pipeline.py

"""
Pipeline for CONLL-U formatting.
"""
# pylint: disable=too-few-public-methods, unused-import, undefined-variable, too-many-nested-blocks
import pathlib

try:
    from networkx import DiGraph
except ImportError:  # pragma: no cover
    DiGraph = None  # type: ignore
    print('No libraries installed. Failed to import.')

from core_utils.article.article import Article
from core_utils.pipeline import (AbstractCoNLLUAnalyzer, CoNLLUDocument, LibraryWrapper,
                                 PipelineProtocol, StanzaDocument, TreeNode)


class CorpusManager:
    """
    Work with articles and store them.
    """

    def __init__(self, path_to_raw_txt_data: pathlib.Path) -> None:
        """
        Initialize an instance of the CorpusManager class.

        Args:
            path_to_raw_txt_data (pathlib.Path): Path to raw txt data
        """

    def _validate_dataset(self) -> None:
        """
        Validate folder with assets.
        """

    def _scan_dataset(self) -> None:
        """
        Register each dataset entry.
        """

    def get_articles(self) -> dict:
        """
        Get storage params.

        Returns:
            dict: Storage params
        """


class TextProcessingPipeline(PipelineProtocol):
    """
    Preprocess and morphologically annotate sentences into the CONLL-U format.
    """

    def __init__(
        self, corpus_manager: CorpusManager, analyzer: LibraryWrapper | None = None
    ) -> None:
        """
        Initialize an instance of the TextProcessingPipeline class.

        Args:
            corpus_manager (CorpusManager): CorpusManager instance
            analyzer (LibraryWrapper | None): Analyzer instance
        """

    def run(self) -> None:
        """
        Perform basic preprocessing and write processed text to files.
        """


class UDPipeAnalyzer(LibraryWrapper):
    """
    Wrapper for udpipe library.
    """

    _analyzer: AbstractCoNLLUAnalyzer

    def __init__(self) -> None:
        """
        Initialize an instance of the UDPipeAnalyzer class.
        """

    def _bootstrap(self) -> AbstractCoNLLUAnalyzer:
        """
        Load and set up the UDPipe model.

        Returns:
            AbstractCoNLLUAnalyzer: Analyzer instance
        """

    def analyze(self, texts: list[str]) -> list[StanzaDocument | str]:
        """
        Process texts into CoNLL-U formatted markup.

        Args:
            texts (list[str]): Collection of texts

        Returns:
            list[StanzaDocument | str]: List of documents
        """

    def to_conllu(self, article: Article) -> None:
        """
        Save content to ConLLU format.

        Args:
            article (Article): Article containing information to save
        """


class StanzaAnalyzer(LibraryWrapper):
    """
    Wrapper for stanza library.
    """

    _analyzer: AbstractCoNLLUAnalyzer

    def __init__(self) -> None:
        """
        Initialize an instance of the StanzaAnalyzer class.
        """

    def _bootstrap(self) -> AbstractCoNLLUAnalyzer:
        """
        Load and set up the Stanza model.

        Returns:
            AbstractCoNLLUAnalyzer: Analyzer instance
        """

    def analyze(self, texts: list[str]) -> list[StanzaDocument]:
        """
        Process texts into CoNLL-U formatted markup.

        Args:
            texts (list[str]): Collection of texts

        Returns:
            list[StanzaDocument]: List of documents
        """

    def to_conllu(self, article: Article) -> None:
        """
        Save content to ConLLU format.

        Args:
            article (Article): Article containing information to save
        """

    def from_conllu(self, article: Article) -> CoNLLUDocument:
        """
        Load ConLLU content from article stored on disk.

        Args:
            article (Article): Article to load

        Returns:
            CoNLLUDocument: Document ready for parsing
        """


class POSFrequencyPipeline:
    """
    Count frequencies of each POS in articles, update meta info and produce graphic report.
    """

    def __init__(self, corpus_manager: CorpusManager, analyzer: LibraryWrapper) -> None:
        """
        Initialize an instance of the POSFrequencyPipeline class.

        Args:
            corpus_manager (CorpusManager): CorpusManager instance
            analyzer (LibraryWrapper): Analyzer instance
        """

    def run(self) -> None:
        """
        Visualize the frequencies of each part of speech.
        """

    def _count_frequencies(self, article: Article) -> dict[str, int]:
        """
        Count POS frequency in Article.

        Args:
            article (Article): Article instance

        Returns:
            dict[str, int]: POS frequencies
        """


class PatternSearchPipeline(PipelineProtocol):
    """
    Search for the required syntactic pattern.
    """

    def __init__(
        self, corpus_manager: CorpusManager, analyzer: LibraryWrapper, pos: tuple[str, ...]
    ) -> None:
        """
        Initialize an instance of the PatternSearchPipeline class.

        Args:
            corpus_manager (CorpusManager): CorpusManager instance
            analyzer (LibraryWrapper): Analyzer instance
            pos (tuple[str, ...]): Root, Dependency, Child part of speech
        """

    def _make_graphs(self, doc: CoNLLUDocument) -> list[DiGraph]:
        """
        Make graphs for a document.

        Args:
            doc (CoNLLUDocument): Document for patterns searching

        Returns:
            list[DiGraph]: Graphs for the sentences in the document
        """

    def _add_children(
        self, graph: DiGraph, subgraph_to_graph: dict, node_id: int, tree_node: TreeNode
    ) -> None:
        """
        Add children to TreeNode.

        Args:
            graph (DiGraph): Sentence graph to search for a pattern
            subgraph_to_graph (dict): Matched subgraph
            node_id (int): ID of root node of the match
            tree_node (TreeNode): Root node of the match
        """

    def _find_pattern(self, doc_graphs: list) -> dict[int, list[TreeNode]]:
        """
        Search for the required pattern.

        Args:
            doc_graphs (list): A list of graphs for the document

        Returns:
            dict[int, list[TreeNode]]: A dictionary with pattern matches
        """

    def run(self) -> None:
        """
        Search for a pattern in documents and writes found information to JSON file.
        """


def main() -> None:
    """
    Entrypoint for pipeline module.
    """


if __name__ == "__main__":
    main()