





import itertools
import string
from collections.abc import Generator, Iterable




def     A_      (	lowercase_   ,		lowercase_	)      ->    Dict:
       _snake_case   :				List[Any]  					=    iter(__lowercase	)
       while True:
              _snake_case   :				Tuple  					=    tuple(itertools.islice(__lowercase   ,		__lowercase	)	)
              if not chunk:
                     return
              yield chunk




def     A_      (	lowercase_	)      ->    Optional[int]:
       _snake_case   :				Any  					=    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]	)
       _snake_case   :				int  					=    ''''''

       if len(__lowercase	) < 2:
              return dirty

       for i in range(len(__lowercase	) - 1	):
              clean += dirty[i]

              if dirty[i] == dirty[i + 1]:
                     clean += "X"

       clean += dirty[-1]

       if len(__lowercase	) & 1:
              clean += "X"

       return clean




def     A_      (	lowercase_	)      ->    str:
       # I and J are used interchangeably to allow
       # us to use a 5x5 table (25 letters)
       _snake_case   :				Optional[int]  					=    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
       # we're using a list instead of a '2d' array because it makes the math
       # for setting up the table and doing the actual encoding/decoding simpler
       _snake_case   :				Any  					=    []

       # copy key chars into the table if they are in `alphabet` ignoring duplicates
       for char in key.upper():
              if char not in table and char in alphabet:
                     table.append(__lowercase	)

    # fill the rest of the table in with the remaining alphabet chars
       for char in alphabet:
              if char not in table:
                     table.append(__lowercase	)

       return table




def     A_      (	lowercase_   ,		lowercase_	)      ->    Tuple:
       _snake_case   :				Any  					=    generate_table(__lowercase	)
       _snake_case   :				Any  					=    prepare_input(__lowercase	)
       _snake_case   :				Optional[Any]  					=    ''''''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__lowercase   ,		2	):
              _snake_case					,     _snake_case   :				int  					=    divmod(table.index(__lowercase	)   ,		5	)
              _snake_case					,     _snake_case   :				Dict  					=    divmod(table.index(__lowercase	)   ,		5	)

              if rowa == rowa:
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
              elif cola == cola:
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
              else:  # rectangle
                     ciphertext += table[rowa * 5 + cola]
                     ciphertext += table[rowa * 5 + cola]

       return ciphertext




def     A_      (	lowercase_   ,		lowercase_	)      ->    Tuple:
       _snake_case   :				Dict  					=    generate_table(__lowercase	)
       _snake_case   :				int  					=    ''''''

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__lowercase   ,		2	):
              _snake_case					,     _snake_case   :				Optional[Any]  					=    divmod(table.index(__lowercase	)   ,		5	)
              _snake_case					,     _snake_case   :				Optional[Any]  					=    divmod(table.index(__lowercase	)   ,		5	)

              if rowa == rowa:
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
              elif cola == cola:
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
              else:  # rectangle
                     plaintext += table[rowa * 5 + cola]
                     plaintext += table[rowa * 5 + cola]

       return plaintext














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





