/* Copyright 2018 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
#define TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_

#include <memory>
#include <unordered_set>

#include "tensorflow/lite/context.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/core/model.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/util.h"

namespace tflite {
namespace optimize {

// Quantizes input_model and populates the provided builder with the new model.
// input_model is required to have min/max information populated in its
// quantization params.
//
// Inputs and output types default to float instead of a quantized type.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, ErrorReporter* error_reporter);

// Same as above, but the types of quantized inputs and outputs are
// configurable.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, const TensorType& input_type,
                           const TensorType& output_type,
                           ErrorReporter* error_reporter);

// Same as above, but can enable allowing float intermediate operations for ops
// that do not yet support quantizable.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           ErrorReporter* error_reporter);

// Same as above but with added option of disabling per channel quantization
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           bool disable_per_channel,
                           ErrorReporter* error_reporter);

// Same as above but with added option of handling quantization of external
// state tensors. This assumes first input and output tensors are ouputs and
// rest are state tensors which are quantized later with type as
// activation type (hence no fake quant ops).
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           bool disable_per_channel,
                           ErrorReporter* error_reporter,
                           bool handle_external_state);

// Same as above, but enables only quantizing an allowlist of operations,
// specified by their operator output name.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* input_model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           const std::unordered_set<string>& operator_names,
                           ErrorReporter* error_reporter);

// Same as above, but enables to provide activation type, which
// could be TensorType_INT16 or TensorType_INT8.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           const std::unordered_set<string>& operator_names,
                           const TensorType& activations_type,
                           const TensorType& bias_type,
                           ErrorReporter* error_reporter);

// Same as above, but all operators supporting quantization are quantized.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModelAllOperators(
    flatbuffers::FlatBufferBuilder* builder, ModelT* model,
    const TensorType& input_type, const TensorType& output_type,
    bool allow_float, const TensorType& activations_type,
    const TensorType& bias_type, ErrorReporter* error_reporter);

// Same as above, but allows disabling per channel quantization.
//
// Note: This is a private API, subject to change.
TfLiteStatus QuantizeModelAllOperators(
    flatbuffers::FlatBufferBuilder* builder, ModelT* model,
    const TensorType& input_type, const TensorType& output_type,
    bool allow_float, const TensorType& activations_type,
    const TensorType& bias_type, bool disable_per_channel,
    ErrorReporter* error_reporter);

// Quantizes input_model and populates the provided builder with the new model
// with all possible input parameters including disabling per_channel
// quantization.
//
// All functions above call this function underneath.
TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
                           ModelT* model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           const std::unordered_set<string>& operator_names,
                           const TensorType& activations_type,
                           const TensorType& bias_type,
                           bool disable_per_channel,
                           ErrorReporter* error_reporter,
                           bool handle_external_state);

}  // namespace optimize
}  // namespace tflite

#endif  // TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
