diff --git a/tensorflow/compiler/xla/client/sharding_builder.cc b/tensorflow/compiler/xla/client/sharding_builder.cc
index b9bff06cbdb..988fc5044b6 100644
--- a/tensorflow/compiler/xla/client/sharding_builder.cc
+++ b/tensorflow/compiler/xla/client/sharding_builder.cc
@@ -20,13 +20,13 @@ namespace sharding_builder {
 
 OpSharding Replicate() {
   OpSharding result;
-  result.set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
+  result.set_type(OpSharding::REPLICATED);
   return result;
 }
 
 OpSharding AssignDevice(int device) {
   OpSharding result;
-  result.set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
+  result.set_type(OpSharding::MAXIMAL);
   result.add_tile_assignment_dimensions(1);
   result.add_tile_assignment_devices(device);
   return result;
@@ -35,7 +35,7 @@ OpSharding AssignDevice(int device) {
 OpSharding Tile(const Shape& tile_shape,
                 const TileAssignment& tile_assignment) {
   OpSharding result;
-  result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
+  result.set_type(OpSharding::OTHER);
   *result.mutable_tile_shape() = tile_shape.ToProto();
   for (int64 dim : tile_assignment.dimensions()) {
     result.add_tile_assignment_dimensions(dim);
@@ -48,7 +48,7 @@ OpSharding Tile(const Shape& tile_shape,
 
 OpSharding Tile1D(const Shape& tile_shape, int64 num_tiles) {
   OpSharding result;
-  result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
+  result.set_type(OpSharding::OTHER);
 
   CHECK_EQ(tile_shape.rank(), 1);
   std::vector<int64> dimensions(1, num_tiles);
@@ -65,7 +65,7 @@ OpSharding Tile1D(const Shape& tile_shape, int64 num_tiles) {
 
 OpSharding Tuple(const ShapeTree<OpSharding>& shardings) {
   OpSharding result;
-  result.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
+  result.set_type(OpSharding::TUPLE);
   for (const auto& index_to_sharding : shardings.leaves()) {
     *result.add_tuple_shardings() = index_to_sharding.second;
   }
diff --git a/tensorflow/compiler/xla/client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc
index 6b28f0bdbf9..318d5f3be35 100644
--- a/tensorflow/compiler/xla/client/xla_builder.cc
+++ b/tensorflow/compiler/xla/client/xla_builder.cc
@@ -1309,14 +1309,13 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) {
     instr.set_infeed_config(config);
 
     if (shape.IsArray() && sharding() &&
-        sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) {
+        sharding()->type() == OpSharding::OTHER) {
       // TODO(b/110793772): Support tiled array-shaped infeeds.
       return InvalidArgument(
           "Tiled sharding is not yet supported for array-shaped infeeds");
     }
 
-    if (sharding() &&
-        sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
+    if (sharding() && sharding()->type() == OpSharding::REPLICATED) {
       return InvalidArgument(
           "Replicated sharding is not yet supported for infeeds");
     }
@@ -1343,8 +1342,7 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) {
     // data and a token. For tuple sharding type, the sharding must be changed
     // to accommodate the token.
     XlaOp infeed;
-    if (sharding() &&
-        sharding()->type() == OpSharding::Type::OpSharding_Type_TUPLE) {
+    if (sharding() && sharding()->type() == OpSharding::TUPLE) {
       // TODO(b/80000000): Remove this when clients have been updated to handle
       // tokens.
       OpSharding infeed_instruction_sharding = *sharding();
@@ -1385,14 +1383,13 @@ XlaOp XlaBuilder::InfeedWithToken(const XlaOp& token, const Shape& shape,
     instr.set_infeed_config(config);
 
     if (shape.IsArray() && sharding() &&
-        sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) {
+        sharding()->type() == OpSharding::OTHER) {
       // TODO(b/110793772): Support tiled array-shaped infeeds.
       return InvalidArgument(
           "Tiled sharding is not yet supported for array-shaped infeeds");
     }
 
-    if (sharding() &&
-        sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
+    if (sharding() && sharding()->type() == OpSharding::REPLICATED) {
       return InvalidArgument(
           "Replicated sharding is not yet supported for infeeds");
     }
diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc
index 4bc5f5c9006..43198311839 100644
--- a/tensorflow/compiler/xla/service/hlo_parser.cc
+++ b/tensorflow/compiler/xla/service/hlo_parser.cc
@@ -1833,7 +1833,7 @@ bool HloParser::ParseSharding(OpSharding* sharding) {
       }
     } while (EatIfPresent(TokKind::kComma));
   }
-  sharding->set_type(OpSharding::Type::OpSharding_Type_TUPLE);
+  sharding->set_type(OpSharding::TUPLE);
 
   return ParseToken(TokKind::kRbrace, "expected '}' to end sharding attribute");
 }
@@ -1915,13 +1915,13 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding,
       return Error(loc,
                    "replicated shardings should not have any devices assigned");
     }
-    sharding->set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
+    sharding->set_type(OpSharding::REPLICATED);
   } else if (maximal) {
     if (devices.size() != 1) {
       return Error(loc,
                    "maximal shardings should have exactly one device assigned");
     }
-    sharding->set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
+    sharding->set_type(OpSharding::MAXIMAL);
     sharding->add_tile_assignment_devices(devices[0]);
   } else {
     if (devices.size() <= 1) {
@@ -1934,7 +1934,7 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding,
           "non-maximal shardings must have a tile assignment list including "
           "dimensions");
     }
-    sharding->set_type(OpSharding::Type::OpSharding_Type_OTHER);
+    sharding->set_type(OpSharding::OTHER);
     for (int64 dim : tile_assignment_dimensions) {
       sharding->add_tile_assignment_dimensions(dim);
     }
diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc
index f1d7e60f2b5..538a99b46ea 100644
--- a/tensorflow/compiler/xla/service/hlo_sharding.cc
+++ b/tensorflow/compiler/xla/service/hlo_sharding.cc
@@ -359,7 +359,7 @@ Status HloSharding::ValidateNonTuple(const Shape& shape,
 
 /*static*/ StatusOr<HloSharding> HloSharding::FromProto(
     const OpSharding& proto) {
-  if (proto.type() == OpSharding::Type::OpSharding_Type_TUPLE) {
+  if (proto.type() == OpSharding::TUPLE) {
     std::vector<HloSharding> tuple_shardings;
     tuple_shardings.reserve(proto.tuple_shardings().size());
     for (const OpSharding& tuple_sharding_proto : proto.tuple_shardings()) {
@@ -368,13 +368,13 @@ Status HloSharding::ValidateNonTuple(const Shape& shape,
       tuple_shardings.push_back(sharding);
     }
     return HloSharding(tuple_shardings);
-  } else if (proto.type() == OpSharding::Type::OpSharding_Type_REPLICATED) {
+  } else if (proto.type() == OpSharding::REPLICATED) {
     return Replicate();
   } else if (proto.tile_assignment_devices().size() == 1) {
     return HloSharding(proto.tile_assignment_devices(0));
   }
 
-  TF_RET_CHECK(proto.type() != OpSharding::Type::OpSharding_Type_MAXIMAL)
+  TF_RET_CHECK(proto.type() != OpSharding::MAXIMAL)
       << "Maximal sharding is expected to have single device assignment, but "
       << proto.tile_assignment_devices().size() << " has provided.";
 
@@ -411,7 +411,7 @@ OpSharding HloSharding::ToProto() const {
     for (const HloSharding& element : tuple_elements_) {
       *result.add_tuple_shardings() = element.ToProto();
     }
-    result.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
+    result.set_type(OpSharding::TUPLE);
     return result;
   }
 
@@ -422,11 +422,11 @@ OpSharding HloSharding::ToProto() const {
     result.add_tile_assignment_devices(device);
   }
   if (IsReplicated()) {
-    result.set_type(OpSharding::Type::OpSharding_Type_REPLICATED);
+    result.set_type(OpSharding::REPLICATED);
   } else if (IsTileMaximal()) {
-    result.set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
+    result.set_type(OpSharding::MAXIMAL);
   } else {
-    result.set_type(OpSharding::Type::OpSharding_Type_OTHER);
+    result.set_type(OpSharding::OTHER);
   }
   return result;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_sharding_test.cc b/tensorflow/compiler/xla/service/hlo_sharding_test.cc
index 9e234e02558..c8ad100c522 100644
--- a/tensorflow/compiler/xla/service/hlo_sharding_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_sharding_test.cc
@@ -132,7 +132,7 @@ TEST_F(HloShardingTest, NestedTuple) {
 
   HloSharding tiled_sharding = HloSharding::Tile(Array<int64>({{0, 1}}));
   OpSharding proto;
-  proto.set_type(OpSharding::Type::OpSharding_Type_TUPLE);
+  proto.set_type(OpSharding::TUPLE);
   *proto.add_tuple_shardings() = HloSharding::Replicate().ToProto();
   *proto.add_tuple_shardings() = HloSharding::AssignDevice(0).ToProto();
   *proto.add_tuple_shardings() = tiled_sharding.ToProto();
diff --git a/tensorflow/opensource_only.files b/tensorflow/opensource_only.files
index 9991800dfb1..e15b2eda7b3 100644
--- a/tensorflow/opensource_only.files
+++ b/tensorflow/opensource_only.files
@@ -1,282 +1,282 @@
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