Fix the multiple typos in our internal task files

PiperOrigin-RevId: 682328927

mediapipe/tasks/python/genai/bundler/llm_bundler.py

@@ -34,7 +34,7 @@ class BundleConfig:
       passed here.
     start_token: Token that will be used to signify the beginning of a sequence.
     stop_tokens: Tokens that will be used to signify the end of a sequence.
-    output_filename: Name of the generated `.task` file containg the Bundle.
+    output_filename: Name of the generated `.task` file containing the Bundle.
     enable_bytes_to_unicode_mapping: Enables GPT-2 style bytes to unicode
       mapping. For more details see:
       https://github.com/openai/gpt-2/blob/master/src/encoder.py#L9

mediapipe/tasks/python/genai/converter/converter_base.py

@@ -23,7 +23,7 @@
 class QuantizationAction:
   """Container of the tensor values and its corresponding quantization settings.
 
-  The contrainer is responsible for hosting all of the information that is
+  The container is responsible for hosting all of the information that is
   required to execute the weight-only quantization.
 
   Attributes:

mediapipe/tasks/python/genai/converter/llm_converter.py

@@ -244,7 +244,7 @@ def convert_bpe_vocab(vocab_model_file: str, output_dir: str) -> str:
   if not os.path.isdir(vocab_model_file):
     raise ValueError(
         'The input BPE vocab model file path is expected to be a directory that'
-        ' conatins both tokenizer.json and tokenizer_config.json files.'
+        ' contains both tokenizer.json and tokenizer_config.json files.'
     )
   output_vocab_file = os.path.join(output_dir, 'spm.model')
   model_ckpt_util.ConvertHfTokenizer(vocab_model_file, output_vocab_file)

mediapipe/tasks/python/genai/converter/quantization_util.py

@@ -162,7 +162,7 @@ def get_best_bound(
     p_value: float = 1.0,
     per_channel: bool = False,
 ) -> JTensor:
-  """Scan mutliple factors on max value to get best bound value.
+  """Scan multiple factors on max value to get best bound value.
 
   This does a scan to get bound value(s) that minimize mean absolute error (MAE)
   between original tensor 't' and quantized tensor. It's (almost) equivalent to
@@ -239,7 +239,7 @@ def reduce_precision(
 
   Args:
     t: Input tensor.
-    contract_dims: Speficies contracting dimesnions of the input tensor.
+    contract_dims: Specifies contracting dimensions of the input tensor.
     need_gradient: If gradient is needed out of this function.
     bits: Target number of bits.
     optimization_on_bound: If MAE bound optimizer is used.
@@ -405,7 +405,7 @@ def pack_4bit(
     packed_dtype: Target type to pack to, int32 or int8.
 
   Returns:
-    int32 or int8 packed tensor where the pack_dim size is dividened by 8
+    int32 or int8 packed tensor where the pack_dim size is dividend by 8
     from the original tensor x.
   """
   x = jnp.asarray(x)

mediapipe/tasks/web/genai/llm_inference/llm_inference_options.d.ts

@@ -32,7 +32,7 @@ export declare interface WebGpuOptions {
 
   // TODO: b/327685206 - Fill Adapter infor for LLM Web task
   /**
-   * The information of WebGPU adapater, which will be used to optimize the
+   * The information of WebGPU adapter, which will be used to optimize the
    * performance for LLM Inference task.
    */
   adapterInfo?: GPUAdapterInfo;

mediapipe/tasks/web/genai_experimental/rag_pipeline/rag_pipeline.ts

@@ -89,7 +89,7 @@ export class RagPipeline {
         // We currently only use a single .wasm file and a single .data file (for
         // the tasks that have to load assets). We need to revisit how we
         // initialize the file locator if we ever need to differentiate between
-        // diffferent files.
+        // different files.
         if (file.endsWith('.wasm')) {
           return wasmFileset.wasmBinaryPath.toString();
         } else if (wasmFileset.assetBinaryPath && file.endsWith('.data')) {