tensorflow/lite/toco/toco_flags.proto - platform/external/tensorflow - Git at Google

 // Copyright 2022 The TensorFlow Authors. All Rights Reserved.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
 // You may obtain a copy of the License at
 //
 //     http://www.apache.org/licenses/LICENSE-2.0
 //
 // Unless required by applicable law or agreed to in writing, software
 // distributed under the License is distributed on an "AS IS" BASIS,
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
 syntax = "proto2";

 package toco;

 import "tensorflow/lite/toco/types.proto";

 // Supported I/O file formats. Some formats may be input-only or output-only.
 enum FileFormat {
   FILE_FORMAT_UNKNOWN = 0;

   // GraphDef, third_party/tensorflow/core/framework/graph.proto
   TENSORFLOW_GRAPHDEF = 1;

   // Tensorflow's mobile inference model.
   // third_party/tensorflow/lite/schema/schema.fbs
   TFLITE = 2;

   // GraphViz
   // Export-only.
   GRAPHVIZ_DOT = 3;
 }

 // TocoFlags encodes extra parameters that drive tooling operations, that
 // are not normally encoded in model files and in general may not be thought
 // of as properties of models, instead describing how models are to be
 // processed in the context of the present tooling job.
 //
 // Next ID to use: 51.
 message TocoFlags {
   // Input file format
   optional FileFormat input_format = 1;

   // Output file format
   optional FileFormat output_format = 2;

   // Similar to inference_type, but allows to control specifically the
   // quantization of input arrays, separately from other arrays.
   //
   // If not set, then the value of inference_type is implicitly used, i.e.
   // by default input arrays are quantized like other arrays.
   //
   // Like inference_type, this only affects real-number arrays. By "real-number"
   // we mean float arrays, and quantized arrays. This excludes plain
   // integer arrays, strings arrays, and every other data type.
   //
   // The typical use for this flag is for vision models taking a bitmap
   // as input, typically with uint8 channels, yet still requiring floating-point
   // inference. For such image models, the uint8 input is quantized, i.e.
   // the uint8 values are interpreted as real numbers, and the quantization
   // parameters used for such input arrays are their mean_value, std_value
   // parameters.
   optional IODataType inference_input_type = 11;

   // Sets the type of real-number arrays in the output file, that is, controls
   // the representation (quantization) of real numbers in the output file,
   // except for input arrays, which are controlled by inference_input_type.
   //
   // NOTE: this flag only impacts real-number arrays. By "real-number"
   // we mean float arrays, and quantized arrays. This excludes plain
   // integer arrays, strings arrays, and every other data type.
   //
   // For real-number arrays, the impact of this flag is to allow the output
   // file to choose a different real-numbers representation (quantization)
   // from what the input file used. For any other types of arrays, changing
   // the data type would not make sense.
   //
   // Specifically:
   //    - If FLOAT, then real-numbers arrays will be of type float in
   //      the output file. If they were quantized in the input file, then
   //      they get dequantized.
   //    - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
   //      as uint8 in the output file. If they were float in the input file,
   //      then they get quantized.
   //    - If not set, then all real-numbers arrays retain the same type in the
   //      output file as they have in the input file.
   //
   optional IODataType inference_type = 4;

   // default_ranges_min and default_ranges_max are helpers to experiment
   // with quantization of models. Normally, quantization requires the input
   // model to have (min, max) range information for every activations array.
   // This is needed in order to know how to quantize arrays and still achieve
   // satisfactory accuracy. However, in some circumstances one would just like
   // to estimate the performance of quantized inference, without caring about
   // accuracy. That is what default_ranges_min and default_ranges_max are for:
   // when specified, they will be used as default (min, max) range boundaries
   // for all activation arrays that lack (min, max) range information, thus
   // allowing for quantization to proceed.
   //
   // It should be clear from the above explanation that these parameters are
   // for experimentation purposes only and should not be used in production:
   // they make it easy to quantize models, but the resulting quantized model
   // will be inaccurate.
   //
   // These values only apply to arrays quantized with the kUint8 data type.
   optional float default_ranges_min = 5;
   optional float default_ranges_max = 6;
   // Equivalent versions of default_ranges_min/_max for arrays quantized with
   // the kInt16 data type.
   optional float default_int16_ranges_min = 15;
   optional float default_int16_ranges_max = 16;

   // Ignore and discard FakeQuant nodes. For instance, that can be used to
   // generate plain float code without fake-quantization from a quantized
   // graph.
   optional bool drop_fake_quant = 7;

   // Normally, FakeQuant nodes must be strict boundaries for graph
   // transformations, in order to ensure that quantized inference has the
   // exact same arithmetic behavior as quantized training --- which is the
   // whole point of quantized training and of FakeQuant nodes in the first
   // place. However, that entails subtle requirements on where exactly
   // FakeQuant nodes must be placed in the graph. Some quantized graphs
   // have FakeQuant nodes at unexpected locations, that prevent graph
   // transformations that are necessary in order to generate inference
   // code for these graphs. Such graphs should be fixed, but as a
   // temporary work-around, setting this reorder_across_fake_quant flag
   // allows toco to perform necessary graph transformations on them,
   // at the cost of no longer faithfully matching inference and training
   // arithmetic.
   optional bool reorder_across_fake_quant = 8;

   // If true, allow TOCO to create TF Lite Custom operators for all the
   // unsupported Tensorflow ops.
   optional bool allow_custom_ops = 10;

   // Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
   // If true, then control dependencies will be immediately dropped during
   // import.
   // If not set, the default behavior is as follows:
   //    - Default to false if the output format is TENSORFLOW_GRAPHDEF.
   //    - Default to true in all other cases.
   optional bool drop_control_dependency = 12;

   // Disables transformations that fuse subgraphs such as known LSTMs (not all
   // LSTMs are identified).
   optional bool debug_disable_recurrent_cell_fusion = 13;

   // Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
   // array data types throughout the graph. The graph must be properly annotated
   // with FakeQuant* ops on at least the edges and may contain additional ops on
   // the interior of the graph to widen/narrow as desired.
   //
   // Input and output array data types may change because of this propagation
   // and users must be sure to query the final data_type values.
   optional bool propagate_fake_quant_num_bits = 14;

   // Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
   // This flag allows nudging them to 1 to allow proceeding, with moderate
   // inaccuracy.
   optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;

   // Minimum size of constant arrays to deduplicate; arrays smaller will not be
   // deduplicated.
   optional int64 dedupe_array_min_size_bytes = 18 [default = 64];

   // Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
   // Ignored if the output format is not TFLite.
   optional bool split_tflite_lstm_inputs = 19 [default = true];

   // Store weights as quantized weights followed by dequantize operations.
   // Computation is still done in float, but reduces model size (at the cost of
   // accuracy and latency).
   // DEPRECATED: Please use post_training_quantize instead.
   optional bool quantize_weights = 20 [default = false];

   // Full filepath of folder to dump the graphs at various stages of processing
   // GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
   // to keep the requirements of the output file.
   optional string dump_graphviz_dir = 24;

   // Boolean indicating whether to dump the graph after every graph
   // transformation.
   optional bool dump_graphviz_include_video = 25;

   // Boolean indicating whether to quantize the weights of the converted float
   // model. Model size will be reduced and there will be latency improvements
   // (at the cost of accuracy).
   optional bool post_training_quantize = 26 [default = false];

   // This flag only works when converting to TensorFlow Lite format.
   // When enabled, unsupported ops will be converted to select TensorFlow ops.
   // TODO(ycling): Consider to rename the following 2 flags and don't call it
   // "Flex".
   // `enable_select_tf_ops` should always be used with `allow_custom_ops`.
   // WARNING: Experimental interface, subject to change
   optional bool enable_select_tf_ops = 27 [default = false];

   // This flag only works when converting to TensorFlow Lite format.
   // When enabled, all TensorFlow ops will be converted to select TensorFlow
   // ops.
   // This will force `enable_select_tf_ops` to true.
   // `force_select_tf_ops` should always be used with `enable_select_tf_ops`.
   // WARNING: Experimental interface, subject to change
   optional bool force_select_tf_ops = 28 [default = false];

   // Boolean indicating whether to convert float32 constant buffers to
   // float16. This is typically done to reduce model size. Delegates may also
   // wish to implement kernels on reduced precision floats for performance
   // gains.
   optional bool quantize_to_float16 = 29 [default = false];

   // Boolean flag indicating whether the converter should allow models with
   // dynamic Tensor shape. When set to False, the converter will generate
   // runtime memory offsets for activation Tensors (with 128 bits alignment)
   // and error out on models with undetermined Tensor shape. (Default: True)
   optional bool allow_dynamic_tensors = 30 [default = true];

   // Full filepath of the folder to dump conversion logs. This includes a global
   // view of the conversion process, and user can choose to submit those logs.
   optional string conversion_summary_dir = 31;

   // String representing the custom ops OpDefs that are included in the
   // GraphDef.
   // Deprecated do not use.
   repeated string custom_opdefs = 32 [deprecated = true];

   // Name of user's defined Tensorflow ops required in the TensorFlow Lite
   // runtime. These ops will be supported as select TensorFlow ops.
   repeated string select_user_tf_ops = 33;

   // Whether to enable tflite resource variables during conversion or not.
   // Note: This is an experimental feature.
   optional bool enable_tflite_resource_variables = 34 [default = true];

   // Whether to unfold tf.BatchMatMul to a set of tfl.fully_connected ops. If
   // not, translate to tfl.batch_matmul.
   // WARNING: Experimental interface, subject to change.
   optional bool unfold_batchmatmul = 35 [default = true];

   // Whether to lower static Tensor List ops to builtin ops. If not, use Flex
   // tensor list ops.
   // WARNING: Experimental interface, subject to change.
   optional bool lower_tensor_list_ops = 36 [default = true];

   // The accumulation type to use when quantize_to_float16 is true. Typical
   // choices would be either float16 or float32.
   optional IODataType accumulation_type = 37;

   // Whether this model supports inference in bfloat16.
   // Note: This is an experimental feature.
   optional bool allow_bfloat16 = 38 [default = false];

   // If true, automatically adds all tf ops into the model as select Tensorflow
   // ops.
   optional bool allow_all_select_tf_ops = 39;

   // Whether to unfold large splat constant tensors in the flatbuffer to reduce
   // model size.
   optional bool unfold_large_splat_constant = 40 [default = false];

   // Name of TFLite backends which are needed to check compatibility.
   // WARNING: Experimental interface, subject to change.
   repeated string supported_backends = 41;

   // Whether to force to use batch size one when the batch size is None during
   // lowering tensor list ops.
   optional bool default_to_single_batch_in_tensor_list_ops = 42
       [default = false];

   // Disable per_channel quantization for dynamic range quantization.
   // Note: This is an experimental feature
   optional bool disable_per_channel_quantization = 43 [default = false];

   // If false, the old TOCO dynamic range quantization is used.
   // Note: This is an experimental feature
   optional bool enable_mlir_dynamic_range_quantizer = 44 [default = false];

   // When the output model is used for TF Quantization, this flag indicates the
   // mode of TF Quantization. Ex: DEFAULT, LEGACY_INTEGER,...
   optional string tf_quantization_mode = 45;

   // Disable inferring tensor range for quantization.
   // Note: This is an experimental feature
   optional bool disable_infer_tensor_range = 46 [default = false];

   // Enable using num bits set in fake quant attributes for quantization.
   // Note: This is an experimental feature
   optional bool use_fake_quant_num_bits = 47 [default = false];

   // Enable converting to DynamicUpdateSlice op (for ops like TensorListSetItem)
   // Note: This is an experimental feature
   optional bool enable_dynamic_update_slice = 48 [default = false];

   // Whether to preserve `TF::AssertOp`.
   optional bool preserve_assert_op = 49 [default = false];

   // Whether to ensure each function has a single use.
   optional bool guarantee_all_funcs_one_use = 50 [default = false];
 }
	// Copyright 2022 The TensorFlow Authors. All Rights Reserved.
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.
	syntax = "proto2";

	package toco;

	import "tensorflow/lite/toco/types.proto";

	// Supported I/O file formats. Some formats may be input-only or output-only.
	enum FileFormat {
	FILE_FORMAT_UNKNOWN = 0;

	// GraphDef, third_party/tensorflow/core/framework/graph.proto
	TENSORFLOW_GRAPHDEF = 1;

	// Tensorflow's mobile inference model.
	// third_party/tensorflow/lite/schema/schema.fbs
	TFLITE = 2;

	// GraphViz
	// Export-only.
	GRAPHVIZ_DOT = 3;
	}

	// TocoFlags encodes extra parameters that drive tooling operations, that
	// are not normally encoded in model files and in general may not be thought
	// of as properties of models, instead describing how models are to be
	// processed in the context of the present tooling job.
	//
	// Next ID to use: 51.
	message TocoFlags {
	// Input file format
	optional FileFormat input_format = 1;

	// Output file format
	optional FileFormat output_format = 2;

	// Similar to inference_type, but allows to control specifically the
	// quantization of input arrays, separately from other arrays.
	//
	// If not set, then the value of inference_type is implicitly used, i.e.
	// by default input arrays are quantized like other arrays.
	//
	// Like inference_type, this only affects real-number arrays. By "real-number"
	// we mean float arrays, and quantized arrays. This excludes plain
	// integer arrays, strings arrays, and every other data type.
	//
	// The typical use for this flag is for vision models taking a bitmap
	// as input, typically with uint8 channels, yet still requiring floating-point
	// inference. For such image models, the uint8 input is quantized, i.e.
	// the uint8 values are interpreted as real numbers, and the quantization
	// parameters used for such input arrays are their mean_value, std_value
	// parameters.
	optional IODataType inference_input_type = 11;

	// Sets the type of real-number arrays in the output file, that is, controls
	// the representation (quantization) of real numbers in the output file,
	// except for input arrays, which are controlled by inference_input_type.
	//
	// NOTE: this flag only impacts real-number arrays. By "real-number"
	// we mean float arrays, and quantized arrays. This excludes plain
	// integer arrays, strings arrays, and every other data type.
	//
	// For real-number arrays, the impact of this flag is to allow the output
	// file to choose a different real-numbers representation (quantization)
	// from what the input file used. For any other types of arrays, changing
	// the data type would not make sense.
	//
	// Specifically:
	// - If FLOAT, then real-numbers arrays will be of type float in
	// the output file. If they were quantized in the input file, then
	// they get dequantized.
	// - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
	// as uint8 in the output file. If they were float in the input file,
	// then they get quantized.
	// - If not set, then all real-numbers arrays retain the same type in the
	// output file as they have in the input file.
	//
	optional IODataType inference_type = 4;

	// default_ranges_min and default_ranges_max are helpers to experiment
	// with quantization of models. Normally, quantization requires the input
	// model to have (min, max) range information for every activations array.
	// This is needed in order to know how to quantize arrays and still achieve
	// satisfactory accuracy. However, in some circumstances one would just like
	// to estimate the performance of quantized inference, without caring about
	// accuracy. That is what default_ranges_min and default_ranges_max are for:
	// when specified, they will be used as default (min, max) range boundaries
	// for all activation arrays that lack (min, max) range information, thus
	// allowing for quantization to proceed.
	//
	// It should be clear from the above explanation that these parameters are
	// for experimentation purposes only and should not be used in production:
	// they make it easy to quantize models, but the resulting quantized model
	// will be inaccurate.
	//
	// These values only apply to arrays quantized with the kUint8 data type.
	optional float default_ranges_min = 5;
	optional float default_ranges_max = 6;
	// Equivalent versions of default_ranges_min/_max for arrays quantized with
	// the kInt16 data type.
	optional float default_int16_ranges_min = 15;
	optional float default_int16_ranges_max = 16;

	// Ignore and discard FakeQuant nodes. For instance, that can be used to
	// generate plain float code without fake-quantization from a quantized
	// graph.
	optional bool drop_fake_quant = 7;

	// Normally, FakeQuant nodes must be strict boundaries for graph
	// transformations, in order to ensure that quantized inference has the
	// exact same arithmetic behavior as quantized training --- which is the
	// whole point of quantized training and of FakeQuant nodes in the first
	// place. However, that entails subtle requirements on where exactly
	// FakeQuant nodes must be placed in the graph. Some quantized graphs
	// have FakeQuant nodes at unexpected locations, that prevent graph
	// transformations that are necessary in order to generate inference
	// code for these graphs. Such graphs should be fixed, but as a
	// temporary work-around, setting this reorder_across_fake_quant flag
	// allows toco to perform necessary graph transformations on them,
	// at the cost of no longer faithfully matching inference and training
	// arithmetic.
	optional bool reorder_across_fake_quant = 8;

	// If true, allow TOCO to create TF Lite Custom operators for all the
	// unsupported Tensorflow ops.
	optional bool allow_custom_ops = 10;

	// Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
	// If true, then control dependencies will be immediately dropped during
	// import.
	// If not set, the default behavior is as follows:
	// - Default to false if the output format is TENSORFLOW_GRAPHDEF.
	// - Default to true in all other cases.
	optional bool drop_control_dependency = 12;

	// Disables transformations that fuse subgraphs such as known LSTMs (not all
	// LSTMs are identified).
	optional bool debug_disable_recurrent_cell_fusion = 13;

	// Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
	// array data types throughout the graph. The graph must be properly annotated
	// with FakeQuant* ops on at least the edges and may contain additional ops on
	// the interior of the graph to widen/narrow as desired.
	//
	// Input and output array data types may change because of this propagation
	// and users must be sure to query the final data_type values.
	optional bool propagate_fake_quant_num_bits = 14;

	// Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
	// This flag allows nudging them to 1 to allow proceeding, with moderate
	// inaccuracy.
	optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;

	// Minimum size of constant arrays to deduplicate; arrays smaller will not be
	// deduplicated.
	optional int64 dedupe_array_min_size_bytes = 18 [default = 64];

	// Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
	// Ignored if the output format is not TFLite.
	optional bool split_tflite_lstm_inputs = 19 [default = true];

	// Store weights as quantized weights followed by dequantize operations.
	// Computation is still done in float, but reduces model size (at the cost of
	// accuracy and latency).
	// DEPRECATED: Please use post_training_quantize instead.
	optional bool quantize_weights = 20 [default = false];

	// Full filepath of folder to dump the graphs at various stages of processing
	// GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
	// to keep the requirements of the output file.
	optional string dump_graphviz_dir = 24;

	// Boolean indicating whether to dump the graph after every graph
	// transformation.
	optional bool dump_graphviz_include_video = 25;

	// Boolean indicating whether to quantize the weights of the converted float
	// model. Model size will be reduced and there will be latency improvements
	// (at the cost of accuracy).
	optional bool post_training_quantize = 26 [default = false];

	// This flag only works when converting to TensorFlow Lite format.
	// When enabled, unsupported ops will be converted to select TensorFlow ops.
	// TODO(ycling): Consider to rename the following 2 flags and don't call it
	// "Flex".
	// `enable_select_tf_ops` should always be used with `allow_custom_ops`.
	// WARNING: Experimental interface, subject to change
	optional bool enable_select_tf_ops = 27 [default = false];

	// This flag only works when converting to TensorFlow Lite format.
	// When enabled, all TensorFlow ops will be converted to select TensorFlow
	// ops.
	// This will force `enable_select_tf_ops` to true.
	// `force_select_tf_ops` should always be used with `enable_select_tf_ops`.
	// WARNING: Experimental interface, subject to change
	optional bool force_select_tf_ops = 28 [default = false];

	// Boolean indicating whether to convert float32 constant buffers to
	// float16. This is typically done to reduce model size. Delegates may also
	// wish to implement kernels on reduced precision floats for performance
	// gains.
	optional bool quantize_to_float16 = 29 [default = false];

	// Boolean flag indicating whether the converter should allow models with
	// dynamic Tensor shape. When set to False, the converter will generate
	// runtime memory offsets for activation Tensors (with 128 bits alignment)
	// and error out on models with undetermined Tensor shape. (Default: True)
	optional bool allow_dynamic_tensors = 30 [default = true];

	// Full filepath of the folder to dump conversion logs. This includes a global
	// view of the conversion process, and user can choose to submit those logs.
	optional string conversion_summary_dir = 31;

	// String representing the custom ops OpDefs that are included in the
	// GraphDef.
	// Deprecated do not use.
	repeated string custom_opdefs = 32 [deprecated = true];

	// Name of user's defined Tensorflow ops required in the TensorFlow Lite
	// runtime. These ops will be supported as select TensorFlow ops.
	repeated string select_user_tf_ops = 33;

	// Whether to enable tflite resource variables during conversion or not.
	// Note: This is an experimental feature.
	optional bool enable_tflite_resource_variables = 34 [default = true];

	// Whether to unfold tf.BatchMatMul to a set of tfl.fully_connected ops. If
	// not, translate to tfl.batch_matmul.
	// WARNING: Experimental interface, subject to change.
	optional bool unfold_batchmatmul = 35 [default = true];

	// Whether to lower static Tensor List ops to builtin ops. If not, use Flex
	// tensor list ops.
	// WARNING: Experimental interface, subject to change.
	optional bool lower_tensor_list_ops = 36 [default = true];

	// The accumulation type to use when quantize_to_float16 is true. Typical
	// choices would be either float16 or float32.
	optional IODataType accumulation_type = 37;

	// Whether this model supports inference in bfloat16.
	// Note: This is an experimental feature.
	optional bool allow_bfloat16 = 38 [default = false];

	// If true, automatically adds all tf ops into the model as select Tensorflow
	// ops.
	optional bool allow_all_select_tf_ops = 39;

	// Whether to unfold large splat constant tensors in the flatbuffer to reduce
	// model size.
	optional bool unfold_large_splat_constant = 40 [default = false];

	// Name of TFLite backends which are needed to check compatibility.
	// WARNING: Experimental interface, subject to change.
	repeated string supported_backends = 41;

	// Whether to force to use batch size one when the batch size is None during
	// lowering tensor list ops.
	optional bool default_to_single_batch_in_tensor_list_ops = 42
	[default = false];

	// Disable per_channel quantization for dynamic range quantization.
	// Note: This is an experimental feature
	optional bool disable_per_channel_quantization = 43 [default = false];

	// If false, the old TOCO dynamic range quantization is used.
	// Note: This is an experimental feature
	optional bool enable_mlir_dynamic_range_quantizer = 44 [default = false];

	// When the output model is used for TF Quantization, this flag indicates the
	// mode of TF Quantization. Ex: DEFAULT, LEGACY_INTEGER,...
	optional string tf_quantization_mode = 45;

	// Disable inferring tensor range for quantization.
	// Note: This is an experimental feature
	optional bool disable_infer_tensor_range = 46 [default = false];

	// Enable using num bits set in fake quant attributes for quantization.
	// Note: This is an experimental feature
	optional bool use_fake_quant_num_bits = 47 [default = false];

	// Enable converting to DynamicUpdateSlice op (for ops like TensorListSetItem)
	// Note: This is an experimental feature
	optional bool enable_dynamic_update_slice = 48 [default = false];

	// Whether to preserve `TF::AssertOp`.
	optional bool preserve_assert_op = 49 [default = false];

	// Whether to ensure each function has a single use.
	optional bool guarantee_all_funcs_one_use = 50 [default = false];
	}