Fix miscellaneous documentation typos

Author: buildwithsuhana

CONTRIBUTING.md

@@ -28,7 +28,7 @@ maintain two lists of github tags for contributors:
 If you would like propose a new symbol or feature, please first review our
 design guide and roadmap linked above, and open an issue to discuss. If you have
 a specific design in mind, please include a Colab notebook showing the proposed
-design in a end-to-end example. Keep in mind that design for a new feature or
+design in an end-to-end example. Keep in mind that design for a new feature or
 use case may take longer than contributing to an open issue with a
 vetted-design.
 

STYLE_GUIDE.md

@@ -21,7 +21,7 @@ is considered as a common noun and not an abbreviation anymore.
 ## Naming of Models and Presets
 
 Naming of models and presets is a difficult and important element of our
-library usability. In general we try to to follow the branding of "upstream"
+library usability. In general we try to follow the branding of "upstream"
 model naming, subject to the consistency constraints laid out here.
 
 - The model and preset names should be recognizable to users familiar with the

keras_hub/src/layers/modeling/box_matcher.py

@@ -38,7 +38,7 @@ class BoxMatcher(keras.layers.Layer):
     Args:
         thresholds: A sorted list of floats to classify the matches into
             different results (e.g. positive or negative or ignored match). The
-            list will be prepended with -Inf and and appended with +Inf.
+            list will be prepended with -Inf and appended with +Inf.
         match_values: A list of integers representing matched results (e.g.
             positive or negative or ignored match). len(`match_values`) must
             equal to len(`thresholds`) + 1.

keras_hub/src/layers/modeling/cached_multi_head_attention.py

@@ -50,7 +50,7 @@ class CachedMultiHeadAttention(keras.layers.MultiHeadAttention):
             `[B, 2, S, num_heads, key_dims]`, where `S` must agree with the
             `attention_mask` shape. This argument is intended for use during
             generation to avoid recomputing intermediate state.
-        cache_update_index: a int or int Tensor, the index at which to update
+        cache_update_index: an int or int Tensor, the index at which to update
             `cache` (usually the index of the current token being processed
             when running generation). If `cache_update_index=None` while `cache`
             is set, the cache will not be updated.

keras_hub/src/layers/modeling/rotary_embedding.py

@@ -164,7 +164,7 @@ def call(self, inputs, start_index=0, positions=None):
 
     def _apply_rotary_pos_emb(self, tensor, cos_emb, sin_emb):
         x1, x2 = ops.split(tensor, 2, axis=-1)
-        # Avoid `ops.concatenate` for now, to avoid a obscure bug with XLA
+        # Avoid `ops.concatenate` for now, to avoid an obscure bug with XLA
         # compilation on jax. We should be able to remove this once the
         # following PR is in all jax releases we care about:
         # https://github.com/openxla/xla/pull/7875

keras_hub/src/models/deit/deit_image_classifier.py

@@ -20,10 +20,11 @@ class DeiTImageClassifier(ImageClassifier):
     `num_classes` argument, controlling the number of predicted output classes.
 
     To fine-tune with `fit()`, pass a dataset containing tuples of `(x, y)`
-    labels where `x` is a string and `y` is a integer from `[0, num_classes)`.
+    labels where `x` is a string and `y` is an integer from `[0, num_classes)`.
 
-    Not that unlike `keras_hub.model.ImageClassifier`, the `DeiTImageClassifier`
-    we pluck out `cls_token` which is first seqence from the backbone.
+    Note that unlike `keras_hub.model.ImageClassifier`, the
+    `DeiTImageClassifier` we pluck out `cls_token` which is first seqence from
+    the backbone.
 
     Args:
         backbone: A `keras_hub.models.DeiTBackbone` instance or a `keras.Model`.

keras_hub/src/models/depth_estimator_preprocessor.py

@@ -16,7 +16,7 @@ class DepthEstimatorPreprocessor(Preprocessor):
 
     All `DepthEstimatorPreprocessor` take inputs three inputs, `x`, `y`, and
     `sample_weight`. `x`, the first input, should always be included. It can
-    be a image or batch of images. See examples below. `y` and `sample_weight`
+    be an image or batch of images. See examples below. `y` and `sample_weight`
     are optional inputs that will be passed through unaltered. Usually, `y` will
     be the depths, and `sample_weight` will not be provided.
 

keras_hub/src/models/flux/flux_text_to_image_preprocessor.py

@@ -18,7 +18,7 @@ class FluxTextToImagePreprocessor(Preprocessor):
 
     Args:
         clip_l_preprocessor: A `keras_hub.models.CLIPPreprocessor` instance.
-        t5_preprocessor: A optional `keras_hub.models.T5Preprocessor` instance.
+        t5_preprocessor: An optional `keras_hub.models.T5Preprocessor` instance.
     """
 
     backbone_cls = FluxBackbone

keras_hub/src/models/image_classifier.py

@@ -14,7 +14,7 @@ class ImageClassifier(Task):
     `num_classes` argument, controlling the number of predicted output classes.
 
     To fine-tune with `fit()`, pass a dataset containing tuples of `(x, y)`
-    labels where `x` is a string and `y` is a integer from `[0, num_classes)`.
+    labels where `x` is a string and `y` is an integer from `[0, num_classes)`.
     All `ImageClassifier` tasks include a `from_preset()` constructor which can
     be used to load a pre-trained config and weights.
 

keras_hub/src/models/image_classifier_preprocessor.py

@@ -16,7 +16,7 @@ class ImageClassifierPreprocessor(Preprocessor):
 
     All `ImageClassifierPreprocessor` take inputs three inputs, `x`, `y`, and
     `sample_weight`. `x`, the first input, should always be included. It can
-    be a image or batch of images. See examples below. `y` and `sample_weight`
+    be an image or batch of images. See examples below. `y` and `sample_weight`
     are optional inputs that will be passed through unaltered. Usually, `y` will
     be the classification label, and `sample_weight` will not be provided.