from typing import Optional, List, Dict, Any, Union, IO
import math
from tqdm import tqdm
import numpy
from ast import literal_eval
from pathlib import Path
from preshed.counter import PreshCounter
import tarfile
import gzip
import zipfile
import srsly
import warnings
from wasabi import Printer

from ._util import app, Arg, Opt
from ..vectors import Vectors
from ..errors import Errors, Warnings
from ..language import Language
from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
from ..lookups import Lookups

try:
    import ftfy
except ImportError:
    ftfy = None


DEFAULT_OOV_PROB = -20


@app.command("init-model")
def init_model_cli(
    # fmt: off
    lang: str = Arg(..., help="Model language"),
    output_dir: Path = Arg(..., help="Model output directory"),
    freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
    clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
    jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
    vectors_loc: Optional[Path] = Opt(None, "--vectors-loc", "-v", help="Optional vectors file in Word2Vec format", exists=True),
    prune_vectors: int = Opt(-1, "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
    truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
    vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
    model_name: Optional[str] = Opt(None, "--model-name", "-mn", help="Optional name for the model meta"),
    base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Base model (for languages with custom tokenizers)")
    # fmt: on
):
    """
    Create a new model from raw data. If vectors are provided in Word2Vec format,
    they can be either a .txt or zipped as a .zip or .tar.gz.
    """
    init_model(
        lang,
        output_dir,
        freqs_loc=freqs_loc,
        clusters_loc=clusters_loc,
        jsonl_loc=jsonl_loc,
        vectors_loc=vectors_loc,
        prune_vectors=prune_vectors,
        truncate_vectors=truncate_vectors,
        vectors_name=vectors_name,
        model_name=model_name,
        base_model=base_model,
        silent=False,
    )


def init_model(
    lang: str,
    output_dir: Path,
    freqs_loc: Optional[Path] = None,
    clusters_loc: Optional[Path] = None,
    jsonl_loc: Optional[Path] = None,
    vectors_loc: Optional[Path] = None,
    prune_vectors: int = -1,
    truncate_vectors: int = 0,
    vectors_name: Optional[str] = None,
    model_name: Optional[str] = None,
    base_model: Optional[str] = None,
    silent: bool = True,
) -> Language:
    msg = Printer(no_print=silent, pretty=not silent)
    if jsonl_loc is not None:
        if freqs_loc is not None or clusters_loc is not None:
            settings = ["-j"]
            if freqs_loc:
                settings.append("-f")
            if clusters_loc:
                settings.append("-c")
            msg.warn(
                "Incompatible arguments",
                "The -f and -c arguments are deprecated, and not compatible "
                "with the -j argument, which should specify the same "
                "information. Either merge the frequencies and clusters data "
                "into the JSONL-formatted file (recommended), or use only the "
                "-f and -c files, without the other lexical attributes.",
            )
        jsonl_loc = ensure_path(jsonl_loc)
        lex_attrs = srsly.read_jsonl(jsonl_loc)
    else:
        clusters_loc = ensure_path(clusters_loc)
        freqs_loc = ensure_path(freqs_loc)
        if freqs_loc is not None and not freqs_loc.exists():
            msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
        lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)

    with msg.loading("Creating model..."):
        nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)

    msg.good("Successfully created model")
    if vectors_loc is not None:
        add_vectors(
            msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
        )
    vec_added = len(nlp.vocab.vectors)
    lex_added = len(nlp.vocab)
    msg.good(
        "Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors",
    )
    if not output_dir.exists():
        output_dir.mkdir()
    nlp.to_disk(output_dir)
    return nlp


def open_file(loc: Union[str, Path]) -> IO:
    """Handle .gz, .tar.gz or unzipped files"""
    loc = ensure_path(loc)
    if tarfile.is_tarfile(str(loc)):
        return tarfile.open(str(loc), "r:gz")
    elif loc.parts[-1].endswith("gz"):
        return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
    elif loc.parts[-1].endswith("zip"):
        zip_file = zipfile.ZipFile(str(loc))
        names = zip_file.namelist()
        file_ = zip_file.open(names[0])
        return (line.decode("utf8") for line in file_)
    else:
        return loc.open("r", encoding="utf8")


def read_attrs_from_deprecated(
    msg: Printer, freqs_loc: Optional[Path], clusters_loc: Optional[Path]
) -> List[Dict[str, Any]]:
    if freqs_loc is not None:
        with msg.loading("Counting frequencies..."):
            probs, _ = read_freqs(freqs_loc)
        msg.good("Counted frequencies")
    else:
        probs, _ = ({}, DEFAULT_OOV_PROB)  # noqa: F841
    if clusters_loc:
        with msg.loading("Reading clusters..."):
            clusters = read_clusters(clusters_loc)
        msg.good("Read clusters")
    else:
        clusters = {}
    lex_attrs = []
    sorted_probs = sorted(probs.items(), key=lambda item: item[1], reverse=True)
    if len(sorted_probs):
        for i, (word, prob) in tqdm(enumerate(sorted_probs)):
            attrs = {"orth": word, "id": i, "prob": prob}
            # Decode as a little-endian string, so that we can do & 15 to get
            # the first 4 bits. See _parse_features.pyx
            if word in clusters:
                attrs["cluster"] = int(clusters[word][::-1], 2)
            else:
                attrs["cluster"] = 0
            lex_attrs.append(attrs)
    return lex_attrs


def create_model(
    lang: str,
    lex_attrs: List[Dict[str, Any]],
    name: Optional[str] = None,
    base_model: Optional[Union[str, Path]] = None,
) -> Language:
    if base_model:
        nlp = load_model(base_model)
        # keep the tokenizer but remove any existing pipeline components due to
        # potentially conflicting vectors
        for pipe in nlp.pipe_names:
            nlp.remove_pipe(pipe)
    else:
        lang_class = get_lang_class(lang)
        nlp = lang_class()
    for lexeme in nlp.vocab:
        lexeme.rank = OOV_RANK
    for attrs in lex_attrs:
        if "settings" in attrs:
            continue
        lexeme = nlp.vocab[attrs["orth"]]
        lexeme.set_attrs(**attrs)
    if len(nlp.vocab):
        oov_prob = min(lex.prob for lex in nlp.vocab) - 1
    else:
        oov_prob = DEFAULT_OOV_PROB
    nlp.vocab.cfg.update({"oov_prob": oov_prob})
    if name:
        nlp.meta["name"] = name
    return nlp


def add_vectors(
    msg: Printer,
    nlp: Language,
    vectors_loc: Optional[Path],
    truncate_vectors: int,
    prune_vectors: int,
    name: Optional[str] = None,
) -> None:
    vectors_loc = ensure_path(vectors_loc)
    if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
        nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
        for lex in nlp.vocab:
            if lex.rank and lex.rank != OOV_RANK:
                nlp.vocab.vectors.add(lex.orth, row=lex.rank)
    else:
        if vectors_loc:
            with msg.loading(f"Reading vectors from {vectors_loc}"):
                vectors_data, vector_keys = read_vectors(
                    msg, vectors_loc, truncate_vectors
                )
            msg.good(f"Loaded vectors from {vectors_loc}")
        else:
            vectors_data, vector_keys = (None, None)
        if vector_keys is not None:
            for word in vector_keys:
                if word not in nlp.vocab:
                    nlp.vocab[word]
        if vectors_data is not None:
            nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
    if name is None:
        nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors"
    else:
        nlp.vocab.vectors.name = name
    nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
    if prune_vectors >= 1:
        nlp.vocab.prune_vectors(prune_vectors)


def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
    f = open_file(vectors_loc)
    shape = tuple(int(size) for size in next(f).split())
    if truncate_vectors >= 1:
        shape = (truncate_vectors, shape[1])
    vectors_data = numpy.zeros(shape=shape, dtype="f")
    vectors_keys = []
    for i, line in enumerate(tqdm(f)):
        line = line.rstrip()
        pieces = line.rsplit(" ", vectors_data.shape[1])
        word = pieces.pop(0)
        if len(pieces) != vectors_data.shape[1]:
            msg.fail(Errors.E094.format(line_num=i, loc=vectors_loc), exits=1)
        vectors_data[i] = numpy.asarray(pieces, dtype="f")
        vectors_keys.append(word)
        if i == truncate_vectors - 1:
            break
    return vectors_data, vectors_keys


def read_freqs(
    freqs_loc: Path, max_length: int = 100, min_doc_freq: int = 5, min_freq: int = 50
):
    counts = PreshCounter()
    total = 0
    with freqs_loc.open() as f:
        for i, line in enumerate(f):
            freq, doc_freq, key = line.rstrip().split("\t", 2)
            freq = int(freq)
            counts.inc(i + 1, freq)
            total += freq
    counts.smooth()
    log_total = math.log(total)
    probs = {}
    with freqs_loc.open() as f:
        for line in tqdm(f):
            freq, doc_freq, key = line.rstrip().split("\t", 2)
            doc_freq = int(doc_freq)
            freq = int(freq)
            if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length:
                try:
                    word = literal_eval(key)
                except SyntaxError:
                    # Take odd strings literally.
                    word = literal_eval(f"'{key}'")
                smooth_count = counts.smoother(int(freq))
                probs[word] = math.log(smooth_count) - log_total
    oov_prob = math.log(counts.smoother(0)) - log_total
    return probs, oov_prob


def read_clusters(clusters_loc: Path) -> dict:
    clusters = {}
    if ftfy is None:
        warnings.warn(Warnings.W004)
    with clusters_loc.open() as f:
        for line in tqdm(f):
            try:
                cluster, word, freq = line.split()
                if ftfy is not None:
                    word = ftfy.fix_text(word)
            except ValueError:
                continue
            # If the clusterer has only seen the word a few times, its
            # cluster is unreliable.
            if int(freq) >= 3:
                clusters[word] = cluster
            else:
                clusters[word] = "0"
    # Expand clusters with re-casing
    for word, cluster in list(clusters.items()):
        if word.lower() not in clusters:
            clusters[word.lower()] = cluster
        if word.title() not in clusters:
            clusters[word.title()] = cluster
        if word.upper() not in clusters:
            clusters[word.upper()] = cluster
    return clusters