// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package runtime

// traceExpWriter is a wrapper around trace writer that produces traceEvExperimentalBatch
// batches. This means that the data written to the writer need not conform to the standard
// trace format.
type traceExpWriter struct {
	traceWriter
	exp traceExperiment
}

				expect(0, 0)
			},
		},
		{
			name: "Should send full updates in batches",
			test: func(updateCh chan *model.PushRequest, expect func(partial, full int32)) {
				updateCh <- &model.PushRequest{Full: true}
				updateCh <- &model.PushRequest{Full: true}
				expect(0, 1)
			},
		},
		{
			name: "Should send full updates in batches, partial updates immediately",

import org.gradle.internal.operations.RunnableBuildOperation;

import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.function.Supplier;

/**
 * Executor for batches of intermediate build actions that can be initiated
 * from the top-level build action or from model builders.
 * <p>
 * All actions are always executed, and <b>may run in {@link #isParallel() parallel}</b>.

  func->Execute(exec_ctx, arguments.values(), results);
  host->Await(results);

  for (auto& result : results) {
    EXPECT_FALSE(result->IsError());
  }
}

// Runs a BEF function that batches a function that does nothing just to measure
// the runtime overhead. The BEF function signature is adapted from a real model
// and is useful for benchmarking ops with large attributes and many
// input/output.

        @Override
        String toString() {
            return "reject " + version
        }
    }

    static class StrictPermutationsProvider implements Iterable<Candidate> {
        private final List<Batch> batches = []
        private int batchCount

        static StrictPermutationsProvider check(Map config) {
            new StrictPermutationsProvider().and(config)
        }

            thread.block()
            stream.received(bytes)
        }

        then:
        1 * dispatch.onOutput({ it instanceof ReadStdInEvent }) >> { instant.requested }
    }

    def "can read multiple batches of input from client"() {
        def dispatch = Mock(OutputEventListener)
        def stream = new StdInStream(dispatch)
        def text1 = "some text"
        def bytes1 = text1.bytes
        def text2 = "more"

// RUN: tf-tfrt-opt -split-input-file -tfrt-reconfig-batch-op="tfrt-min-num-batch-threads=2 tfrt-min-max-enqueued-batches=3" %s | FileCheck %s --dump-input=always

// -----

// The num_batch_threads is lowered bound to 2 from the original attribute of 1

// CHECK-LABEL: func private @batched_function
func.func private @batched_function(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> {
  %2 = "tf.Identity"(%arg0) : (tensor<1x3xf32>) -> tensor<1x3xf32>

  // The TFLite fused op does not support batching yet.
  // TODO(b/158709815): Add support for batches with padded NMS.
  auto boxes_type =
      mlir::dyn_cast<RankedTensorType>(func_.getFunctionType().getInput(0));
  if (boxes_type == nullptr || !boxes_type.hasRank() ||
      boxes_type.getRank() != 2) {
    return func_.emitWarning() << "TFLite does not support batched input for "
                                  "non_max_suppression_padded";
  }

    ): List<SmallSubprojectBucket> {
        // splitIntoBuckets() method expects us to split large element into N elements,
        // but we want to have a single bucket with N batches.
        // As a workaround, we repeat the bucket N times, and deduplicate the result at the end
        val resultIncludingDuplicates = splitIntoBuckets(
            LinkedList(subProjectTestClassTimes),

	lastTime traceTime // when we wrote the last event
	pos      int       // next write offset in arr
	lenPos   int       // position of batch length value
}

// traceBuf is per-M tracing buffer.
//
// TODO(mknyszek): Rename traceBuf to traceBatch, since they map 1:1 with event batches.
type traceBuf struct {
	_ sys.NotInHeap
	traceBufHeader