from typing import Optional, Dict, List, Union, Sequence
from pydantic import BaseModel, FilePath
import plac
import tqdm
from pathlib import Path
from wasabi import msg
import thinc
import thinc.schedules
from thinc.api import Model

from ..gold import GoldCorpus
from .. import util


registry = util.registry

CONFIG_STR = """
[training]
patience = 10
eval_frequency = 10
dropout = 0.2
init_tok2vec = null
vectors = null
max_epochs = 100
orth_variant_level = 0.0
gold_preproc = false
max_length = 0
use_gpu = 0
scores = ["ents_p",  "ents_r", "ents_f"]
score_weights = {"ents_f": 1.0}
limit = 0

[training.batch_size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001

[optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999

[nlp]
lang = "en"
vectors = ${training:vectors}

[nlp.pipeline.tok2vec]
factory = "tok2vec"

[nlp.pipeline.ner]
factory = "ner"

[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 64
maxout_pieces = 3

[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}

[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
width = 128
depth = 4
window_size = 1
embed_size = 10000
maxout_pieces = 3
subword_features = true
"""


class PipelineComponent(BaseModel):
    factory: str
    model: Model

    class Config:
        arbitrary_types_allowed = True


class ConfigSchema(BaseModel):
    optimizer: Optional["Optimizer"]

    class training(BaseModel):
        patience: int = 10
        eval_frequency: int = 100
        dropout: float = 0.2
        init_tok2vec: Optional[FilePath] = None
        vectors: Optional[str] = None
        max_epochs: int = 100
        orth_variant_level: float = 0.0
        gold_preproc: bool = False
        max_length: int = 0
        use_gpu: int = 0
        scores: List[str] = ["ents_p", "ents_r", "ents_f"]
        score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
        limit: int = 0
        batch_size: Union[Sequence[int], int]

    class nlp(BaseModel):
        lang: str
        vectors: Optional[str]
        pipeline: Optional[Dict[str, PipelineComponent]]

    class Config:
        extra = "allow"


@plac.annotations(
    # fmt: off
    train_path=("Location of JSON-formatted training data", "positional", None, Path),
    dev_path=("Location of JSON-formatted development data", "positional", None, Path),
    config_path=("Path to config file", "positional", None, Path),
    output_path=("Output directory to store model in", "option", "o", Path),
    meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
    raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
    # fmt: on
)
def train_from_config_cli(
    train_path,
    dev_path,
    config_path,
    output_path=None,
    meta_path=None,
    raw_text=None,
    debug=False,
    verbose=False,
):
    """
    Train or update a spaCy model. Requires data to be formatted in spaCy's
    JSON format. To convert data from other formats, use the `spacy convert`
    command.
    """
    if not config_path or not config_path.exists():
        msg.fail("Config file not found", config_path, exits=1)
    if not train_path or not train_path.exists():
        msg.fail("Training data not found", train_path, exits=1)
    if not dev_path or not dev_path.exists():
        msg.fail("Development data not found", dev_path, exits=1)
    if meta_path is not None and not meta_path.exists():
        msg.fail("Can't find model meta.json", meta_path, exits=1)
    if output_path is not None and not output_path.exists():
        output_path.mkdir()

    try:
        train_from_config(
            config_path,
            {"train": train_path, "dev": dev_path},
            output_path=output_path,
            meta_path=meta_path,
            raw_text=raw_text,
        )
    except KeyboardInterrupt:
        msg.warn("Cancelled.")


def train_from_config(
    config_path, data_paths, raw_text=None, meta_path=None, output_path=None,
):
    msg.info(f"Loading config from: {config_path}")
    config = util.load_config(config_path, create_objects=True)
    use_gpu = config["training"]["use_gpu"]
    if use_gpu >= 0:
        msg.info("Using GPU")
    else:
        msg.info("Using CPU")
    msg.info("Creating nlp from config")
    nlp_config = util.load_config(config_path, create_objects=False)["nlp"]
    nlp = util.load_model_from_config(nlp_config)
    optimizer = config["optimizer"]
    training = config["training"]
    limit = training["limit"]
    msg.info("Loading training corpus")
    corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit)
    msg.info("Initializing the nlp pipeline")
    nlp.begin_training(lambda: corpus.train_examples, device=use_gpu)

    train_batches = create_train_batches(nlp, corpus, training)
    evaluate = create_evaluation_callback(nlp, optimizer, corpus, training)

    # Create iterator, which yields out info after each optimization step.
    msg.info("Start training")
    training_step_iterator = train_while_improving(
        nlp,
        optimizer,
        train_batches,
        evaluate,
        training["dropout"],
        training["patience"],
        training["eval_frequency"],
    )

    msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
    print_row = setup_printer(training, nlp)

    try:
        progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
        for batch, info, is_best_checkpoint in training_step_iterator:
            progress.update(1)
            if is_best_checkpoint is not None:
                progress.close()
                print_row(info)
                if is_best_checkpoint and output_path is not None:
                    nlp.to_disk(output_path)
                progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
    finally:
        if output_path is not None:
            with nlp.use_params(optimizer.averages):
                final_model_path = output_path / "model-final"
                nlp.to_disk(final_model_path)
            msg.good("Saved model to output directory", final_model_path)
        # with msg.loading("Creating best model..."):
        #     best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
        # msg.good("Created best model", best_model_path)


def create_train_batches(nlp, corpus, cfg):
    while True:
        train_examples = corpus.train_dataset(
            nlp,
            noise_level=0.0,
            orth_variant_level=cfg["orth_variant_level"],
            gold_preproc=cfg["gold_preproc"],
            max_length=cfg["max_length"],
            ignore_misaligned=True,
        )
        for batch in util.minibatch_by_words(train_examples, size=cfg["batch_size"]):
            yield batch


def create_evaluation_callback(nlp, optimizer, corpus, cfg):
    def evaluate():
        with nlp.use_params(optimizer.averages):
            dev_examples = list(
                corpus.dev_dataset(
                    nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True
                )
            )
            scorer = nlp.evaluate(dev_examples)
            scores = scorer.scores
            # Calculate a weighted sum based on score_weights for the main score
            weights = cfg["score_weights"]
            weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
        return weighted_score, scorer.scores

    return evaluate


def train_while_improving(
    nlp, optimizer, train_data, evaluate, dropout, patience, eval_frequency
):
    """Train until an evaluation stops improving. Works as a generator,
    with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
    where info is a dict, and is_best_checkpoint is in [True, False, None] --
    None indicating that the iteration was not evaluated as a checkpoint.
    The evaluation is conducted by calling the evaluate callback, which should

    Positional arguments:
        nlp: The spaCy pipeline to evaluate.
        train_data (Iterable[Batch]): A generator of batches, with the training
            data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
            data iterable needs to take care of iterating over the epochs and
            shuffling.
        evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation.
            The callback should take no arguments and return a tuple
            `(main_score, other_scores)`. The main_score should be a float where
            higher is better. other_scores can be any object.

    Every iteration, the function yields out a tuple with:

    * batch: A zipped sequence of Tuple[Doc, GoldParse] pairs.
    * info: A dict with various information about the last update (see below).
    * is_best_checkpoint: A value in None, False, True, indicating whether this
        was the best evaluation so far. You should use this to save the model
        checkpoints during training. If None, evaluation was not conducted on
        that iteration. False means evaluation was conducted, but a previous
        evaluation was better.

    The info dict provides the following information:

        epoch (int): How many passes over the data have been completed.
        step (int): How many steps have been completed.
        score (float): The main score form the last evaluation.
        other_scores: : The other scores from the last evaluation.
        loss: The accumulated losses throughout training.
        checkpoints: A list of previous results, where each result is a
            (score, step, epoch) tuple.
    """
    if isinstance(dropout, float):
        dropouts = thinc.schedules.constant(dropout)
    else:
        dropouts = dropout
    results = []
    losses = {}
    for step, batch in enumerate(train_data):
        dropout = next(dropouts)
        for subbatch in subdivide_batch(batch):
            nlp.update(subbatch, drop=dropout, losses=losses, sgd=False)
        for name, proc in nlp.pipeline:
            if hasattr(proc, "model"):
                proc.model.finish_update(optimizer)
        optimizer.step_schedules()
        if not (step % eval_frequency):
            score, other_scores = evaluate()
            results.append((score, step))
            is_best_checkpoint = score == max(results)[0]
        else:
            score, other_scores = (None, None)
            is_best_checkpoint = None
        info = {
            "step": step,
            "score": score,
            "other_scores": other_scores,
            "losses": losses,
            "checkpoints": results,
        }
        yield batch, info, is_best_checkpoint
        if is_best_checkpoint is not None:
            losses = {}
        # Stop if no improvement in `patience` updates
        best_score, best_step = max(results)
        if (step - best_step) >= patience:
            break


def subdivide_batch(batch):
    return [batch]


def setup_printer(training, nlp):
    score_cols = training["scores"]
    score_widths = [max(len(col), 6) for col in score_cols]
    loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names]
    loss_widths = [max(len(col), 8) for col in loss_cols]
    table_header = ["#"] + loss_cols + score_cols + ["Score"]
    table_header = [col.upper() for col in table_header]
    table_widths = [6] + loss_widths + score_widths + [6]
    table_aligns = ["r" for _ in table_widths]

    msg.row(table_header, widths=table_widths)
    msg.row(["-" * width for width in table_widths])

    def print_row(info):
        losses = [
            "{0:.2f}".format(info["losses"].get(pipe_name, 0.0))
            for pipe_name in nlp.pipe_names
        ]
        scores = [
            "{0:.2f}".format(info["other_scores"].get(col, 0.0)) for col in score_cols
        ]
        data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
        msg.row(data, widths=table_widths, aligns=table_aligns)

    return print_row