spaCy/spacy/cli/debug_config.py

from typing import Optional, Dict, Any, Union, List
from pathlib import Path
from wasabi import msg, table
from thinc.api import Config
from thinc.config import VARIABLE_RE
import typer

from ._util import Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli
from .. import util


@debug_cli.command(
    "config",
    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def debug_config_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True),
    code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    show_funcs: bool = Opt(False, "--show-functions", "-F", help="Show an overview of all registered functions used in the config and where they come from (modules, files etc.)"),
    show_vars: bool = Opt(False, "--show-variables", "-V", help="Show an overview of all variables referenced in the config and their values. This will also reflect variables overwritten on the CLI.")
    # fmt: on
):
    """Debug a config.cfg file and show validation errors. The command will
    create all objects in the tree and validate them. Note that some config
    validation errors are blocking and will prevent the rest of the config from
    being resolved. This means that you may not see all validation errors at
    once and some issues are only shown once previous errors have been fixed.
    Similar as with the 'train' command, you can override settings from the config
    as command line options. For instance, --training.batch_size 128 overrides
    the value of "batch_size" in the block "[training]".
    """
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    debug_config(
        config_path, overrides=overrides, show_funcs=show_funcs, show_vars=show_vars
    )


def debug_config(
    config_path: Path,
    *,
    overrides: Dict[str, Any] = {},
    show_funcs: bool = False,
    show_vars: bool = False,
):
    msg.divider("Config validation")
    with show_validation_error(config_path):
        config = util.load_config(config_path, overrides=overrides)
        nlp, _ = util.load_model_from_config(config)
    msg.good("Config is valid")
    if show_vars:
        variables = get_variables(config)
        msg.divider(f"Variables ({len(variables)})")
        head = ("Variable", "Value")
        msg.table(variables, header=head, divider=True, widths=(41, 34), spacing=2)
    if show_funcs:
        funcs = get_registered_funcs(config)
        msg.divider(f"Registered functions ({len(funcs)})")
        for func in funcs:
            func_data = {
                "Registry": f"@{func['registry']}",
                "Name": func["name"],
                "Module": func["module"],
                "File": f"{func['file']} (line {func['line_no']})",
            }
            msg.info(f"[{func['path']}]")
            print(table(func_data).strip())


def get_registered_funcs(config: Config) -> List[Dict[str, Optional[Union[str, int]]]]:
    result = []
    for key, value in util.walk_dict(config):
        if not key[-1].startswith("@"):
            continue
        # We have a reference to a registered function
        reg_name = key[-1][1:]
        registry = getattr(util.registry, reg_name)
        path = ".".join(key[:-1])
        info = registry.find(value)
        result.append({"name": value, "registry": reg_name, "path": path, **info})
    return result


def get_variables(config: Config) -> Dict[str, Any]:
    result = {}
    for variable in sorted(set(VARIABLE_RE.findall(config.to_str()))):
        path = variable[2:-1].replace(":", ".")
        value = util.dot_to_object(config, path)
        result[variable] = repr(value)
    return result