






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 						SCREAMING_SNAKE_CASE				( datasets.BeamBasedBuilder					):





							"""simple docstring"""

							def       __A (  self:   Dict   )    ->				int:
												return datasets.DatasetInfo(
												    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )		,     supervised_keys=__A		,     )

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



							def       __A (  self:   List[str]		,     __A:   str		,     __A:   List[str]   )    ->				Dict:
												import apache_beam as beam

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





class 						SCREAMING_SNAKE_CASE				( datasets.BeamBasedBuilder					):





							"""simple docstring"""

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

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



							def       __A (  self:   Optional[Any]		,     __A:   Dict		,     __A:   int   )    ->				Tuple:
												import apache_beam as beam

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




def      __A					(     ):

					'''simple docstring'''







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




def      __A					(     ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

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

							@require_beam
							def       __A (  self:   Tuple   )    ->				Dict:
												import apache_beam as beam

												_A								= beam.io.parquetio.WriteToParquet

												_A								= len(get_test_dummy_examples()   )
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	_A								= DummyBeamDataset(cache_dir=__A		,     beam_runner='''DirectRunner'''   )
																	with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
																						_A								= partial(__A		,     num_shards=2   )
																						builder.download_and_prepare()
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(
																	            __A		,     builder.name		,     '''default'''		,     '''0.0.0'''		,     f"""{builder.name}-train-00000-of-00002.arrow"""   )   )   )
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(
																	            __A		,     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'''   )}   )   )
																	_A								= builder.as_dataset()
																	self.assertEqual(dset['''train'''].num_rows		,     __A   )
																	self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples		,     __A   )
																	# 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(__A		,     builder.name		,     '''default'''		,     '''0.0.0'''		,     '''dataset_info.json'''   )   )   )
																	del dset

							@require_beam
							def       __A (  self:   List[str]   )    ->				int:
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	_A								= DummyBeamDataset(cache_dir=__A   )
																	self.assertRaises(datasets.builder.MissingBeamOptions		,     builder.download_and_prepare   )



							@require_beam
							def       __A (  self:   List[str]   )    ->				List[Any]:
												_A								= len(get_test_nested_examples()   )
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																	_A								= NestedBeamDataset(cache_dir=__A		,     beam_runner='''DirectRunner'''   )
																	builder.download_and_prepare()
																	self.assertTrue(
																	    os.path.exists(
																	        os.path.join(__A		,     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'''   )}   )}   )   )
																	_A								= builder.as_dataset()
																	self.assertEqual(dset['''train'''].num_rows		,     __A   )
																	self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples		,     __A   )
																	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(__A		,     builder.name		,     '''default'''		,     '''0.0.0'''		,     '''dataset_info.json'''   )   )   )
																	del dset











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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					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 ):

					'''simple docstring'''







					_A								= '''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
					_A								= []

					# 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 ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

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

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

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



