# coding: utf8
# cython: infer_types=True
# cython: bounds_check=False
# cython: profile=True
from __future__ import unicode_literals

from libc.string cimport memcpy, memset
from libc.stdlib cimport malloc, free

import numpy
from cymem.cymem cimport Pool
from thinc.neural.util import get_array_module

from .doc cimport Doc, set_children_from_heads, token_by_start, token_by_end
from .span cimport Span
from .token cimport Token
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..structs cimport LexemeC, TokenC
from ..attrs cimport TAG
from ..attrs import intify_attrs
from ..util import SimpleFrozenDict
from ..errors import Errors

cdef class Retokenizer:
    """Helper class for doc.retokenize() context manager."""
    cdef Doc doc
    cdef list merges
    cdef list splits
    cdef set tokens_to_merge
    def __init__(self, doc):
        self.doc = doc
        self.merges = []
        self.splits = []
        self.tokens_to_merge = set()

    def merge(self, Span span, attrs=SimpleFrozenDict()):
        """Mark a span for merging. The attrs will be applied to the resulting
        token.
        """
        for token in span:
            if token.i in self.tokens_to_merge:
                raise ValueError(Errors.E102.format(token=repr(token)))
            self.tokens_to_merge.add(token.i)

        attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
        self.merges.append((span, attrs))

    def split(self, Token token, orths, attrs=SimpleFrozenDict()):
        """Mark a Token for splitting, into the specified orths. The attrs
        will be applied to each subtoken.
        """
        attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings)
        self.splits.append((token.start_char, orths, attrs))

    def __enter__(self):
        self.merges = []
        self.splits = []
        return self

    def __exit__(self, *args):
        # Do the actual merging here
        if len(self.merges) > 1:
            _bulk_merge(self.doc, self.merges)
        elif len(self.merges) == 1:
            (span, attrs) = self.merges[0]
            start = span.start
            end = span.end
            _merge(self.doc, start, end, attrs)

        for start_char, orths, attrs in self.splits:
            raise NotImplementedError

def _merge(Doc doc, int start, int end, attributes):
    """Retokenize the document, such that the span at
    `doc.text[start_idx : end_idx]` is merged into a single token. If
    `start_idx` and `end_idx `do not mark start and end token boundaries,
    the document remains unchanged.
    start_idx (int): Character index of the start of the slice to merge.
    end_idx (int): Character index after the end of the slice to merge.
    **attributes: Attributes to assign to the merged token. By default,
        attributes are inherited from the syntactic root of the span.
    RETURNS (Token): The newly merged token, or `None` if the start and end
        indices did not fall at token boundaries.
    """
    cdef Span span = doc[start:end]
    cdef int start_char = span.start_char
    cdef int end_char = span.end_char
    # Resize the doc.tensor, if it's set. Let the last row for each token stand
    # for the merged region. To do this, we create a boolean array indicating
    # whether the row is to be deleted, then use numpy.delete
    if doc.tensor is not None and doc.tensor.size != 0:
        doc.tensor = _resize_tensor(doc.tensor, [(start, end)])
    # Get LexemeC for newly merged token
    new_orth = ''.join([t.text_with_ws for t in span])
    if span[-1].whitespace_:
        new_orth = new_orth[:-len(span[-1].whitespace_)]
    cdef const LexemeC* lex = doc.vocab.get(doc.mem, new_orth)
    # House the new merged token where it starts
    cdef TokenC* token = &doc.c[start]
    token.spacy = doc.c[end-1].spacy
    for attr_name, attr_value in attributes.items():
        if attr_name == TAG:
            doc.vocab.morphology.assign_tag(token, attr_value)
        else:
            Token.set_struct_attr(token, attr_name, attr_value)
    # Make sure ent_iob remains consistent
    if doc.c[end].ent_iob == 1 and token.ent_iob in (0, 2):
        if token.ent_type == doc.c[end].ent_type:
            token.ent_iob = 3
        else:
            # If they're not the same entity type, let them be two entities
            doc.c[end].ent_iob = 3
    # Begin by setting all the head indices to absolute token positions
    # This is easier to work with for now than the offsets
    # Before thinking of something simpler, beware the case where a
    # dependency bridges over the entity. Here the alignment of the
    # tokens changes.
    span_root = span.root.i
    token.dep = span.root.dep
    # We update token.lex after keeping span root and dep, since
    # setting token.lex will change span.start and span.end properties
    # as it modifies the character offsets in the doc
    token.lex = lex
    for i in range(doc.length):
        doc.c[i].head += i
    # Set the head of the merged token, and its dep relation, from the Span
    token.head = doc.c[span_root].head
    # Adjust deps before shrinking tokens
    # Tokens which point into the merged token should now point to it
    # Subtract the offset from all tokens which point to >= end
    offset = (end - start) - 1
    for i in range(doc.length):
        head_idx = doc.c[i].head
        if start <= head_idx < end:
            doc.c[i].head = start
        elif head_idx >= end:
            doc.c[i].head -= offset
    # Now compress the token array
    for i in range(end, doc.length):
        doc.c[i - offset] = doc.c[i]
    for i in range(doc.length - offset, doc.length):
        memset(&doc.c[i], 0, sizeof(TokenC))
        doc.c[i].lex = &EMPTY_LEXEME
    doc.length -= offset
    for i in range(doc.length):
        # ...And, set heads back to a relative position
        doc.c[i].head -= i
    # Set the left/right children, left/right edges
    set_children_from_heads(doc.c, doc.length)
    # Clear the cached Python objects
    # Return the merged Python object
    return doc[start]

def _bulk_merge(Doc doc, merges):
    """Retokenize the document, such that the spans described in 'merges'
     are merged into a single token. This method assumes that the merges
     are in the same order at which they appear in the doc, and that merges
     do not intersect each other in any way.

    merges: Tokens to merge, and corresponding attributes to assign to the
        merged token. By default, attributes are inherited from the
        syntactic root of the span.
    RETURNS (Token): The first newly merged token.
    """
    cdef Span span
    cdef const LexemeC* lex
    cdef Pool mem = Pool()
    tokens = <TokenC**>mem.alloc(len(merges), sizeof(TokenC))
    spans = []

    def _get_start(merge):
        return merge[0].start
    merges.sort(key=_get_start)

    for merge_index, (span, attributes) in enumerate(merges):
        start = span.start
        end = span.end
        spans.append(span)

        # House the new merged token where it starts
        token = &doc.c[start]

        tokens[merge_index] = token

        # Assign attributes
        for attr_name, attr_value in attributes.items():
            if attr_name == TAG:
                doc.vocab.morphology.assign_tag(token, attr_value)
            else:
                Token.set_struct_attr(token, attr_name, attr_value)

    # Resize the doc.tensor, if it's set. Let the last row for each token stand
    # for the merged region. To do this, we create a boolean array indicating
    # whether the row is to be deleted, then use numpy.delete
    if doc.tensor is not None and doc.tensor.size != 0:
        doc.tensor = _resize_tensor(doc.tensor,
            [(m[1][0].start, m[1][0].end) for m in merges])
    # Memorize span roots and sets dependencies of the newly merged
    # tokens to the dependencies of their roots.
    span_roots = []
    for i, span in enumerate(spans):
        span_roots.append(span.root.i)
        tokens[i].dep = span.root.dep

    # We update token.lex after keeping span root and dep, since
    # setting token.lex will change span.start and span.end properties
    # as it modifies the character offsets in the doc
    for token_index in range(len(merges)):
        new_orth = ''.join([t.text_with_ws for t in spans[token_index]])
        if spans[token_index][-1].whitespace_:
            new_orth = new_orth[:-len(spans[token_index][-1].whitespace_)]
        lex = doc.vocab.get(doc.mem, new_orth)
        tokens[token_index].lex = lex
        # We set trailing space here too
        tokens[token_index].spacy = doc.c[spans[token_index].end-1].spacy

    # Begin by setting all the head indices to absolute token positions
    # This is easier to work with for now than the offsets
    # Before thinking of something simpler, beware the case where a
    # dependency bridges over the entity. Here the alignment of the
    # tokens changes.
    for i in range(doc.length):
        doc.c[i].head += i

    # Set the head of the merged token from the Span
    for i in range(len(merges)):
        tokens[i].head = doc.c[span_roots[i]].head

    # Adjust deps before shrinking tokens
    # Tokens which point into the merged token should now point to it
    # Subtract the offset from all tokens which point to >= end
    offsets = []
    current_span_index = 0
    current_offset = 0
    for i in range(doc.length):
        if current_span_index < len(spans) and i == spans[current_span_index].end:
            #last token was the last of the span
            current_offset += (spans[current_span_index].end - spans[current_span_index].start) -1
            current_span_index += 1

        if current_span_index < len(spans) and \
                spans[current_span_index].start <= i < spans[current_span_index].end:
            offsets.append(spans[current_span_index].start - current_offset)
        else:
            offsets.append(i - current_offset)

    for i in range(doc.length):
        doc.c[i].head = offsets[doc.c[i].head]

    # Now compress the token array
    offset = 0
    in_span = False
    span_index = 0
    for i in range(doc.length):
        if in_span and i == spans[span_index].end:
            # First token after a span
            in_span = False
            span_index += 1
        if span_index < len(spans) and i == spans[span_index].start:
            # First token in a span
            doc.c[i - offset] = doc.c[i] # move token to its place
            offset += (spans[span_index].end - spans[span_index].start) - 1
            in_span = True
        if not in_span:
            doc.c[i - offset] = doc.c[i] # move token to its place

    for i in range(doc.length - offset, doc.length):
        memset(&doc.c[i], 0, sizeof(TokenC))
        doc.c[i].lex = &EMPTY_LEXEME
    doc.length -= offset

    # ...And, set heads back to a relative position
    for i in range(doc.length):
        doc.c[i].head -= i

    # Set the left/right children, left/right edges
    set_children_from_heads(doc.c, doc.length)

    # Make sure ent_iob remains consistent
    for (span, _) in merges:
        if(span.end < len(offsets)):
        #if it's not the last span
            token_after_span_position = offsets[span.end]
            if doc.c[token_after_span_position].ent_iob == 1\
                    and doc.c[token_after_span_position - 1].ent_iob in (0, 2):
                if doc.c[token_after_span_position - 1].ent_type == doc.c[token_after_span_position].ent_type:
                    doc.c[token_after_span_position - 1].ent_iob = 3
                else:
                    # If they're not the same entity type, let them be two entities
                    doc.c[token_after_span_position].ent_iob = 3
    # Return the merged Python object
    return doc[spans[0].start]


def _resize_tensor(tensor, ranges):
    delete = []
    for start, end in ranges:
        for i in range(start, end-1):
            delete.append(i)
    xp = get_array_module(tensor)
    return xp.delete(tensor, delete, axis=0)