Fix pipeline/.

spacy/pipeline/_edit_tree_internals/edit_trees.pxd

@@ -46,11 +46,18 @@ cdef struct EditTreeC:
     bint is_match_node
     NodeC inner
 
-cdef inline EditTreeC edittree_new_match(len_t prefix_len, len_t suffix_len,
+cdef inline EditTreeC edittree_new_match(
-        uint32_t prefix_tree, uint32_t suffix_tree):
+    len_t prefix_len,
-    cdef MatchNodeC match_node = MatchNodeC(prefix_len=prefix_len,
+    len_t suffix_len,
-            suffix_len=suffix_len, prefix_tree=prefix_tree,
+    uint32_t prefix_tree,
-            suffix_tree=suffix_tree)
+    uint32_t suffix_tree
+):
+    cdef MatchNodeC match_node = MatchNodeC(
+        prefix_len=prefix_len,
+        suffix_len=suffix_len,
+        prefix_tree=prefix_tree,
+        suffix_tree=suffix_tree
+    )
     cdef NodeC inner = NodeC(match_node=match_node)
     return EditTreeC(is_match_node=True, inner=inner)
 

spacy/pipeline/_edit_tree_internals/edit_trees.pyx

@@ -5,8 +5,6 @@ from libc.string cimport memset
 from libcpp.pair cimport pair
 from libcpp.vector cimport vector
 
-from pathlib import Path
-
 from ...typedefs cimport hash_t
 
 from ... import util
@@ -25,17 +23,16 @@ cdef LCS find_lcs(str source, str target):
     target (str): The second string.
     RETURNS (LCS): The spans of the longest common subsequences.
     """
-    cdef Py_ssize_t source_len = len(source)
     cdef Py_ssize_t target_len = len(target)
-    cdef size_t longest_align = 0;
+    cdef size_t longest_align = 0
     cdef int source_idx, target_idx
     cdef LCS lcs
     cdef Py_UCS4 source_cp, target_cp
 
     memset(&lcs, 0, sizeof(lcs))
 
-    cdef vector[size_t] prev_aligns = vector[size_t](target_len);
+    cdef vector[size_t] prev_aligns = vector[size_t](target_len)
-    cdef vector[size_t] cur_aligns = vector[size_t](target_len);
+    cdef vector[size_t] cur_aligns = vector[size_t](target_len)
 
     for (source_idx, source_cp) in enumerate(source):
         for (target_idx, target_cp) in enumerate(target):
@@ -89,7 +86,7 @@ cdef class EditTrees:
         cdef LCS lcs = find_lcs(form, lemma)
 
         cdef EditTreeC tree
-        cdef uint32_t tree_id, prefix_tree, suffix_tree
+        cdef uint32_t prefix_tree, suffix_tree
         if lcs_is_empty(lcs):
             tree = edittree_new_subst(self.strings.add(form), self.strings.add(lemma))
         else:
@@ -108,7 +105,7 @@ cdef class EditTrees:
         return self._tree_id(tree)
 
     cdef uint32_t _tree_id(self, EditTreeC tree):
-         # If this tree has been constructed before, return its identifier.
+        # If this tree has been constructed before, return its identifier.
         cdef hash_t hash = edittree_hash(tree)
         cdef unordered_map[hash_t, uint32_t].iterator iter = self.map.find(hash)
         if iter != self.map.end():
@@ -289,6 +286,7 @@ def _tree2dict(tree):
         tree = tree["inner"]["subst_node"]
     return(dict(tree))
 
+
 def _dict2tree(tree):
     errors = validate_edit_tree(tree)
     if errors:

spacy/pipeline/_parser_internals/_beam_utils.pyx

@@ -1,17 +1,14 @@
 # cython: infer_types=True
 # cython: profile=True
-cimport numpy as np
-
 import numpy
 
-from cpython.ref cimport Py_XDECREF, PyObject
 from thinc.extra.search cimport Beam
 
 from thinc.extra.search import MaxViolation
 
 from thinc.extra.search cimport MaxViolation
 
-from ...typedefs cimport class_t, hash_t
+from ...typedefs cimport class_t
 from .transition_system cimport Transition, TransitionSystem
 
 from ...errors import Errors
@@ -146,7 +143,6 @@ def update_beam(TransitionSystem moves, states, golds, model, int width, beam_de
     cdef MaxViolation violn
     pbeam = BeamBatch(moves, states, golds, width=width, density=beam_density)
     gbeam = BeamBatch(moves, states, golds, width=width, density=0.0)
-    cdef StateClass state
     beam_maps = []
     backprops = []
     violns = [MaxViolation() for _ in range(len(states))]

spacy/pipeline/_parser_internals/_state.pxd

@@ -277,7 +277,6 @@ cdef cppclass StateC:
 
         return n
 
-
     int n_L(int head) nogil const:
         return n_arcs(this._left_arcs, head)
 

spacy/pipeline/_parser_internals/arc_eager.pyx

@@ -9,7 +9,7 @@ from ...strings cimport hash_string
 from ...structs cimport TokenC
 from ...tokens.doc cimport Doc, set_children_from_heads
 from ...tokens.token cimport MISSING_DEP
-from ...typedefs cimport attr_t, hash_t
+from ...typedefs cimport attr_t
 
 from ...training import split_bilu_label
 
@@ -68,8 +68,9 @@ cdef struct GoldParseStateC:
     weight_t pop_cost
 
 
-cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
+cdef GoldParseStateC create_gold_state(
-        heads, labels, sent_starts) except *:
+    Pool mem, const StateC* state, heads, labels, sent_starts
+) except *:
     cdef GoldParseStateC gs
     gs.length = len(heads)
     gs.stride = 1
@@ -82,7 +83,7 @@ cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
     gs.n_kids_in_stack = <int32_t*>mem.alloc(gs.length, sizeof(gs.n_kids_in_stack[0]))
 
     for i, is_sent_start in enumerate(sent_starts):
-        if is_sent_start == True:
+        if is_sent_start is True:
             gs.state_bits[i] = set_state_flag(
                 gs.state_bits[i],
                 IS_SENT_START,
@@ -210,6 +211,7 @@ cdef class ArcEagerGold:
     def update(self, StateClass stcls):
         update_gold_state(&self.c, stcls.c)
 
+
 def _get_aligned_sent_starts(example):
     """Get list of SENT_START attributes aligned to the predicted tokenization.
     If the reference has not sentence starts, return a list of None values.
@@ -524,7 +526,6 @@ cdef class Break:
     """
     @staticmethod
     cdef bint is_valid(const StateC* st, attr_t label) nogil:
-        cdef int i
         if st.buffer_length() < 2:
             return False
         elif st.B(1) != st.B(0) + 1:
@@ -556,8 +557,8 @@ cdef class Break:
                 cost -= 1
             if gold.heads[si] == b0:
                 cost -= 1
-        if not is_sent_start(gold, state.B(1)) \
+        if not is_sent_start(gold, state.B(1)) and\
-        and not is_sent_start_unknown(gold, state.B(1)):
+                not is_sent_start_unknown(gold, state.B(1)):
             cost += 1
         return cost
 
@@ -803,7 +804,6 @@ cdef class ArcEager(TransitionSystem):
             raise TypeError(Errors.E909.format(name="ArcEagerGold"))
         cdef ArcEagerGold gold_ = gold
         gold_state = gold_.c
-        n_gold = 0
         if self.c[i].is_valid(stcls.c, self.c[i].label):
             cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
         else:
@@ -875,7 +875,7 @@ cdef class ArcEager(TransitionSystem):
             print("Gold")
             for token in example.y:
                 print(token.i, token.text, token.dep_, token.head.text)
-            aligned_heads, aligned_labels = example.get_aligned_parse()
+            aligned_heads, _aligned_labels = example.get_aligned_parse()
             print("Aligned heads")
             for i, head in enumerate(aligned_heads):
                 print(example.x[i], example.x[head] if head is not None else "__")

spacy/pipeline/_parser_internals/ner.pyx

@@ -1,6 +1,3 @@
-import os
-import random
-
 from cymem.cymem cimport Pool
 from libc.stdint cimport int32_t
 
@@ -14,7 +11,7 @@ from ...tokens.span import Span
 
 from ...attrs cimport IS_SPACE
 from ...lexeme cimport Lexeme
-from ...structs cimport SpanC, TokenC
+from ...structs cimport SpanC
 from ...tokens.span cimport Span
 from ...typedefs cimport attr_t, weight_t
 
@@ -141,11 +138,10 @@ cdef class BiluoPushDown(TransitionSystem):
             OUT: Counter()
         }
         actions[OUT][''] = 1  # Represents a token predicted to be outside of any entity
-        actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
+        actions[UNIT][''] = 1  # Represents a token prohibited to be in an entity
         for entity_type in kwargs.get('entity_types', []):
             for action in (BEGIN, IN, LAST, UNIT):
                 actions[action][entity_type] = 1
-        moves = ('M', 'B', 'I', 'L', 'U')
         for example in kwargs.get('examples', []):
             for token in example.y:
                 ent_type = token.ent_type_
@@ -164,7 +160,7 @@ cdef class BiluoPushDown(TransitionSystem):
             if token.ent_type:
                 labels.add(token.ent_type_)
         return labels
-    
+
     def move_name(self, int move, attr_t label):
         if move == OUT:
             return 'O'
@@ -325,7 +321,6 @@ cdef class BiluoPushDown(TransitionSystem):
             raise TypeError(Errors.E909.format(name="BiluoGold"))
         cdef BiluoGold gold_ = gold
         gold_state = gold_.c
-        n_gold = 0
         if self.c[i].is_valid(stcls.c, self.c[i].label):
             cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
         else:
@@ -486,10 +481,8 @@ cdef class In:
     @staticmethod
     cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
         gold = <GoldNERStateC*>_gold
-        move = IN
         cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT
         cdef int g_act = gold.ner[s.B(0)].move
-        cdef attr_t g_tag = gold.ner[s.B(0)].label
         cdef bint is_sunk = _entity_is_sunk(s, gold.ner)
 
         if g_act == MISSING:
@@ -549,12 +542,10 @@ cdef class Last:
     @staticmethod
     cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
         gold = <GoldNERStateC*>_gold
-        move = LAST
         b0 = s.B(0)
         ent_start = s.E(0)
 
         cdef int g_act = gold.ner[b0].move
-        cdef attr_t g_tag = gold.ner[b0].label
 
         cdef int cost = 0
 
@@ -650,7 +641,6 @@ cdef class Unit:
                 cost += 1
                 break
         return cost
- 
 
 
 cdef class Out:
@@ -675,7 +665,6 @@ cdef class Out:
     cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
         gold = <GoldNERStateC*>_gold
         cdef int g_act = gold.ner[s.B(0)].move
-        cdef attr_t g_tag = gold.ner[s.B(0)].label
         cdef weight_t cost = 0
         if g_act == MISSING:
             pass

spacy/pipeline/_parser_internals/nonproj.pyx

@@ -125,14 +125,17 @@ def decompose(label):
 def is_decorated(label):
     return DELIMITER in label
 
+
 def count_decorated_labels(gold_data):
     freqs = {}
     for example in gold_data:
         proj_heads, deco_deps = projectivize(example.get_aligned("HEAD"),
                                              example.get_aligned("DEP"))
         # set the label to ROOT for each root dependent
-        deco_deps = ['ROOT' if head == i else deco_deps[i]
+        deco_deps = [
-                       for i, head in enumerate(proj_heads)]
+            'ROOT' if head == i else deco_deps[i]
+            for i, head in enumerate(proj_heads)
+        ]
         # count label frequencies
         for label in deco_deps:
             if is_decorated(label):
@@ -160,9 +163,9 @@ def projectivize(heads, labels):
 
 
 cdef vector[int] _heads_to_c(heads):
-    cdef vector[int] c_heads;
+    cdef vector[int] c_heads
     for head in heads:
-        if head == None:
+        if head is None:
             c_heads.push_back(-1)
         else:
             assert head < len(heads)
@@ -199,6 +202,7 @@ def _decorate(heads, proj_heads, labels):
             deco_labels.append(labels[tokenid])
     return deco_labels
 
+
 def get_smallest_nonproj_arc_slow(heads):
     cdef vector[int] c_heads = _heads_to_c(heads)
     return _get_smallest_nonproj_arc(c_heads)

spacy/pipeline/_parser_internals/stateclass.pyx

@@ -1,6 +1,4 @@
 # cython: infer_types=True
-import numpy
-
 from libcpp.vector cimport vector
 
 from ...tokens.doc cimport Doc
@@ -38,11 +36,11 @@ cdef class StateClass:
         cdef vector[ArcC] arcs
         self.c.get_arcs(&arcs)
         return list(arcs)
-        #py_arcs = []
+        # py_arcs = []
-        #for arc in arcs:
+        # for arc in arcs:
-        #    if arc.head != -1 and arc.child != -1:
+        #     if arc.head != -1 and arc.child != -1:
-        #        py_arcs.append((arc.head, arc.child, arc.label))
+        #         py_arcs.append((arc.head, arc.child, arc.label))
-        #return arcs
+        # return arcs
 
     def add_arc(self, int head, int child, int label):
         self.c.add_arc(head, child, label)
@@ -52,10 +50,10 @@ cdef class StateClass:
 
     def H(self, int child):
         return self.c.H(child)
-    
+
     def L(self, int head, int idx):
         return self.c.L(head, idx)
-    
+
     def R(self, int head, int idx):
         return self.c.R(head, idx)
 
@@ -98,7 +96,7 @@ cdef class StateClass:
 
     def H(self, int i):
         return self.c.H(i)
-    
+
     def E(self, int i):
         return self.c.E(i)
 
@@ -116,7 +114,7 @@ cdef class StateClass:
 
     def H_(self, int i):
         return self.doc[self.c.H(i)]
-    
+
     def E_(self, int i):
         return self.doc[self.c.E(i)]
 
@@ -125,7 +123,7 @@ cdef class StateClass:
 
     def R_(self, int i, int idx):
         return self.doc[self.c.R(i, idx)]
- 
+
     def empty(self):
         return self.c.empty()
 
@@ -134,7 +132,7 @@ cdef class StateClass:
 
     def at_break(self):
         return False
-        #return self.c.at_break()
+        # return self.c.at_break()
 
     def has_head(self, int i):
         return self.c.has_head(i)

spacy/pipeline/_parser_internals/transition_system.pxd

@@ -20,11 +20,15 @@ cdef struct Transition:
     int (*do)(StateC* state, attr_t label) nogil
 
 
-ctypedef weight_t (*get_cost_func_t)(const StateC* state, const void* gold,
+ctypedef weight_t (*get_cost_func_t)(
-        attr_tlabel) nogil
+    const StateC* state, const void* gold, attr_tlabel
-ctypedef weight_t (*move_cost_func_t)(const StateC* state, const void* gold) nogil
+) nogil
-ctypedef weight_t (*label_cost_func_t)(const StateC* state, const void*
+ctypedef weight_t (*move_cost_func_t)(
-        gold, attr_t label) nogil
+        const StateC* state, const void* gold
+) nogil
+ctypedef weight_t (*label_cost_func_t)(
+    const StateC* state, const void* gold, attr_t label
+) nogil
 
 ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil
 

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -8,9 +8,7 @@ from collections import Counter
 import srsly
 
 from ...structs cimport TokenC
-from ...tokens.doc cimport Doc
 from ...typedefs cimport attr_t, weight_t
-from . cimport _beam_utils
 from .stateclass cimport StateClass
 
 from ... import util
@@ -231,7 +229,6 @@ cdef class TransitionSystem:
         return self
 
     def to_bytes(self, exclude=tuple()):
-        transitions = []
         serializers = {
             'moves': lambda: srsly.json_dumps(self.labels),
             'strings': lambda: self.strings.to_bytes(),

spacy/pipeline/dep_parser.pyx

@@ -1,6 +1,6 @@
 # cython: infer_types=True, profile=True, binding=True
 from collections import defaultdict
-from typing import Callable, Iterable, Optional
+from typing import Callable, Optional
 
 from thinc.api import Config, Model
 
@@ -124,6 +124,7 @@ def make_parser(
         scorer=scorer,
     )
 
+
 @Language.factory(
     "beam_parser",
     assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],

spacy/pipeline/morphologizer.pyx

@@ -2,7 +2,6 @@
 from itertools import islice
 from typing import Callable, Dict, Optional, Union
 
-import srsly
 from thinc.api import Config, Model, SequenceCategoricalCrossentropy
 
 from ..morphology cimport Morphology
@@ -14,10 +13,8 @@ from ..errors import Errors
 from ..language import Language
 from ..parts_of_speech import IDS as POS_IDS
 from ..scorer import Scorer
-from ..symbols import POS
 from ..training import validate_examples, validate_get_examples
 from ..util import registry
-from .pipe import deserialize_config
 from .tagger import Tagger
 
 # See #9050
@@ -76,8 +73,11 @@ def morphologizer_score(examples, **kwargs):
     results = {}
     results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
     results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs))
-    results.update(Scorer.score_token_attr_per_feat(examples,
+    results.update(
-        "morph", getter=morph_key_getter, **kwargs))
+        Scorer.score_token_attr_per_feat(
+            examples, "morph", getter=morph_key_getter, **kwargs
+        )
+    )
     return results
 
 
@@ -233,7 +233,6 @@ class Morphologizer(Tagger):
         if isinstance(docs, Doc):
             docs = [docs]
         cdef Doc doc
-        cdef Vocab vocab = self.vocab
         cdef bint overwrite = self.cfg["overwrite"]
         cdef bint extend = self.cfg["extend"]
         labels = self.labels

spacy/pipeline/multitask.pyx

@@ -4,13 +4,10 @@ from typing import Optional
 import numpy
 from thinc.api import Config, CosineDistance, Model, set_dropout_rate, to_categorical
 
-from ..tokens.doc cimport Doc
+from ..attrs import ID
-
-from ..attrs import ID, POS
 from ..errors import Errors
 from ..language import Language
 from ..training import validate_examples
-from ._parser_internals import nonproj
 from .tagger import Tagger
 from .trainable_pipe import TrainablePipe
 
@@ -103,10 +100,9 @@ class MultitaskObjective(Tagger):
         cdef int idx = 0
         correct = numpy.zeros((scores.shape[0],), dtype="i")
         guesses = scores.argmax(axis=1)
-        docs = [eg.predicted for eg in examples]
         for i, eg in enumerate(examples):
             # Handles alignment for tokenization differences
-            doc_annots = eg.get_aligned()  # TODO
+            _doc_annots = eg.get_aligned()  # TODO
             for j in range(len(eg.predicted)):
                 tok_annots = {key: values[j] for key, values in tok_annots.items()}
                 label = self.make_label(j, tok_annots)
@@ -206,7 +202,6 @@ class ClozeMultitask(TrainablePipe):
             losses[self.name] = 0.
         set_dropout_rate(self.model, drop)
         validate_examples(examples, "ClozeMultitask.rehearse")
-        docs = [eg.predicted for eg in examples]
         predictions, bp_predictions = self.model.begin_update()
         loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions)
         bp_predictions(d_predictions)

spacy/pipeline/ner.pyx

@@ -1,6 +1,6 @@
 # cython: infer_types=True, profile=True, binding=True
 from collections import defaultdict
-from typing import Callable, Iterable, Optional
+from typing import Callable, Optional
 
 from thinc.api import Config, Model
 
@@ -10,7 +10,7 @@ from ._parser_internals.ner cimport BiluoPushDown
 from .transition_parser cimport Parser
 
 from ..language import Language
-from ..scorer import PRFScore, get_ner_prf
+from ..scorer import get_ner_prf
 from ..training import remove_bilu_prefix
 from ..util import registry
 
@@ -100,6 +100,7 @@ def make_ner(
         scorer=scorer,
     )
 
+
 @Language.factory(
     "beam_ner",
     assigns=["doc.ents", "token.ent_iob", "token.ent_type"],

spacy/pipeline/pipe.pyx

@@ -1,6 +1,6 @@
 # cython: infer_types=True, profile=True, binding=True
 import warnings
-from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple, Union
+from typing import Callable, Dict, Iterable, Iterator, Tuple, Union
 
 import srsly
 
@@ -40,7 +40,7 @@ cdef class Pipe:
         """
         raise NotImplementedError(Errors.E931.format(parent="Pipe", method="__call__", name=self.name))
 
-    def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
+    def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
         """Apply the pipe to a stream of documents. This usually happens under
         the hood when the nlp object is called on a text and all components are
         applied to the Doc.
@@ -59,7 +59,7 @@ cdef class Pipe:
             except Exception as e:
                 error_handler(self.name, self, [doc], e)
 
-    def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
+    def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
         """Initialize the pipe. For non-trainable components, this method
         is optional. For trainable components, which should inherit
         from the subclass TrainablePipe, the provided data examples

spacy/pipeline/sentencizer.pyx

@@ -7,13 +7,13 @@ from ..tokens.doc cimport Doc
 
 from .. import util
 from ..language import Language
-from ..scorer import Scorer
 from .pipe import Pipe
 from .senter import senter_score
 
 # see #9050
 BACKWARD_OVERWRITE = False
 
+
 @Language.factory(
     "sentencizer",
     assigns=["token.is_sent_start", "doc.sents"],
@@ -36,17 +36,19 @@ class Sentencizer(Pipe):
     DOCS: https://spacy.io/api/sentencizer
     """
 
-    default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
+    default_punct_chars = [
-            '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
+        '!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
-            '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
+        '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
-            '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
+        '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
-            '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
+        '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
-            '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
+        '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
-            '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
+        '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
-            '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
+        '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
-            '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
+        '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
-            '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
+        '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
-            '｡', '。']
+        '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
+        '｡', '。'
+    ]
 
     def __init__(
         self,
@@ -128,7 +130,6 @@ class Sentencizer(Pipe):
         if isinstance(docs, Doc):
             docs = [docs]
         cdef Doc doc
-        cdef int idx = 0
         for i, doc in enumerate(docs):
             doc_tag_ids = batch_tag_ids[i]
             for j, tag_id in enumerate(doc_tag_ids):
@@ -169,7 +170,6 @@ class Sentencizer(Pipe):
         path = path.with_suffix(".json")
         srsly.write_json(path, {"punct_chars": list(self.punct_chars), "overwrite": self.overwrite})
 
-
     def from_disk(self, path, *, exclude=tuple()):
         """Load the sentencizer from disk.
 

spacy/pipeline/senter.pyx

@@ -2,7 +2,6 @@
 from itertools import islice
 from typing import Callable, Optional
 
-import srsly
 from thinc.api import Config, Model, SequenceCategoricalCrossentropy
 
 from ..tokens.doc cimport Doc

spacy/pipeline/tagger.pyx

@@ -1,26 +1,17 @@
 # cython: infer_types=True, profile=True, binding=True
-import warnings
 from itertools import islice
 from typing import Callable, Optional
 
 import numpy
-import srsly
 from thinc.api import Config, Model, SequenceCategoricalCrossentropy, set_dropout_rate
-from thinc.types import Floats2d
 
-from ..morphology cimport Morphology
 from ..tokens.doc cimport Doc
-from ..vocab cimport Vocab
 
 from .. import util
-from ..attrs import ID, POS
-from ..errors import Errors, Warnings
 from ..language import Language
-from ..parts_of_speech import X
 from ..scorer import Scorer
 from ..training import validate_examples, validate_get_examples
 from ..util import registry
-from .pipe import deserialize_config
 from .trainable_pipe import TrainablePipe
 
 # See #9050
@@ -169,7 +160,6 @@ class Tagger(TrainablePipe):
         if isinstance(docs, Doc):
             docs = [docs]
         cdef Doc doc
-        cdef Vocab vocab = self.vocab
         cdef bint overwrite = self.cfg["overwrite"]
         labels = self.labels
         for i, doc in enumerate(docs):

spacy/pipeline/trainable_pipe.pyx

@@ -55,7 +55,7 @@ cdef class TrainablePipe(Pipe):
         except Exception as e:
             error_handler(self.name, self, [doc], e)
 
-    def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
+    def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
         """Apply the pipe to a stream of documents. This usually happens under
         the hood when the nlp object is called on a text and all components are
         applied to the Doc.
@@ -102,9 +102,9 @@ cdef class TrainablePipe(Pipe):
     def update(self,
                examples: Iterable["Example"],
                *,
-               drop: float=0.0,
+               drop: float = 0.0,
-               sgd: Optimizer=None,
+               sgd: Optimizer = None,
-               losses: Optional[Dict[str, float]]=None) -> Dict[str, float]:
+               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.
 
@@ -138,8 +138,8 @@ cdef class TrainablePipe(Pipe):
     def rehearse(self,
                  examples: Iterable[Example],
                  *,
-                 sgd: Optimizer=None,
+                 sgd: Optimizer = None,
-                 losses: Dict[str, float]=None,
+                 losses: Dict[str, float] = None,
                  **config) -> Dict[str, float]:
         """Perform a "rehearsal" update from a batch of data. Rehearsal updates
         teach the current model to make predictions similar to an initial model,
@@ -177,7 +177,7 @@ cdef class TrainablePipe(Pipe):
         """
         return util.create_default_optimizer()
 
-    def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
+    def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
         """Initialize the pipe for training, using data examples if available.
         This method needs to be implemented by each TrainablePipe component,
         ensuring the internal model (if available) is initialized properly

spacy/pipeline/transition_parser.pxd

@@ -13,8 +13,18 @@ cdef class Parser(TrainablePipe):
     cdef readonly TransitionSystem moves
     cdef public object _multitasks
 
-    cdef void _parseC(self, CBlas cblas, StateC** states,
+    cdef void _parseC(
-            WeightsC weights, SizesC sizes) nogil
+        self,
+        CBlas cblas,
+        StateC** states,
+        WeightsC weights,
+        SizesC sizes
+    ) nogil
 
-    cdef void c_transition_batch(self, StateC** states, const float* scores,
+    cdef void c_transition_batch(
-            int nr_class, int batch_size) nogil
+        self,
+        StateC** states,
+        const float* scores,
+        int nr_class,
+        int batch_size
+    ) nogil

spacy/pipeline/transition_parser.pyx

@@ -7,17 +7,14 @@ from cymem.cymem cimport Pool
 from itertools import islice
 
 from libc.stdlib cimport calloc, free
-from libc.string cimport memcpy, memset
+from libc.string cimport memset
 from libcpp.vector cimport vector
 
 import random
 
 import srsly
-from thinc.api import CupyOps, NumpyOps, get_ops, set_dropout_rate
+from thinc.api import CupyOps, NumpyOps, set_dropout_rate
 
-from thinc.extra.search cimport Beam
-
-import warnings
 
 import numpy
 import numpy.random
@@ -42,7 +39,7 @@ from .trainable_pipe import TrainablePipe
 from ._parser_internals cimport _beam_utils
 
 from .. import util
-from ..errors import Errors, Warnings
+from ..errors import Errors
 from ..training import validate_examples, validate_get_examples
 from ._parser_internals import _beam_utils
 
@@ -258,7 +255,6 @@ cdef class Parser(TrainablePipe):
             except Exception as e:
                 error_handler(self.name, self, batch_in_order, e)
 
-
     def predict(self, docs):
         if isinstance(docs, Doc):
             docs = [docs]
@@ -300,8 +296,6 @@ cdef class Parser(TrainablePipe):
         return batch
 
     def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
-        cdef Beam beam
-        cdef Doc doc
         self._ensure_labels_are_added(docs)
         batch = _beam_utils.BeamBatch(
             self.moves,
@@ -321,16 +315,18 @@ cdef class Parser(TrainablePipe):
         del model
         return list(batch)
 
-    cdef void _parseC(self, CBlas cblas, StateC** states,
+    cdef void _parseC(
-            WeightsC weights, SizesC sizes) nogil:
+        self, CBlas cblas, StateC** states, WeightsC weights, SizesC sizes
-        cdef int i, j
+    ) nogil:
+        cdef int i
         cdef vector[StateC*] unfinished
         cdef ActivationsC activations = alloc_activations(sizes)
         while sizes.states >= 1:
             predict_states(cblas, &activations, states, &weights, sizes)
             # Validate actions, argmax, take action.
-            self.c_transition_batch(states,
+            self.c_transition_batch(
-                activations.scores, sizes.classes, sizes.states)
+                states, activations.scores, sizes.classes, sizes.states
+            )
             for i in range(sizes.states):
                 if not states[i].is_final():
                     unfinished.push_back(states[i])
@@ -342,7 +338,6 @@ cdef class Parser(TrainablePipe):
 
     def set_annotations(self, docs, states_or_beams):
         cdef StateClass state
-        cdef Beam beam
         cdef Doc doc
         states = _beam_utils.collect_states(states_or_beams, docs)
         for i, (state, doc) in enumerate(zip(states, docs)):
@@ -359,8 +354,13 @@ cdef class Parser(TrainablePipe):
         self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
         return [state for state in states if not state.c.is_final()]
 
-    cdef void c_transition_batch(self, StateC** states, const float* scores,
+    cdef void c_transition_batch(
-            int nr_class, int batch_size) nogil:
+        self,
+        StateC** states,
+        const float* scores,
+        int nr_class,
+        int batch_size
+    ) nogil:
         # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
         with gil:
             assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
@@ -380,7 +380,6 @@ cdef class Parser(TrainablePipe):
         free(is_valid)
 
     def update(self, examples, *, drop=0., sgd=None, losses=None):
-        cdef StateClass state
         if losses is None:
             losses = {}
         losses.setdefault(self.name, 0.)
@@ -419,8 +418,7 @@ cdef class Parser(TrainablePipe):
         if not states:
             return losses
         model, backprop_tok2vec = self.model.begin_update([eg.x for eg in examples])
- 
+
-        all_states = list(states)
         states_golds = list(zip(states, golds))
         n_moves = 0
         while states_golds:
@@ -500,8 +498,16 @@ cdef class Parser(TrainablePipe):
         del tutor
         return losses
 
-    def update_beam(self, examples, *, beam_width,
+    def update_beam(
-            drop=0., sgd=None, losses=None, beam_density=0.0):
+        self,
+        examples,
+        *,
+        beam_width,
+        drop=0.,
+        sgd=None,
+        losses=None,
+        beam_density=0.0
+    ):
         states, golds, _ = self.moves.init_gold_batch(examples)
         if not states:
             return losses
@@ -531,8 +537,9 @@ cdef class Parser(TrainablePipe):
 
         is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
         costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
-        cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
+        cdef np.ndarray d_scores = numpy.zeros(
-                                        dtype='f', order='C')
+            (len(states), self.moves.n_moves), dtype='f', order='C'
+        )
         c_d_scores = <float*>d_scores.data
         unseen_classes = self.model.attrs["unseen_classes"]
         for i, (state, gold) in enumerate(zip(states, golds)):
@@ -542,8 +549,9 @@ cdef class Parser(TrainablePipe):
             for j in range(self.moves.n_moves):
                 if costs[j] <= 0.0 and j in unseen_classes:
                     unseen_classes.remove(j)
-            cpu_log_loss(c_d_scores,
+            cpu_log_loss(
-                costs, is_valid, &scores[i, 0], d_scores.shape[1])
+                c_d_scores, costs, is_valid, &scores[i, 0], d_scores.shape[1]
+            )
             c_d_scores += d_scores.shape[1]
         # Note that we don't normalize this. See comment in update() for why.
         if losses is not None: