file(impl_name)
    assert src is not None, f"Could not find {impl_name}.py"
    assert check_pad_token_setting(src), (
        f"{impl_name}.py must set `tokenizer.pad_token = tokenizer.eos_token` "
        "or provide a fallback pad_token_id in generate()"
    )

def test_implementation_runs_and_calls_generate(implementation):
    """
    Execute the file, ensure model.generate() actually runs at least once,
    and that we either set tokenizer.pad_token or passed a non‑None pad_token_id.
    """
    impl_name, _ = implementation
    path, code = find_implementation_file(impl_name)
    assert code is not None, f"Could not find {impl_name}.py"

    tokenizer, model, dataset, device, max_new_tokens, collator = setup_mock_env()
    namespace = {
        "__name__": "__main__",
        "tokenizer": tokenizer,
        "model":     model,
        "dataset":   dataset,
        "device":    device,
        "max_new_tokens": max_new_tokens,
        "default_data_collator": collator,
        "DataLoader": DataLoader,
        "torch": torch,
    }

    # Run the user’s script
    exec(code, namespace)

    # Must have called generate()
    assert model.generate.call_count > 0, f"{impl_name}.py never called model.generate()"

    # check pad handling
    pad_ok = False
    if tokenizer.pad_token is not None:
        pad_ok = True
    else:
        for _, kwargs in model.generate.call_args_list:
            pid = kwargs.get("pad_token_id", None)
            if pid is not None:
                pad_ok = True
                break

    assert pad_ok, (
        f"{impl_name}.py called generate() but did not set "
        "`tokenizer.pad_token` nor pass a non‑None pad_token_id"
    )

def test_dataloader_created(implementation):
    """
    Your script must instantiate at least one DataLoader from
    torch.utils.data.DataLoader(...)
    """
    impl_name, _ = implementation
    path, code = find_implementation_file(impl_name)
    assert code is not None, f"Could not find {impl_name}.py"

    tokenizer, model, dataset, device, max_new_tokens, collator = setup_mock_env()
    namespace = {
        "__name__": "__main__",
        "tokenizer": tokenizer,
        "model":     model,
        "dataset":   dataset,
        "device":    device,
        "max_new_tokens": max_new_tokens,
        "default_data_collator": collator,
        "DataLoader": DataLoader,
        "torch": torch,
    }

    exec(code, namespace)

    found = any(isinstance(v, DataLoader) for v in namespace.values())
    assert found, f"{impl_name}.py never created a `DataLoader(...)`"

def test_model_generate_parameters(implementation):
    """
    Inspect the last call to model.generate(...) and ensure it got
    input_ids, attention_mask and max_new_tokens, plus a valid pad_token_id.
    """
    impl_name, _ = implementation
    path, code = find_implementation_file(impl_name)
    assert code is not None, f"Could not find {impl_name}.py"

    tokenizer, model, dataset, device, max_new_tokens, collator = setup_mock_env()
    namespace = {
        "__name__": "__main__",
        "tokenizer": tokenizer,
        "model":     model,
        "dataset":   dataset,
        "device":    device,
        "max_new_tokens": max_new_tokens,
        "default_data_collator": collator,
        "DataLoader": DataLoader,
        "torch": torch,
    }

    exec(code, namespace)

    # if generate() never called, that’s an outright failure
    assert model.generate.call_count > 0, f"{impl_name}.py never called model.generate()"

    last_kwargs = model.generate.call_args_list[-1][1]

    for key in ("input_ids", "attention_mask", "max_new_tokens"):
        assert key in last_kwargs, f"{impl_name}.py generate(...) missing `{key}`"

    # pad_token_id must not be None if tokenizer.pad_token was never set
    pid = last_kwargs.get("pad_token_id", None)
    assert pid is not None or tokenizer.pad_token is not None, (
        f"{impl_name}.py generate(...) must pass a non‑None pad_token_id "
        "or set tokenizer.pad_token beforehand"
    )

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