我有一个制图器和一个缩小器。整个代码是从wordcount示例修改的，但是输入和输出类型是根据我的需要修改的。
错误似乎是由于输入/输出类型不匹配造成的，但不确定出了什么问题。

public static class TokenizerMapper
            extends Mapper<Object, Text, Text, Text>{

        private Text word = new Text();

        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            //blah blah
        }
    }

    public static class IntSumReducer
            extends Reducer<Text,Text,IntWritable,Text> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<Text> values,
                           Context context
        ) throws IOException, InterruptedException {
            //blah blah
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileInputFormat.addInputPath(job, new Path(args[1]));
        FileOutputFormat.setOutputPath(job, new Path(args[2]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

当我运行代码时，出现如下错误：

java.io.IOException: wrong key class: class org.apache.hadoop.io.IntWritable is not class org.apache.hadoop.io.Text
    at org.apache.hadoop.mapred.IFile$Writer.append(IFile.java:191)
    at org.apache.hadoop.mapred.Task$CombineOutputCollector.collect(Task.java:1574)
    at org.apache.hadoop.mapred.Task$NewCombinerRunner$OutputConverter.write(Task.java:1891)
    at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
    at org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer$Context.write(WrappedReducer.java:105)
    at WordCount$IntSumReducer.reduce(WordCount.java:47)
    at WordCount$IntSumReducer.reduce(WordCount.java:35)
    at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
    at org.apache.hadoop.mapred.Task$NewCombinerRunner.combine(Task.java:1912)
    at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.sortAndSpill(MapTask.java:1662)
    at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.flush(MapTask.java:1505)
    at org.apache.hadoop.mapred.MapTask$NewOutputCollector.close(MapTask.java:735)
    at org.apache.hadoop.mapred.MapTask.closeQuietly(MapTask.java:2076)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:809)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:347)
    at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:271)
    at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
    at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
    at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    at java.base/java.lang.Thread.run(Thread.java:834)