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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
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package org.nd4j.samediff.frameworkimport.onnx.definitions.implementations

import org.nd4j.autodiff.samediff.SDVariable
import org.nd4j.autodiff.samediff.SameDiff
import org.nd4j.autodiff.samediff.internal.SameDiffOp
import org.nd4j.samediff.frameworkimport.ImportGraph
import org.nd4j.samediff.frameworkimport.hooks.PreImportHook
import org.nd4j.samediff.frameworkimport.hooks.annotations.PreHookRule
import org.nd4j.samediff.frameworkimport.registry.OpMappingRegistry
import org.nd4j.shade.protobuf.GeneratedMessageV3
import org.nd4j.shade.protobuf.ProtocolMessageEnum

/**
 * Implementation of Microsoft ONNX BiasGelu operation.
 * 
 * BiasGelu combines bias addition with GELU activation:
 * 1. Add bias to input: x = input + bias
 * 2. Apply GELU: output = GELU(x) = 0.5 * x * (1 + erf(x / sqrt(2)))
 * 
 * @author Adam Gibson
 */
@PreHookRule(nodeNames = [], opNames = ["BiasGelu"], frameworkName = "onnx")
class BiasGelu: PreImportHook {
    
    override fun doImport(
        sd: SameDiff,
        attributes: Map<String, Any>,
        outputNames: List<String>,
        op: SameDiffOp,
        mappingRegistry: OpMappingRegistry<GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, ProtocolMessageEnum, GeneratedMessageV3, GeneratedMessageV3>,
        importGraph: ImportGraph<GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, ProtocolMessageEnum>,
        dynamicVariables: Map<String, GeneratedMessageV3>
    ): Map<String, List<SDVariable>> {
        
        val input = sd.getVariable(op.inputsToOp[0])
        val bias = sd.getVariable(op.inputsToOp[1])
        
        // Add bias to input
        val biasedInput = input.add(bias)
        
        // Apply GELU activation: 0.5 * x * (1 + erf(x / sqrt(2)))
        val x = biasedInput
        val sqrt2 = sd.constant(kotlin.math.sqrt(2.0))
        val erfInput = x.div(sqrt2)
        val erfResult = sd.math().erf(erfInput)
        val onePlusErf = erfResult.add(sd.constant(1.0))
        val result = x.mul(onePlusErf).mul(outputNames[0],sd.constant(0.5))
        return mapOf(outputNames[0] to listOf(result))
    }
}