old_contents: """Adapters for working with pipeline frameworks."""
  
  import abc
  import apache_beam as beam
  import apache_beam.transforms.combiners as combiners
  
  
  class PipelineOperations(abc.ABC):
    """Interface for pipeline frameworks adapters."""
  
    @abc.abstractmethod
    def map(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def map_tuple(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def map_values(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def group_by_key(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def filter(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def keys(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def values(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      pass
  
    @abc.abstractmethod
    def count_per_element(self, col, stage_name: str):
      pass
  
  
  class BeamOperations(PipelineOperations):
    """Apache Beam adapter."""
  
    def map(self, col, fn, stage_name: str):
      return col | stage_name >> beam.Map(fn)
  
    def map_tuple(self, col, fn, stage_name: str):
      return col | stage_name >> beam.MapTuple(fn)
  
    def map_values(self, col, fn, stage_name: str):
      return col | stage_name >> beam.MapTuple(lambda k, v: (k, fn(v)))
  
    def group_by_key(self, col, stage_name: str):
      return col | stage_name >> beam.GroupByKey()
  
    def filter(self, col, fn, stage_name: str):
      return col | stage_name >> beam.Filter(fn)
  
    def keys(self, col, stage_name: str):
      return col | stage_name >> beam.Keys()
  
    def values(self, col, stage_name: str):
      return col | stage_name >> beam.Values()
  
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      return col | stage_name >> combiners.Sample.FixedSizePerKey(n)
  
    def count_per_element(self, col, stage_name: str):
      return col | stage_name >> combiners.Count.PerElement()
  
  class LocalPipelineOperations(PipelineOperations):
    """Local Pipeline adapter."""
    
    def map(self, col, fn, stage_name: str):
      pass
  
    def map_tuple(self, col, fn, stage_name: str):
      pass
  
    def map_values(self, col, fn, stage_name: str):
      pass
  
    def group_by_key(self, col, stage_name: str):
      pass
  
    def filter(self, col, fn, stage_name: str):
      pass
  
    def keys(self, col, stage_name: str):
      pass
  
    def values(self, col, stage_name: str):
      pass
  
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      pass
  
    def count_per_element(self, col, stage_name: str):
      pass
new_contents: """Adapters for working with pipeline frameworks."""
  
  import abc
  import apache_beam as beam
  import apache_beam.transforms.combiners as combiners
  
  
  class PipelineOperations(abc.ABC):
    """Interface for pipeline frameworks adapters."""
  
    @abc.abstractmethod
    def map(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def map_tuple(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def map_values(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def group_by_key(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def filter(self, col, fn, stage_name: str):
      pass
  
    @abc.abstractmethod
    def keys(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def values(self, col, stage_name: str):
      pass
  
    @abc.abstractmethod
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      pass
  
    @abc.abstractmethod
    def count_per_element(self, col, stage_name: str):
      pass
  
  
  class BeamOperations(PipelineOperations):
    """Apache Beam adapter."""
  
    def map(self, col, fn, stage_name: str):
      return col | stage_name >> beam.Map(fn)
  
    def map_tuple(self, col, fn, stage_name: str):
      return col | stage_name >> beam.MapTuple(fn)
  
    def map_values(self, col, fn, stage_name: str):
      return col | stage_name >> beam.MapTuple(lambda k, v: (k, fn(v)))
  
    def group_by_key(self, col, stage_name: str):
      return col | stage_name >> beam.GroupByKey()
  
    def filter(self, col, fn, stage_name: str):
      return col | stage_name >> beam.Filter(fn)
  
    def keys(self, col, stage_name: str):
      return col | stage_name >> beam.Keys()
  
    def values(self, col, stage_name: str):
      return col | stage_name >> beam.Values()
  
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      return col | stage_name >> combiners.Sample.FixedSizePerKey(n)
  
    def count_per_element(self, col, stage_name: str):
      return col | stage_name >> combiners.Count.PerElement()
  
  class LocalPipelineOperations(PipelineOperations):
    """Local Pipeline adapter."""
    
    def map(self, col, fn, stage_name: str):
      pass
  
    def map_tuple(self, col, fn, stage_name: str):
      pass
  
    def map_values(self, col, fn, stage_name: str):
      pass
  
    def group_by_key(self, col, stage_name: str):
      pass
  
    def filter(self, col, fn, stage_name: str):
      pass
  
    def keys(self, col, stage_name: str):
      pass
  
    def values(self, col, stage_name: str):
      pass
  
    def sample_fixed_per_key(self, col, n: int, stage_name: str):
      pass
  
    def count_per_element(self, col, stage_name: str):
      pass
  
subject: Add newline to end of pipeline_operations