src/submission/check_validity.py

import json
import os
import shutil
import re
import numpy as np
import pandas as pd
import gradio as gr
from urllib.parse import urlparse
from collections import defaultdict
from datetime import datetime, timedelta, timezone
from typing import Literal, Tuple, Union

from huggingface_hub import HfApi, HfFileSystem, hf_hub_url, get_hf_file_metadata
from huggingface_hub import ModelCard
from huggingface_hub.hf_api import ModelInfo
from transformers import AutoConfig
from transformers.models.auto.tokenization_auto import AutoTokenizer

from src.envs import EVAL_REQUESTS_SUBGRAPH, EVAL_REQUESTS_CAUSALGRAPH

TASKS = ["ioi", "mcqa", "arithmetic-addition", "arithmetic-subtraction", "arc-easy", "arc-challenge"]
MODELS = ["gpt2", "qwen2.5", "gemma2", "llama3", "interpbench"]

class FeaturizerValidator:
    def __init__(self, base_featurizer_class):
        self.base_featurizer_class = base_featurizer_class
        self.featurizer_class_name = None
        
        # torch.nn.Module
        self.module_value, self.module_attr = "torch", "Module"
        self.featurizer_module_class_name_1 = None
        self.featurizer_module_class_name_2 = None


    def find_featurizer_subclass(self, module_path: str) -> Tuple[bool, Union[str, None]]:
        """
        Finds the first class in the module that inherits from Featurizer.
        
        Args:
            module_path: Path to the uploaded Python file
                
        Returns:
            Tuple of (success, class_name, message)
        """
        # First try with AST for safety
        try:
            with open(module_path, 'r') as file:
                tree = ast.parse(file.read(), filename=module_path)
            
            for node in ast.walk(tree):
                if isinstance(node, ast.ClassDef):
                    for base in node.bases:
                        if isinstance(base, ast.Name) and base.id == self.base_featurizer_class.__name__:
                            return True, node.name, f"Found class '{node.name}' that inherits from {self.base_featurizer_class.__name__}"
            
            return False, None, f"No class inheriting from {self.base_featurizer_class.__name__} found"
        
        except Exception as e:
            return False, None, f"Error during static analysis: {str(e)}"


    def find_featurizer_module_classes(self, module_path: str) -> Tuple[bool, Union[str, None]]:
        try:
            with open(module_path, 'r') as file:
                tree = ast.parse(file.read(), filename=module_path)
            
            for node in ast.walk(tree):
                if isinstance(node, ast.ClassDef):
                    for base in node.bases:
                        if (isinstance(base, ast.Attribute) and base.attr == self.module_attr):
                            if self.featurizer_module_class_name_1 is None:
                                self.featurizer_module_class_name_1 = node.name
                            else:
                                self.featurizer_module_class_name_2 = node.name
                                return True, f"Found two featurizer modules: {self.featurizer_module_class_name_1}, {self.featurizer_module_class_name_2}"
            
            if self.featurizer_module_class_name_1:
                return True, f"Found one featurizer module: {self.featurizer_module_class_name_1}"
            return False, f"Found no featurizer modules."
        
        except Exception as e:
            return False, f"Error during static analysis: {e}"
    

    def validate_uploaded_module(self, module_path: str) -> Tuple[bool, str]:
        """
        Validates an uploaded module to ensure it properly extends the Featurizer class.
        
        Args:
            module_path: Path to the uploaded Python file
            class_name: Name of the class to validate
            
        Returns:
            Tuple of (is_valid, message)
        """
        # First, find the name of the featurizer class we're verifying
        found, class_name, message = self.find_featurizer_subclass(module_path)
        if not found:
            return False, message
        else:
            print("Verified featurizer subclass.")

        # Second, find the name of the featurizer and inverse featurizer modules
        modules_found, modules_message = self.find_featurizer_module_classes(module_path)
        if not modules_found:
            return False, modules_message
        else:
            print(f"Verified featurizer module(s): {modules_message}")

        # Then, perform static code analysis on the featurizer class for basic safety
        inheritance_check, ast_message = self._verify_inheritance_with_ast(module_path, class_name)
        if not inheritance_check:
            return False, ast_message
            
        # Then, try to load and validate the featurizer class
        return self._verify_inheritance_with_import(module_path, class_name)

        # TODO: try directly loading featurizer module and inverse featurizer module?
    

    def _verify_inheritance_with_ast(self, module_path: str, class_name: str) -> Tuple[bool, str]:
        """Verify inheritance using AST without executing code"""
        try:
            with open(module_path, 'r') as file:
                tree = ast.parse(file.read(), filename=module_path)
            
            # Look for class definitions that match the target class name
            for node in ast.walk(tree):
                if isinstance(node, ast.ClassDef) and node.name == class_name:
                    # Check if any base class name matches 'Featurizer'
                    for base in node.bases:
                        if isinstance(base, ast.Name) and base.id == self.base_featurizer_class.__name__:
                            return True, "Static analysis indicates proper inheritance"
                        
                    return False, f"Class '{class_name}' does not appear to inherit from {self.base_featurizer_class.__name__}"
            
            return False, f"Class '{class_name}' not found in the uploaded module"
            
        except Exception as e:
            return False, f"Error during static analysis: {str(e)}"
    

    def _verify_inheritance_with_import(self, module_path: str, class_name: str) -> Tuple[bool, str]:
        """Safely import the module and verify inheritance using Python's introspection"""
        try:
            # Dynamically import the module
            spec = importlib.util.spec_from_file_location("uploaded_module", module_path)
            if spec is None or spec.loader is None:
                return False, "Could not load the module specification"
            
            uploaded_module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(uploaded_module)
            
            # Get the class from the module
            if not hasattr(uploaded_module, class_name):
                return False, f"Class '{class_name}' not found in the uploaded module"
            
            uploaded_class = getattr(uploaded_module, class_name)
            
            # Check if it's a proper subclass
            if not inspect.isclass(uploaded_class):
                return False, f"'{class_name}' is not a class"
            
            if not issubclass(uploaded_class, self.base_featurizer_class):
                return False, f"'{class_name}' does not inherit from {self.base_featurizer_class.__name__}"
            
            # Optional: Check method resolution order
            mro = inspect.getmro(uploaded_class)
            if self.base_featurizer_class not in mro:
                return False, f"{self.base_featurizer_class.__name__} not in the method resolution order"
                
            return True, f"Class '{class_name}' properly extends {self.base_featurizer_class.__name__}"
            
        except Exception as e:
            return False, f"Error during dynamic validation: {str(e)}"


def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
    """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
    try:
        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
        if test_tokenizer:
            try:
                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
            except ValueError as e:
                return (
                    False,
                    f"uses a tokenizer which is not in a transformers release: {e}",
                    None
                )
            except Exception as e:
                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
        return True, None, config

    except ValueError:
        return (
            False,
            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
            None
        )

    except Exception as e:
        return False, "was not found on hub!", None


def get_model_size(model_info: ModelInfo, precision: str):
    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
    try:
        model_size = round(model_info.safetensors["total"] / 1e9, 3)
    except (AttributeError, TypeError):
        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py

    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
    model_size = size_factor * model_size
    return model_size


def get_model_arch(model_info: ModelInfo):
    """Gets the model architecture from the configuration"""
    return model_info.config.get("architectures", "Unknown")


def already_submitted_models(requested_models_dir: str) -> set[str]:
    """Gather a list of already submitted models to avoid duplicates"""
    depth = 1
    file_names = []
    users_to_submission_dates = defaultdict(list)

    for root, _, files in os.walk(requested_models_dir):
        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
        if current_depth == depth:
            for file in files:
                if not file.endswith(".json"):
                    continue
                with open(os.path.join(root, file), "r") as f:
                    info = json.load(f)
                    file_names.append(f"{info['model']}_{info['revision']}_{info['track']}")

                    # Select organisation
                    if info["model"].count("/") == 0 or "submitted_time" not in info:
                        continue
                    organisation, _ = info["model"].split("/")
                    users_to_submission_dates[organisation].append(info["submitted_time"])

    return set(file_names), users_to_submission_dates


def _format_time(earliest_time):
    time_left = (earliest_time.tz_convert("UTC") + timedelta(weeks=1)) - pd.Timestamp.utcnow()
    hours = time_left.seconds // 3600
    minutes, seconds = divmod(time_left.seconds % 3600, 60)
    time_left_formatted = f"{hours:02}:{minutes:02}:{seconds:02}"
    if time_left.days > 0:
        time_left_formatted = f"{time_left.days} days, {time_left_formatted}"
    return time_left_formatted


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requests"""
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
    all_evals = []

    for entry in entries:
        if ".json" in entry:
            file_path = os.path.join(save_path, entry)
            with open(file_path) as fp:
                data = json.load(fp)

            # if "still_on_hub" in data and data["still_on_hub"]:
            #     data[EvalQueueColumn.model.name] = make_clickable_model(data["hf_repo"], data["model"])
            #     data[EvalQueueColumn.revision.name] = data.get("revision", "main")
            # else:
            #     data[EvalQueueColumn.model.name] = data["model"]
            #     data[EvalQueueColumn.revision.name] = "N/A"

            all_evals.append(data)

        elif ".md" not in entry:
            # this is a folder
            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
            for sub_entry in sub_entries:
                file_path = os.path.join(save_path, entry, sub_entry)
                with open(file_path) as fp:
                    data = json.load(fp)
                all_evals.append(data)

    return pd.DataFrame(all_evals)

def check_rate_limit(track, user_name, contact_email):    
    if "Circuit" in track:
        save_path = EVAL_REQUESTS_SUBGRAPH
    else:
        save_path = EVAL_REQUESTS_CAUSALGRAPH

    

    evaluation_queue = get_evaluation_queue_df(save_path, ["user_name", "contact_email"])

    if evaluation_queue.empty or user_name == "atticusg" or user_name == "yiksiu":
        return True, None

    one_week_ago = pd.Timestamp.utcnow() - timedelta(weeks=1)
    
    user_name_occurrences = evaluation_queue[evaluation_queue["user_name"] == user_name]
    user_name_occurrences["submit_time"] = pd.to_datetime(user_name_occurrences["submit_time"], utc=True)
    user_name_occurrences = user_name_occurrences[user_name_occurrences["submit_time"] >= one_week_ago]
    email_occurrences = evaluation_queue[evaluation_queue["contact_email"] == contact_email.lower()]
    email_occurrences["submit_time"] = pd.to_datetime(email_occurrences["submit_time"], utc=True)
    email_occurrences = email_occurrences[email_occurrences["submit_time"] >= one_week_ago]
    if user_name_occurrences.shape[0] >= 2:
        earliest_time = user_name_occurrences["submit_time"].min()
        time_left_formatted = _format_time(earliest_time)
        return False, time_left_formatted
    if email_occurrences.shape[0] >= 2:
        earliest_time = email_occurrences["submit_time"].min()
        time_left_formatted = _format_time(earliest_time)
        return False, time_left_formatted
    
    return True, None

def parse_huggingface_url(url: str):
    """
    Extracts repo_id and subfolder path from a Hugging Face URL.
    Returns (repo_id, folder_path).
    """
    # Handle cases where the input is already a repo_id (no URL)
    if not url.startswith(("http://", "https://")):
        return url, None
    
    parsed = urlparse(url)
    path_parts = parsed.path.strip("/").split("/")
    revision = "main"
    
    # Extract repo_id (username/repo_name)
    if len(path_parts) < 2:
        return None, None, None     # Can't extract repo_id
    else:
        repo_id = f"{path_parts[0]}/{path_parts[1]}"
    
    # Extract folder path (if in /tree/ or /blob/)
    if "tree" in path_parts or "blob" in path_parts:
        try:
            branch_idx = path_parts.index("tree") if "tree" in path_parts else path_parts.index("blob")
            folder_path = "/".join(path_parts[branch_idx + 2:])  # Skip "tree/main" or "blob/main"
            revision = path_parts[branch_idx + 1]
        except (ValueError, IndexError):
            folder_path = None
    else:
        folder_path = None
    
    return repo_id, folder_path, revision


def validate_directory_circuit(fs: HfFileSystem, repo_id: str, dirname: str, curr_tm: str, circuit_level:Literal['edge', 'node','neuron']='edge'):
    errors = []
    warnings = []

    task, model = curr_tm.split("_")
    curr_tm_display = curr_tm.replace("_", "/")

    files = fs.ls(dirname)

    # Detect whether multi-circuit or importances
    is_multiple_circuits = False
    files = [f["name"] for f in files if (f["name"].endswith(".json") or f["name"].endswith(".pt"))]
    if len(files) == 1:
        is_multiple_circuits = False
    elif len(files) > 1:
        is_multiple_circuits = True
        if len(files) < 9:
            errors.append(f"Folder for {curr_tm_display} contains multiple circuits, but not enough. If you intended to submit importances, include only one circuit in the folder. Otherwise, please add the rest of the circuits.")
    else:
        warnings.append(f"Directory present for {curr_tm_display} but is empty")
    
    offset = 0
    for idx, file in enumerate(files):
        file_suffix = file.split(repo_id + "/")[1]
        file_url = hf_hub_url(repo_id=repo_id, filename=file_suffix)
        file_info = get_hf_file_metadata(file_url)
        file_size_mb = file_info.size / (1024 * 1024)
        if file_size_mb > 150:
            warnings.append(f"Will skip file >150MB: {file}")
            offset -= 1
            continue

        if is_multiple_circuits and idx + offset >= 9:
            break

    return errors, warnings


def verify_circuit_submission(hf_repo, level, progress=gr.Progress()):
    VALID_COMBINATIONS = [
        "ioi_gpt2", "ioi_qwen2.5", "ioi_gemma2", "ioi_llama3", "ioi_interpbench",
        "mcqa_qwen2.5", "mcqa_gemma2", "mcqa_llama3",
        "arithmetic-addition_llama3", "arithmetic-subtraction_llama3",
        "arc-easy_gemma2", "arc-easy_llama3",
        "arc-challenge_llama3"
    ]

    errors = []
    warnings = []

    directories_present = {tm: False for tm in VALID_COMBINATIONS}
    directories_valid = {tm: False for tm in VALID_COMBINATIONS}

    fs = HfFileSystem()

    path = hf_repo
    level = level

    try:
        repo_id, folder_path, revision = parse_huggingface_url(hf_repo)
        folder_path = repo_id + "/" + folder_path
        files = fs.listdir(folder_path, revision=revision)
    except Exception as e:
        errors.append(f"Could not open Huggingface URL: {e}")
        return errors, warnings

    file_counts = 0
    for dirname in progress.tqdm(files, desc="Validating directories in repo"):
        file_counts += 1
        if file_counts >= 30:
            warnings.append("Folder contains many files/directories; stopped at 30.")
            break
        circuit_dir = dirname["name"]
        dirname_proc = circuit_dir.lower().split("/")[-1]
        if not fs.isdir(circuit_dir):
            continue
        curr_task = None
        curr_model = None
        # Look for task names in filename
        for task in TASKS:
            if dirname_proc.startswith(task) or f"_{task}" in dirname_proc:
                curr_task = task
        # Look for model names in filename
        for model in MODELS:
            if dirname_proc.startswith(model) or f"_{model}" in dirname_proc:
                curr_model = model
        if curr_task is not None and curr_model is not None:
            curr_tm = f"{curr_task}_{curr_model}"
            if curr_tm in VALID_COMBINATIONS:
                directories_present[curr_tm] = True
            else:
                continue
        else:
            continue
        
        # Parse circuits directory
        print(f"validating {circuit_dir}")
        vd_errors, vd_warnings = validate_directory_circuit(fs, repo_id, circuit_dir, curr_tm, level)
        errors.extend(vd_errors)
        warnings.extend(vd_warnings)

        if len(vd_errors) == 0:
            directories_valid[curr_tm] = True
    
    task_set, model_set = set(), set()
    for tm in directories_present:
        if not directories_present[tm]:
            continue
        if not directories_valid[tm]:
            warnings.append(f"Directory found for {tm.replace('_', '/')}, but circuits not valid or present")
            continue
        task, model = tm.split("_")
        task_set.add(task)
        model_set.add(model)
    if len(task_set) < 2:
        errors.append("At least 2 tasks are required")
    if len(model_set) < 2:
        errors.append("At least 2 models are required")

    no_tm_display = [tm.replace("_", "/") for tm in directories_valid if not directories_valid[tm]]
    if len(no_tm_display) > 0:
        warnings.append(f"No valid circuits or importance scores found for the following tasks/models: {*no_tm_display,}")
        
    return errors, warnings


def validate_directory_causalgraph(fs: HfFileSystem, repo_id: str, dirname: str):
    errors = []
    warnings = []

    files = fs.ls(dirname)
    files = [f["name"] for f in files if "_featurizer" in f["name"] or "_indices" in f["name"]]

    valid_triplet = False

    offset = 0
    for idx, file in enumerate(files):
        file_suffix = file.split(repo_id + "/")[1]
        file_url = hf_hub_url(repo_id=repo_id, filename=file_suffix)
        file_info = get_hf_file_metadata(file_url)
        file_size_mb = file_info.size / (1024 * 1024)
        if file_size_mb > 150:
            warnings.append(f"Will skip file >150MB: {file}")
            offset -= 1
            continue

        if idx + offset > 30:
            warnings.append("Many files in directory; stopping at 30")
            break
        
        if file.endswith("_featurizer") or file.endswith("_indices"):
            prefix = "_".join(file.split("_")[:-1])
            this_suffix = "_" + file.split("_")[-1]
            suffixes = ("_featurizer", "_inverse_featurizer", "_indices")
            for idx, suffix in enumerate(suffixes):
                if file.replace(this_suffix, suffix) not in files:
                    warnings.append(f"For {prefix}, found a {this_suffix} file but no associated {suffix}")
                    break
                if idx == len(suffixes) - 1:
                    valid_triplet = True
            if valid_triplet:
                found_submodule = False
                found_layer = False
                found_token = False
                if "residual" or "attention" in prefix.lower():
                    found_submodule = True
                if "layer" in prefix.lower():
                    found_layer = True
                if "token" in prefix.lower():
                    found_token = True
                if not found_submodule or not found_layer or not found_token:
                    errors.append("Could not derive where featurizer should be applied from featurizer filenames.")
        
        if valid_triplet:
            break

    if not valid_triplet:
        errors.append("No valid featurizer/inverse featurizer/indices triplets.")
    return errors, warnings


def verify_causal_variable_submission(hf_repo, progress=gr.Progress()):
    CV_TASKS = set(["ioi_task", "4_answer_MCQA", "ARC_easy", "arithmetic", "ravel_task"])
    CV_TASK_VARIABLES = {"ioi_task": ["output_token", "output_position"],
                    "4_answer_MCQA": ["answer_pointer", "answer"],
                    "ARC_easy": ["answer_pointer", "answer"],
                    "arithmetic": ["ones_carry"],
                    "ravel_task": ["Country", "Continent", "Language"]}
    CV_MODELS = set(["GPT2LMHeadModel", "Qwen2ForCausalLM", "Gemma2ForCausalLM", "LlamaForCausalLM"])
    # create pairs of valid task/model combinations
    CV_VALID_TASK_MODELS = set([("ioi_task", "GPT2LMHeadModel"),
                         ("ioi_task", "Qwen2ForCausalLM"),
                         ("ioi_task", "Gemma2ForCausalLM"),
                         ("ioi_task", "LlamaForCausalLM"),
                         ("4_answer_MCQA", "Qwen2ForCausalLM"),
                         ("4_answer_MCQA", "Gemma2ForCausalLM"),
                         ("4_answer_MCQA", "LlamaForCausalLM"),
                         ("ARC_easy", "Gemma2ForCausalLM"),
                         ("ARC_easy", "LlamaForCausalLM"),
                         ("arithmetic", "Gemma2ForCausalLM"),
                         ("arithmetic", "LlamaForCausalLM"),
                         ("ravel_task", "Gemma2ForCausalLM"),
                         ("ravel_task", "LlamaForCausalLM")])
    
    errors = []
    warnings = []

    num_py_files = 0
    directories_present = {tm: False for tm in CV_VALID_TASK_MODELS}
    directories_valid = {tm: False for tm in CV_VALID_TASK_MODELS}
    variables_valid = {}

    fs = HfFileSystem()

    path = hf_repo

    try:
        repo_id, folder_path, revision = parse_huggingface_url(hf_repo)
        folder_path = repo_id + "/" + folder_path
        files = fs.listdir(folder_path, revision=revision)
    except Exception as e:
        errors.append(f"Could not open Huggingface URL: {e}")
        return errors, warnings

    file_counts = 0
    for file in progress.tqdm(files, desc="Validating files in repo"):
        filename = file["name"]
        file_counts += 1
        if file_counts >= 30:
            warnings.append("Folder contains many files/directories; stopped at 30.")
            break
        
        if filename.endswith(".py"):
            num_py_files += 1

        causalgraph_dir = filename
        dirname_proc = causalgraph_dir.lower().split("/")[-1]
        if not fs.isdir(causalgraph_dir):
            continue
        curr_task = None
        curr_model = None
        curr_variable = None
        # Look for task names in filename
        for task in CV_TASKS:
            if dirname_proc.startswith(task.lower()) or f"_{task.lower()}" in dirname_proc:
                curr_task = task
                if curr_task not in variables_valid:
                    variables_valid[curr_task] = {v: False for v in CV_TASK_VARIABLES[curr_task]}
                for variable in CV_TASK_VARIABLES[curr_task]:
                    if dirname_proc.startswith(variable.lower()) or f"_{variable.lower()}" in dirname_proc or f"_{variable.lower().replace('_', '-')}" in dirname_proc:
                        curr_variable = variable
                        break
        # Look for model names in filename
        for model in CV_MODELS:
            if dirname_proc.startswith(model.lower()) or f"_{model.lower()}" in dirname_proc:
                curr_model = model
        if curr_task is not None and curr_model is not None and curr_variable is not None:
            curr_tm = (curr_task, curr_model)
            if curr_tm in CV_VALID_TASK_MODELS:
                directories_present[curr_tm] = True
            else:
                continue
        else:
            continue

        print(f"validating {causalgraph_dir}")
        vd_errors, vd_warnings = validate_directory_causalgraph(fs, repo_id, causalgraph_dir)
        errors.extend(vd_errors)
        warnings.extend(vd_warnings)

        if len(vd_errors) == 0:
            directories_valid[curr_tm] = True
            variables_valid[curr_task][curr_variable] = True

    if num_py_files == 0:
        warnings.append("No featurizer.py or token_position.py files detected in root of provided repo. We will load from the code used for baseline evaluations.")
    elif num_py_files == 1:
        warnings.append("Either featurizer.py or token_position.py files missing in root of provided repo. We will load from the code used for baseline evaluations.")

    task_set, model_set = set(), set()
    for tm in directories_present:
        if not directories_present[tm]:
            continue
        if not directories_valid[tm]:
            warnings.append(f"Directory found for {tm[0]}/{tm[1]}, but contents not valid")
            continue
    
    for tm in directories_valid:
        if directories_valid[tm]:
            task, model = tm
            task_set.add(task)
            model_set.add(model)

    if len(task_set) == 0 or len(model_set) == 0:
        errors.append("No valid directories found for any task/model.")

    # no_tm_display = [f"{tm[0]}/{tm[1]}" for tm in directories_valid if not directories_valid[tm]]
    # if len(no_tm_display) > 0:
    #     warnings.append(f"No valid submission found for the following tasks/models: {*no_tm_display,}")
    
    for task in variables_valid:
        found_variable_display = [v for v in variables_valid[task] if variables_valid[task][v]]
        no_variable_display = [v for v in variables_valid[task] if not variables_valid[task][v]]
        if no_variable_display:
            warnings.append(f"For {task}, found variables {*found_variable_display,}, but not variables {*no_variable_display,}")
        
    return errors, warnings