// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
 
using System.Collections.Generic;
 
namespace Microsoft.ML.AutoML
{
    internal class ColumnConcatenatingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.ColumnConcatenating.ToString(),
                PipelineNodeType.Transform, inColumns, outColumn);
            var estimator = CreateInstance(context, inColumns, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string outColumn)
        {
            return context.Transforms.Concatenate(outColumn, inColumns);
        }
    }
 
    internal class ColumnCopyingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.ColumnCopying.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.CopyColumns(outColumn, inColumn);
        }
    }
 
    internal class KeyToValueMappingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.KeyToValueMapping.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.Conversion.MapKeyToValue(outColumn, inColumn);
        }
    }
 
    internal class HashingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.Hashing.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.Conversion.Hash(outColumn, inColumn);
        }
    }
 
    internal class MissingValueIndicatingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pipelineNode = new PipelineNode(EstimatorName.MissingValueIndicating.ToString(),
                PipelineNodeType.Transform, inColumns, outColumns);
            var estimator = CreateInstance(context, inColumns, outColumns);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pairs = new InputOutputColumnPair[inColumns.Length];
            for (var i = 0; i < inColumns.Length; i++)
            {
                var pair = new InputOutputColumnPair(outColumns[i], inColumns[i]);
                pairs[i] = pair;
            }
            return context.Transforms.IndicateMissingValues(pairs);
        }
    }
 
    internal class MissingValueReplacingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pipelineNode = new PipelineNode(EstimatorName.MissingValueReplacing.ToString(),
                PipelineNodeType.Transform, inColumns, outColumns);
            var estimator = CreateInstance(context, inColumns, outColumns);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pairs = new InputOutputColumnPair[inColumns.Length];
            for (var i = 0; i < inColumns.Length; i++)
            {
                var pair = new InputOutputColumnPair(outColumns[i], inColumns[i]);
                pairs[i] = pair;
            }
            return context.Transforms.ReplaceMissingValues(pairs);
        }
    }
 
    internal class NormalizingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.Normalizing.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.NormalizeMinMax(outColumn, inColumn);
        }
    }
 
    internal class OneHotEncodingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pipelineNode = new PipelineNode(EstimatorName.OneHotEncoding.ToString(),
                PipelineNodeType.Transform, inColumns, outColumns);
            var estimator = CreateInstance(context, inColumns, outColumns);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        public static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string[] outColumns)
        {
            var cols = new InputOutputColumnPair[inColumns.Length];
            for (var i = 0; i < cols.Length; i++)
            {
                cols[i] = new InputOutputColumnPair(outColumns[i], inColumns[i]);
            }
            return context.Transforms.Categorical.OneHotEncoding(cols);
        }
    }
 
    internal class OneHotHashEncodingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            return CreateSuggestedTransform(context, new[] { inColumn }, new[] { outColumn });
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pipelineNode = new PipelineNode(EstimatorName.OneHotHashEncoding.ToString(),
                PipelineNodeType.Transform, inColumns, outColumns);
            var estimator = CreateInstance(context, inColumns, outColumns);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string[] outColumns)
        {
            var cols = new InputOutputColumnPair[inColumns.Length];
            for (var i = 0; i < cols.Length; i++)
            {
                cols[i] = new InputOutputColumnPair(outColumns[i], inColumns[i]);
            }
            return context.Transforms.Categorical.OneHotHashEncoding(cols);
        }
    }
 
    internal class TextFeaturizingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.TextFeaturizing.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.Text.FeaturizeText(outColumn, inColumn);
        }
    }
 
    internal class TypeConvertingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns, pipelineNode.OutColumns);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string[] inColumns, string[] outColumns)
        {
            var pipelineNode = new PipelineNode(EstimatorName.TypeConverting.ToString(),
                PipelineNodeType.Transform, inColumns, outColumns);
            var estimator = CreateInstance(context, inColumns, outColumns);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string[] inColumns, string[] outColumns)
        {
            var cols = new InputOutputColumnPair[inColumns.Length];
            for (var i = 0; i < cols.Length; i++)
            {
                cols[i] = new InputOutputColumnPair(outColumns[i], inColumns[i]);
            }
            return context.Transforms.Conversion.ConvertType(cols);
        }
    }
 
    internal class ValueToKeyMappingExtension : IEstimatorExtension
    {
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNode = new PipelineNode(EstimatorName.ValueToKeyMapping.ToString(),
                PipelineNodeType.Transform, inColumn, outColumn);
 
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.Conversion.MapValueToKey(outColumn, inColumn);
        }
    }
 
    internal class RawByteImageLoading : IEstimatorExtension
    {
        public static string ImageFolder { private get; set; }
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNodeProperty = new Dictionary<string, object>()
            {
                { "imageFolder", ImageFolder },
            };
            var pipelineNode = new PipelineNode(EstimatorName.RawByteImageLoading.ToString(), PipelineNodeType.Transform, inColumn, outColumn, pipelineNodeProperty);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.LoadRawImageBytes(outColumn, ImageFolder, inColumn);
        }
    }
 
    internal class ImageLoading : IEstimatorExtension
    {
        public static string ImageFolder { private get; set; }
        public IEstimator<ITransformer> CreateInstance(MLContext context, PipelineNode pipelineNode)
        {
            return CreateInstance(context, pipelineNode.InColumns[0], pipelineNode.OutColumns[0]);
        }
 
        public static SuggestedTransform CreateSuggestedTransform(MLContext context, string inColumn, string outColumn)
        {
            var pipelineNodeProperty = new Dictionary<string, object>()
            {
                { "imageFolder", ImageFolder },
            };
            var pipelineNode = new PipelineNode(EstimatorName.ImageLoading.ToString(), PipelineNodeType.Transform, inColumn, outColumn, pipelineNodeProperty);
            var estimator = CreateInstance(context, inColumn, outColumn);
            return new SuggestedTransform(pipelineNode, estimator);
        }
 
        private static IEstimator<ITransformer> CreateInstance(MLContext context, string inColumn, string outColumn)
        {
            return context.Transforms.LoadImages(outColumn, ImageFolder, inColumn);
        }
    }
}