Fixes Typos in multiple files

examples/classification.py

@@ -96,7 +96,7 @@ def _make_dataset(training: bool) -> kd.data.Pipeline:
           gm.data.FormatText(
               key=_INPUT_FIELD,
               template="""<start_of_turn>user
-              Please classify whether the following sentence is grammaticaly correct, please answer only with Yes or No.
+              Please classify whether the following sentence is grammatically correct, please answer only with Yes or No.
               Sentence: {text}<end_of_turn>
               <start_of_turn>model""",
           ),
@@ -113,7 +113,7 @@ def _make_dataset(training: bool) -> kd.data.Pipeline:
           gm.data.MapInts(
               key=_LABEL_FIELD,
               # Rather than predicting the token 0 and 1, we are using the
-              # token 1294 and 3553 which respectivelly correspond to "No" and
+              # token 1294 and 3553 which respectively correspond to "No" and
               # "Yes". We do this because those token already contain semantic
               # information, so even zero-shot prediction without any
               # finetuning has better than random performances.

gemma/gm/nn/_transformer.py

@@ -175,7 +175,7 @@ def vision_encoder(self) -> gemma_vision.SigLiPFromPatches | None:
           'return_hidden_states',
       ),
   )
-  # The function accepts/returns aribtrary batch shape, but inside the
+  # The function accepts/returns arbitrary batch shape, but inside the
   # function, the batch dimension is flattened to a single dimension.
   @_jax_utils.flatten_unflatten_batch_dim()
   @typechecked

gemma/gm/nn/gemma3n/_transformer.py

@@ -218,7 +218,7 @@ def vision_encoder(self) -> gemma_vision.SigLiPFromPatches | None:
           'return_hidden_states',
       ),
   )
-  # The function accepts/returns aribtrary batch shape, but inside the
+  # The function accepts/returns arbitrary batch shape, but inside the
   # function, the batch dimension is flattened to a single dimension.
   @_jax_utils.flatten_unflatten_batch_dim()
   @typechecked

gemma/gm/text/_chat_sampler.py

@@ -68,7 +68,7 @@ class ChatSampler:
       conversation can have (prompts, answers, images for all turns). Setting
       this to a fixed value avoids re-compilation between turns.
     max_out_length: Length of the output buffer for a single turn. Static value
-      used to avoid trigering a jit recompilation. Shouldn't be changed unless
+      used to avoid triggering a jit recompilation. Shouldn't be changed unless
       you have a task where the model generates really long outputs.
     last_state: Last state of the sampler, automatically handled by the sampler,
       but exposed for power users to access the logits, cache, ... or initialize

gemma/gm/text/_sampler.py

@@ -108,7 +108,7 @@ class Sampler:
       conversation can have (prompts, answers, images for all turns). Setting
       this to a fixed value avoids re-compilation between turns.
     max_out_length: Length of the output buffer for a single turn. Static value
-      used to avoid trigering a jit recompilation. Shouldn't be changed unless
+      used to avoid triggering a jit recompilation. Shouldn't be changed unless
       you have a task where the model generates really long outputs.
     pad_length: If provided, pad the prompt to this length. This ensure the
       prompt is always the same length, to avoid jit re-compilation.

gemma/gm/vision/_token_utils.py

@@ -219,7 +219,7 @@ def _get_new_mm_tokens(
     offset_by: int,
     length_with_mm: int,
 ) -> Int['B max_num_images num_tokens_per_image+4']:
-  # Jax vmap does not support positional argiments, so need the
+  # Jax vmap does not support positional arguments, so need the
   # _get_new_mm_tokens_inner indirection.
   return jax.vmap(
       _get_new_mm_tokens_inner, in_axes=(0, None, None, None, None)