old_contents: # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
  # SPDX-License-Identifier: Apache-2.0
  
  """
  Purpose
  
  Shows how to run an electronic medical record file system (EMRFS) command as a job step on an Amazon EMR cluster. This
  can be used to automate EMRFS commands and is an alternative to connecting through
  SSH to run the commands manually.
  """
  
  # snippet-start:[emr.python.addstep.emrfs]
  import boto3
  from botocore.exceptions import ClientError
  
  
  def add_emrfs_step(command, bucket_url, cluster_id, emr_client):
      """
      Add an EMRFS command as a job flow step to an existing cluster.
  
      :param command: The EMRFS command to run.
      :param bucket_url: The URL of a bucket that contains tracking metadata.
      :param cluster_id: The ID of the cluster to update.
      :param emr_client: The Boto3 Amazon EMR client object.
      :return: The ID of the added job flow step. Status can be tracked by calling
               the emr_client.describe_step() function.
      """
      job_flow_step = {
          'Name': 'Example EMRFS Command Step',
          'ActionOnFailure': 'CONTINUE',
          'HadoopJarStep': {
              'Jar': 'command-runner.jar',
              'Args': [
                  '/usr/bin/emrfs',
                  command,
                  bucket_url
              ]
          }
      }
  
      try:
          response = emr_client.add_job_flow_steps(
              JobFlowId=cluster_id, Steps=[job_flow_step])
          step_id = response['StepIds'][0]
          print(f"Added step {step_id} to cluster {cluster_id}.")
      except ClientError:
          print(f"Couldn't add a step to cluster {cluster_id}.")
          raise
      else:
          return step_id
  
  
  def usage_demo():
      emr_client = boto3.client('emr')
      # Assumes the first waiting cluster has EMRFS enabled and has created metadata
      # with the default name of 'EmrFSMetadata'.
      cluster = emr_client.list_clusters(ClusterStates=['WAITING'])['Clusters'][0]
      add_emrfs_step(
          'sync', 's3://elasticmapreduce/samples/cloudfront', cluster['Id'], emr_client)
  
  
  if __name__ == '__main__':
      usage_demo()
  # snippet-end:[emr.python.addstep.emrfs]
  
new_contents: # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
  # SPDX-License-Identifier: Apache-2.0
  
  """
  Purpose
  
  Shows how to run an elastic map reduce file system (EMRFS) command as a job step on
  an Amazon EMR cluster. This can be used to automate EMRFS commands and is an
  alternative to connecting through SSH to run the commands manually.
  """
  
  # snippet-start:[emr.python.addstep.emrfs]
  import boto3
  from botocore.exceptions import ClientError
  
  
  def add_emrfs_step(command, bucket_url, cluster_id, emr_client):
      """
      Add an EMRFS command as a job flow step to an existing cluster.
  
      :param command: The EMRFS command to run.
      :param bucket_url: The URL of a bucket that contains tracking metadata.
      :param cluster_id: The ID of the cluster to update.
      :param emr_client: The Boto3 Amazon EMR client object.
      :return: The ID of the added job flow step. Status can be tracked by calling
               the emr_client.describe_step() function.
      """
      job_flow_step = {
          'Name': 'Example EMRFS Command Step',
          'ActionOnFailure': 'CONTINUE',
          'HadoopJarStep': {
              'Jar': 'command-runner.jar',
              'Args': [
                  '/usr/bin/emrfs',
                  command,
                  bucket_url
              ]
          }
      }
  
      try:
          response = emr_client.add_job_flow_steps(
              JobFlowId=cluster_id, Steps=[job_flow_step])
          step_id = response['StepIds'][0]
          print(f"Added step {step_id} to cluster {cluster_id}.")
      except ClientError:
          print(f"Couldn't add a step to cluster {cluster_id}.")
          raise
      else:
          return step_id
  
  
  def usage_demo():
      emr_client = boto3.client('emr')
      # Assumes the first waiting cluster has EMRFS enabled and has created metadata
      # with the default name of 'EmrFSMetadata'.
      cluster = emr_client.list_clusters(ClusterStates=['WAITING'])['Clusters'][0]
      add_emrfs_step(
          'sync', 's3://elasticmapreduce/samples/cloudfront', cluster['Id'], emr_client)
  
  
  if __name__ == '__main__':
      usage_demo()
  # snippet-end:[emr.python.addstep.emrfs]
  
subject: Fix wrong definition of 'EMR'.