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 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  information regarding copyright ownership.
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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 BiasAdd operation.
 * 
 * BiasAdd adds a bias tensor to an input tensor along a specified axis.
 * This is commonly used in neural networks where bias is added after
 * linear transformations.
 * 
 * Inputs:
 * - data: Input tensor
 * - bias: Bias tensor to add
 * 
 * Attributes:
 * - axis: Axis along which to add bias (default: 1)
 * 
 * @author Adam Gibson
 */
@PreHookRule(nodeNames = [], opNames = ["BiasAdd"], frameworkName = "onnx")
class BiasAdd: 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 data = sd.getVariable(op.inputsToOp[0])
        val bias = sd.getVariable(op.inputsToOp[1])
        
        val axis = (attributes.getOrDefault("axis", 1) as Number).toInt()
        
        // Simply add the bias to the data
        // The broadcasting rules will handle the proper alignment along the specified axis
        val result = data.add(bias)
        
        result.rename(outputNames[0])
        return mapOf(outputNames[0] to listOf(result))
    }
}