from collections.abc import Iterable as IterableInstance
import warnings
import numpy
from murmurhash.mrmr cimport hash64

from ..tokens.doc cimport Doc
from ..tokens.span cimport Span
from ..tokens.span import Span
from ..attrs import IDS
from .alignment import Alignment
from .iob_utils import biluo_to_iob, offsets_to_biluo_tags, doc_to_biluo_tags
from .iob_utils import biluo_tags_to_spans, remove_bilu_prefix
from ..errors import Errors, Warnings
from ..pipeline._parser_internals import nonproj
from ..tokens.token cimport MISSING_DEP
from ..util import logger, to_ternary_int, all_equal


cpdef Doc annotations_to_doc(vocab, tok_annot, doc_annot):
    """ Create a Doc from dictionaries with token and doc annotations. """
    attrs, array = _annot2array(vocab, tok_annot, doc_annot)
    output = Doc(vocab, words=tok_annot["ORTH"], spaces=tok_annot["SPACY"])
    if "entities" in doc_annot:
       _add_entities_to_doc(output, doc_annot["entities"])
    if "spans" in doc_annot:
       _add_spans_to_doc(output, doc_annot["spans"])
    if array.size:
        output = output.from_array(attrs, array)
    # links are currently added with ENT_KB_ID on the token level
    output.cats.update(doc_annot.get("cats", {}))
    return output


def validate_examples(examples, method):
    """Check that a batch of examples received during processing is valid.
    This function lives here to prevent circular imports.

    examples (Iterable[Examples]): A batch of examples.
    method (str): The method name to show in error messages.
    """
    if not isinstance(examples, IterableInstance):
        err = Errors.E978.format(name=method, types=type(examples))
        raise TypeError(err)
    wrong = set([type(eg) for eg in examples if not isinstance(eg, Example)])
    if wrong:
        err = Errors.E978.format(name=method, types=wrong)
        raise TypeError(err)


def validate_get_examples(get_examples, method):
    """Check that a generator of a batch of examples received during processing is valid:
    the callable produces a non-empty list of Example objects.
    This function lives here to prevent circular imports.

    get_examples (Callable[[], Iterable[Example]]): A function that produces a batch of examples.
    method (str): The method name to show in error messages.
    """
    if get_examples is None or not hasattr(get_examples, "__call__"):
        err = Errors.E930.format(method=method, obj=type(get_examples))
        raise TypeError(err)
    examples = get_examples()
    if not examples:
        err = Errors.E930.format(method=method, obj=examples)
        raise TypeError(err)
    validate_examples(examples, method)


cdef class Example:
    def __init__(self, Doc predicted, Doc reference, *, alignment=None):
        if predicted is None:
            raise TypeError(Errors.E972.format(arg="predicted"))
        if reference is None:
            raise TypeError(Errors.E972.format(arg="reference"))
        self.predicted = predicted
        self.reference = reference
        self._cached_alignment = alignment

    def __len__(self):
        return len(self.predicted)

    property predicted:
        def __get__(self):
            return self.x

        def __set__(self, doc):
            self.x = doc
            self._cached_alignment = None
            self._cached_words_x = [t.text for t in doc]

    property reference:
        def __get__(self):
            return self.y

        def __set__(self, doc):
            self.y = doc
            self._cached_alignment = None
            self._cached_words_y = [t.text for t in doc]

    def copy(self):
        return Example(
            self.x.copy(),
            self.y.copy()
        )

    @classmethod
    def from_dict(cls, Doc predicted, dict example_dict):
        if predicted is None:
            raise ValueError(Errors.E976.format(n="first", type="Doc"))
        if example_dict is None:
            raise ValueError(Errors.E976.format(n="second", type="dict"))
        example_dict = _fix_legacy_dict_data(example_dict)
        tok_dict, doc_dict = _parse_example_dict_data(example_dict)
        if "ORTH" not in tok_dict:
            tok_dict["ORTH"] = [tok.text for tok in predicted]
            tok_dict["SPACY"] = [tok.whitespace_ for tok in predicted]
        return Example(
            predicted,
            annotations_to_doc(predicted.vocab, tok_dict, doc_dict)
        )

    @property
    def alignment(self):
        x_sig = hash64(self.x.c, sizeof(self.x.c[0]) * self.x.length, 0)
        y_sig = hash64(self.y.c, sizeof(self.y.c[0]) * self.y.length, 0)
        if self._cached_alignment is None:
            words_x = [token.text for token in self.x]
            words_y = [token.text for token in self.y]
            self._x_sig = x_sig
            self._y_sig = y_sig
            self._cached_words_x = words_x
            self._cached_words_y = words_y
            self._cached_alignment = Alignment.from_strings(words_x, words_y)
            return self._cached_alignment
        elif self._x_sig == x_sig and self._y_sig == y_sig:
            # If we have a cached alignment, check whether the cache is invalid
            # due to retokenization. To make this check fast in loops, we first
            # check a hash of the TokenC arrays.
            return self._cached_alignment
        else:
            words_x = [token.text for token in self.x]
            words_y = [token.text for token in self.y]
            if words_x == self._cached_words_x and words_y == self._cached_words_y:
                self._x_sig = x_sig
                self._y_sig = y_sig
                return self._cached_alignment
            else:
                self._cached_alignment = Alignment.from_strings(words_x, words_y)
                self._cached_words_x = words_x
                self._cached_words_y = words_y
                self._x_sig = x_sig
                self._y_sig = y_sig
                return self._cached_alignment


    def _get_aligned_vectorized(self, align, gold_values):
        # Fast path for Doc attributes/fields that are predominantly a single value,
        # i.e., TAG, POS, MORPH.
        x2y_single_toks = []
        x2y_single_toks_i = []

        x2y_multiple_toks = []
        x2y_multiple_toks_i = []

        # Gather indices of gold tokens aligned to the candidate tokens into two buckets.
        #   Bucket 1: All tokens that have a one-to-one alignment.
        #   Bucket 2: All tokens that have a one-to-many alignment.
        for idx, token in enumerate(self.predicted):
            aligned_gold_i = align[token.i]
            aligned_gold_len = len(aligned_gold_i)

            if aligned_gold_len == 1:
                x2y_single_toks.append(aligned_gold_i.item())
                x2y_single_toks_i.append(idx)
            elif aligned_gold_len > 1:
                x2y_multiple_toks.append(aligned_gold_i)
                x2y_multiple_toks_i.append(idx)

        # Map elements of the first bucket directly to the output array.
        output = numpy.full(len(self.predicted), None)
        output[x2y_single_toks_i] = gold_values[x2y_single_toks].squeeze()

        # Collapse many-to-one alignments into one-to-one alignments if they
        # share the same value. Map to None in all other cases.
        for i in range(len(x2y_multiple_toks)):
            aligned_gold_values = gold_values[x2y_multiple_toks[i]]

            # If all aligned tokens have the same value, use it.
            if all_equal(aligned_gold_values):
                x2y_multiple_toks[i] = aligned_gold_values[0].item()
            else:
                x2y_multiple_toks[i] = None

        output[x2y_multiple_toks_i] = x2y_multiple_toks

        return output.tolist()


    def _get_aligned_non_vectorized(self, align, gold_values):
        # Slower path for fields that return multiple values (resulting
        # in ragged arrays that cannot be vectorized trivially).
        output = [None] * len(self.predicted)

        for token in self.predicted:
            aligned_gold_i = align[token.i]
            values = gold_values[aligned_gold_i].ravel()
            if len(values) == 1:
                output[token.i] = values.item()
            elif all_equal(values):
                # If all aligned tokens have the same value, use it.
                output[token.i] = values[0].item()

        return output


    def get_aligned(self, field, as_string=False):
        """Return an aligned array for a token attribute."""
        align = self.alignment.x2y
        gold_values = self.reference.to_array([field])

        if len(gold_values.shape) == 1:
            output = self._get_aligned_vectorized(align, gold_values)
        else:
            output = self._get_aligned_non_vectorized(align, gold_values)

        vocab = self.reference.vocab
        if as_string and field not in ["ENT_IOB", "SENT_START"]:
            output = [vocab.strings[o] if o is not None else o for o in output]

        return output

    def get_aligned_parse(self, projectivize=True):
        cand_to_gold = self.alignment.x2y
        gold_to_cand = self.alignment.y2x
        heads = [token.head.i for token in self.y]
        deps = [token.dep_ for token in self.y]

        if projectivize:
            proj_heads, proj_deps = nonproj.projectivize(heads, deps)
            has_deps = [token.has_dep() for token in self.y]
            has_heads = [token.has_head() for token in self.y]

            # ensure that missing data remains missing
            heads = [h if has_heads[i] else heads[i] for i, h in enumerate(proj_heads)]
            deps = [d if has_deps[i] else deps[i] for i, d in enumerate(proj_deps)]

        # Select all candidate tokens that are aligned to a single gold token.
        c2g_single_toks = numpy.where(cand_to_gold.lengths == 1)[0]

        # Fetch all aligned gold token incides.
        if c2g_single_toks.shape == cand_to_gold.lengths.shape:
            # This the most likely case.
            gold_i = cand_to_gold[:]
        else:
            gold_i = numpy.vectorize(lambda x: cand_to_gold[int(x)][0], otypes='i')(c2g_single_toks)

        # Fetch indices of all gold heads for the aligned gold tokens.
        heads = numpy.asarray(heads, dtype='i')
        gold_head_i = heads[gold_i]

        # Select all gold tokens that are heads of the previously selected 
        # gold tokens (and are aligned to a single candidate token).
        g2c_len_heads = gold_to_cand.lengths[gold_head_i]
        g2c_len_heads = numpy.where(g2c_len_heads == 1)[0]
        g2c_i = numpy.vectorize(lambda x: gold_to_cand[int(x)][0], otypes='i')(gold_head_i[g2c_len_heads]).squeeze()

        # Update head/dep alignments with the above.
        aligned_heads = numpy.full((self.x.length), None)
        aligned_heads[c2g_single_toks[g2c_len_heads]] = g2c_i

        deps = numpy.asarray(deps)
        aligned_deps = numpy.full((self.x.length), None)
        aligned_deps[c2g_single_toks] = deps[gold_i]

        return aligned_heads.tolist(), aligned_deps.tolist()

    def get_aligned_sent_starts(self):
        """Get list of SENT_START attributes aligned to the predicted tokenization.
        If the reference does not have sentence starts, return a list of None values.
        """
        if self.y.has_annotation("SENT_START"):
            align = self.alignment.y2x
            sent_starts = [False] * len(self.x)
            for y_sent in self.y.sents:
                x_start = int(align[y_sent.start][0])
                sent_starts[x_start] = True
            return sent_starts
        else:
            return [None] * len(self.x)

    def get_aligned_spans_x2y(self, x_spans, allow_overlap=False):
        return self._get_aligned_spans(self.y, x_spans, self.alignment.x2y, allow_overlap)

    def get_aligned_spans_y2x(self, y_spans, allow_overlap=False):
        return self._get_aligned_spans(self.x, y_spans, self.alignment.y2x, allow_overlap)

    def _get_aligned_spans(self, doc, spans, align, allow_overlap):
        seen = set()
        output = []
        for span in spans:
            indices = align[span.start : span.end]
            if not allow_overlap:
                indices = [idx for idx in indices if idx not in seen]
            if len(indices) >= 1:
                aligned_span = Span(doc, indices[0], indices[-1] + 1, label=span.label)
                target_text = span.text.lower().strip().replace(" ", "")
                our_text = aligned_span.text.lower().strip().replace(" ", "")
                if our_text == target_text:
                    output.append(aligned_span)
                    seen.update(indices)
        return output

    def get_aligned_ents_and_ner(self):
        if not self.y.has_annotation("ENT_IOB"):
            return [], [None] * len(self.x)
        x_ents = self.get_aligned_spans_y2x(self.y.ents, allow_overlap=False)
        # Default to 'None' for missing values
        x_tags = offsets_to_biluo_tags(
            self.x,
            [(e.start_char, e.end_char, e.label_) for e in x_ents],
            missing=None
        )
        # Now fill the tokens we can align to O.
        O = 2 # I=1, O=2, B=3
        for i, ent_iob in enumerate(self.get_aligned("ENT_IOB")):
            if x_tags[i] is None:
                if ent_iob == O:
                    x_tags[i] = "O"
                elif self.x[i].is_space:
                    x_tags[i] = "O"
        return x_ents, x_tags

    def get_aligned_ner(self):
        x_ents, x_tags = self.get_aligned_ents_and_ner()
        return x_tags

    def get_matching_ents(self, check_label=True):
        """Return entities that are shared between predicted and reference docs.

        If `check_label` is True, entities must have matching labels to be
        kept. Otherwise only the character indices need to match.
        """
        gold = {}
        for ent in self.reference.ents:
            gold[(ent.start_char, ent.end_char)] = ent.label

        keep = []
        for ent in self.predicted.ents:
            key = (ent.start_char, ent.end_char)
            if key not in gold:
                continue

            if check_label and ent.label != gold[key]:
                continue

            keep.append(ent)

        return keep

    def to_dict(self):
        return {
            "doc_annotation": {
                "cats": dict(self.reference.cats),
                "entities": doc_to_biluo_tags(self.reference),
                "links": self._links_to_dict()
            },
            "token_annotation": {
                "ORTH": [t.text for t in self.reference],
                "SPACY": [bool(t.whitespace_) for t in self.reference],
                "TAG": [t.tag_ for t in self.reference],
                "LEMMA": [t.lemma_ for t in self.reference],
                "POS": [t.pos_ for t in self.reference],
                "MORPH": [str(t.morph) for t in self.reference],
                "HEAD": [t.head.i for t in self.reference],
                "DEP": [t.dep_ for t in self.reference],
                "SENT_START": [int(bool(t.is_sent_start)) for t in self.reference]
            }
        }

    def _links_to_dict(self):
        links = {}
        for ent in self.reference.ents:
            if ent.kb_id_:
                links[(ent.start_char, ent.end_char)] = {ent.kb_id_: 1.0}
        return links

    def split_sents(self):
        """ Split the token annotations into multiple Examples based on
        sent_starts and return a list of the new Examples"""
        if not self.reference.has_annotation("SENT_START"):
            return [self]

        align = self.alignment.y2x
        seen_indices = set()
        output = []
        for y_sent in self.reference.sents:
            indices = align[y_sent.start : y_sent.end]
            indices = [idx for idx in indices if idx not in seen_indices]
            if indices:
                x_sent = self.predicted[indices[0] : indices[-1] + 1]
                output.append(Example(x_sent.as_doc(), y_sent.as_doc()))
                seen_indices.update(indices)
        return output

    property text:
        def __get__(self):
            return self.x.text

    def __str__(self):
        return str(self.to_dict())

    def __repr__(self):
        return str(self.to_dict())


def _annot2array(vocab, tok_annot, doc_annot):
    attrs = []
    values = []

    for key, value in doc_annot.items():
        if value:
            if key in ["entities", "cats", "spans"]:
                pass
            elif key == "links":
                ent_kb_ids = _parse_links(vocab, tok_annot["ORTH"], tok_annot["SPACY"], value)
                tok_annot["ENT_KB_ID"] = ent_kb_ids
            else:
                raise ValueError(Errors.E974.format(obj="doc", key=key))

    for key, value in tok_annot.items():
        if key not in IDS:
            raise ValueError(Errors.E974.format(obj="token", key=key))
        elif key in ["ORTH", "SPACY"]:
            pass
        elif key == "HEAD":
            attrs.append(key)
            values.append([h-i if h is not None else 0 for i, h in enumerate(value)])
        elif key == "DEP":
            attrs.append(key)
            values.append([vocab.strings.add(h) if h is not None else MISSING_DEP for h in value])
        elif key == "SENT_START":
            attrs.append(key)
            values.append([to_ternary_int(v) for v in value])
        elif key == "MORPH":
            attrs.append(key)
            values.append([vocab.morphology.add(v) for v in value])
        else:
            attrs.append(key)
            if not all(isinstance(v, str) for v in value):
                types = set([type(v) for v in value])
                raise TypeError(Errors.E969.format(field=key, types=types)) from None
            values.append([vocab.strings.add(v) for v in value])
    array = numpy.asarray(values, dtype="uint64")
    return attrs, array.T


def _add_spans_to_doc(doc, spans_data):
    if not isinstance(spans_data, dict):
        raise ValueError(Errors.E879)
    for key, span_list in spans_data.items():
        spans = []
        if not isinstance(span_list, list):
            raise ValueError(Errors.E879)
        for span_tuple in span_list:
            if not isinstance(span_tuple, (list, tuple)) or len(span_tuple) < 2:
                raise ValueError(Errors.E879)
            start_char = span_tuple[0]
            end_char = span_tuple[1]
            label = 0
            kb_id = 0
            if len(span_tuple) > 2:
                label = span_tuple[2]
            if len(span_tuple) > 3:
                kb_id = span_tuple[3]
            span = doc.char_span(start_char, end_char, label=label, kb_id=kb_id)
            spans.append(span)
        doc.spans[key] = spans


def _add_entities_to_doc(doc, ner_data):
    if ner_data is None:
        return
    elif ner_data == []:
        doc.ents = []
    elif not isinstance(ner_data, (list, tuple)):
        raise ValueError(Errors.E973)
    elif isinstance(ner_data[0], (list, tuple)):
        return _add_entities_to_doc(
            doc,
            offsets_to_biluo_tags(doc, ner_data)
        )
    elif isinstance(ner_data[0], str) or ner_data[0] is None:
        return _add_entities_to_doc(
            doc,
            biluo_tags_to_spans(doc, ner_data)
        )
    elif isinstance(ner_data[0], Span):
        entities = []
        missing = []
        for span in ner_data:
            if span.label:
                entities.append(span)
            else:
                missing.append(span)
        doc.set_ents(entities, missing=missing)
    else:
        raise ValueError(Errors.E973)


def _parse_example_dict_data(example_dict):
    return (
        example_dict["token_annotation"],
        example_dict["doc_annotation"]
    )


def _fix_legacy_dict_data(example_dict):
    token_dict = example_dict.get("token_annotation", {})
    doc_dict = example_dict.get("doc_annotation", {})
    for key, value in example_dict.items():
        if value is not None:
            if key in ("token_annotation", "doc_annotation"):
                pass
            elif key == "ids":
                pass
            elif key in ("cats", "links", "spans"):
                doc_dict[key] = value
            elif key in ("ner", "entities"):
                doc_dict["entities"] = value
            else:
                token_dict[key] = value
    # Remap keys
    remapping = {
        "words": "ORTH",
        "tags": "TAG",
        "pos": "POS",
        "lemmas": "LEMMA",
        "deps": "DEP",
        "heads": "HEAD",
        "sent_starts": "SENT_START",
        "morphs": "MORPH",
        "spaces": "SPACY",
    }
    old_token_dict = token_dict
    token_dict = {}
    for key, value in old_token_dict.items():
        if key in ("text", "ids", "brackets"):
            pass
        elif key in remapping.values():
            token_dict[key] = value
        elif key.lower() in remapping:
            token_dict[remapping[key.lower()]] = value
        else:
            all_keys = set(remapping.values())
            all_keys.update(remapping.keys())
            raise KeyError(Errors.E983.format(key=key, dict="token_annotation", keys=all_keys))
    text = example_dict.get("text", example_dict.get("raw"))
    if _has_field(token_dict, "ORTH") and not _has_field(token_dict, "SPACY"):
        token_dict["SPACY"] = _guess_spaces(text, token_dict["ORTH"])
    if "HEAD" in token_dict and "SENT_START" in token_dict:
        # If heads are set, we don't also redundantly specify SENT_START.
        token_dict.pop("SENT_START")
        logger.debug(Warnings.W092)
    return {
        "token_annotation": token_dict,
        "doc_annotation": doc_dict
    }

def _has_field(annot, field):
    if field not in annot:
        return False
    elif annot[field] is None:
        return False
    elif len(annot[field]) == 0:
        return False
    elif all([value is None for value in annot[field]]):
        return False
    else:
        return True


def _parse_ner_tags(biluo_or_offsets, vocab, words, spaces):
    if isinstance(biluo_or_offsets[0], (list, tuple)):
        # Convert to biluo if necessary
        # This is annoying but to convert the offsets we need a Doc
        # that has the target tokenization.
        reference = Doc(vocab, words=words, spaces=spaces)
        biluo = offsets_to_biluo_tags(reference, biluo_or_offsets)
    else:
        biluo = biluo_or_offsets
    ent_iobs = []
    ent_types = []
    for iob_tag in biluo_to_iob(biluo):
        if iob_tag in (None, "-"):
            ent_iobs.append("")
            ent_types.append("")
        else:
            ent_iobs.append(iob_tag.split("-")[0])
            if iob_tag.startswith("I") or iob_tag.startswith("B"):
                ent_types.append(remove_bilu_prefix(iob_tag))
            else:
                ent_types.append("")
    return ent_iobs, ent_types

def _parse_links(vocab, words, spaces, links):
    reference = Doc(vocab, words=words, spaces=spaces)
    starts = {token.idx: token.i for token in reference}
    ends = {token.idx + len(token): token.i for token in reference}
    ent_kb_ids = ["" for _ in reference]

    for index, annot_dict in links.items():
        true_kb_ids = []
        for key, value in annot_dict.items():
            if value == 1.0:
                true_kb_ids.append(key)
        if len(true_kb_ids) > 1:
            raise ValueError(Errors.E980)

        if len(true_kb_ids) == 1:
            start_char, end_char = index
            start_token = starts.get(start_char)
            end_token = ends.get(end_char)
            if start_token is None or end_token is None:
                raise ValueError(Errors.E981)
            for i in range(start_token, end_token+1):
                ent_kb_ids[i] = true_kb_ids[0]

    return ent_kb_ids


def _guess_spaces(text, words):
    if text is None:
        return None
    spaces = []
    text_pos = 0
    # align words with text
    for word in words:
        try:
            word_start = text[text_pos:].index(word)
        except ValueError:
            spaces.append(True)
            continue
        text_pos += word_start + len(word)
        if text_pos < len(text) and text[text_pos] == " ":
            spaces.append(True)
        else:
            spaces.append(False)
    return spaces