#   Licensed to the Apache Software Foundation (ASF) under one
#   or more contributor license agreements.  See the NOTICE file
#   distributed with this work for additional information
#   regarding copyright ownership.  The ASF licenses this file
#   to you under the Apache License, Version 2.0 (the
#   "License"); you may not use this file except in compliance
#   with the License.  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

# beam-playground:
#   name: TextIO
#   description: TextIO example.
#   multifile: false
#   context_line: 43
#   categories:
#     - Quickstart
#   complexity: BASIC
#   tags:
#     - hellobeam

import apache_beam as beam

# Output PCollection
class Output(beam.PTransform):
    class _OutputFn(beam.DoFn):
        def __init__(self, prefix=''):
            super().__init__()
            self.prefix = prefix

        def process(self, element):
            print(self.prefix+str(element))

    def __init__(self, label=None,prefix=''):
        super().__init__(label)
        self.prefix = prefix

    def expand(self, input):
        input | beam.ParDo(self._OutputFn(self.prefix))


with beam.Pipeline() as p:
  lines = p | 'Log lines' >> beam.io.ReadFromText('gs://apache-beam-samples/shakespeare/kinglear.txt') \
            | beam.Filter(lambda line: line != "")

  lines | 'Log fixed lines' >> beam.combiners.Sample.FixedSizeGlobally(10) \
    | beam.FlatMap(lambda sentence: sentence) \
    | Output(prefix = 'Fixed first 10 lines: ')

  words = p | 'Log words' >> beam.io.ReadFromText('gs://apache-beam-samples/shakespeare/kinglear.txt') \
            | beam.FlatMap(lambda sentence: sentence.split()) \
            | beam.Filter(lambda word: not word.isspace() or word.isalnum()) \
            | beam.combiners.Sample.FixedSizeGlobally(10) \
            | beam.FlatMap(lambda word: word) \
            | 'Log output words' >> Output(prefix = 'Word: ')


