Resolve chartboost/ruff-action@v1 violations across the repository

pyproject.toml

@@ -75,3 +75,16 @@ sacrebleu = ["py.typed"]
 
 [tool.setuptools_scm]
 version_file = "sacrebleu/version.py"
+
+[tool.ruff.lint]
+ignore = [
+    "B019",
+    "B020",
+    "PLR1704",
+    "PLW1508",
+    "RUF012",
+    "SIM115",
+    "SIM117",
+    "TRY002",
+    "TRY203",
+]

sacrebleu/__init__.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 
 # Copyright 2017--2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
@@ -42,25 +40,25 @@
 from .version import __version__
 
 __all__ = [
-    "smart_open",
-    "SACREBLEU_DIR",
-    "download_test_set",
-    "get_source_file",
-    "get_reference_files",
-    "get_available_testsets",
-    "get_langpairs_for_testset",
-    "extract_word_ngrams",
-    "extract_char_ngrams",
-    "DATASETS",
     "BLEU",
     "CHRF",
+    "DATASETS",
+    "SACREBLEU_DIR",
     "TER",
+    "__version__",
     "corpus_bleu",
+    "corpus_chrf",
+    "corpus_ter",
+    "download_test_set",
+    "extract_char_ngrams",
+    "extract_word_ngrams",
+    "get_available_testsets",
+    "get_langpairs_for_testset",
+    "get_reference_files",
+    "get_source_file",
     "raw_corpus_bleu",
     "sentence_bleu",
-    "corpus_chrf",
     "sentence_chrf",
-    "corpus_ter",
     "sentence_ter",
-    "__version__",
+    "smart_open",
 ]

sacrebleu/__main__.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 
 # Copyright 2017--2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #

sacrebleu/compat.py

@@ -1,4 +1,6 @@
-from typing import Sequence, Optional
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 from .metrics import BLEU, CHRF, TER, BLEUScore, CHRFScore, TERScore
 
@@ -39,7 +41,7 @@ def corpus_bleu(hypotheses: Sequence[str],
 
 def raw_corpus_bleu(hypotheses: Sequence[str],
                     references: Sequence[Sequence[str]],
-                    smooth_value: Optional[float] = BLEU.SMOOTH_DEFAULTS['floor']) -> BLEUScore:
+                    smooth_value: float | None = BLEU.SMOOTH_DEFAULTS['floor']) -> BLEUScore:
     """Computes BLEU for a corpus against a single (or multiple) reference(s).
     This convenience function assumes a particular set of arguments i.e.
     it disables tokenization and applies a `floor` smoothing with value `0.1`.
@@ -64,7 +66,7 @@ def raw_corpus_bleu(hypotheses: Sequence[str],
 def sentence_bleu(hypothesis: str,
                   references: Sequence[str],
                   smooth_method: str = 'exp',
-                  smooth_value: Optional[float] = None,
+                  smooth_value: float | None = None,
                   lowercase: bool = False,
                   tokenize=BLEU.TOKENIZER_DEFAULT,
                   use_effective_order: bool = True) -> BLEUScore:

sacrebleu/dataset/__init__.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # Copyright 2017--2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
@@ -69,8 +67,8 @@
     k: {d.split("=")[0]: d.split("=")[1] for d in v.split()}
     for (k, v) in _SUBSETS.items()
 }
-COUNTRIES = sorted(list({v.split("-")[0] for v in SUBSETS["wmt19"].values()}))
-DOMAINS = sorted(list({v.split("-")[1] for v in SUBSETS["wmt19"].values()}))
+COUNTRIES = sorted({v.split("-")[0] for v in SUBSETS["wmt19"].values()})
+DOMAINS = sorted({v.split("-")[1] for v in SUBSETS["wmt19"].values()})
 
 DATASETS = {
     # wmt

sacrebleu/dataset/__main__.py

@@ -28,8 +28,8 @@
             print("Downloading ", url)
             with urllib.request.urlopen(url) as f:
                 data = f.read()
-        except Exception as exc:
-            raise (exc)
+        except Exception:
+            raise
 
         if hashlib.md5(data).hexdigest() != md5_hash:
             print("MD5 check failed for", url)

sacrebleu/dataset/base.py

@@ -1,10 +1,11 @@
 """
 The base class for all types of datasets.
 """
+from __future__ import annotations
+
 import os
 import re
 from abc import ABCMeta, abstractmethod
-from typing import Dict, List, Optional
 
 from ..utils import SACREBLEU_DIR, download_file, smart_open
 
@@ -13,11 +14,11 @@ class Dataset(metaclass=ABCMeta):
     def __init__(
         self,
         name: str,
-        data: Optional[List[str]] = None,
-        description: Optional[str] = None,
-        citation: Optional[str] = None,
-        md5: Optional[List[str]] = None,
-        langpairs=Dict[str, List[str]],
+        data: list[str] | None = None,
+        description: str | None = None,
+        citation: str | None = None,
+        md5: list[str] | None = None,
+        langpairs=None,
         **kwargs,
     ):
         """
@@ -35,7 +36,7 @@ def __init__(
         self.description = description
         self.citation = citation
         self.md5 = md5
-        self.langpairs = langpairs
+        self.langpairs = {} if langpairs is None else langpairs
         self.kwargs = kwargs
 
         # Don't do any downloading or further processing now.
@@ -118,9 +119,8 @@ def process_to_text(self, langpair=None) -> None:
 
         :param langpair: The language pair to process. e.g. "en-de". If None, all files will be processed.
         """
-        pass
 
-    def fieldnames(self, langpair) -> List[str]:
+    def fieldnames(self, langpair) -> list[str]:
         """
         Return a list of all the field names. For most source, this is just
         the source and the reference. For others, it might include the document
@@ -141,8 +141,7 @@ def __iter__(self, langpair):
         all_files = self.get_files(langpair)
         all_fins = [smart_open(f) for f in all_files]
 
-        for item in zip(*all_fins):
-            yield item
+        yield from zip(*all_fins)
 
     def source(self, langpair):
         """
@@ -160,8 +159,7 @@ def references(self, langpair):
         ref_files = self.get_reference_files(langpair)
         ref_fins = [smart_open(f) for f in ref_files]
 
-        for item in zip(*ref_fins):
-            yield item
+        yield from zip(*ref_fins)
 
     def get_source_file(self, langpair):
         all_files = self.get_files(langpair)

sacrebleu/dataset/fake_sgml.py

@@ -66,7 +66,7 @@ def process_to_text(self, langpair=None):
                 origin_file = os.path.join(self._rawdir, origin_file)
                 output_file = self._get_txt_file_path(langpair, field)
 
-                if field.startswith("src") or field.startswith("ref"):
+                if field.startswith(("src", "ref")):
                     self._convert_format(origin_file, output_file)
                 else:
                     # document metadata keys

sacrebleu/dataset/iwslt_xml.py

@@ -5,4 +5,3 @@ class IWSLTXMLDataset(FakeSGMLDataset):
     """IWSLT dataset format. Can be parsed with the lxml parser."""
 
     # Same as FakeSGMLDataset. Nothing to do here.
-    pass

sacrebleu/dataset/wmt_xml.py

@@ -1,12 +1,11 @@
 import os
+from collections import defaultdict
 
 import lxml.etree as ET
 
 from ..utils import smart_open
 from .base import Dataset
 
-from collections import defaultdict
-
 
 def _get_field_by_translator(translator):
     if not translator:
@@ -92,14 +91,14 @@ def get_sents(doc):
 
             for seg_id in sorted(src_sents.keys()):
                 # no ref translation is available for this segment
-                if not any([value.get(seg_id, "") for value in trans_to_ref.values()]):
+                if not any(value.get(seg_id, "") for value in trans_to_ref.values()):
                     continue
                 for translator in translators:
                     refs[_get_field_by_translator(translator)].append(
                         trans_to_ref.get(translator, {translator: {}}).get(seg_id, "")
                     )
                 src.append(src_sents[seg_id])
-                for system_name in hyps.keys():
+                for system_name in hyps:
                     systems[system_name].append(hyps[system_name][seg_id])
                 docids.append(doc.attrib["id"])
                 orig_langs.append(doc.attrib["origlang"])

sacrebleu/metrics/__init__.py

@@ -1,8 +1,8 @@
 """The implementation of various metrics."""
 
-from .bleu import BLEU, BLEUScore   # noqa: F401
-from .chrf import CHRF, CHRFScore   # noqa: F401
-from .ter import TER, TERScore      # noqa: F401
+from .bleu import BLEU, BLEUScore  # noqa: F401
+from .chrf import CHRF, CHRFScore  # noqa: F401
+from .ter import TER, TERScore  # noqa: F401
 
 METRICS = {
     'BLEU': BLEU,

sacrebleu/metrics/base.py

@@ -4,12 +4,14 @@
 of abstract methods. This way, a correctly implemented metric will work
 seamlessly with the rest of the codebase.
 """
+from __future__ import annotations
 
 import json
 import logging
 import statistics
 from abc import ABCMeta, abstractmethod
-from typing import Any, Dict, List, Optional, Sequence
+from collections.abc import Sequence
+from typing import Any
 
 from ..version import __version__
 
@@ -88,7 +90,7 @@ def format(
 
         return full_score
 
-    def estimate_ci(self, scores: List["Score"]):
+    def estimate_ci(self, scores: list[Score]):
         """Takes a list of scores and stores mean, stdev and 95% confidence
         interval around the mean.
 
@@ -237,7 +239,7 @@ def _check_sentence_score_args(self, hyp: str, refs: Sequence[str]):
             raise TypeError(f"{prefix}: {err_msg}")
 
     def _check_corpus_score_args(
-        self, hyps: Sequence[str], refs: Optional[Sequence[Sequence[str]]]
+        self, hyps: Sequence[str], refs: Sequence[Sequence[str]] | None
     ):
         """Performs sanity checks on `corpus_score` method's arguments.
 
@@ -289,22 +291,20 @@ def _check_corpus_score_args(
             raise TypeError(f"{prefix}: {err_msg}")
 
     @abstractmethod
-    def _aggregate_and_compute(self, stats: List[List[Any]]) -> Any:
+    def _aggregate_and_compute(self, stats: list[list[Any]]) -> Any:
         """Computes the final score given the pre-computed match statistics.
 
         :param stats: A list of segment-level statistics.
         :return: A `Score` instance.
         """
-        pass
 
     @abstractmethod
-    def _compute_score_from_stats(self, stats: List[Any]) -> Any:
+    def _compute_score_from_stats(self, stats: list[Any]) -> Any:
         """Computes the final score from already aggregated statistics.
 
         :param stats: A list or numpy array of segment-level statistics.
         :return: A `Score` object.
         """
-        pass
 
     @abstractmethod
     def _preprocess_segment(self, sent: str) -> str:
@@ -314,22 +314,20 @@ def _preprocess_segment(self, sent: str) -> str:
         :param sent: The input sentence.
         :return: The pre-processed output sentence.
         """
-        pass
 
     @abstractmethod
-    def _extract_reference_info(self, refs: Sequence[str]) -> Dict[str, Any]:
+    def _extract_reference_info(self, refs: Sequence[str]) -> dict[str, Any]:
         """Given a list of reference segments, extract the required
         information (such as n-grams for BLEU and chrF). This should be implemented
         for the generic `_cache_references()` to work across all metrics.
 
         :param refs: A sequence of strings.
         """
-        pass
 
     @abstractmethod
     def _compute_segment_statistics(
-        self, hypothesis: str, ref_kwargs: Dict
-    ) -> List[Any]:
+        self, hypothesis: str, ref_kwargs: dict
+    ) -> list[Any]:
         """Given a (pre-processed) hypothesis sentence and already computed
         reference info, returns the best match statistics across the
         references. The return type is usually a List of ints or floats.
@@ -339,9 +337,8 @@ def _compute_segment_statistics(
         within. This is formulated as a dictionary as different metrics may
         require different information regarding a reference segment.
         """
-        pass
 
-    def _cache_references(self, references: Sequence[Sequence[str]]) -> List[Any]:
+    def _cache_references(self, references: Sequence[Sequence[str]]) -> list[Any]:
         """Given the full set of document references, extract segment n-grams
         (or other necessary information) for caching purposes.
 
@@ -371,15 +368,15 @@ def _cache_references(self, references: Sequence[Sequence[str]]) -> List[Any]:
             ref_cache.append(self._extract_reference_info(lines))
 
         if len(num_refs) == 1:
-            self.num_refs = list(num_refs)[0]
+            self.num_refs = next(iter(num_refs))
         else:
             # A variable number of refs exist
             self.num_refs = -1
 
         return ref_cache
 
     def _extract_corpus_statistics(
-        self, hypotheses: Sequence[str], references: Optional[Sequence[Sequence[str]]]
+        self, hypotheses: Sequence[str], references: Sequence[Sequence[str]] | None
     ) -> Any:
         """Reads the corpus and returns sentence-level match statistics for
         faster re-computations esp. during statistical tests.
@@ -440,7 +437,7 @@ def sentence_score(self, hypothesis: str, references: Sequence[str]) -> Any:
     def corpus_score(
         self,
         hypotheses: Sequence[str],
-        references: Optional[Sequence[Sequence[str]]],
+        references: Sequence[Sequence[str]] | None,
         n_bootstrap: int = 1,
     ) -> Any:
         """Compute the metric for a corpus against a single (or multiple) reference(s).