Fix typos (#1284)

Author: omahs

.github/workflows/quality.yml

@@ -29,7 +29,7 @@ jobs:
         run: |
           pip install --upgrade pip
           # .[quality] installs too many dependencies
-          # TODO: we should remove the the version pinning at some point
+          # TODO: we should remove the version pinning at some point
           pip install "black~=23.1" "ruff==0.4.4"
 
       - name: Check style with black

docs/README.md

@@ -29,7 +29,7 @@ This command will generate the HTML files that will be rendered as the
 documentation on the [Hugging Face
 website](https://huggingface.co/docs/optimum/index). You can inspect them in
 your favorite browser. You can also adapt the `BUILD_DIR` argument to any
-temporary folder that you prefer. By default, the comamnd builds a Docker container
+temporary folder that you prefer. By default, the command builds a Docker container
 with the latest files from the main branch. To build documentation for a different
 commit or a fork, use the `DEFAULT_CLONE_URL` and `COMMIT_SHA_SUBPACKAGE`
 environment variables.
@@ -262,7 +262,7 @@ includes an example of how to transcribe speech to text in the [docstring of its
 forward
 function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward).
 
-## Writing documenation examples
+## Writing documentation examples
 
 The syntax for Example docstrings can look as follows:
 

optimum/exporters/openvino/model_configs.py

@@ -478,7 +478,7 @@ def add_past_key_values(self, inputs_or_outputs: Dict[str, Dict[int, str]], dire
             decoder_sequence_name = "past_sequence_length"
             name = "past_key_values"
         else:
-            decoder_sequence_name = "past_sequence_length + present_lenght"
+            decoder_sequence_name = "past_sequence_length + present_length"
             name = "present"
 
         is_v4 = hasattr(self._normalized_config, "rope_ratio")

optimum/intel/openvino/modeling_base.py

@@ -177,7 +177,7 @@ def to(self, device: str):
         """
         if self._compile_only and isinstance(device, str):
             raise ValueError(
-                "`to()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`to()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         if isinstance(device, str):
@@ -739,7 +739,7 @@ def reshape(self, batch_size: int, sequence_length: int, height: int = None, wid
         """
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         self.is_dynamic = True if batch_size == -1 and sequence_length == -1 else False

optimum/intel/openvino/modeling_base_seq2seq.py

@@ -475,7 +475,7 @@ def reshape(self, batch_size: int, sequence_length: int):
         """
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         logger.warning("Some part of the model's decoder do not support static shapes and will be kept dynamic.")
         self.is_dynamic = True if batch_size == -1 and sequence_length == -1 else False
@@ -490,7 +490,7 @@ def half(self):
         """
         if self._compile_only:
             raise ValueError(
-                "`half()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`half()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         for submodel in self.ov_submodels.values():
             apply_moc_transformations(submodel, cf=False)

optimum/intel/openvino/modeling_decoder.py

@@ -249,7 +249,7 @@ def _save_pretrained(self, save_directory: Union[str, Path]):
 
         if self._compile_only:
             raise ValueError(
-                "`save_pretrained()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`save_pretrained()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         model_to_save = (
             self.model
@@ -368,7 +368,7 @@ def _reshape(
     ):
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         if height is not None:
@@ -895,7 +895,7 @@ def _from_pretrained(
 
             if compile_only:
                 raise ValueError(
-                    "quantization is not supported with `compile_only` mode, please intialize model without this option"
+                    "quantization is not supported with `compile_only` mode, please initialize model without this option"
                 )
 
             from optimum.intel.openvino.quantization import OVQuantizer

optimum/intel/openvino/modeling_diffusion.py

@@ -306,7 +306,7 @@ def _save_pretrained(self, save_directory: Union[str, Path]):
         """
         if self._compile_only:
             raise ValueError(
-                "`save_pretrained()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`save_pretrained()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         save_directory = Path(save_directory)
@@ -888,7 +888,7 @@ def _reshape_vae_decoder(
     def reshape(self, batch_size: int, height: int, width: int, num_images_per_prompt: int = -1, num_frames: int = -1):
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         self.is_dynamic = -1 in {batch_size, height, width, num_images_per_prompt}
@@ -958,7 +958,7 @@ def half(self):
         """
         if self._compile_only:
             raise ValueError(
-                "`half()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`half()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         for submodel in self.ov_submodels.values():
@@ -971,7 +971,7 @@ def half(self):
     def clear_requests(self):
         if self._compile_only:
             raise ValueError(
-                "`clear_requests()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`clear_requests()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         for component in [
@@ -1031,7 +1031,7 @@ def __call__(self, *args, **kwargs):
 
         height, width = None, None
         height_idx, width_idx = None, None
-        shapes_overriden = False
+        shapes_overridden = False
         sig = inspect.signature(self.auto_model_class.__call__)
         sig_height_idx = list(sig.parameters).index("height") if "height" in sig.parameters else len(sig.parameters)
         sig_width_idx = list(sig.parameters).index("width") if "width" in sig.parameters else len(sig.parameters)
@@ -1059,7 +1059,7 @@ def __call__(self, *args, **kwargs):
             else:
                 kwargs["height"] = height
 
-            shapes_overriden = True
+            shapes_overridden = True
 
         if self.width != -1:
             if width is not None and width != self.width:
@@ -1072,11 +1072,11 @@ def __call__(self, *args, **kwargs):
                 args[width_idx] = width
             else:
                 kwargs["width"] = width
-            shapes_overriden = True
+            shapes_overridden = True
 
         # Sana generates images in specific resolution grid size and then resize to requested size by default, it may contradict with pipeline height / width
         # Disable this behavior for static shape pipeline
-        if self.auto_model_class.__name__.startswith("Sana") and shapes_overriden:
+        if self.auto_model_class.__name__.startswith("Sana") and shapes_overridden:
             sig_resolution_bining_idx = (
                 list(sig.parameters).index("use_resolution_binning")
                 if "use_resolution_binning" in sig.parameters

optimum/intel/openvino/modeling_sam.py

@@ -301,7 +301,7 @@ def reshape(self, batch_size: int = -1, point_batch_size: int = -1, num_points_p
         """
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         vision_encoder_shapes = {}
         for inputs in self.vision_encoder_model.inputs:

optimum/intel/openvino/modeling_seq2seq.py

@@ -436,7 +436,7 @@ def reshape(self, batch_size: int, sequence_length: int):
         """
         if self._compile_only:
             raise ValueError(
-                "`reshape()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`reshape()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         super().reshape(batch_size, sequence_length)
         self.clear_requests()
@@ -453,7 +453,7 @@ def half(self):
     def clear_requests(self):
         if self._compile_only:
             raise ValueError(
-                "`clear_requests()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`clear_requests()` is not supported with `compile_only` mode, please initialize model without this option"
             )
         self.encoder.request = None
         self.decoder.request = None

optimum/intel/openvino/modeling_text2speech.py

@@ -235,7 +235,7 @@ def __init__(
     def clear_requests(self):
         if self._compile_only:
             raise ValueError(
-                "`clear_requests()` is not supported with `compile_only` mode, please intialize model without this option"
+                "`clear_requests()` is not supported with `compile_only` mode, please initialize model without this option"
             )
 
         for _, component in self.components.items():