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Implement EagerRunner. · MSavameri/TensorFlow.NET@2602fb9 · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit 2602fb9

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Oceania2018
committed
Implement EagerRunner.
1 parent 91fb30f commit 2602fb9

11 files changed

Lines changed: 726 additions & 15 deletions

‎src/TensorFlowNET.Core/Eager/EagerRunner.RecordGradient.cs‎

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using Tensorflow.Gradients;
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using static Tensorflow.Binding;
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using static Tensorflow.tensorflow;
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namespace Tensorflow.Eager
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{
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public partial class EagerRunner
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{
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bool RecordGradient(string op_name,
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Tensor[] inputs,
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object[] attrs,
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Tensor[] results)
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{
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var input_ids = MakeTensorIDList(inputs);
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var input_dtypes = MakeTensorDtypeList(inputs);
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bool should_record = false;
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foreach (var tape in tf.GetTapeSet())
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{
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if(tape.ShouldRecord(input_ids, input_dtypes))
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{
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should_record = true;
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break;
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}
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}
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if (!should_record)
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{
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/*for (TFE_Py_ForwardAccumulator* accumulator : SafeAccumulatorSet())
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{
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if (accumulator->accumulator->ShouldRecord(input_ids, input_dtypes))
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{
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should_record = true;
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break;
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}
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}*/
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}
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if (!should_record) return should_record;
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Tensor[] op_outputs;
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bool op_outputs_tuple_created = false;
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var unused_output_indices = gradient_exclustions.OpGradientUnusedOutputIndices(op_name);
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if (unused_output_indices != null)
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{
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if (unused_output_indices.Length == 0)
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op_outputs = new Tensor[0];
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else
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{
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op_outputs_tuple_created = true;
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// op_outputs = CopySequenceSettingIndicesToNull(results, *unused_output_indices);
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}
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}
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else
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op_outputs = results;
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Tensor[] op_inputs;
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bool op_inputs_tuple_created = false;
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var unused_input_indices = gradient_exclustions.OpGradientUnusedInputIndices(op_name);
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if(unused_input_indices != null)
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{
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if (unused_input_indices.Length == 0)
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op_inputs = new Tensor[0];
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else
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{
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op_inputs_tuple_created = true;
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// op_inputs = CopySequenceSettingIndicesToNull(inputs, *unused_input_indices);
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}
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}
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else
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op_inputs = inputs;
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TapeSetRecordOperation(op_name, inputs, results, input_ids, input_dtypes,
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() => GetGradientFunction(op_name, inputs, attrs, results));
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return true;
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}
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BackwardFunction GetGradientFunction(string op_name,
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Tensor[] op_inputs,
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object[] attrs,
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Tensor[] op_outputs)
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=> (output_grads, unneeded_gradients) =>
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{
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var gradients = ops.gradientFunctions[op_name](new EagerOperation
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{
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Name = op_name,
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NumInputs = op_inputs.Length,
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Inputs = op_inputs,
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NumOutputs = op_outputs.Length,
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Outputs = op_outputs,
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SkipInputIndices = unneeded_gradients,
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Attrs = attrs
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}, output_grads);
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return gradients;
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};
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bool CouldForwardprop()
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{
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return HasAccumulator();
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}
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bool CouldBackprop()
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{
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return HasGradientTape();
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}
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long[] MakeTensorIDList(Tensor[] tensors)
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{
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return tensors.Select(x => x.Id).ToArray();
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}
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TF_DataType[] MakeTensorDtypeList(Tensor[] tensors)
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{
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return tensors.Select(x => x.dtype).ToArray();
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}
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}
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}

‎src/TensorFlowNET.Core/Eager/EagerRunner.RunCallbacks.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Eager
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{
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public partial class EagerRunner
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{
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bool RunCallbacks(FastPathOpExecInfo op_exec_info,
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int num_inferred_attrs,
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Tensor[] inputs,
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object[] attrs,
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Tensor[] flattened_result)
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{
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if (op_exec_info.run_gradient_callback)
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{
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if (!RecordGradient(op_exec_info.op_name, inputs, attrs,
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flattened_result))
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{
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return false;
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}
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}
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if (op_exec_info.run_post_exec_callbacks)
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{
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}
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return true;
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}
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}
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}

‎src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs‎

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using System.Collections.Generic;
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using System.Linq;
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using System;
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using static Tensorflow.Binding;
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namespace Tensorflow.Eager
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{
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/// <summary>
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/// python\eager\pywrap_tfe_src.cc
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/// </summary>
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public partial class EagerRunner
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{
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public Tensor[] TFE_Execute(Context ctx,
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string device_name,
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string op_name,
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Tensor[] inputs,
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object[] attrs,
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int num_outputs)
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=> TFE_ExecuteCancelable(ctx, device_name, op_name, inputs, attrs, num_outputs);
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public Tensor[] TFE_ExecuteCancelable(Context ctx,
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string device_name,
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string op_name,
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Tensor[] inputs,
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object[] attrs,
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int num_outputs)
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{
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var status = tf.status;
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var op = GetOp(ctx, op_name, status);
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status.Check(true);
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c_api.TFE_OpSetDevice(op, device_name, status.Handle);
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if (status.ok())
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{
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for (int i = 0; i < inputs.Length; ++i)
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{
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IntPtr tensor_handle;
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switch (inputs[i])
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{
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case EagerTensor et:
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tensor_handle = et.EagerTensorHandle;
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break;
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default:
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tensor_handle = c_api.TFE_NewTensorHandle(inputs[i], status.Handle);
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break;
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}
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c_api.TFE_OpAddInput(op, tensor_handle, status.Handle);
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}
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}
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if (status.ok())
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SetOpAttrs(op, attrs, status.Handle);
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var outputs = new IntPtr[num_outputs];
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if (status.ok())
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{
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c_api.TFE_Execute(op, outputs, ref num_outputs, status.Handle);
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status.Check(true);
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}
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return outputs.Select(x => new EagerTensor(x)).ToArray();
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}
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}
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}

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