OpenHands/opendevin/core/config.py

import argparse
import os
import pathlib
import platform
import uuid
from dataclasses import dataclass, field, fields, is_dataclass
from enum import Enum
from types import UnionType
from typing import Any, ClassVar, MutableMapping, get_args, get_origin

import toml
from dotenv import load_dotenv

from opendevin.core import logger
from opendevin.core.utils import Singleton

load_dotenv()


LLM_SENSITIVE_FIELDS = ['api_key', 'aws_access_key_id', 'aws_secret_access_key']
_DEFAULT_AGENT = 'CodeActAgent'
_MAX_ITERATIONS = 100


@dataclass
class LLMConfig:
    """Configuration for the LLM model.

    Attributes:
        model: The model to use.
        api_key: The API key to use.
        base_url: The base URL for the API. This is necessary for local LLMs. It is also used for Azure embeddings.
        api_version: The version of the API.
        embedding_model: The embedding model to use.
        embedding_base_url: The base URL for the embedding API.
        embedding_deployment_name: The name of the deployment for the embedding API. This is used for Azure OpenAI.
        aws_access_key_id: The AWS access key ID.
        aws_secret_access_key: The AWS secret access key.
        aws_region_name: The AWS region name.
        num_retries: The number of retries to attempt.
        retry_multiplier: The multiplier for the exponential backoff.
        retry_min_wait: The minimum time to wait between retries, in seconds. This is exponential backoff minimum. For models with very low limits, this can be set to 15-20.
        retry_max_wait: The maximum time to wait between retries, in seconds. This is exponential backoff maximum.
        timeout: The timeout for the API.
        max_message_chars: The approximate max number of characters in the content of an event included in the prompt to the LLM. Larger observations are truncated.
        temperature: The temperature for the API.
        top_p: The top p for the API.
        custom_llm_provider: The custom LLM provider to use. This is undocumented in opendevin, and normally not used. It is documented on the litellm side.
        max_input_tokens: The maximum number of input tokens. Note that this is currently unused, and the value at runtime is actually the total tokens in OpenAI (e.g. 128,000 tokens for GPT-4).
        max_output_tokens: The maximum number of output tokens. This is sent to the LLM.
        input_cost_per_token: The cost per input token. This will available in logs for the user to check.
        output_cost_per_token: The cost per output token. This will available in logs for the user to check.
        ollama_base_url: The base URL for the OLLAMA API.
        drop_params: Drop any unmapped (unsupported) params without causing an exception.
    """

    model: str = 'gpt-4o'
    api_key: str | None = None
    base_url: str | None = None
    api_version: str | None = None
    embedding_model: str = 'local'
    embedding_base_url: str | None = None
    embedding_deployment_name: str | None = None
    aws_access_key_id: str | None = None
    aws_secret_access_key: str | None = None
    aws_region_name: str | None = None
    num_retries: int = 10
    retry_multiplier: float = 2
    retry_min_wait: int = 3
    retry_max_wait: int = 300
    timeout: int | None = None
    max_message_chars: int = 10_000  # maximum number of characters in an observation's content when sent to the llm
    temperature: float = 0
    top_p: float = 0.5
    custom_llm_provider: str | None = None
    max_input_tokens: int | None = None
    max_output_tokens: int | None = None
    input_cost_per_token: float | None = None
    output_cost_per_token: float | None = None
    ollama_base_url: str | None = None
    drop_params: bool | None = None

    def defaults_to_dict(self) -> dict:
        """Serialize fields to a dict for the frontend, including type hints, defaults, and whether it's optional."""
        result = {}
        for f in fields(self):
            result[f.name] = get_field_info(f)
        return result

    def __str__(self):
        attr_str = []
        for f in fields(self):
            attr_name = f.name
            attr_value = getattr(self, f.name)

            if attr_name in LLM_SENSITIVE_FIELDS:
                attr_value = '******' if attr_value else None

            attr_str.append(f'{attr_name}={repr(attr_value)}')

        return f"LLMConfig({', '.join(attr_str)})"

    def __repr__(self):
        return self.__str__()

    def to_safe_dict(self):
        """Return a dict with the sensitive fields replaced with ******."""
        ret = self.__dict__.copy()
        for k, v in ret.items():
            if k in LLM_SENSITIVE_FIELDS:
                ret[k] = '******' if v else None
        return ret

    def set_missing_attributes(self):
        """Set any missing attributes to their default values."""
        for field_name, field_obj in self.__dataclass_fields__.items():
            if not hasattr(self, field_name):
                setattr(self, field_name, field_obj.default)


@dataclass
class AgentConfig:
    """Configuration for the agent.

    Attributes:
        memory_enabled: Whether long-term memory (embeddings) is enabled.
        memory_max_threads: The maximum number of threads indexing at the same time for embeddings.
        llm_config: The name of the llm config to use. If specified, this will override global llm config.
    """

    memory_enabled: bool = False
    memory_max_threads: int = 2
    llm_config: str | None = None

    def defaults_to_dict(self) -> dict:
        """Serialize fields to a dict for the frontend, including type hints, defaults, and whether it's optional."""
        result = {}
        for f in fields(self):
            result[f.name] = get_field_info(f)
        return result


@dataclass
class SandboxConfig(metaclass=Singleton):
    """Configuration for the sandbox.

    Attributes:
        api_hostname: The hostname for the EventStream Runtime API.
        container_image: The container image to use for the sandbox.
        user_id: The user ID for the sandbox.
        timeout: The timeout for the sandbox.
        enable_auto_lint: Whether to enable auto-lint.
        use_host_network: Whether to use the host network.
        initialize_plugins: Whether to initialize plugins.
        od_runtime_extra_deps: The extra dependencies to install in the runtime image (typically used for evaluation).
            This will be rendered into the end of the Dockerfile that builds the runtime image.
            It can contain any valid shell commands (e.g., pip install numpy).
            The path to the interpreter is available as $OD_INTERPRETER_PATH,
            which can be used to install dependencies for the OD-specific Python interpreter.
        od_runtime_startup_env_vars: The environment variables to set at the launch of the runtime.
            This is a dictionary of key-value pairs.
            This is useful for setting environment variables that are needed by the runtime.
            For example, for specifying the base url of website for browsergym evaluation.
        browsergym_eval_env: The BrowserGym environment to use for evaluation.
            Default is None for general purpose browsing. Check evaluation/miniwob and evaluation/webarena for examples.
    """

    api_hostname: str = 'localhost'
    container_image: str = (
        'ubuntu:22.04'  # default to ubuntu:22.04 for eventstream runtime
    )
    user_id: int = os.getuid() if hasattr(os, 'getuid') else 1000
    timeout: int = 120
    enable_auto_lint: bool = (
        False  # once enabled, OpenDevin would lint files after editing
    )
    use_host_network: bool = False
    initialize_plugins: bool = True
    od_runtime_extra_deps: str | None = None
    od_runtime_startup_env_vars: dict[str, str] = field(default_factory=dict)
    browsergym_eval_env: str | None = None

    def defaults_to_dict(self) -> dict:
        """Serialize fields to a dict for the frontend, including type hints, defaults, and whether it's optional."""
        dict = {}
        for f in fields(self):
            dict[f.name] = get_field_info(f)
        return dict

    def __str__(self):
        attr_str = []
        for f in fields(self):
            attr_name = f.name
            attr_value = getattr(self, f.name)

            attr_str.append(f'{attr_name}={repr(attr_value)}')

        return f"SandboxConfig({', '.join(attr_str)})"

    def __repr__(self):
        return self.__str__()


class UndefinedString(str, Enum):
    UNDEFINED = 'UNDEFINED'


@dataclass
class AppConfig(metaclass=Singleton):
    """Configuration for the app.

    Attributes:
        llms: A dictionary of name -> LLM configuration. Default config is under 'llm' key.
        agents: A dictionary of name -> Agent configuration. Default config is under 'agent' key.
        default_agent: The name of the default agent to use.
        sandbox: The sandbox configuration.
        runtime: The runtime environment.
        file_store: The file store to use.
        file_store_path: The path to the file store.
        workspace_base: The base path for the workspace. Defaults to ./workspace as an absolute path.
        workspace_mount_path: The path to mount the workspace. This is set to the workspace base by default.
        workspace_mount_path_in_sandbox: The path to mount the workspace in the sandbox. Defaults to /workspace.
        workspace_mount_rewrite: The path to rewrite the workspace mount path to.
        cache_dir: The path to the cache directory. Defaults to /tmp/cache.
        run_as_devin: Whether to run as devin.
        max_iterations: The maximum number of iterations.
        max_budget_per_task: The maximum budget allowed per task, beyond which the agent will stop.
        e2b_api_key: The E2B API key.
        disable_color: Whether to disable color. For terminals that don't support color.
        debug: Whether to enable debugging.
        enable_cli_session: Whether to enable saving and restoring the session when run from CLI.
        file_uploads_max_file_size_mb: Maximum file size for uploads in megabytes. 0 means no limit.
        file_uploads_restrict_file_types: Whether to restrict file types for file uploads. Defaults to False.
        file_uploads_allowed_extensions: List of allowed file extensions for uploads. ['.*'] means all extensions are allowed.
    """

    llms: dict[str, LLMConfig] = field(default_factory=dict)
    agents: dict = field(default_factory=dict)
    default_agent: str = _DEFAULT_AGENT
    sandbox: SandboxConfig = field(default_factory=SandboxConfig)
    runtime: str = 'eventstream'
    file_store: str = 'memory'
    file_store_path: str = '/tmp/file_store'
    # TODO: clean up workspace path after the removal of ServerRuntime
    workspace_base: str = os.path.join(os.getcwd(), 'workspace')
    workspace_mount_path: str | None = (
        UndefinedString.UNDEFINED  # this path should always be set when config is fully loaded
    )  # when set to None, do not mount the workspace
    workspace_mount_path_in_sandbox: str = '/workspace'
    workspace_mount_rewrite: str | None = None
    cache_dir: str = '/tmp/cache'
    run_as_devin: bool = True
    confirmation_mode: bool = False
    max_iterations: int = _MAX_ITERATIONS
    max_budget_per_task: float | None = None
    e2b_api_key: str = ''
    disable_color: bool = False
    jwt_secret: str = uuid.uuid4().hex
    debug: bool = False
    enable_cli_session: bool = False
    file_uploads_max_file_size_mb: int = 0
    file_uploads_restrict_file_types: bool = False
    file_uploads_allowed_extensions: list[str] = field(default_factory=lambda: ['.*'])

    defaults_dict: ClassVar[dict] = {}

    def get_llm_config(self, name='llm') -> LLMConfig:
        """Llm is the name for default config (for backward compatibility prior to 0.8)"""
        if name in self.llms:
            return self.llms[name]
        if name is not None and name != 'llm':
            logger.opendevin_logger.warning(
                f'llm config group {name} not found, using default config'
            )
        if 'llm' not in self.llms:
            self.llms['llm'] = LLMConfig()
        return self.llms['llm']

    def set_llm_config(self, value: LLMConfig, name='llm'):
        self.llms[name] = value

    def get_agent_config(self, name='agent') -> AgentConfig:
        """Agent is the name for default config (for backward compability prior to 0.8)"""
        if name in self.agents:
            return self.agents[name]
        if 'agent' not in self.agents:
            self.agents['agent'] = AgentConfig()
        return self.agents['agent']

    def set_agent_config(self, value: AgentConfig, name='agent'):
        self.agents[name] = value

    def get_agent_to_llm_config_map(self) -> dict[str, LLMConfig]:
        """Get a map of agent names to llm configs."""
        return {name: self.get_llm_config_from_agent(name) for name in self.agents}

    def get_llm_config_from_agent(self, name='agent') -> LLMConfig:
        agent_config: AgentConfig = self.get_agent_config(name)
        llm_config_name = agent_config.llm_config
        return self.get_llm_config(llm_config_name)

    def __post_init__(self):
        """Post-initialization hook, called when the instance is created with only default values."""
        AppConfig.defaults_dict = self.defaults_to_dict()

    def defaults_to_dict(self) -> dict:
        """Serialize fields to a dict for the frontend, including type hints, defaults, and whether it's optional."""
        result = {}
        for f in fields(self):
            field_value = getattr(self, f.name)

            # dataclasses compute their defaults themselves
            if is_dataclass(type(field_value)):
                result[f.name] = field_value.defaults_to_dict()
            else:
                result[f.name] = get_field_info(f)
        return result

    def __str__(self):
        attr_str = []
        for f in fields(self):
            attr_name = f.name
            attr_value = getattr(self, f.name)

            if attr_name in [
                'e2b_api_key',
                'github_token',
                'jwt_secret',
            ]:
                attr_value = '******' if attr_value else None

            attr_str.append(f'{attr_name}={repr(attr_value)}')

        return f"AppConfig({', '.join(attr_str)}"

    def __repr__(self):
        return self.__str__()


def get_field_info(f):
    """Extract information about a dataclass field: type, optional, and default.

    Args:
        f: The field to extract information from.

    Returns: A dict with the field's type, whether it's optional, and its default value.
    """
    field_type = f.type
    optional = False

    # for types like str | None, find the non-None type and set optional to True
    # this is useful for the frontend to know if a field is optional
    # and to show the correct type in the UI
    # Note: this only works for UnionTypes with None as one of the types
    if get_origin(field_type) is UnionType:
        types = get_args(field_type)
        non_none_arg = next((t for t in types if t is not type(None)), None)
        if non_none_arg is not None:
            field_type = non_none_arg
            optional = True

    # type name in a pretty format
    type_name = (
        field_type.__name__ if hasattr(field_type, '__name__') else str(field_type)
    )

    # default is always present
    default = f.default

    # return a schema with the useful info for frontend
    return {'type': type_name.lower(), 'optional': optional, 'default': default}


def load_from_env(cfg: AppConfig, env_or_toml_dict: dict | MutableMapping[str, str]):
    """Reads the env-style vars and sets config attributes based on env vars or a config.toml dict.
    Compatibility with vars like LLM_BASE_URL, AGENT_MEMORY_ENABLED, SANDBOX_TIMEOUT and others.

    Args:
        cfg: The AppConfig object to set attributes on.
        env_or_toml_dict: The environment variables or a config.toml dict.
    """

    def get_optional_type(union_type: UnionType) -> Any:
        """Returns the non-None type from a Union."""
        types = get_args(union_type)
        return next((t for t in types if t is not type(None)), None)

    # helper function to set attributes based on env vars
    def set_attr_from_env(sub_config: Any, prefix=''):
        """Set attributes of a config dataclass based on environment variables."""
        for field_name, field_type in sub_config.__annotations__.items():
            # compute the expected env var name from the prefix and field name
            # e.g. LLM_BASE_URL
            env_var_name = (prefix + field_name).upper()

            if is_dataclass(field_type):
                # nested dataclass
                nested_sub_config = getattr(sub_config, field_name)
                set_attr_from_env(nested_sub_config, prefix=field_name + '_')
            elif env_var_name in env_or_toml_dict:
                # convert the env var to the correct type and set it
                value = env_or_toml_dict[env_var_name]

                # skip empty config values (fall back to default)
                if not value:
                    continue

                try:
                    # if it's an optional type, get the non-None type
                    if get_origin(field_type) is UnionType:
                        field_type = get_optional_type(field_type)

                    # Attempt to cast the env var to type hinted in the dataclass
                    if field_type is bool:
                        cast_value = str(value).lower() in ['true', '1']
                    else:
                        cast_value = field_type(value)
                    setattr(sub_config, field_name, cast_value)
                except (ValueError, TypeError):
                    logger.opendevin_logger.error(
                        f'Error setting env var {env_var_name}={value}: check that the value is of the right type'
                    )

    # Start processing from the root of the config object
    set_attr_from_env(cfg)

    # load default LLM config from env
    default_llm_config = cfg.get_llm_config()
    set_attr_from_env(default_llm_config, 'LLM_')
    # load default agent config from env
    default_agent_config = cfg.get_agent_config()
    set_attr_from_env(default_agent_config, 'AGENT_')


def load_from_toml(cfg: AppConfig, toml_file: str = 'config.toml'):
    """Load the config from the toml file. Supports both styles of config vars.

    Args:
        cfg: The AppConfig object to update attributes of.
        toml_file: The path to the toml file. Defaults to 'config.toml'.
    """
    # try to read the config.toml file into the config object
    try:
        with open(toml_file, 'r', encoding='utf-8') as toml_contents:
            toml_config = toml.load(toml_contents)
    except FileNotFoundError:
        return
    except toml.TomlDecodeError as e:
        logger.opendevin_logger.warning(
            f'Cannot parse config from toml, toml values have not been applied.\nError: {e}',
            exc_info=False,
        )
        return

    # if there was an exception or core is not in the toml, try to use the old-style toml
    if 'core' not in toml_config:
        # re-use the env loader to set the config from env-style vars
        load_from_env(cfg, toml_config)
        return

    core_config = toml_config['core']

    # load llm configs and agent configs
    for key, value in toml_config.items():
        if isinstance(value, dict):
            try:
                if key is not None and key.lower() == 'agent':
                    logger.opendevin_logger.info(
                        'Attempt to load default agent config from config toml'
                    )
                    non_dict_fields = {
                        k: v for k, v in value.items() if not isinstance(v, dict)
                    }
                    agent_config = AgentConfig(**non_dict_fields)
                    cfg.set_agent_config(agent_config, 'agent')
                    for nested_key, nested_value in value.items():
                        if isinstance(nested_value, dict):
                            logger.opendevin_logger.info(
                                f'Attempt to load group {nested_key} from config toml as agent config'
                            )
                            agent_config = AgentConfig(**nested_value)
                            cfg.set_agent_config(agent_config, nested_key)
                if key is not None and key.lower() == 'llm':
                    logger.opendevin_logger.info(
                        'Attempt to load default LLM config from config toml'
                    )
                    non_dict_fields = {
                        k: v for k, v in value.items() if not isinstance(v, dict)
                    }
                    llm_config = LLMConfig(**non_dict_fields)
                    cfg.set_llm_config(llm_config, 'llm')
                    for nested_key, nested_value in value.items():
                        if isinstance(nested_value, dict):
                            logger.opendevin_logger.info(
                                f'Attempt to load group {nested_key} from config toml as llm config'
                            )
                            llm_config = LLMConfig(**nested_value)
                            cfg.set_llm_config(llm_config, nested_key)
            except (TypeError, KeyError) as e:
                logger.opendevin_logger.warning(
                    f'Cannot parse config from toml, toml values have not been applied.\n Error: {e}',
                    exc_info=False,
                )

    try:
        # set sandbox config from the toml file
        sandbox_config = cfg.sandbox

        # migrate old sandbox configs from [core] section to sandbox config
        keys_to_migrate = [key for key in core_config if key.startswith('sandbox_')]
        for key in keys_to_migrate:
            new_key = key.replace('sandbox_', '')
            if new_key in sandbox_config.__annotations__:
                # read the key in sandbox and remove it from core
                setattr(sandbox_config, new_key, core_config.pop(key))
            else:
                logger.opendevin_logger.warning(f'Unknown sandbox config: {key}')

        # the new style values override the old style values
        if 'sandbox' in toml_config:
            sandbox_config = SandboxConfig(**toml_config['sandbox'])

        # update the config object with the new values
        AppConfig(sandbox=sandbox_config, **core_config)
    except (TypeError, KeyError) as e:
        logger.opendevin_logger.warning(
            f'Cannot parse config from toml, toml values have not been applied.\nError: {e}',
            exc_info=False,
        )


def finalize_config(cfg: AppConfig):
    """More tweaks to the config after it's been loaded."""
    # Set workspace_mount_path if not set by the user
    if cfg.workspace_mount_path is UndefinedString.UNDEFINED:
        cfg.workspace_mount_path = os.path.abspath(cfg.workspace_base)
    cfg.workspace_base = os.path.abspath(cfg.workspace_base)

    if cfg.workspace_mount_rewrite:  # and not config.workspace_mount_path:
        # TODO why do we need to check if workspace_mount_path is None?
        base = cfg.workspace_base or os.getcwd()
        parts = cfg.workspace_mount_rewrite.split(':')
        cfg.workspace_mount_path = base.replace(parts[0], parts[1])

    for llm in cfg.llms.values():
        if llm.embedding_base_url is None:
            llm.embedding_base_url = llm.base_url

    if cfg.sandbox.use_host_network and platform.system() == 'Darwin':
        logger.opendevin_logger.warning(
            'Please upgrade to Docker Desktop 4.29.0 or later to use host network mode on macOS. '
            'See https://github.com/docker/roadmap/issues/238#issuecomment-2044688144 for more information.'
        )

    # make sure cache dir exists
    if cfg.cache_dir:
        pathlib.Path(cfg.cache_dir).mkdir(parents=True, exist_ok=True)


# Utility function for command line --group argument
def get_llm_config_arg(
    llm_config_arg: str, toml_file: str = 'config.toml'
) -> LLMConfig | None:
    """Get a group of llm settings from the config file.

    A group in config.toml can look like this:

    ```
    [llm.gpt-3.5-for-eval]
    model = 'gpt-3.5-turbo'
    api_key = '...'
    temperature = 0.5
    num_retries = 10
    ...
    ```

    The user-defined group name, like "gpt-3.5-for-eval", is the argument to this function. The function will load the LLMConfig object
    with the settings of this group, from the config file, and set it as the LLMConfig object for the app.

    Note that the group must be under "llm" group, or in other words, the group name must start with "llm.".

    Args:
        llm_config_arg: The group of llm settings to get from the config.toml file.

    Returns:
        LLMConfig: The LLMConfig object with the settings from the config file.
    """
    # keep only the name, just in case
    llm_config_arg = llm_config_arg.strip('[]')

    # truncate the prefix, just in case
    if llm_config_arg.startswith('llm.'):
        llm_config_arg = llm_config_arg[4:]

    logger.opendevin_logger.info(f'Loading llm config from {llm_config_arg}')

    # load the toml file
    try:
        with open(toml_file, 'r', encoding='utf-8') as toml_contents:
            toml_config = toml.load(toml_contents)
    except FileNotFoundError as e:
        logger.opendevin_logger.error(f'Config file not found: {e}')
        return None
    except toml.TomlDecodeError as e:
        logger.opendevin_logger.error(
            f'Cannot parse llm group from {llm_config_arg}. Exception: {e}'
        )
        return None

    # update the llm config with the specified section
    if 'llm' in toml_config and llm_config_arg in toml_config['llm']:
        return LLMConfig(**toml_config['llm'][llm_config_arg])
    logger.opendevin_logger.debug(f'Loading from toml failed for {llm_config_arg}')
    return None


# Command line arguments
def get_parser() -> argparse.ArgumentParser:
    """Get the parser for the command line arguments."""
    parser = argparse.ArgumentParser(description='Run an agent with a specific task')
    parser.add_argument(
        '-d',
        '--directory',
        type=str,
        help='The working directory for the agent',
    )
    parser.add_argument(
        '-t', '--task', type=str, default='', help='The task for the agent to perform'
    )
    parser.add_argument(
        '-f',
        '--file',
        type=str,
        help='Path to a file containing the task. Overrides -t if both are provided.',
    )
    parser.add_argument(
        '-c',
        '--agent-cls',
        default=_DEFAULT_AGENT,
        type=str,
        help='Name of the default agent to use',
    )
    parser.add_argument(
        '-i',
        '--max-iterations',
        default=_MAX_ITERATIONS,
        type=int,
        help='The maximum number of iterations to run the agent',
    )
    parser.add_argument(
        '-b',
        '--max-budget-per-task',
        type=float,
        help='The maximum budget allowed per task, beyond which the agent will stop.',
    )
    # --eval configs are for evaluations only
    parser.add_argument(
        '--eval-output-dir',
        default='evaluation/evaluation_outputs/outputs',
        type=str,
        help='The directory to save evaluation output',
    )
    parser.add_argument(
        '--eval-n-limit',
        default=None,
        type=int,
        help='The number of instances to evaluate',
    )
    parser.add_argument(
        '--eval-num-workers',
        default=4,
        type=int,
        help='The number of workers to use for evaluation',
    )
    parser.add_argument(
        '--eval-note',
        default=None,
        type=str,
        help='The note to add to the evaluation directory',
    )
    parser.add_argument(
        '-l',
        '--llm-config',
        default=None,
        type=str,
        help='Replace default LLM ([llm] section in config.toml) config with the specified LLM config, e.g. "llama3" for [llm.llama3] section in config.toml',
    )
    return parser


def parse_arguments() -> argparse.Namespace:
    """Parse the command line arguments."""
    parser = get_parser()
    parsed_args, _ = parser.parse_known_args()
    return parsed_args


def load_app_config(set_logging_levels: bool = True) -> AppConfig:
    """Load the configuration from the config.toml file and environment variables.

    Args:
        set_logger_levels: Whether to set the global variables for logging levels.
    """
    config = AppConfig()
    load_from_toml(config)
    load_from_env(config, os.environ)
    finalize_config(config)
    if set_logging_levels:
        logger.DEBUG = config.debug
        logger.DISABLE_COLOR_PRINTING = config.disable_color
    return config