remove further type ignore

Author: lars20070

stubs/transformers/__init__.pyi

@@ -1,24 +1,26 @@
 from typing import Any
 
+from typing_extensions import Self
+
 from . import modeling_utils, processing_utils, tokenization_utils
 from .modeling_utils import PreTrainedModel
 from .processing_utils import ProcessorMixin
 from .tokenization_utils import PreTrainedTokenizer
 
 class AutoModelForCausalLM(PreTrainedModel):
     @classmethod
-    def from_pretrained(cls, *args: Any, **kwargs: Any) -> PreTrainedModel: ...
+    def from_pretrained(cls, *args: Any, **kwargs: Any) -> Self: ...
 
 class AutoTokenizer(PreTrainedTokenizer):
     @classmethod
-    def from_pretrained(cls, *args: Any, **kwargs: Any) -> PreTrainedTokenizer: ...
+    def from_pretrained(cls, *args: Any, **kwargs: Any) -> Self: ...
 
 class AutoProcessor(ProcessorMixin):
     @classmethod
-    def from_pretrained(cls, *args: Any, **kwargs: Any) -> ProcessorMixin: ...
+    def from_pretrained(cls, *args: Any, **kwargs: Any) -> Self: ...
 
 class LlavaForConditionalGeneration(PreTrainedModel):
     @classmethod
-    def from_pretrained(cls, *args: Any, **kwargs: Any) -> PreTrainedModel: ...
+    def from_pretrained(cls, *args: Any, **kwargs: Any) -> Self: ...
 
 def from_pretrained(*args: Any, **kwargs: Any) -> Any: ...

tests/models/test_outlines.py

@@ -122,11 +122,11 @@ async def generate_stream(
 
 @pytest.fixture
 def transformers_model() -> OutlinesModel:
-    hf_model = transformers.AutoModelForCausalLM.from_pretrained(  # type: ignore
+    hf_model = transformers.AutoModelForCausalLM.from_pretrained(
         'erwanf/gpt2-mini',
         device_map='cpu',
     )
-    hf_tokenizer = transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini')  # type: ignore
+    hf_tokenizer = transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini')
     chat_template = '{% for message in messages %}{{ message.role }}: {{ message.content }}{% endfor %}'
     hf_tokenizer.chat_template = chat_template
     outlines_model = outlines.models.transformers.from_transformers(  # type: ignore[reportUnknownMemberType]
@@ -138,13 +138,11 @@ def transformers_model() -> OutlinesModel:
 
 @pytest.fixture
 def transformers_multimodal_model() -> OutlinesModel:
-    hf_model = transformers.LlavaForConditionalGeneration.from_pretrained(  # type: ignore
+    hf_model = transformers.LlavaForConditionalGeneration.from_pretrained(
         'trl-internal-testing/tiny-LlavaForConditionalGeneration',
         device_map='cpu',
     )
-    hf_processor = transformers.AutoProcessor.from_pretrained(  # type: ignore
-        'trl-internal-testing/tiny-LlavaForConditionalGeneration'
-    )
+    hf_processor = transformers.AutoProcessor.from_pretrained('trl-internal-testing/tiny-LlavaForConditionalGeneration')
     outlines_model = outlines.models.transformers.from_transformers(  # type: ignore[reportUnknownMemberType]
         hf_model,
         hf_processor,  # type: ignore
@@ -198,11 +196,11 @@ def binary_image() -> BinaryImage:
     pytest.param(
         'from_transformers',
         lambda: (
-            transformers.AutoModelForCausalLM.from_pretrained(  # type: ignore
+            transformers.AutoModelForCausalLM.from_pretrained(
                 'erwanf/gpt2-mini',
                 device_map='cpu',
             ),
-            transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini'),  # type: ignore
+            transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini'),
         ),
         marks=skip_if_transformers_imports_unsuccessful,
     ),
@@ -257,11 +255,11 @@ def test_init(model_loading_function_name: str, args: Callable[[], tuple[Any]])
     pytest.param(
         'from_transformers',
         lambda: (
-            transformers.AutoModelForCausalLM.from_pretrained(  # type: ignore
+            transformers.AutoModelForCausalLM.from_pretrained(
                 'erwanf/gpt2-mini',
                 device_map='cpu',
             ),
-            transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini'),  # type: ignore
+            transformers.AutoTokenizer.from_pretrained('erwanf/gpt2-mini'),
         ),
         marks=skip_if_transformers_imports_unsuccessful,
     ),