from thinc.api import Model, set_dropout_rate

from .pipes import Pipe
from ..gold import Example
from ..tokens import Doc
from ..vocab import Vocab
from ..language import component
from ..util import link_vectors_to_models, minibatch, eg2doc


@component("tok2vec", assigns=["doc.tensor"])
class Tok2Vec(Pipe):
    @classmethod
    def from_nlp(cls, nlp, model, **cfg):
        return cls(nlp.vocab, model, **cfg)

    def __init__(self, vocab, model, **cfg):
        """Construct a new statistical model. Weights are not allocated on
        initialisation.
        vocab (Vocab): A `Vocab` instance. The model must share the same `Vocab`
            instance with the `Doc` objects it will process.
        **cfg: Config parameters.
        """
        self.vocab = vocab
        self.model = model
        self.cfg = dict(cfg)
        self.listeners = []

    def create_listener(self):
        listener = Tok2VecListener(
            upstream_name="tok2vec", width=self.model.get_dim("nO")
        )
        self.listeners.append(listener)

    def add_listener(self, listener):
        self.listeners.append(listener)

    def find_listeners(self, model):
        for node in model.walk():
            if isinstance(node, Tok2VecListener) and node.upstream_name == self.name:
                self.add_listener(node)

    def __call__(self, doc):
        """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
        model. Vectors are set to the `Doc.tensor` attribute.
        docs (Doc or iterable): One or more documents to add vectors to.
        RETURNS (dict or None): Intermediate computations.
        """
        tokvecses = self.predict([doc])
        self.set_annotations([doc], tokvecses)
        return doc

    def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
        """Process `Doc` objects as a stream.
        stream (iterator): A sequence of `Doc` objects to process.
        batch_size (int): Number of `Doc` objects to group.
        n_threads (int): Number of threads.
        YIELDS (iterator): A sequence of `Doc` objects, in order of input.
        """
        for batch in minibatch(stream, batch_size):
            batch = list(batch)
            if as_example:
                docs = [eg2doc(doc) for doc in batch]
            else:
                docs = batch
            tokvecses = self.predict(docs)
            self.set_annotations(docs, tokvecses)
            yield from batch

    def predict(self, docs):
        """Return a single tensor for a batch of documents.
        docs (iterable): A sequence of `Doc` objects.
        RETURNS (object): Vector representations for each token in the documents.
        """
        tokvecs = self.model.predict(docs)
        batch_id = Tok2VecListener.get_batch_id(docs)
        for listener in self.listeners:
            listener.receive(batch_id, tokvecs, None)
        return tokvecs

    def set_annotations(self, docs, tokvecses):
        """Set the tensor attribute for a batch of documents.
        docs (iterable): A sequence of `Doc` objects.
        tokvecs (object): Vector representation for each token in the documents.
        """
        for doc, tokvecs in zip(docs, tokvecses):
            assert tokvecs.shape[0] == len(doc)
            doc.tensor = tokvecs

    def update(self, examples, drop=0.0, sgd=None, losses=None, set_annotations=False):
        """Update the model.
        examples (iterable): A batch of examples
        drop (float): The droput rate.
        sgd (callable): An optimizer.
        RETURNS (dict): Results from the update.
        """
        if losses is None:
            losses = {}
        examples = Example.to_example_objects(examples)
        docs = [eg.doc for eg in examples]
        if isinstance(docs, Doc):
            docs = [docs]
        set_dropout_rate(self.model, drop)
        tokvecs, bp_tokvecs = self.model.begin_update(docs)

        def capture_losses(d_tokvecs):
            """Accumulate tok2vec loss before doing backprop."""
            l2_loss = sum((d_t2v ** 2).sum() for d_t2v in d_tokvecs)
            if self.name in losses:
                losses[self.name] += l2_loss / len(d_tokvecs)
            else:
                losses[self.name] = l2_loss / len(d_tokvecs)
            return bp_tokvecs(d_tokvecs)

        batch_id = Tok2VecListener.get_batch_id(docs)
        for listener in self.listeners:
            listener.receive(batch_id, tokvecs, capture_losses)
        if sgd is not None:
            self.model.finish_update(sgd)
        if set_annotations:
            self.set_annotations(docs, tokvecs)

    def get_loss(self, docs, golds, scores):
        pass

    def begin_training(
        self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs
    ):
        """Allocate models and pre-process training data

        get_examples (function): Function returning example training data.
        pipeline (list): The pipeline the model is part of.
        """
        # TODO: charembed does not play nicely with dim inference yet
        # docs = [Doc(Vocab(), words=["hello"])]
        # self.model.initialize(X=docs)
        self.model.initialize()
        link_vectors_to_models(self.vocab)


class Tok2VecListener(Model):
    """A layer that gets fed its answers from an upstream connection,
    for instance from a component earlier in the pipeline.
    """

    name = "tok2vec-listener"

    def __init__(self, upstream_name, width):
        Model.__init__(self, name=self.name, forward=forward, dims={"nO": width})
        self.upstream_name = upstream_name
        self._batch_id = None
        self._outputs = None
        self._backprop = None

    @classmethod
    def get_batch_id(cls, inputs):
        return sum(sum(token.orth for token in doc) for doc in inputs)

    def receive(self, batch_id, outputs, backprop):
        self._batch_id = batch_id
        self._outputs = outputs
        self._backprop = backprop

    def verify_inputs(self, inputs):
        if self._batch_id is None and self._outputs is None:
            raise ValueError
        else:
            batch_id = self.get_batch_id(inputs)
            if batch_id != self._batch_id:
                raise ValueError(f"Mismatched IDs! {batch_id} vs {self._batch_id}")
            else:
                return True


def forward(model: Tok2VecListener, inputs, is_train):
    if is_train:
        model.verify_inputs(inputs)
        return model._outputs, model._backprop
    else:
        return [doc.tensor for doc in inputs], lambda dX: []