# cython: infer_types=True, bounds_check=False, profile=True from libc.string cimport memcpy, memset from libc.stdlib cimport malloc, free from cymem.cymem cimport Pool from thinc.api import get_array_module import numpy 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 MORPH, NORM from ..vocab cimport Vocab from .underscore import is_writable_attr from ..attrs import intify_attrs from ..util import SimpleFrozenDict from ..errors import Errors from ..strings import get_string_id cdef class Retokenizer: """Helper class for doc.retokenize() context manager. DOCS: https://spacy.io/api/doc#retokenize USAGE: https://spacy.io/usage/linguistic-features#retokenization """ cdef Doc doc cdef list merges cdef list splits cdef set tokens_to_merge cdef list _spans_to_merge def __init__(self, doc): self.doc = doc self.merges = [] self.splits = [] self.tokens_to_merge = set() self._spans_to_merge = [] # keep a record to filter out duplicates def merge(self, Span span, attrs=SimpleFrozenDict()): """Mark a span for merging. The attrs will be applied to the resulting token. span (Span): The span to merge. attrs (dict): Attributes to set on the merged token. DOCS: https://spacy.io/api/doc#retokenizer.merge """ if (span.start, span.end) in self._spans_to_merge: return if span.end - span.start <= 0: raise ValueError(Errors.E199.format(start=span.start, end=span.end)) 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) self._spans_to_merge.append((span.start, span.end)) attrs = normalize_token_attrs(self.doc.vocab, attrs) self.merges.append((span, attrs)) def split(self, Token token, orths, heads, attrs=SimpleFrozenDict()): """Mark a Token for splitting, into the specified orths. The attrs will be applied to each subtoken. token (Token): The token to split. orths (list): The verbatim text of the split tokens. Needs to match the text of the original token. heads (list): List of token or `(token, subtoken)` tuples specifying the tokens to attach the newly split subtokens to. attrs (dict): Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values. DOCS: https://spacy.io/api/doc#retokenizer.split """ if ''.join(orths) != token.text: raise ValueError(Errors.E117.format(new=''.join(orths), old=token.text)) if "_" in attrs: # Extension attributes extensions = attrs["_"] for extension in extensions: _validate_extensions(extension) attrs = {key: value for key, value in attrs.items() if key != "_"} # NB: Since we support {"KEY": [value, value]} syntax here, this # will only "intify" the keys, not the values attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings) attrs["_"] = extensions else: # NB: Since we support {"KEY": [value, value]} syntax here, this # will only "intify" the keys, not the values attrs = intify_attrs(attrs, strings_map=self.doc.vocab.strings) if MORPH in attrs: for i, morph in enumerate(attrs[MORPH]): # add and set to normalized value morph = self.doc.vocab.morphology.add(self.doc.vocab.strings.as_string(morph)) attrs[MORPH][i] = morph head_offsets = [] for head in heads: if isinstance(head, Token): head_offsets.append((head.idx, 0)) else: head_offsets.append((head[0].idx, head[1])) self.splits.append((token.idx, orths, head_offsets, attrs)) def __enter__(self): self.merges = [] self.splits = [] return self def __exit__(self, *args): # Do the actual merging here if len(self.merges) >= 1: _merge(self.doc, self.merges) # Iterate in order, to keep things simple. for start_char, orths, heads, attrs in sorted(self.splits): # Resolve token index token_index = token_by_start(self.doc.c, self.doc.length, start_char) # Check we're still able to find tokens starting at the character offsets # referred to in the splits. If we merged these tokens previously, we # have to raise an error if token_index == -1: raise IndexError(Errors.E122) head_indices = [] for head_char, subtoken in heads: head_index = token_by_start(self.doc.c, self.doc.length, head_char) if head_index == -1: raise IndexError(Errors.E123) # We want to refer to the token index of the head *after* the # mergery. We need to account for the extra tokens introduced. # e.g., let's say we have [ab, c] and we want a and b to depend # on c. The correct index for c will be 2, not 1. if head_index > token_index: head_index += len(orths)-1 head_indices.append(head_index+subtoken) _split(self.doc, token_index, orths, head_indices, attrs) def _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 int i, merge_index, start, end, token_index, current_span_index, current_offset, offset, span_index cdef Span span cdef const LexemeC* lex cdef TokenC* token cdef Pool mem = Pool() cdef int merged_iob = 0 # merges should not be empty, but make sure to avoid zero-length mem alloc assert len(merges) > 0 tokens = 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] start_ent_iob = doc.c[start].ent_iob start_ent_type = doc.c[start].ent_type # Initially set attributes to attributes of span root token.tag = doc.c[span.root.i].tag token.pos = doc.c[span.root.i].pos token.morph = doc.c[span.root.i].morph token.ent_iob = doc.c[span.root.i].ent_iob token.ent_type = doc.c[span.root.i].ent_type merged_iob = token.ent_iob # If span root is part of an entity, merged token is B-ENT if token.ent_iob in (1, 3): merged_iob = 3 # If start token is I-ENT and previous token is of the same # type, then I-ENT (could check I-ENT from start to span root) if start_ent_iob == 1 and start > 0 \ and start_ent_type == token.ent_type \ and doc.c[start - 1].ent_type == token.ent_type: merged_iob = 1 token.ent_iob = merged_iob # Set lemma to concatenated lemmas merged_lemma = "" for span_token in span: merged_lemma += span_token.lemma_ if doc.c[span_token.i].spacy: merged_lemma += " " merged_lemma = merged_lemma.strip() token.lemma = doc.vocab.strings.add(merged_lemma) # Unset attributes that don't match new token token.norm = 0 tokens[merge_index] = token # 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[0].start, m[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, (span, attributes) in enumerate(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_)] # add the vector of the (merged) entity to the vocab if not doc.vocab.get_vector(new_orth).any(): if doc.vocab.vectors_length > 0: doc.vocab.set_vector(new_orth, span.vector) token = tokens[token_index] lex = doc.vocab.get(doc.mem, new_orth) token.lex = lex # We set trailing space here too token.spacy = doc.c[spans[token_index].end-1].spacy set_token_attrs(span[0], attributes) # 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 if doc.has_annotation("DEP"): set_children_from_heads(doc.c, 0, doc.length) # Make sure ent_iob remains consistent make_iob_consistent(doc.c, doc.length) # 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) if xp is numpy: return xp.delete(tensor, delete, axis=0) else: offset = 0 copy_start = 0 resized_shape = (tensor.shape[0] - len(delete), tensor.shape[1]) for start, end in ranges: if copy_start > 0: tensor[copy_start - offset:start - offset] = tensor[copy_start: start] offset += end - start - 1 copy_start = end - 1 tensor[copy_start - offset:resized_shape[0]] = tensor[copy_start:] return xp.asarray(tensor[:resized_shape[0]]) def _split(Doc doc, int token_index, orths, heads, attrs): """Retokenize the document, such that the token at `doc[token_index]` is split into tokens with the orth 'orths' token_index(int): token index of the token to split. orths: IDs of the verbatim text content of the tokens to create **attributes: Attributes to assign to each of the newly created tokens. By default, attributes are inherited from the original token. RETURNS (Token): The first newly created token. """ cdef int nb_subtokens = len(orths) cdef const LexemeC* lex cdef TokenC* token cdef TokenC orig_token = doc.c[token_index] cdef int orig_length = len(doc) if(len(heads) != nb_subtokens): raise ValueError(Errors.E115) # First, make the dependencies absolutes for i in range(doc.length): doc.c[i].head += i # Adjust dependencies, so they refer to post-split indexing offset = nb_subtokens - 1 for i in range(doc.length): if doc.c[i].head > token_index: doc.c[i].head += offset # Double doc.c max_length if necessary (until big enough for all new tokens) while doc.length + nb_subtokens - 1 >= doc.max_length: doc._realloc(doc.max_length * 2) # Move tokens after the split to create space for the new tokens doc.length = len(doc) + nb_subtokens -1 to_process_tensor = (doc.tensor is not None and doc.tensor.size != 0) if to_process_tensor: xp = get_array_module(doc.tensor) if xp is numpy: doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0) else: shape = (doc.tensor.shape[0] + nb_subtokens, doc.tensor.shape[1]) resized_array = xp.zeros(shape, dtype="float32") resized_array[:doc.tensor.shape[0]] = doc.tensor[:doc.tensor.shape[0]] doc.tensor = resized_array for token_to_move in range(orig_length - 1, token_index, -1): doc.c[token_to_move + nb_subtokens - 1] = doc.c[token_to_move] if to_process_tensor: doc.tensor[token_to_move + nb_subtokens - 1] = doc.tensor[token_to_move] # Host the tokens in the newly created space cdef int idx_offset = 0 for i, orth in enumerate(orths): token = &doc.c[token_index + i] lex = doc.vocab.get(doc.mem, orth) token.lex = lex # If lemma is currently set, set default lemma to orth if token.lemma != 0: token.lemma = lex.orth token.norm = 0 # reset norm if to_process_tensor: # setting the tensors of the split tokens to array of zeros doc.tensor[token_index + i:token_index + i + 1] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32") # Update the character offset of the subtokens if i != 0: token.idx = orig_token.idx + idx_offset idx_offset += len(orth) # Set token.spacy to False for all non-last split tokens, and # to origToken.spacy for the last token if (i < nb_subtokens - 1): token.spacy = False else: token.spacy = orig_token.spacy # Make IOB consistent if (orig_token.ent_iob == 3): if i == 0: token.ent_iob = 3 else: token.ent_iob = 1 else: # In all other cases subtokens inherit iob from origToken token.ent_iob = orig_token.ent_iob # Apply attrs to each subtoken for attr_name, attr_values in attrs.items(): for i, attr_value in enumerate(attr_values): token = &doc.c[token_index + i] if attr_name == "_": for ext_attr_key, ext_attr_value in attr_value.items(): doc[token_index + i]._.set(ext_attr_key, ext_attr_value) # NB: We need to call get_string_id here because only the keys are # "intified" (since we support "KEY": [value, value] syntax here). else: # Set attributes on both token and lexeme to take care of token # attribute vs. lexical attribute without having to enumerate # them. If an attribute name is not valid, set_struct_attr will # ignore it. Exception: set NORM only on tokens. Token.set_struct_attr(token, attr_name, get_string_id(attr_value)) if attr_name != NORM: Lexeme.set_struct_attr(token.lex, attr_name, get_string_id(attr_value)) # Assign correct dependencies to the inner token for i, head in enumerate(heads): doc.c[token_index + i].head = head # Transform the dependencies into relative ones again for i in range(doc.length): doc.c[i].head -= i # set children from head if doc.has_annotation("DEP"): set_children_from_heads(doc.c, 0, doc.length) def _validate_extensions(extensions): if not isinstance(extensions, dict): raise ValueError(Errors.E120.format(value=repr(extensions))) for key, value in extensions.items(): # Get the extension and make sure it's available and writable extension = Token.get_extension(key) if not extension: # Extension attribute doesn't exist raise ValueError(Errors.E118.format(attr=key)) if not is_writable_attr(extension): raise ValueError(Errors.E119.format(attr=key)) cdef make_iob_consistent(TokenC* tokens, int length): cdef int i if tokens[0].ent_iob == 1: tokens[0].ent_iob = 3 for i in range(1, length): if tokens[i].ent_iob == 1 and tokens[i - 1].ent_type != tokens[i].ent_type: tokens[i].ent_iob = 3 def normalize_token_attrs(Vocab vocab, attrs): if "_" in attrs: # Extension attributes extensions = attrs["_"] _validate_extensions(extensions) attrs = {key: value for key, value in attrs.items() if key != "_"} attrs = intify_attrs(attrs, strings_map=vocab.strings) attrs["_"] = extensions else: attrs = intify_attrs(attrs, strings_map=vocab.strings) if MORPH in attrs: # add and set to normalized value morph = vocab.morphology.add(vocab.strings.as_string(attrs[MORPH])) attrs[MORPH] = morph return attrs def set_token_attrs(Token py_token, attrs): cdef TokenC* token = py_token.c cdef const LexemeC* lex = token.lex cdef Doc doc = py_token.doc # Assign attributes for attr_name, attr_value in attrs.items(): if attr_name == "_": # Set extension attributes for ext_attr_key, ext_attr_value in attr_value.items(): py_token._.set(ext_attr_key, ext_attr_value) else: # Set attributes on both token and lexeme to take care of token # attribute vs. lexical attribute without having to enumerate # them. If an attribute name is not valid, set_struct_attr will # ignore it. Exception: set NORM only on tokens. Token.set_struct_attr(token, attr_name, attr_value) if attr_name != NORM: Lexeme.set_struct_attr(lex, attr_name, attr_value)