ruff stylechecking

.pre-commit-config.yaml

@@ -18,22 +18,6 @@ repos:
       - id: end-of-file-fixer
         exclude: LICENSE
 
-  - repo: local
-    hooks:
-      - id: pyupgrade
-        name: pyupgrade
-        entry: poetry run pyupgrade --py38-plus
-        types: [python]
-        language: system
-
-  - repo: local
-    hooks:
-      - id: isort
-        name: isort
-        entry: poetry run isort --settings-path pyproject.toml
-        types: [python]
-        language: system
-
   - repo: local
     hooks:
       - id: black
@@ -42,13 +26,12 @@ repos:
         types: [python]
         language: system
 
-  - repo: local
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    rev: 'v0.0.227'
     hooks:
-      - id: flake8
-        name: flake8
-        entry: poetry run flake8 --config setup.cfg
-        types: [python]
-        language: system
+      - id: ruff
+        # Respect `exclude` and `extend-exclude` settings.
+        args: ["--force-exclude"]
 
   - repo: local
     hooks:

Makefile

@@ -78,26 +78,23 @@ update-deps:
 #* Linting
 .PHONY: check-style
 check-style:
-	poetry run isort --diff --check-only --settings-path pyproject.toml ./
 	poetry run black --diff --check --config pyproject.toml ./
+	poetry run ruff  --no-fix --config pyproject.toml ./
 #   poetry run darglint --verbosity 2 inseq tests
-	poetry run flake8 --config setup.cfg ./
 #	poetry run mypy --config-file pyproject.toml ./
 
 .PHONY: fix-style
 fix-style:
-	poetry run pyupgrade --exit-zero-even-if-changed --py38-plus **/*.py
-	poetry run isort --settings-path pyproject.toml ./
 	poetry run black --config pyproject.toml ./
+	poetry run ruff --config pyproject.toml ./
 
 .PHONY: check-safety
 check-safety:
 	poetry check
 	poetry run safety check --full-report -i 51499 -i 51457
-	poetry run bandit -ll --recursive inseq tests
 
 .PHONY: lint
-lint: check-style check-safety
+lint: fix-style check-safety
 
 #* Linting
 .PHONY: test

inseq/attr/__init__.py

@@ -7,7 +7,6 @@
     register_step_score,
 )
 
-
 __all__ = [
     "FeatureAttribution",
     "list_feature_attribution_methods",

inseq/attr/attribution_decorators.py

@@ -13,14 +13,12 @@
 # limitations under the License.
 """ Decorators for attribution methods. """
 
-from typing import Any, Callable, List, Optional, Sequence
-
 import logging
 from functools import wraps
+from typing import Any, Callable, List, Optional, Sequence
 
 from ..data.data_utils import TensorWrapper
 
-
 logger = logging.getLogger(__name__)
 
 

inseq/attr/feat/__init__.py

@@ -13,7 +13,6 @@
     SaliencyAttribution,
 )
 
-
 __all__ = [
     "FeatureAttribution",
     "extract_args",

inseq/attr/feat/attention_attribution.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 """ Attention-based feature attribution methods. """
 
-from typing import Any, Callable, Dict, Union
-
 import logging
+from typing import Any, Callable, Dict, Union
 
 from ...data import Batch, EncoderDecoderBatch, FeatureAttributionStepOutput
 from ...utils import Registry, pretty_tensor
@@ -25,7 +24,6 @@
 from .feature_attribution import FeatureAttribution
 from .ops import Attention
 
-
 logger = logging.getLogger(__name__)
 
 

inseq/attr/feat/attribution_utils.py

@@ -1,7 +1,6 @@
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
-
 import logging
 import math
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -20,7 +19,6 @@
     TokenWithId,
 )
 
-
 if TYPE_CHECKING:
     from ...models import AttributionModel
     from .feature_attribution import FeatureAttribution
@@ -163,8 +161,8 @@ def extract_args(
         if step_score not in STEP_SCORES_MAP:
             raise AttributeError(
                 f"Step score {step_score} not found. Available step scores are: "
-                f"{', '.join([x for x in STEP_SCORES_MAP.keys()])}. Use the inseq.register_step_score"
-                f"function to register a custom step score."
+                f"{', '.join(list(STEP_SCORES_MAP.keys()))}. Use the inseq.register_step_score"
+                "function to register a custom step score."
             )
         extra_step_scores_args.update(
             **extract_signature_args(

inseq/attr/feat/feature_attribution.py

@@ -16,11 +16,10 @@
 Todo:
     * 🟡: Allow custom arguments for model loading in the :class:`FeatureAttribution` :meth:`load` method.
 """
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
-
 import logging
 from abc import abstractmethod
 from datetime import datetime
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 from torchtyping import TensorType
 
@@ -46,7 +45,6 @@
 from ..attribution_decorators import batched, set_hook, unset_hook
 from .attribution_utils import STEP_SCORES_MAP, check_attribute_positions, get_step_scores, tok2string
 
-
 if TYPE_CHECKING:
     from ...models import AttributionModel
 
@@ -465,8 +463,8 @@ def filtered_attribute_step(
             if step_score not in STEP_SCORES_MAP:
                 raise AttributeError(
                     f"Step score {step_score} not found. Available step scores are: "
-                    f"{', '.join([x for x in STEP_SCORES_MAP.keys()])}. Use the inseq.register_step_score"
-                    f"function to register a custom step score."
+                    f"{', '.join(list(STEP_SCORES_MAP.keys()))}. Use the inseq.register_step_score"
+                    "function to register a custom step score."
                 )
             step_output.step_scores[step_score] = get_step_scores(
                 self.attribution_model, batch, target_ids, step_score, step_scores_args

inseq/attr/feat/gradient_attribution.py

@@ -13,9 +13,8 @@
 # limitations under the License.
 """ Gradient-based feature attribution methods. """
 
-from typing import Any, Dict
-
 import logging
+from typing import Any, Dict
 
 from captum.attr import (
     DeepLift,
@@ -34,7 +33,6 @@
 from .feature_attribution import FeatureAttribution
 from .ops import DiscretetizedIntegratedGradients
 
-
 logger = logging.getLogger(__name__)
 
 

inseq/attr/feat/ops/__init__.py

@@ -2,5 +2,4 @@
 from .discretized_integrated_gradients import DiscretetizedIntegratedGradients
 from .monotonic_path_builder import MonotonicPathBuilder
 
-
 __all__ = ["DiscretetizedIntegratedGradients", "MonotonicPathBuilder", "Attention"]

inseq/attr/feat/ops/basic_attention.py

@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List, Protocol, Tuple, Union
-
 import logging
+from typing import Any, Dict, List, Protocol, Tuple, Union
 
 import torch
 from captum._utils.typing import TensorOrTupleOfTensorsGeneric
@@ -24,7 +23,6 @@
 from ....data import Batch, EncoderDecoderBatch
 from ....utils.typing import AggregatedLayerAttentionTensor, FullAttentionOutput, FullLayerAttentionTensor
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -115,7 +113,7 @@ def _aggregate_attention_heads(
             if isinstance(aggregate_fn, str):
                 if aggregate_fn not in cls.AGGREGATE_FN_OPTIONS:
                     raise RuntimeError(
-                        "Invalid aggregation method specified." f"Valid methods are: {cls.AGGREGATE_FN_OPTIONS.keys()}"
+                        f"Invalid aggregation method specified.Valid methods are: {cls.AGGREGATE_FN_OPTIONS.keys()}"
                     )
                 aggregate_fn = cls.AGGREGATE_FN_OPTIONS[aggregate_fn]
             if heads is None:
@@ -201,7 +199,7 @@ def _aggregate_layers(
             if isinstance(aggregate_fn, str):
                 if aggregate_fn not in cls.AGGREGATE_FN_OPTIONS:
                     raise RuntimeError(
-                        "Invalid aggregation method specified." f"Valid methods are: {cls.AGGREGATE_FN_OPTIONS.keys()}"
+                        f"Invalid aggregation method specified.Valid methods are: {cls.AGGREGATE_FN_OPTIONS.keys()}"
                     )
                 aggregate_fn = cls.AGGREGATE_FN_OPTIONS[aggregate_fn]
             if layers is None:

inseq/attr/feat/ops/discretized_integrated_gradients.py

@@ -16,9 +16,8 @@
 # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
 # OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-from typing import Any, Callable, List, Tuple, Union
-
 from pathlib import Path
+from typing import Any, Callable, List, Tuple, Union
 
 import torch
 from captum._utils.common import (

inseq/attr/feat/ops/monotonic_path_builder.py

@@ -17,21 +17,19 @@
 # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
 # OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-from typing import Any, List, Optional, Tuple, Union
-
 import logging
 import os
 from enum import Enum
 from itertools import islice
 from pathlib import Path
+from typing import Any, List, Optional, Tuple, Union
 
 import torch
 from scipy.sparse import csr_matrix
 from torchtyping import TensorType
 
 from ....utils import is_joblib_available, is_scikitlearn_available
 
-
 if is_joblib_available():
     from joblib import Parallel, delayed
 
@@ -41,7 +39,6 @@
 from ....utils import INSEQ_ARTIFACTS_CACHE, cache_results, euclidean_distance
 from ....utils.typing import MultiStepEmbeddingsTensor, VocabularyEmbeddingsTensor
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -59,8 +56,10 @@ def __init__(
         *args: Tuple[Any],
     ) -> None:
         super().__init__(
-            f"Unknown strategy: {strategy}.\nAvailable strategies: "
-            f"{','.join([s.value for s in PathBuildingStrategies])}",
+            (
+                f"Unknown strategy: {strategy}.\nAvailable strategies: "
+                f"{','.join([s.value for s in PathBuildingStrategies])}"
+            ),
             *args,
         )
 

inseq/commands/attribute.py

@@ -1,7 +1,6 @@
-from typing import List, Optional
-
 import logging
 from dataclasses import dataclass, field
+from typing import List, Optional
 
 from .. import list_feature_attribution_methods, load_model
 from ..utils import get_default_device
@@ -123,9 +122,9 @@ def __post_init__(self):
         if self.input_texts is None:
             raise RuntimeError("Input texts must be specified.")
         if isinstance(self.input_texts, str):
-            self.input_texts = [t for t in self.input_texts]
+            self.input_texts = list(self.input_texts)
         if isinstance(self.generated_texts, str):
-            self.generated_texts = [t for t in self.generated_texts]
+            self.generated_texts = list(self.generated_texts)
 
 
 def attribute(input_texts, generated_texts, args: AttributeBaseArgs):

inseq/commands/attribute_dataset.py

@@ -1,12 +1,10 @@
-from typing import List, Optional, Tuple
-
 from dataclasses import dataclass, field
+from typing import List, Optional, Tuple
 
 from ..utils import is_datasets_available
 from .attribute import AttributeBaseArgs, attribute
 from .base import BaseCLICommand
 
-
 if is_datasets_available():
     from datasets import load_dataset
 

inseq/commands/base.py

@@ -1,12 +1,10 @@
-from typing import Any, Iterable, NewType, Union
-
 import dataclasses
 from abc import ABC, abstractstaticmethod
 from argparse import Namespace
+from typing import Any, Iterable, NewType, Union
 
 from ..utils import InseqArgumentParser
 
-
 DataClassType = NewType("DataClassType", Any)
 OneOrMoreDataClasses = Union[DataClassType, Iterable[DataClassType]]
 

inseq/commands/cli.py

@@ -1,10 +1,10 @@
 """Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/commands/transformers_cli.py"""
+import sys
 
 from ..utils import InseqArgumentParser
 from .attribute import AttributeCommand
 from .attribute_dataset import AttributeDatasetCommand
 
-
 COMMANDS = [AttributeCommand, AttributeDatasetCommand]
 
 
@@ -19,7 +19,7 @@ def main():
 
     if not hasattr(args, "factory_method"):
         parser.print_help()
-        exit(1)
+        sys.exit(1)
 
     # Run
     command, command_args = args.factory_method(args)

inseq/data/__init__.py

@@ -16,7 +16,6 @@
 from .batch import Batch, BatchEmbedding, BatchEncoding, DecoderOnlyBatch, EncoderDecoderBatch
 from .viz import show_attributions
 
-
 __all__ = [
     "Aggregator",
     "AggregatorPipeline",