from typing import Optional, Iterable, Callable, Dict, Union, List
from pathlib import Path
from itertools import islice
import srsly
import random
from thinc.api import CosineDistance, Model, Optimizer, Config
from thinc.api import set_dropout_rate
import warnings

from ..kb import KnowledgeBase, Candidate
from ..ml import empty_kb
from ..tokens import Doc
from .pipe import deserialize_config
from .trainable_pipe import TrainablePipe
from ..language import Language
from ..vocab import Vocab
from ..training import Example, validate_examples, validate_get_examples
from ..errors import Errors, Warnings
from ..util import SimpleFrozenList
from .. import util
from ..scorer import Scorer


default_model_config = """
[model]
@architectures = "spacy.EntityLinker.v1"

[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 2
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""
DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]


@Language.factory(
    "entity_linker",
    requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
    assigns=["token.ent_kb_id"],
    default_config={
        "model": DEFAULT_NEL_MODEL,
        "labels_discard": [],
        "n_sents": 0,
        "incl_prior": True,
        "incl_context": True,
        "entity_vector_length": 64,
        "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
    },
    default_score_weights={
        "nel_micro_f": 1.0,
        "nel_micro_r": None,
        "nel_micro_p": None,
    },
)
def make_entity_linker(
    nlp: Language,
    name: str,
    model: Model,
    *,
    labels_discard: Iterable[str],
    n_sents: int,
    incl_prior: bool,
    incl_context: bool,
    entity_vector_length: int,
    get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
):
    """Construct an EntityLinker component.

    model (Model[List[Doc], Floats2d]): A model that learns document vector
        representations. Given a batch of Doc objects, it should return a single
        array, with one row per item in the batch.
    labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
    n_sents (int): The number of neighbouring sentences to take into account.
    incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
    incl_context (bool): Whether or not to include the local context in the model.
    entity_vector_length (int): Size of encoding vectors in the KB.
    get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
        produces a list of candidates, given a certain knowledge base and a textual mention.
    """
    return EntityLinker(
        nlp.vocab,
        model,
        name,
        labels_discard=labels_discard,
        n_sents=n_sents,
        incl_prior=incl_prior,
        incl_context=incl_context,
        entity_vector_length=entity_vector_length,
        get_candidates=get_candidates,
    )


class EntityLinker(TrainablePipe):
    """Pipeline component for named entity linking.

    DOCS: https://spacy.io/api/entitylinker
    """

    NIL = "NIL"  # string used to refer to a non-existing link

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "entity_linker",
        *,
        labels_discard: Iterable[str],
        n_sents: int,
        incl_prior: bool,
        incl_context: bool,
        entity_vector_length: int,
        get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
    ) -> None:
        """Initialize an entity linker.

        vocab (Vocab): The shared vocabulary.
        model (thinc.api.Model): The Thinc Model powering the pipeline component.
        name (str): The component instance name, used to add entries to the
            losses during training.
        labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
        n_sents (int): The number of neighbouring sentences to take into account.
        incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
        incl_context (bool): Whether or not to include the local context in the model.
        entity_vector_length (int): Size of encoding vectors in the KB.
        get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
            produces a list of candidates, given a certain knowledge base and a textual mention.

        DOCS: https://spacy.io/api/entitylinker#init
        """
        self.vocab = vocab
        self.model = model
        self.name = name
        self.labels_discard = list(labels_discard)
        self.n_sents = n_sents
        self.incl_prior = incl_prior
        self.incl_context = incl_context
        self.get_candidates = get_candidates
        self.cfg = {}
        self.distance = CosineDistance(normalize=False)
        # how many neightbour sentences to take into account
        # create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
        self.kb = empty_kb(entity_vector_length)(self.vocab)

    def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
        """Define the KB of this pipe by providing a function that will
        create it using this object's vocab."""
        if not callable(kb_loader):
            raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))

        self.kb = kb_loader(self.vocab)

    def validate_kb(self) -> None:
        # Raise an error if the knowledge base is not initialized.
        if len(self.kb) == 0:
            raise ValueError(Errors.E139.format(name=self.name))

    def initialize(
        self,
        get_examples: Callable[[], Iterable[Example]],
        *,
        nlp: Optional[Language] = None,
        kb_loader: Callable[[Vocab], KnowledgeBase] = None,
    ):
        """Initialize the pipe for training, using a representative set
        of data examples.

        get_examples (Callable[[], Iterable[Example]]): Function that
            returns a representative sample of gold-standard Example objects.
        nlp (Language): The current nlp object the component is part of.
        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
            Note that providing this argument, will overwrite all data accumulated in the current KB.
            Use this only when loading a KB as-such from file.

        DOCS: https://spacy.io/api/entitylinker#initialize
        """
        validate_get_examples(get_examples, "EntityLinker.initialize")
        if kb_loader is not None:
            self.set_kb(kb_loader)
        self.validate_kb()
        nO = self.kb.entity_vector_length
        doc_sample = []
        vector_sample = []
        for example in islice(get_examples(), 10):
            doc_sample.append(example.x)
            vector_sample.append(self.model.ops.alloc1f(nO))
        assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
        assert len(vector_sample) > 0, Errors.E923.format(name=self.name)
        self.model.initialize(
            X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
        )

    def update(
        self,
        examples: Iterable[Example],
        *,
        drop: float = 0.0,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> Dict[str, float]:
        """Learn from a batch of documents and gold-standard information,
        updating the pipe's model. Delegates to predict and get_loss.

        examples (Iterable[Example]): A batch of Example objects.
        drop (float): The dropout rate.
        sgd (thinc.api.Optimizer): The optimizer.
        losses (Dict[str, float]): Optional record of the loss during training.
            Updated using the component name as the key.
        RETURNS (Dict[str, float]): The updated losses dictionary.

        DOCS: https://spacy.io/api/entitylinker#update
        """
        self.validate_kb()
        if losses is None:
            losses = {}
        losses.setdefault(self.name, 0.0)
        if not examples:
            return losses
        validate_examples(examples, "EntityLinker.update")
        sentence_docs = []
        for eg in examples:
            sentences = [s for s in eg.reference.sents]
            kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
            for ent in eg.reference.ents:
                # KB ID of the first token is the same as the whole span
                kb_id = kb_ids[ent.start]
                if kb_id:
                    try:
                        # find the sentence in the list of sentences.
                        sent_index = sentences.index(ent.sent)
                    except AttributeError:
                        # Catch the exception when ent.sent is None and provide a user-friendly warning
                        raise RuntimeError(Errors.E030) from None
                    # get n previous sentences, if there are any
                    start_sentence = max(0, sent_index - self.n_sents)
                    # get n posterior sentences, or as many < n as there are
                    end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
                    # get token positions
                    start_token = sentences[start_sentence].start
                    end_token = sentences[end_sentence].end
                    # append that span as a doc to training
                    sent_doc = eg.predicted[start_token:end_token].as_doc()
                    sentence_docs.append(sent_doc)
        set_dropout_rate(self.model, drop)
        if not sentence_docs:
            warnings.warn(Warnings.W093.format(name="Entity Linker"))
            return losses
        sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
        loss, d_scores = self.get_loss(
            sentence_encodings=sentence_encodings, examples=examples
        )
        bp_context(d_scores)
        if sgd is not None:
            self.finish_update(sgd)
        losses[self.name] += loss
        return losses

    def get_loss(self, examples: Iterable[Example], sentence_encodings):
        validate_examples(examples, "EntityLinker.get_loss")
        entity_encodings = []
        for eg in examples:
            kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
            for ent in eg.reference.ents:
                kb_id = kb_ids[ent.start]
                if kb_id:
                    entity_encoding = self.kb.get_vector(kb_id)
                    entity_encodings.append(entity_encoding)
        entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
        if sentence_encodings.shape != entity_encodings.shape:
            err = Errors.E147.format(
                method="get_loss", msg="gold entities do not match up"
            )
            raise RuntimeError(err)
        gradients = self.distance.get_grad(sentence_encodings, entity_encodings)
        loss = self.distance.get_loss(sentence_encodings, entity_encodings)
        loss = loss / len(entity_encodings)
        return float(loss), gradients

    def predict(self, docs: Iterable[Doc]) -> List[str]:
        """Apply the pipeline's model to a batch of docs, without modifying them.
        Returns the KB IDs for each entity in each doc, including NIL if there is
        no prediction.

        docs (Iterable[Doc]): The documents to predict.
        RETURNS (List[int]): The models prediction for each document.

        DOCS: https://spacy.io/api/entitylinker#predict
        """
        self.validate_kb()
        entity_count = 0
        final_kb_ids = []
        if not docs:
            return final_kb_ids
        if isinstance(docs, Doc):
            docs = [docs]
        for i, doc in enumerate(docs):
            sentences = [s for s in doc.sents]
            if len(doc) > 0:
                # Looping through each sentence and each entity
                # This may go wrong if there are entities across sentences - which shouldn't happen normally.
                for sent_index, sent in enumerate(sentences):
                    if sent.ents:
                        # get n_neightbour sentences, clipped to the length of the document
                        start_sentence = max(0, sent_index - self.n_sents)
                        end_sentence = min(
                            len(sentences) - 1, sent_index + self.n_sents
                        )
                        start_token = sentences[start_sentence].start
                        end_token = sentences[end_sentence].end
                        sent_doc = doc[start_token:end_token].as_doc()
                        # currently, the context is the same for each entity in a sentence (should be refined)
                        xp = self.model.ops.xp
                        if self.incl_context:
                            sentence_encoding = self.model.predict([sent_doc])[0]
                            sentence_encoding_t = sentence_encoding.T
                            sentence_norm = xp.linalg.norm(sentence_encoding_t)
                        for ent in sent.ents:
                            entity_count += 1
                            if ent.label_ in self.labels_discard:
                                # ignoring this entity - setting to NIL
                                final_kb_ids.append(self.NIL)
                            else:
                                candidates = self.get_candidates(self.kb, ent)
                                if not candidates:
                                    # no prediction possible for this entity - setting to NIL
                                    final_kb_ids.append(self.NIL)
                                elif len(candidates) == 1:
                                    # shortcut for efficiency reasons: take the 1 candidate
                                    # TODO: thresholding
                                    final_kb_ids.append(candidates[0].entity_)
                                else:
                                    random.shuffle(candidates)
                                    # set all prior probabilities to 0 if incl_prior=False
                                    prior_probs = xp.asarray(
                                        [c.prior_prob for c in candidates]
                                    )
                                    if not self.incl_prior:
                                        prior_probs = xp.asarray(
                                            [0.0 for _ in candidates]
                                        )
                                    scores = prior_probs
                                    # add in similarity from the context
                                    if self.incl_context:
                                        entity_encodings = xp.asarray(
                                            [c.entity_vector for c in candidates]
                                        )
                                        entity_norm = xp.linalg.norm(
                                            entity_encodings, axis=1
                                        )
                                        if len(entity_encodings) != len(prior_probs):
                                            raise RuntimeError(
                                                Errors.E147.format(
                                                    method="predict",
                                                    msg="vectors not of equal length",
                                                )
                                            )
                                        # cosine similarity
                                        sims = xp.dot(
                                            entity_encodings, sentence_encoding_t
                                        ) / (sentence_norm * entity_norm)
                                        if sims.shape != prior_probs.shape:
                                            raise ValueError(Errors.E161)
                                        scores = (
                                            prior_probs + sims - (prior_probs * sims)
                                        )
                                    # TODO: thresholding
                                    best_index = scores.argmax().item()
                                    best_candidate = candidates[best_index]
                                    final_kb_ids.append(best_candidate.entity_)
        if not (len(final_kb_ids) == entity_count):
            err = Errors.E147.format(
                method="predict", msg="result variables not of equal length"
            )
            raise RuntimeError(err)
        return final_kb_ids

    def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
        """Modify a batch of documents, using pre-computed scores.

        docs (Iterable[Doc]): The documents to modify.
        kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.

        DOCS: https://spacy.io/api/entitylinker#set_annotations
        """
        count_ents = len([ent for doc in docs for ent in doc.ents])
        if count_ents != len(kb_ids):
            raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
        i = 0
        for doc in docs:
            for ent in doc.ents:
                kb_id = kb_ids[i]
                i += 1
                for token in ent:
                    token.ent_kb_id_ = kb_id

    def score(self, examples, **kwargs):
        """Score a batch of examples.

        examples (Iterable[Example]): The examples to score.
        RETURNS (Dict[str, Any]): The scores.

        DOCS TODO: https://spacy.io/api/entity_linker#score
        """
        validate_examples(examples, "EntityLinker.score")
        return Scorer.score_links(examples, negative_labels=[self.NIL])

    def to_disk(
        self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
    ) -> None:
        """Serialize the pipe to disk.

        path (str / Path): Path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.

        DOCS: https://spacy.io/api/entitylinker#to_disk
        """
        serialize = {}
        serialize["vocab"] = lambda p: self.vocab.to_disk(p)
        serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
        serialize["kb"] = lambda p: self.kb.to_disk(p)
        serialize["model"] = lambda p: self.model.to_disk(p)
        util.to_disk(path, serialize, exclude)

    def from_disk(
        self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
    ) -> "EntityLinker":
        """Load the pipe from disk. Modifies the object in place and returns it.

        path (str / Path): Path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.
        RETURNS (EntityLinker): The modified EntityLinker object.

        DOCS: https://spacy.io/api/entitylinker#from_disk
        """

        def load_model(p):
            try:
                self.model.from_bytes(p.open("rb").read())
            except AttributeError:
                raise ValueError(Errors.E149) from None

        deserialize = {}
        deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
        deserialize["kb"] = lambda p: self.kb.from_disk(p)
        deserialize["model"] = load_model
        util.from_disk(path, deserialize, exclude)
        return self

    def rehearse(self, examples, *, sgd=None, losses=None, **config):
        raise NotImplementedError

    def add_label(self, label):
        raise NotImplementedError
