Reformat with black 25

Author: honnibal

spacy/cli/_util.py

@@ -225,13 +225,11 @@ def get_git_version(
 
 
 @overload
-def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]:
-    ...
+def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]: ...
 
 
 @overload
-def string_to_list(value: str, intify: Literal[True]) -> List[int]:
-    ...
+def string_to_list(value: str, intify: Literal[True]) -> List[int]: ...
 
 
 def string_to_list(value: str, intify: bool = False) -> Union[List[str], List[int]]:

spacy/cli/debug_data.py

@@ -968,16 +968,14 @@ def _compile_gold(
 
 
 @overload
-def _format_labels(labels: Iterable[str], counts: Literal[False] = False) -> str:
-    ...
+def _format_labels(labels: Iterable[str], counts: Literal[False] = False) -> str: ...
 
 
 @overload
 def _format_labels(
     labels: Iterable[Tuple[str, int]],
     counts: Literal[True],
-) -> str:
-    ...
+) -> str: ...
 
 
 def _format_labels(

spacy/cli/find_threshold.py

@@ -157,9 +157,11 @@ def filter_config(
                 exits=1,
             )
         return {
-            keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
-            if len(keys) > 1
-            else config[keys[0]]
+            keys[0]: (
+                filter_config(config[keys[0]], keys[1:], full_key)
+                if len(keys) > 1
+                else config[keys[0]]
+            )
         }
 
     # Evaluate with varying threshold values.
@@ -216,12 +218,14 @@ def filter_config(
     if len(set(scores.values())) == 1:
         wasabi.msg.warn(
             title="All scores are identical. Verify that all settings are correct.",
-            text=""
-            if (
-                not isinstance(pipe, MultiLabel_TextCategorizer)
-                or scores_key in ("cats_macro_f", "cats_micro_f")
-            )
-            else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
+            text=(
+                ""
+                if (
+                    not isinstance(pipe, MultiLabel_TextCategorizer)
+                    or scores_key in ("cats_macro_f", "cats_micro_f")
+                )
+                else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`."
+            ),
         )
 
     else:

spacy/cli/init_config.py

@@ -195,9 +195,11 @@ def init_config(
         "Pipeline": ", ".join(pipeline),
         "Optimize for": optimize,
         "Hardware": variables["hardware"].upper(),
-        "Transformer": template_vars.transformer.get("name")  # type: ignore[attr-defined]
-        if template_vars.use_transformer  # type: ignore[attr-defined]
-        else None,
+        "Transformer": (
+            template_vars.transformer.get("name")  # type: ignore[attr-defined]
+            if template_vars.use_transformer  # type: ignore[attr-defined]
+            else None
+        ),
     }
     msg.info("Generated config template specific for your use case")
     for label, value in use_case.items():

spacy/compat.py

@@ -1,4 +1,5 @@
 """Helpers for Python and platform compatibility."""
+
 import sys
 
 from thinc.util import copy_array

spacy/displacy/__init__.py

@@ -4,6 +4,7 @@
 DOCS: https://spacy.io/api/top-level#displacy
 USAGE: https://spacy.io/usage/visualizers
 """
+
 import warnings
 from typing import Any, Callable, Dict, Iterable, Optional, Union
 

spacy/lang/am/examples.py

@@ -5,7 +5,6 @@
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "አፕል የዩኬን ጅምር ድርጅት በ 1 ቢሊዮን ዶላር ለመግዛት አስቧል።",
     "የራስ ገዝ መኪኖች የኢንሹራንስ ኃላፊነትን ወደ አምራቾች ያዛውራሉ",

spacy/lang/az/examples.py

@@ -4,7 +4,6 @@
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Bu bir cümlədir.",
     "Necəsən?",

spacy/lang/bg/stop_words.py

@@ -3,6 +3,7 @@
     https://github.com/Alir3z4/stop-words - Original list, serves as a base.
     https://postvai.com/books/stop-dumi.pdf - Additions to the original list in order to improve it.
 """
+
 STOP_WORDS = set(
     """
 а автентичен аз ако ала

spacy/lang/bn/examples.py

@@ -5,5 +5,4 @@
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = ["তুই খুব ভালো", "আজ আমরা ডাক্তার দেখতে যাবো", "আমি জানি না "]