Merge branch 'v4' into refactor/el-candidates

# Conflicts:
#	spacy/errors.py

spacy/errors.py

@@ -960,8 +960,10 @@ class Errors(metaclass=ErrorsWithCodes):
     E4003 = ("Training examples for distillation must have the exact same tokens in the "
              "reference and predicted docs.")
     E4004 = ("Backprop is not supported when is_train is not set.")
+    E4005 = ("EntityLinker_v1 is not supported in spaCy v4. Update your configuration.")
     E4006 = ("Expected `entity_id` to be of type {exp_type}, but is of type {found_type}.")
 
+
 RENAMED_LANGUAGE_CODES = {"xx": "mul", "is": "isl"}
 
 # fmt: on

spacy/pipeline/entity_linker.py

@@ -117,28 +117,9 @@ def make_entity_linker(
         prediction is discarded. If None, predictions are not filtered by any threshold.
     save_activations (bool): save model activations in Doc when annotating.
     """
-
     if not model.attrs.get("include_span_maker", False):
-        try:
+        raise ValueError(Errors.E4005)
-            from spacy_legacy.components.entity_linker import EntityLinker_v1
+
-        except:
-            raise ImportError(
-                "In order to use v1 of the EntityLinker, you must use spacy-legacy>=3.0.12."
-            )
-        # The only difference in arguments here is that use_gold_ents and threshold aren't available.
-        return EntityLinker_v1(
-            nlp.vocab,
-            model,
-            name,
-            labels_discard=labels_discard,
-            n_sents=n_sents,
-            incl_prior=incl_prior,
-            incl_context=incl_context,
-            entity_vector_length=entity_vector_length,
-            get_candidates=get_candidates,
-            overwrite=overwrite,
-            scorer=scorer,
-        )
     return EntityLinker(
         nlp.vocab,
         model,

spacy/tests/pipeline/test_entity_linker.py

@@ -991,13 +991,11 @@ def test_scorer_links():
 @pytest.mark.parametrize(
     "name,config",
     [
-        ("entity_linker", {"@architectures": "spacy.EntityLinker.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL}),
         ("entity_linker", {"@architectures": "spacy.EntityLinker.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL}),
     ],
 )
 # fmt: on
 def test_legacy_architectures(name, config):
-    from spacy_legacy.components.entity_linker import EntityLinker_v1
 
     # Ensure that the legacy architectures still work
     vector_length = 3
@@ -1020,9 +1018,6 @@ def test_legacy_architectures(name, config):
         return mykb
 
     entity_linker = nlp.add_pipe(name, config={"model": config})
-    if config["@architectures"] == "spacy.EntityLinker.v1":
-        assert isinstance(entity_linker, EntityLinker_v1)
-    else:
     assert isinstance(entity_linker, EntityLinker)
     entity_linker.set_kb(create_kb)
     optimizer = nlp.initialize(get_examples=lambda: train_examples)