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

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src/distilabel/models/llms/mlx.py

@@ -33,9 +33,9 @@
 from distilabel.models.llms.utils import compute_tokens, prepare_output
 from distilabel.models.mixins.magpie import MagpieChatTemplateMixin
 from distilabel.typing import (
-    StandardInput,
     GenerateOutput,
     OutlinesStructuredOutputType,
+    StandardInput,
 )
 
 if TYPE_CHECKING:
@@ -218,10 +218,11 @@ def generate(  # type: ignore
 
             output: List[str] = []
             for _ in range(num_generations):
-
                 configured_processors = list(logits_processors or [])
                 if self.structured_output:
-                    structured_processors = self._prepare_structured_output(self.structured_output)
+                    structured_processors = self._prepare_structured_output(
+                        self.structured_output
+                    )
                     configured_processors.extend(structured_processors)
 
                 prompt = self.prepare_input(input)
@@ -257,15 +258,17 @@ def generate(  # type: ignore
             )
         return result
 
-
     def _prepare_structured_output(
-            self, structured_output: Optional[OutlinesStructuredOutputType] = None
+        self, structured_output: Optional[OutlinesStructuredOutputType] = None
     ) -> List[Callable]:
         """Creates the appropriate function to filter tokens to generate structured outputs."""
         if structured_output is None:
             return []
 
-        from distilabel.steps.tasks.structured_outputs.outlines import prepare_guided_output
+        from distilabel.steps.tasks.structured_outputs.outlines import (
+            prepare_guided_output,
+        )
+
         result = prepare_guided_output(structured_output, "mlx", self._wrapped_model)
         if (schema := result.get("schema")) and self.structured_output:
             self.structured_output["schema"] = schema

src/distilabel/steps/tasks/structured_outputs/outlines.py

@@ -1,3 +1,17 @@
+# Copyright 2023-present, Argilla, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import importlib
 import importlib.util
 import inspect
@@ -15,17 +29,18 @@
 )
 
 from pydantic import BaseModel
-from distilabel.errors import DistilabelUserError
 
+from distilabel.errors import DistilabelUserError
 from distilabel.steps.tasks.structured_outputs.utils import schema_as_dict
 
 if TYPE_CHECKING:  # noqa
     from llama_cpp import Llama  # noqa
     from transformers import Pipeline  # noqa
     from vllm import LLM as _vLLM  # noqa
-    from distilabel.models.llms.mlx import MlxModel #noqa
+    from distilabel.models.llms.mlx import MlxModel  # noqa
 
 from distilabel.typing import OutlinesStructuredOutputType  # noqa
+
 Frameworks = Literal["transformers", "llamacpp", "vllm", "mlx"]
 
 
@@ -106,20 +121,24 @@ def _get_logits_processor(framework: Frameworks) -> Tuple[Callable, Callable]:
 
 
 def _get_tokenizer_from_model(
-        llm: Union["_vLLM", "Pipeline", "Llama", "MlxModel"],
-        framework: Frameworks,
+    llm: Union["_vLLM", "Pipeline", "Llama", "MlxModel"],
+    framework: Frameworks,
 ) -> Callable:
     if framework == "llamacpp":
         from outlines.models.llamacpp import LlamaCppTokenizer
+
         return LlamaCppTokenizer(llm)
     if framework == "transformers":
         from outlines.models.transformers import TransformerTokenizer
+
         return TransformerTokenizer(llm.tokenizer)
     if framework == "vllm":
         from outlines.models.vllm import adapt_tokenizer
+
         return adapt_tokenizer(llm.get_tokenizer())
     if framework == "mlx":
         from outlines.models.transformers import TransformerTokenizer
+
         return TransformerTokenizer(llm.tokenizer)
 
 
@@ -186,4 +205,4 @@ def prepare_guided_output(
     raise DistilabelUserError(
         f"Invalid format '{format}'. Must be either 'json' or 'regex'.",
         page="sections/how_to_guides/advanced/structured_generation/",
-    )
+    )