/rasa/shared/data.py

import os
import shutil
import tempfile
import uuid
from enum import Enum
from pathlib import Path
from typing import Text, Optional, Union, List, Callable, Set, Iterable

YAML_FILE_EXTENSIONS = [".yml", ".yaml"]
JSON_FILE_EXTENSIONS = [".json"]
TRAINING_DATA_EXTENSIONS = set(JSON_FILE_EXTENSIONS + YAML_FILE_EXTENSIONS)


def yaml_file_extension() -> Text:
    """Return YAML file extension."""
    return YAML_FILE_EXTENSIONS[0]


def is_likely_yaml_file(file_path: Union[Text, Path]) -> bool:
    """Check if a file likely contains yaml.

    Arguments:
        file_path: path to the file

    Returns:
        `True` if the file likely contains data in yaml format, `False` otherwise.
    """
    return Path(file_path).suffix in set(YAML_FILE_EXTENSIONS)


def is_likely_json_file(file_path: Text) -> bool:
    """Check if a file likely contains json.

    Arguments:
        file_path: path to the file

    Returns:
        `True` if the file likely contains data in json format, `False` otherwise.
    """
    return Path(file_path).suffix in set(JSON_FILE_EXTENSIONS)


def get_core_directory(paths: Optional[Union[Text, List[Text]]]) -> Text:
    """Recursively collects all Core training files from a list of paths.

    Args:
        paths: List of paths to training files or folders containing them.

    Returns:
        Path to temporary directory containing all found Core training files.
    """
    from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
        YAMLStoryReader,
    )

    core_files = get_data_files(paths, YAMLStoryReader.is_stories_file)
    return _copy_files_to_new_dir(core_files)


def get_nlu_directory(paths: Optional[Union[Text, List[Text]]]) -> Text:
    """Recursively collects all NLU training files from a list of paths.

    Args:
        paths: List of paths to training files or folders containing them.

    Returns:
        Path to temporary directory containing all found NLU training files.
    """
    nlu_files = get_data_files(paths, is_nlu_file)
    return _copy_files_to_new_dir(nlu_files)


def get_data_files(
    paths: Optional[Union[Text, List[Text]]], filter_predicate: Callable[[Text], bool]
) -> List[Text]:
    """Recursively collects all training files from a list of paths.

    Args:
        paths: List of paths to training files or folders containing them.
        filter_predicate: property to use when filtering the paths, e.g. `is_nlu_file`.

    Returns:
        Paths of training data files.
    """
    data_files = set()

    if paths is None:
        paths = []
    elif isinstance(paths, str):
        paths = [paths]

    for path in set(paths):
        if not path:
            continue

        if is_valid_filetype(path):
            if filter_predicate(path):
                data_files.add(os.path.abspath(path))
        else:
            new_data_files = _find_data_files_in_directory(path, filter_predicate)
            data_files.update(new_data_files)

    return sorted(data_files)


def _find_data_files_in_directory(
    directory: Text, filter_property: Callable[[Text], bool]
) -> Set[Text]:
    filtered_files = set()

    for root, _, files in os.walk(directory, followlinks=True):
        # we sort the files here to ensure consistent order for repeatable training
        # results
        for f in sorted(files):
            full_path = os.path.join(root, f)

            if not is_valid_filetype(full_path):
                continue

            if filter_property(full_path):
                filtered_files.add(full_path)

    return filtered_files


def is_valid_filetype(path: Union[Path, Text]) -> bool:
    """Checks if given file has a supported extension.

    Args:
        path: Path to the source file.

    Returns:
        `True` is given file has supported extension, `False` otherwise.
    """
    return Path(path).is_file() and Path(path).suffix in TRAINING_DATA_EXTENSIONS


def is_nlu_file(file_path: Text) -> bool:
    """Checks if a file is a Rasa compatible nlu file.

    Args:
        file_path: Path of the file which should be checked.

    Returns:
        `True` if it's a nlu file, otherwise `False`.
    """
    from rasa.shared.nlu.training_data import loading as nlu_loading

    return nlu_loading.guess_format(file_path) != nlu_loading.UNK


def is_config_file(file_path: Text) -> bool:
    """Checks whether the given file path is a Rasa config file.

    Args:
        file_path: Path of the file which should be checked.

    Returns:
        `True` if it's a Rasa config file, otherwise `False`.
    """
    file_name = os.path.basename(file_path)

    return file_name in ["config.yml", "config.yaml"]


def _copy_files_to_new_dir(files: Iterable[Text]) -> Text:
    directory = tempfile.mkdtemp()
    for f in files:
        # makes sure files do not overwrite each other, hence the prefix
        unique_prefix = uuid.uuid4().hex
        unique_file_name = unique_prefix + "_" + os.path.basename(f)
        shutil.copy2(f, os.path.join(directory, unique_file_name))

    return directory


class TrainingType(Enum):
    """Enum class for defining explicitly what training types exist."""

    NLU = 1
    CORE = 2
    BOTH = 3
    END_TO_END = 4

    @property
    def model_type(self) -> Text:
        """Returns the type of model which this training yields."""
        if self == TrainingType.NLU:
            return "nlu"
        if self == TrainingType.CORE:
            return "core"
        return "rasa"