"""
MALT-style dependency parser
"""
# coding: utf-8
# cython: infer_types=True
from __future__ import unicode_literals

from collections import Counter
import ujson

cimport cython
cimport cython.parallel

import numpy.random

from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals
from libc.stdint cimport uint32_t, uint64_t
from libc.string cimport memset, memcpy
from libc.stdlib cimport malloc, calloc, free
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
from thinc.extra.eg cimport Example
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get

from numpy import exp

from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
from ._state cimport StateC
from .nonproj import PseudoProjectivity
from .transition_system import OracleError
from .transition_system cimport TransitionSystem, Transition
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
from ..gold cimport GoldParse
from ..attrs cimport TAG, DEP

from .._ml import build_state2vec, build_model, precompute_hiddens


USE_FTRL = True
DEBUG = False
def set_debug(val):
    global DEBUG
    DEBUG = val


def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, feat_maps, upper_model):
    is_valid = model.ops.allocate((len(docs), system.n_moves), dtype='i')
    costs = model.ops.allocate((len(docs), system.n_moves), dtype='f')
    token_ids = model.ops.allocate((len(docs), StateClass.nr_context_tokens()),
                                    dtype='uint64')
    cached, backprops = zip(*[lyr.begin_update(tokvecs) for lyr in feat_maps)

    def forward(states, drop=0.):
        nonlocal is_valid, costs, token_ids, features
        is_valid = is_valid[:len(states)]
        costs = costs[:len(states)]
        token_ids = token_ids[:len(states)]
        is_valid = is_valid[:len(states)]
        for state in states:
            state.set_context_tokens(&token_ids[i])
            moves.set_valid(&is_valid[i], state.c)

        features = cached[token_ids].sum(axis=1)

        scores, bp_scores = upper_model.begin_update(features, drop=drop)
        softmaxed = model.ops.softmax(scores)
        # Renormalize for invalid actions
        softmaxed *= is_valid
        softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))

        def backward(golds, sgd=None):
            nonlocal costs_, is_valid_, moves_
            cdef TransitionSystem moves = moves_
            cdef int[:, :] is_valid
            cdef float[:, :] costs
            for i, (state, gold) in enumerate(zip(states, golds)):
                moves.set_costs(&costs[i], &is_valid[i],
                    state, gold)
            set_log_loss(model.ops, d_scores,
                scores, is_valid, costs)
            d_tokens = bp_scores(d_scores, sgd)
            return d_tokens

        return softmaxed, backward

    return layerize(forward)


def set_log_loss(ops, gradients, scores, is_valid, costs):
    """Do multi-label log loss"""
    n = gradients.shape[0]
    scores = scores * is_valid
    g_scores = scores * is_valid * (costs <= 0.)
    exps = ops.xp.exp(scores - scores.max(axis=1).reshape((n, 1)))
    exps *= is_valid
    g_exps = ops.xp.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
    g_exps *= costs <= 0.
    g_exps *= is_valid
    gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
    gradients -= g_exps / g_exps.sum(axis=1).reshape((n, 1))


def transition_batch(TransitionSystem moves, states, scores):
    cdef StateClass state
    cdef int guess
    for state, guess in zip(states, scores.argmax(axis=1)):
        action = moves.c[guess]
        action.do(state.c, action.label)


cdef class Parser:
    """
    Base class of the DependencyParser and EntityRecognizer.
    """
    @classmethod
    def load(cls, path, Vocab vocab, TransitionSystem=None, require=False, **cfg):
        """
        Load the statistical model from the supplied path.

        Arguments:
            path (Path):
                The path to load from.
            vocab (Vocab):
                The vocabulary. Must be shared by the documents to be processed.
            require (bool):
                Whether to raise an error if the files are not found.
        Returns (Parser):
            The newly constructed object.
        """
        with (path / 'config.json').open() as file_:
            cfg = ujson.load(file_)
        self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
        if (path / 'model').exists():
            self.model.load(str(path / 'model'))
        elif require:
            raise IOError(
                "Required file %s/model not found when loading" % str(path))
        return self

    def __init__(self, Vocab vocab, TransitionSystem=None, model=None, **cfg):
        """
        Create a Parser.

        Arguments:
            vocab (Vocab):
                The vocabulary object. Must be shared with documents to be processed.
            model (thinc Model):
                The statistical model.
        Returns (Parser):
            The newly constructed object.
        """
        if TransitionSystem is None:
            TransitionSystem = self.TransitionSystem
        self.vocab = vocab
        cfg['actions'] = TransitionSystem.get_actions(**cfg)
        self.moves = TransitionSystem(vocab.strings, cfg['actions'])
        if model is None:
            model = self.build_model(**cfg)
        self.model = model
        self.cfg = cfg

    def __reduce__(self):
        return (Parser, (self.vocab, self.moves, self.model), None, None)

    def build_model(self, width=32, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
        nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
        self.model = build_model(width*2, 2, self.moves.n_moves)
        self.feature_maps = build_feature_maps(nr_context_tokens, width, nr_vector))

    def __call__(self, Doc tokens):
        """
        Apply the parser or entity recognizer, setting the annotations onto the Doc object.

        Arguments:
            doc (Doc): The document to be processed.
        Returns:
            None
        """
        self.parse_batch([tokens])

    def pipe(self, stream, int batch_size=1000, int n_threads=2):
        """
        Process a stream of documents.

        Arguments:
            stream: The sequence of documents to process.
            batch_size (int):
                The number of documents to accumulate into a working set.
            n_threads (int):
                The number of threads with which to work on the buffer in parallel.
        Yields (Doc): Documents, in order.
        """
        cdef Pool mem = Pool()
        cdef int* lengths = <int*>mem.alloc(batch_size, sizeof(int))
        cdef Doc doc
        cdef int i
        cdef int nr_feat = self.model.nr_feat
        cdef int status
        queue = []
        for doc in stream:
            queue.append(doc)
            if len(queue) == batch_size:
                self.parse_batch(queue)
                for doc in queue:
                    self.moves.finalize_doc(doc)
                    yield doc
                queue = []
        if queue:
            self.parse_batch(queue)
            for doc in queue:
                self.moves.finalize_doc(doc)
                yield doc

    def parse_batch(self, docs):
        cdef Doc doc
        cdef StateClass state
        model = get_greedy_model_for_batch([d.tensor for d in docs],
                    self.moves, self.model, self.feat_maps)
        states = [StateClass.init(doc.c, doc.length) for doc in docs]
        todo = list(states)
        while todo:
            scores = model(todo)
            transition_batch(self.moves, todo, scores)
            todo = [st for st in states if not st.is_final()]
        for state, doc in zip(states, docs):
            self.moves.finalize_state(state.c)
            for i in range(doc.length):
                doc.c[i] = state.c._sent[i]
        for doc in docs:
            self.moves.finalize_parse(doc)

    def update(self, docs, golds, drop=0., sgd=None):
        if isinstance(docs, Doc) and isinstance(golds, GoldParse):
            return self.update([docs], [golds], drop=drop)
        for gold in golds:
            self.moves.preprocess_gold(gold)

        model = get_greedy_model_for_batch([d.tensor for d in docs],
                    self.moves, self.model, self.feat_maps)

        d_tokens = [self.model.ops.allocate(d.tensor.shape) for d in docs]
        output = list(d_tokens)
        todo = zip(states, golds, d_tokens)
        while todo:
            states, golds, d_tokens = zip(*todo)
            scores, finish_update = model.begin_update(token_ids)
            d_state_features = finish_update(golds, sgd=sgd)
            for i, token_ids in enumerate(token_ids):
                d_tokens[i][token_ids] += d_state_features[i]
            transition_batch(self.moves, states)
            # Get unfinished states (and their matching gold and token gradients)
            todo = filter(lambda sp: not sp[0].py_is_final(), todo)
        return output, sum(losses)

    def begin_training(self, docs, golds):
        for gold in golds:
            self.moves.preprocess_gold(gold)
        states = self._init_states(docs)
        tokvecs = [d.tensor for d in docs]

        features = self._get_features(states, tokvecs)
        self.model.begin_training(features)


    def step_through(self, Doc doc, GoldParse gold=None):
        """
        Set up a stepwise state, to introspect and control the transition sequence.

        Arguments:
            doc (Doc): The document to step through.
            gold (GoldParse): Optional gold parse
        Returns (StepwiseState):
            A state object, to step through the annotation process.
        """
        return StepwiseState(self, doc, gold=gold)

    def from_transition_sequence(self, Doc doc, sequence):
        """Control the annotations on a document by specifying a transition sequence
        to follow.

        Arguments:
            doc (Doc): The document to annotate.
            sequence: A sequence of action names, as unicode strings.
        Returns: None
        """
        with self.step_through(doc) as stepwise:
            for transition in sequence:
                stepwise.transition(transition)

    def add_label(self, label):
        # Doesn't set label into serializer -- subclasses override it to do that.
        for action in self.moves.action_types:
            added = self.moves.add_action(action, label)
            if added:
                # Important that the labels be stored as a list! We need the
                # order, or the model goes out of synch
                self.cfg.setdefault('extra_labels', []).append(label)


def _begin_update(self, model, states, tokvecs, drop=0.):
    nr_class = self.moves.n_moves
    attr_names = self.model.ops.allocate((2,), dtype='i')
    attr_names[0] = TAG
    attr_names[1] = DEP

    features = self._get_features(states, tokvecs, attr_names)
    scores, finish_update = self.model.begin_update(features, drop=drop)
    assert scores.shape[0] == len(states), (len(states), scores.shape)
    assert len(scores.shape) == 2
    is_valid = self.model.ops.allocate((len(states), nr_class), dtype='i')
    self._validate_batch(is_valid, states)
    softmaxed = self.model.ops.softmax(scores)
    softmaxed *= is_valid
    softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))
    def backward(golds, sgd=None, losses=[], force_gold=False):
        nonlocal softmaxed
        costs = self.model.ops.allocate((len(states), nr_class), dtype='f')
        d_scores = self.model.ops.allocate((len(states), nr_class), dtype='f')

        self._cost_batch(costs, is_valid, states, golds)
        self._set_gradient(d_scores, scores, is_valid, costs)
        losses.append(numpy.abs(d_scores).sum())
        if force_gold:
            softmaxed *= costs <= 0
        return finish_update(d_scores, sgd=sgd)
    return softmaxed, backward

def _init_states(self, docs):
    states = []
    cdef Doc doc
    cdef StateClass state
    for i, doc in enumerate(docs):
        state = StateClass.init(doc.c, doc.length)
        self.moves.initialize_state(state.c)
        states.append(state)
    return states

def _validate_batch(self, int[:, ::1] is_valid, states):
    cdef StateClass state
    cdef int i
    for i, state in enumerate(states):
        self.moves.set_valid(&is_valid[i, 0], state.c)

def _cost_batch(self, weight_t[:, ::1] costs, int[:, ::1] is_valid,
        states, golds):
    cdef int i
    cdef StateClass state
    cdef GoldParse gold
    for i, (state, gold) in enumerate(zip(states, golds)):
        self.moves.set_costs(&is_valid[i, 0], &costs[i, 0], state, gold)



def _get_features(self, states, all_tokvecs, attr_names,
        nF=1, nB=0, nS=2, nL=2, nR=2):
    n_tokens = states[0].nr_context_tokens(nF, nB, nS, nL, nR)
    vector_length = all_tokvecs[0].shape[1]
    tokens = self.model.ops.allocate((len(states), n_tokens), dtype='int32')
    features = self.model.ops.allocate((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
    tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
    for i, state in enumerate(states):
        state.set_context_tokens(tokens[i], nF, nB, nS, nL, nR)
        state.set_attributes(features[i], tokens[i], attr_names)
        state.set_token_vectors(tokvecs[i], all_tokvecs[i], tokens[i])
    return (tokens, features, tokvecs)



cdef int dropout(FeatureC* feats, int nr_feat, float prob) except -1:
    if prob <= 0 or prob >= 1.:
        return 0
    cdef double[::1] py_probs = numpy.random.uniform(0., 1., nr_feat)
    cdef double* probs = &py_probs[0]
    for i in range(nr_feat):
        if probs[i] >= prob:
            feats[i].value /= prob
        else:
            feats[i].value = 0.


cdef class StepwiseState:
    cdef readonly StateClass stcls
    cdef readonly Example eg
    cdef readonly Doc doc
    cdef readonly GoldParse gold
    cdef readonly Parser parser

    def __init__(self, Parser parser, Doc doc, GoldParse gold=None):
        self.parser = parser
        self.doc = doc
        if gold is not None:
            self.gold = gold
            self.parser.moves.preprocess_gold(self.gold)
        else:
            self.gold = GoldParse(doc)
        self.stcls = StateClass.init(doc.c, doc.length)
        self.parser.moves.initialize_state(self.stcls.c)
        self.eg = Example(
            nr_class=self.parser.moves.n_moves,
            nr_atom=CONTEXT_SIZE,
            nr_feat=self.parser.model.nr_feat)

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):
        self.finish()

    @property
    def is_final(self):
        return self.stcls.is_final()

    @property
    def stack(self):
        return self.stcls.stack

    @property
    def queue(self):
        return self.stcls.queue

    @property
    def heads(self):
        return [self.stcls.H(i) for i in range(self.stcls.c.length)]

    @property
    def deps(self):
        return [self.doc.vocab.strings[self.stcls.c._sent[i].dep]
                for i in range(self.stcls.c.length)]

    @property
    def costs(self):
        """
        Find the action-costs for the current state.
        """
        if not self.gold:
            raise ValueError("Can't set costs: No GoldParse provided")
        self.parser.moves.set_costs(self.eg.c.is_valid, self.eg.c.costs,
                self.stcls, self.gold)
        costs = {}
        for i in range(self.parser.moves.n_moves):
            if not self.eg.c.is_valid[i]:
                continue
            transition = self.parser.moves.c[i]
            name = self.parser.moves.move_name(transition.move, transition.label)
            costs[name] = self.eg.c.costs[i]
        return costs

    def predict(self):
        self.eg.reset()
        #self.eg.c.nr_feat = self.parser.model.set_featuresC(self.eg.c.atoms, self.eg.c.features,
        #                                                    self.stcls.c)
        self.parser.moves.set_valid(self.eg.c.is_valid, self.stcls.c)
        #self.parser.model.set_scoresC(self.eg.c.scores,
        #    self.eg.c.features, self.eg.c.nr_feat)

        cdef Transition action = self.parser.moves.c[self.eg.guess]
        return self.parser.moves.move_name(action.move, action.label)

    def transition(self, action_name=None):
        if action_name is None:
            action_name = self.predict()
        moves = {'S': 0, 'D': 1, 'L': 2, 'R': 3}
        if action_name == '_':
            action_name = self.predict()
            action = self.parser.moves.lookup_transition(action_name)
        elif action_name == 'L' or action_name == 'R':
            self.predict()
            move = moves[action_name]
            clas = _arg_max_clas(self.eg.c.scores, move, self.parser.moves.c,
                                 self.eg.c.nr_class)
            action = self.parser.moves.c[clas]
        else:
            action = self.parser.moves.lookup_transition(action_name)
        action.do(self.stcls.c, action.label)

    def finish(self):
        if self.stcls.is_final():
            self.parser.moves.finalize_state(self.stcls.c)
        self.doc.set_parse(self.stcls.c._sent)
        self.parser.moves.finalize_doc(self.doc)


class ParserStateError(ValueError):
    def __init__(self, doc):
        ValueError.__init__(self,
            "Error analysing doc -- no valid actions available. This should "
            "never happen, so please report the error on the issue tracker. "
            "Here's the thread to do so --- reopen it if it's closed:\n"
            "https://github.com/spacy-io/spaCy/issues/429\n"
            "Please include the text that the parser failed on, which is:\n"
            "%s" % repr(doc.text))

cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, int n) nogil:
    cdef int best = -1
    for i in range(n):
        if costs[i] <= 0:
            if best == -1 or scores[i] > scores[best]:
                best = i
    return best


cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
                       int nr_class) except -1:
    cdef weight_t score = 0
    cdef int mode = -1
    cdef int i
    for i in range(nr_class):
        if actions[i].move == move and (mode == -1 or scores[i] >= score):
            mode = i
            score = scores[i]
    return mode