Contributing: fix typos

tensorflow/c/eager/c_api_experimental.h

@@ -27,7 +27,7 @@ extern "C" {
 // creating a new op every time. If `raw_device_name` is `NULL` or empty, it
 // does not set the device name. If it's not `NULL`, then it attempts to parse
 // and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
-// than seperately calling it because if the existing op has the same
+// than separately calling it because if the existing op has the same
 // `raw_device_name`, it skips parsing and just leave as it is.
 TF_CAPI_EXPORT extern void TFE_OpReset(TFE_Op* op_to_reset,
                                        const char* op_or_function_name,

tensorflow/c/experimental/filesystem/modular_filesystem_test.cc

@@ -1569,7 +1569,7 @@ TEST_P(ModularFileSystemTest, TestRoundTrip) {
   if (!status.ok())
     GTEST_SKIP() << "NewRandomAccessFile() not supported: " << status;
 
-  char scratch[64 /* big enough to accomodate test_data */] = {0};
+  char scratch[64 /* big enough to accommodate test_data */] = {0};
   StringPiece result;
   status = read_file->Read(0, test_data.size(), &result, scratch);
   EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OK);

tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc

@@ -937,7 +937,7 @@ class ConvertFusedBatchNormGradBase
     // Gets the result values.
     Value x_backprop, scale_backprop, offset_backprop;
     if (op.is_training()) {  // training
-      // TODO(b/145536565): handle GPU logic seperately.
+      // TODO(b/145536565): handle GPU logic separately.
       // Infers the output type with the converted `act`.
       Type feature_type = RankedTensorType::get(
           {GetDimSize(act_type, feature_dim)}, kernel_type);

tensorflow/core/grappler/optimizers/data/auto_shard.cc

@@ -405,7 +405,7 @@ Status OptimizeGraph(const GrapplerItem& item, int64 num_workers, int64 index,
   // the latest occurrence of a ReaderDataset (e.g. CSVDataset, TFRecordDataset,
   // etc...). We then add a shard after that dataset to shard the outputs of
   // that dataset, in effect giving a piece to each worker. Finally, we remove
-  // occurences from randomness from before that point in the graph (e.g. things
+  // occurrences from randomness from before that point in the graph (e.g. things
   // like ShuffleDataset) to ensure that `shard` returns a sensible result.
   switch (policy) {
     case AutoShardPolicy::OFF:

tensorflow/core/protobuf/eager_service.proto

@@ -248,7 +248,7 @@ service EagerService {
 
   // Contexts are always created with a deadline and no RPCs within a deadline
   // will trigger a context garbage collection. KeepAlive calls can be used to
-  // delay this. It can also be used to validate the existance of a context ID
+  // delay this. It can also be used to validate the existence of a context ID
   // on remote eager worker. If the context is on remote worker, return the same
   // ID and the current context view ID. This is useful for checking if the
   // remote worker (potentially with the same task name and hostname / port) is

tensorflow/lite/experimental/ruy/prepacked_cache_test.cc

@@ -156,7 +156,7 @@ TEST(PrepackedCacheTest, TestCacheOnCacheable) {
   dst.data = dst_data;
 
   ruy::BasicSpec<float, float> spec;
-  // Perform the multiplication and confirm no caching occured.
+  // Perform the multiplication and confirm no caching occurred.
   ruy::Mul<ruy::kAllPaths>(lhs, rhs, spec, &context, &dst);
   EXPECT_EQ(cache->TotalSize(), 0);
 

tensorflow/lite/micro/tools/make/transform_arduino_source.py

@@ -41,7 +41,7 @@ def replace_includes(line, supplied_headers_list):
 
 
 def replace_main(line):
-  """Updates any occurences of a bare main definition to the Arduino equivalent."""
+  """Updates any occurrences of a bare main definition to the Arduino equivalent."""
   main_match = re.match(r'(.*int )(main)(\(.*)', line)
   if main_match:
     line = main_match.group(1) + 'tflite_micro_main' + main_match.group(3)

tensorflow/lite/micro/tools/make/transform_source.py

@@ -48,7 +48,7 @@ def replace_arduino_includes(line, supplied_headers_list):
 
 
 def replace_arduino_main(line):
-  """Updates any occurences of a bare main definition to the Arduino equivalent."""
+  """Updates any occurrences of a bare main definition to the Arduino equivalent."""
   main_match = re.match(r'(.*int )(main)(\(.*)', line)
   if main_match:
     line = main_match.group(1) + 'tflite_micro_main' + main_match.group(3)

tensorflow/lite/toco/logging/conversion_log_util.h

@@ -32,7 +32,7 @@ std::vector<string> GetOperatorNames(const Model& model);
 // Counts the number of different types of operators in the model:
 // Built-in ops, custom ops and select ops.
 // Each map is mapping from the name of the operator (such as 'Conv') to its
-// total number of occurences in the model.
+// total number of occurrences in the model.
 void CountOperatorsByType(const Model& model,
                           std::map<string, int>* built_in_ops,
                           std::map<string, int>* custom_ops,

tensorflow/python/data/experimental/kernel_tests/bucket_by_sequence_length_test.py

@@ -107,7 +107,7 @@ def testBucketDropReminder(self, param_no_padding):
       # Calculate the expected occurrence of individual batch sizes.
       expected_batch_sizes[length] = \
           [batch_size] * (bucket_elements // batch_size)
-      # Calculate the expected occurence of individual sequence lengths.
+      # Calculate the expected occurrence of individual sequence lengths.
       expected_lengths.extend([length] * (bucket_elements // batch_size))
 
     def build_dataset(sparse):

tensorflow/python/keras/layers/preprocessing/categorical_encoding.py

@@ -307,7 +307,7 @@ def merge(self, accumulators):
           # Any newly created token counts in 'base_accumulator''s
           # per_doc_count_dict will have a last_doc_id of -1. This is always
           # less than the next doc id (which are strictly positive), so any
-          # future occurences are guaranteed to be counted.
+          # future occurrences are guaranteed to be counted.
           base_accumulator.per_doc_count_dict[token]["count"] += value["count"]
 
     return base_accumulator

tensorflow/python/keras/layers/preprocessing/text_vectorization.py

@@ -756,7 +756,7 @@ def merge(self, accumulators):
           # Any newly created token counts in 'base_accumulator''s
           # per_doc_count_dict will have a last_doc_id of -1. This is always
           # less than the next doc id (which are strictly positive), so any
-          # future occurences are guaranteed to be counted.
+          # future occurrences are guaranteed to be counted.
           base_accumulator.per_doc_count_dict[token]["count"] += value["count"]
 
     return base_accumulator

tensorflow/python/ops/image_grad.py

@@ -173,8 +173,8 @@ def _RGBToHSVGrad(op, grad):
 
   This function is a piecewise continuous function as defined here:
   https://en.wikipedia.org/wiki/HSL_and_HSV#From_RGB
-  We perform the multi variate derivative and compute all partial derivates
-  seperately before adding them in the end. Formulas are given before each
+  We perform the multivariate derivative and compute all partial derivatives
+  separately before adding them in the end. Formulas are given before each
   partial derivative calculation.
 
   Args:

tensorflow/python/tpu/client/pip_package/README

@@ -1,3 +1,3 @@
-Client responsible for communicating the Cloud TPU API. Released seperately from tensorflow.
+Client responsible for communicating the Cloud TPU API. Released separately from tensorflow.
 
 https://pypi.org/project/cloud-tpu-client/

tensorflow/python/training/tracking/data_structures.py

@@ -434,8 +434,8 @@ def _non_append_mutation(self):
 
   @_non_append_mutation.setter
   def _non_append_mutation(self, value):
-    # Trackable only cares that a mutation occured at some point; when
-    # attempting to save it checks whether a mutation occured and the object is
+    # Trackable only cares that a mutation occurred at some point; when
+    # attempting to save it checks whether a mutation occurred and the object is
     # in a "dirty" state but otherwise the specifics of how it got to that state
     # are ignored. By contrast, the attribute cache needs to signal the mutation
     # immediately since a caller could query the value of an attribute (And