



"""simple docstring"""






lowerCAmelCase__              =							{
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCAmelCase__              =							{value: key for key, value in MORSE_CODE_DICT.items()}






def 			snake_case_    ( A_   :						str		):


       '''simple docstring'''





       return " ".join(MORSE_CODE_DICT[char] for char in message.upper()		)






def 			snake_case_    ( A_   :						str		):


       '''simple docstring'''





       return "".join(REVERSE_DICT[char] for char in message.split()		)






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							List[Any]            =					'''Morse code here!'''
       print(A_		)
       _lowerCamelCase		:							Optional[Any]            =					encrypt(A_		)
       print(A_		)
       _lowerCamelCase		:							Optional[Any]            =					decrypt(A_		)
       print(A_		)


if __name__ == "__main__":
   main()










import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class     _a							(	datasets.BeamBasedBuilder     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return datasets.DatasetInfo(
         features=datasets.Features({'content': datasets.Value('string'			)}			)		,		supervised_keys=_UpperCAmelCase		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[Any]:
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs={'examples': get_test_dummy_examples()}			)]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase			)




class     _a							(	datasets.BeamBasedBuilder     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Any:
     return datasets.DatasetInfo(
         features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'			)}			)}			)		,		supervised_keys=_UpperCAmelCase		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     return [
         datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs={'examples': get_test_nested_examples()}			)
     ]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     import apache_beam as beam

     return pipeline | "Load Examples" >> beam.Create(_UpperCAmelCase			)






def      _snake_case     ():
   return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar'])]






def      _snake_case     ():
   return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar'])]


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  len(get_test_dummy_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train.arrow"""			)			)			)
        self.assertDictEqual(builder.info.features		,		datasets.Features({'content': datasets.Value('string'			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        self.assertDictEqual(dset['train'][0]		,		get_test_dummy_examples()[0][1]			)
        self.assertDictEqual(
            dset['train'][expected_num_examples - 1]		,		get_test_dummy_examples()[expected_num_examples - 1][1]			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     import apache_beam as beam

     UpperCamelCase_						=  beam.io.parquetio.WriteToParquet

     UpperCamelCase_						=  len(get_test_dummy_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        with patch('apache_beam.io.parquetio.WriteToParquet'			) as write_parquet_mock:
           UpperCamelCase_						=  partial(_UpperCAmelCase		,		num_shards=2			)
           builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(
                    _UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train-00000-of-00002.arrow"""			)			)			)
        self.assertTrue(
            os.path.exists(
                os.path.join(
                    _UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train-00000-of-00002.arrow"""			)			)			)
        self.assertDictEqual(builder.info.features		,		datasets.Features({'content': datasets.Value('string'			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        # Order is not preserved when sharding, so we just check that all the elements are there
        self.assertListEqual(sorted(dset['train']['content']			)		,		sorted(['foo', 'bar', 'foobar']			)			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Any:
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  DummyBeamDataset(cache_dir=_UpperCAmelCase			)
        self.assertRaises(datasets.builder.MissingBeamOptions		,		builder.download_and_prepare			)




  @require_beam
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  len(get_test_nested_examples()			)
     with tempfile.TemporaryDirectory() as tmp_cache_dir:
        UpperCamelCase_						=  NestedBeamDataset(cache_dir=_UpperCAmelCase		,		beam_runner='DirectRunner'			)
        builder.download_and_prepare()
        self.assertTrue(
            os.path.exists(
                os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		f"""{builder.name}-train.arrow"""			)			)			)
        self.assertDictEqual(
            builder.info.features		,		datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'			)}			)}			)			)
        UpperCamelCase_						=  builder.as_dataset()
        self.assertEqual(dset['train'].num_rows		,		_UpperCAmelCase			)
        self.assertEqual(dset['train'].info.splits['train'].num_examples		,		_UpperCAmelCase			)
        self.assertDictEqual(dset['train'][0]		,		get_test_nested_examples()[0][1]			)
        self.assertDictEqual(
            dset['train'][expected_num_examples - 1]		,		get_test_nested_examples()[expected_num_examples - 1][1]			)
        self.assertTrue(
            os.path.exists(os.path.join(_UpperCAmelCase		,		builder.name		,		'default'		,		'0.0.0'		,		'dataset_info.json'			)			)			)
        del dset





