[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

docs/_source/conf.py

@@ -82,9 +82,8 @@
     "pipversion": "" if "dev" in release else "==" + release,
     "dockertag": "master" if "dev" in release else "v" + release,
 }
-myst_substitutions[
-    "dockercomposeyaml"
-] = """```yaml
+myst_substitutions["dockercomposeyaml"] = (
+    """```yaml
 # docker-compose.yaml
 version: "3"
 
@@ -98,11 +97,11 @@
      ARGILLA_AUTH_SECRET_KEY: Please generate a 32 character random string with: openssl rand -hex 32
    restart: unless-stopped
 ```""".format(
-    myst_substitutions["dockertag"]
+        myst_substitutions["dockertag"]
+    )
 )
-myst_substitutions[
-    "dockercomposeuseryaml"
-] = """```yaml
+myst_substitutions["dockercomposeuseryaml"] = (
+    """```yaml
 # docker-compose.yaml
 services:
   argilla:
@@ -119,7 +118,8 @@
       - ${}/.users.yaml:/config/.users.yaml
   ...
 ```""".format(
-    myst_substitutions["dockertag"], "PWD"
+        myst_substitutions["dockertag"], "PWD"
+    )
 )
 
 # Do not execute the notebooks when building the docs

src/argilla/cli/datasets/__main__.py

@@ -44,9 +44,11 @@ def callback(
         dataset = FeedbackDataset.from_argilla(name=name, workspace=workspace)
     except ValueError as e:
         echo_in_panel(
-            f"`FeedbackDataset` with name={name} not found in Argilla. Try using '--workspace' option."
-            if not workspace
-            else f"`FeedbackDataset with name={name} and workspace={workspace} not found in Argilla.",
+            (
+                f"`FeedbackDataset` with name={name} not found in Argilla. Try using '--workspace' option."
+                if not workspace
+                else f"`FeedbackDataset with name={name} and workspace={workspace} not found in Argilla."
+            ),
             title="Dataset not found",
             title_align="left",
             success=False,

src/argilla/client/datasets.py

@@ -744,16 +744,20 @@ def _to_datasets_dict(self) -> Dict:
         for key in self._RECORD_TYPE.__fields__:
             if key == "prediction":
                 ds_dict[key] = [
-                    [{"label": pred[0], "score": pred[1]} for pred in rec.prediction]
-                    if rec.prediction is not None
-                    else None
+                    (
+                        [{"label": pred[0], "score": pred[1]} for pred in rec.prediction]
+                        if rec.prediction is not None
+                        else None
+                    )
                     for rec in self._records
                 ]
             elif key == "explanation":
                 ds_dict[key] = [
-                    {key: list(map(dict, tokattrs)) for key, tokattrs in rec.explanation.items()}
-                    if rec.explanation is not None
-                    else None
+                    (
+                        {key: list(map(dict, tokattrs)) for key, tokattrs in rec.explanation.items()}
+                        if rec.explanation is not None
+                        else None
+                    )
                     for rec in self._records
                 ]
             elif key == "id":
@@ -1255,9 +1259,11 @@ def entities_to_dict(
             if entities is None:
                 return None
             return [
-                {"label": ent[0], "start": ent[1], "end": ent[2]}
-                if len(ent) == 3
-                else {"label": ent[0], "start": ent[1], "end": ent[2], "score": ent[3]}
+                (
+                    {"label": ent[0], "start": ent[1], "end": ent[2]}
+                    if len(ent) == 3
+                    else {"label": ent[0], "start": ent[1], "end": ent[2], "score": ent[3]}
+                )
                 for ent in entities
             ]
 
@@ -1281,9 +1287,11 @@ def __entities_to_tuple__(
         entities,
     ) -> List[Union[Tuple[str, int, int], Tuple[str, int, int, float]]]:
         return [
-            (ent["label"], ent["start"], ent["end"])
-            if len(ent) == 3
-            else (ent["label"], ent["start"], ent["end"], ent["score"] or 0.0)
+            (
+                (ent["label"], ent["start"], ent["end"])
+                if len(ent) == 3
+                else (ent["label"], ent["start"], ent["end"], ent["score"] or 0.0)
+            )
             for ent in entities
         ]
 

src/argilla/client/feedback/config.py

@@ -43,9 +43,9 @@ class DatasetConfig(BaseModel):
     fields: List[AllowedFieldTypes]
     questions: List[Annotated[AllowedQuestionTypes, Field(..., discriminator="type")]]
     guidelines: Optional[str] = None
-    metadata_properties: Optional[
-        List[Annotated[AllowedMetadataPropertyTypes, Field(..., discriminator="type")]]
-    ] = None
+    metadata_properties: Optional[List[Annotated[AllowedMetadataPropertyTypes, Field(..., discriminator="type")]]] = (
+        None
+    )
     allow_extra_metadata: bool = True
     vectors_settings: Optional[List[VectorSettings]] = None
 

src/argilla/client/feedback/dataset/local/mixins.py

@@ -504,23 +504,27 @@ def for_text_classification(
         return cls(
             fields=[TextField(name="text", use_markdown=use_markdown)],
             questions=[
-                LabelQuestion(
-                    name="label",
-                    labels=labels,
-                    description=description,
-                )
-                if not multi_label
-                else MultiLabelQuestion(
-                    name="label",
-                    labels=labels,
-                    description=description,
+                (
+                    LabelQuestion(
+                        name="label",
+                        labels=labels,
+                        description=description,
+                    )
+                    if not multi_label
+                    else MultiLabelQuestion(
+                        name="label",
+                        labels=labels,
+                        description=description,
+                    )
                 )
             ],
-            guidelines=guidelines
-            if guidelines is not None
-            else default_guidelines
-            if multi_label
-            else default_guidelines.replace("one or more labels", "one label"),
+            guidelines=(
+                guidelines
+                if guidelines is not None
+                else (
+                    default_guidelines if multi_label else default_guidelines.replace("one or more labels", "one label")
+                )
+            ),
             metadata_properties=metadata_properties,
             vectors_settings=vectors_settings,
         )
@@ -739,11 +743,15 @@ def for_supervised_fine_tuning(
                     name="response", description="Write the response to the instruction.", use_markdown=use_markdown
                 )
             ],
-            guidelines=guidelines
-            if guidelines is not None
-            else default_guidelines + " Take the context into account when writing the response."
-            if context
-            else default_guidelines,
+            guidelines=(
+                guidelines
+                if guidelines is not None
+                else (
+                    default_guidelines + " Take the context into account when writing the response."
+                    if context
+                    else default_guidelines
+                )
+            ),
             metadata_properties=metadata_properties,
             vectors_settings=vectors_settings,
         )
@@ -977,23 +985,27 @@ def for_multi_modal_classification(
         return cls(
             fields=[TextField(name="content", use_markdown=True, required=True)],
             questions=[
-                LabelQuestion(
-                    name="label",
-                    labels=labels,
-                    description=description,
-                )
-                if not multi_label
-                else MultiLabelQuestion(
-                    name="label",
-                    labels=labels,
-                    description=description,
+                (
+                    LabelQuestion(
+                        name="label",
+                        labels=labels,
+                        description=description,
+                    )
+                    if not multi_label
+                    else MultiLabelQuestion(
+                        name="label",
+                        labels=labels,
+                        description=description,
+                    )
                 )
             ],
-            guidelines=guidelines
-            if guidelines is not None
-            else default_guidelines
-            if multi_label
-            else default_guidelines.replace("one or more labels", "one label"),
+            guidelines=(
+                guidelines
+                if guidelines is not None
+                else (
+                    default_guidelines if multi_label else default_guidelines.replace("one or more labels", "one label")
+                )
+            ),
             metadata_properties=metadata_properties,
             vectors_settings=vectors_settings,
         )

src/argilla/client/feedback/integrations/textdescriptives.py

@@ -308,11 +308,11 @@ def _add_text_descriptives_to_metadata(
                 filtered_metrics = {key: value for key, value in metrics.items() if not pd.isna(value)}
                 if metadata_prop_types is not None:
                     filtered_metrics = {
-                        key: int(value)
-                        if metadata_prop_types.get(key) == "integer"
-                        else float(value)
-                        if metadata_prop_types.get(key) == "float"
-                        else value
+                        key: (
+                            int(value)
+                            if metadata_prop_types.get(key) == "integer"
+                            else float(value) if metadata_prop_types.get(key) == "float" else value
+                        )
                         for key, value in filtered_metrics.items()
                     }
                 record.metadata.update(filtered_metrics)

src/argilla/client/feedback/schemas/metadata.py

@@ -71,8 +71,7 @@ def title_must_have_value(cls, v: Optional[str], values: Dict[str, Any]) -> str:
 
     @property
     @abstractmethod
-    def server_settings(self) -> Dict[str, Any]:
-        ...
+    def server_settings(self) -> Dict[str, Any]: ...
 
     def to_server_payload(self) -> Dict[str, Any]:
         return {
@@ -84,20 +83,17 @@ def to_server_payload(self) -> Dict[str, Any]:
 
     @property
     @abstractmethod
-    def _pydantic_field_with_validator(self) -> Tuple[Dict[str, Tuple[Any, ...]], Dict[str, Callable]]:
-        ...
+    def _pydantic_field_with_validator(self) -> Tuple[Dict[str, Tuple[Any, ...]], Dict[str, Callable]]: ...
 
     @abstractmethod
     def _validate_filter(self, metadata_filter: "MetadataFilters") -> None:
         pass
 
     @abstractmethod
-    def _check_allowed_value_type(self, value: Any) -> Any:
-        ...
+    def _check_allowed_value_type(self, value: Any) -> Any: ...
 
     @abstractmethod
-    def _validator(self, value: Any) -> Any:
-        ...
+    def _validator(self, value: Any) -> Any: ...
 
 
 def _validator_definition(schema: MetadataPropertySchema) -> Dict[str, Any]:
@@ -395,8 +391,7 @@ class Config:
 
     @property
     @abstractmethod
-    def query_string(self) -> str:
-        ...
+    def query_string(self) -> str: ...
 
 
 class TermsMetadataFilter(MetadataFilterSchema):

src/argilla/client/feedback/schemas/remote/records.py

@@ -207,9 +207,9 @@ def __updated_record_data(self) -> None:
 
         updated_record = self.from_api(
             payload=response.parsed,
-            question_id_to_name={value: key for key, value in self.question_name_to_id.items()}
-            if self.question_name_to_id
-            else None,
+            question_id_to_name=(
+                {value: key for key, value in self.question_name_to_id.items()} if self.question_name_to_id else None
+            ),
             client=self.client,
         )
 
@@ -306,15 +306,17 @@ def from_api(
             id=payload.id,
             client=client,
             fields=payload.fields,
-            responses=[RemoteResponseSchema.from_api(response) for response in payload.responses]
-            if payload.responses
-            else [],
-            suggestions=[
-                RemoteSuggestionSchema.from_api(suggestion, question_id_to_name=question_id_to_name, client=client)
-                for suggestion in payload.suggestions
-            ]
-            if payload.suggestions
-            else [],
+            responses=(
+                [RemoteResponseSchema.from_api(response) for response in payload.responses] if payload.responses else []
+            ),
+            suggestions=(
+                [
+                    RemoteSuggestionSchema.from_api(suggestion, question_id_to_name=question_id_to_name, client=client)
+                    for suggestion in payload.suggestions
+                ]
+                if payload.suggestions
+                else []
+            ),
             metadata=payload.metadata if payload.metadata else {},
             vectors=payload.vectors if payload.vectors else {},
             external_id=payload.external_id if payload.external_id else None,

src/argilla/client/sdk/text2text/models.py

@@ -48,9 +48,11 @@ def from_client(cls, record: ClientText2TextRecord):
         if record.prediction is not None:
             prediction = Text2TextAnnotation(
                 sentences=[
-                    Text2TextPrediction(text=pred[0], score=pred[1])
-                    if isinstance(pred, tuple)
-                    else Text2TextPrediction(text=pred)
+                    (
+                        Text2TextPrediction(text=pred[0], score=pred[1])
+                        if isinstance(pred, tuple)
+                        else Text2TextPrediction(text=pred)
+                    )
                     for pred in record.prediction
                 ],
                 agent=record.prediction_agent or MACHINE_NAME,
@@ -81,9 +83,9 @@ class Text2TextRecord(CreationText2TextRecord):
     def to_client(self) -> ClientText2TextRecord:
         return ClientText2TextRecord(
             text=self.text,
-            prediction=[(sentence.text, sentence.score) for sentence in self.prediction.sentences]
-            if self.prediction
-            else None,
+            prediction=(
+                [(sentence.text, sentence.score) for sentence in self.prediction.sentences] if self.prediction else None
+            ),
             prediction_agent=self.prediction.agent if self.prediction else None,
             annotation=self.annotation.sentences[0].text if self.annotation else None,
             annotation_agent=self.annotation.agent if self.annotation else None,

src/argilla/client/sdk/text_classification/models.py

@@ -100,19 +100,21 @@ def to_client(self) -> ClientTextClassificationRecord:
             multi_label=self.multi_label,
             status=self.status,
             metadata=self.metadata or {},
-            prediction=[(label.class_label, label.score) for label in self.prediction.labels]
-            if self.prediction
-            else None,
+            prediction=(
+                [(label.class_label, label.score) for label in self.prediction.labels] if self.prediction else None
+            ),
             prediction_agent=self.prediction.agent if self.prediction else None,
             annotation=annotations,
             annotation_agent=self.annotation.agent if self.annotation else None,
             vectors=self._to_client_vectors(self.vectors),
-            explanation={
-                key: [ClientTokenAttributions.parse_obj(attribution) for attribution in attributions]
-                for key, attributions in self.explanation.items()
-            }
-            if self.explanation
-            else None,
+            explanation=(
+                {
+                    key: [ClientTokenAttributions.parse_obj(attribution) for attribution in attributions]
+                    for key, attributions in self.explanation.items()
+                }
+                if self.explanation
+                else None
+            ),
             metrics=self.metrics or None,
             search_keywords=self.search_keywords or None,
         )