
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     :   List[Any]     ):


				'''simple docstring'''


				return datasets.DatasetInfo(
				    features=datasets.Features({'content': datasets.Value('string'     )}     )      ,  supervised_keys=snake_case_      ,  )



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   str     ):


				'''simple docstring'''


				return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'examples': get_test_dummy_examples()}     )]





	def 			A							(  self     :   List[str]      ,  lowercase     :   int      ,  lowercase     :   Any     ):


				'''simple docstring'''


				import apache_beam as beam

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




class        SCREAMING_SNAKE_CASE__					(     datasets.BeamBasedBuilder ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				return datasets.DatasetInfo(
				    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'     )}     )}     )      ,  supervised_keys=snake_case_      ,  )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				return [
				    datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={'examples': get_test_nested_examples()}     )
				]





	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str     ):


				'''simple docstring'''


				import apache_beam as beam

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






def 	a_		(	)			->  Optional[Any]:
			return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']			)]






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




class        SCREAMING_SNAKE_CASE__					(     _a ):





	'''simple docstring'''



	@require_beam
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


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



	@require_beam
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				import apache_beam as beam

				_snake_case          =      beam.io.parquetio.WriteToParquet

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



	@require_beam
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				with tempfile.TemporaryDirectory() as tmp_cache_dir:
							_snake_case          =      DummyBeamDataset(cache_dir=snake_case_     )
							self.assertRaises(datasets.builder.MissingBeamOptions      ,  builder.download_and_prepare     )





	@require_beam
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      len(get_test_nested_examples()     )
				with tempfile.TemporaryDirectory() as tmp_cache_dir:
							_snake_case          =      NestedBeamDataset(cache_dir=snake_case_      ,  beam_runner='DirectRunner'     )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(snake_case_      ,  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'     )}     )}     )     )
							_snake_case          =      builder.as_dataset()
							self.assertEqual(dset['train'].num_rows      ,  snake_case_     )
							self.assertEqual(dset['train'].info.splits['train'].num_examples      ,  snake_case_     )
							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(snake_case_      ,  builder.name      ,  'default'      ,  '0.0.0'      ,  'dataset_info.json'     )     )     )
							del dset

import random




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	@staticmethod
	def 			A							(  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      [ord(lowercase     ) for i in text]
				_snake_case          =      []
				_snake_case          =      []
				for i in plain:
							_snake_case          =      random.randint(1      ,  300     )
							_snake_case          =      (i + k) * k
							cipher.append(lowercase     )
							key.append(lowercase     )
				return cipher, key





	@staticmethod
	def 			A							(  lowercase     :   list[int]      ,  lowercase     :   list[int]     ):


				'''simple docstring'''


				_snake_case          =      []
				for i in range(len(lowercase     )     ):
							_snake_case          =      int((cipher[i] - (key[i]) ** 2) / key[i]     )
							plain.append(chr(lowercase     )     )
				return "".join(lowercase     )


if __name__ == "__main__":
			_lowerCamelCase   ,					_lowerCamelCase	:    Optional[int]          =      Onepad().encrypt('''Hello''')
			print(c, k)
			print(Onepad().decrypt(c, k))