





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        a_	(      self       :   Union[str, Any]       )     ->     List[Any]:







					"""simple docstring"""






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






			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Optional[Any]       )     ->     Optional[int]:







					"""simple docstring"""






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







			def        a_	(      self       :   Any							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   str       )     ->     Optional[int]:







					"""simple docstring"""






					import apache_beam as beam

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


class   A      (datasets.BeamBasedBuilder   ):
			'''simple docstring'''




			def        a_	(      self       :   Optional[int]       )     ->     Optional[Any]:







					"""simple docstring"""






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






			def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Optional[Any]       )     ->     str:







					"""simple docstring"""






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







			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Any							,     __lowerCAmelCase       :   List[Any]       )     ->     Optional[Any]:







					"""simple docstring"""






					import apache_beam as beam

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







def        __lowerCamelCase (							)		->    Dict:






		"""simple docstring"""

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







def        __lowerCamelCase (							)		->    Union[str, Any]:






		"""simple docstring"""

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






class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@require_beam
			def        a_	(      self       :   List[Any]       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       len(get_test_dummy_examples()       )
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__  				=       DummyBeamDataset(cache_dir=__lowerCAmelCase							,     beam_runner="""DirectRunner"""       )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(__lowerCAmelCase							,     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							,     __lowerCAmelCase       )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples							,     __lowerCAmelCase       )
							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(__lowerCAmelCase							,     builder.name							,     """default"""							,     """0.0.0"""							,     """dataset_info.json"""       )       )       )
							del dset






			@require_beam
			def        a_	(      self       :   List[Any]       )     ->     List[Any]:







					"""simple docstring"""






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






			@require_beam
			def        a_	(      self       :   str       )     ->     Tuple:







					"""simple docstring"""






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







			@require_beam
			def        a_	(      self       :   Optional[int]       )     ->     int:







					"""simple docstring"""






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










from string import ascii_uppercase

A   : List[str]			=				{str(ord(c) - 5_5): c for c in ascii_uppercase}







def        __lowerCamelCase (							__a       :int   ,					__a       :int   )		->    str:






		"""simple docstring"""

		if isinstance(__a   ,					__a   ):
				raise TypeError("""int() can't convert non-string with explicit base"""   )
		if num < 0:
				raise ValueError("""parameter must be positive int"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'str' object cannot be interpreted as an integer"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'float' object cannot be interpreted as an integer"""   )
		if base in (0, 1):
				raise ValueError("""base must be >= 2"""   )
		if base > 3_6:
				raise ValueError("""base must be <= 36"""   )
		A__  				=       """"""
		A__  				=       0
		A__  				=       0
		while div != 1:
				A__				,      A__  				=       divmod(__a   ,					__a   )
				if base >= 1_1 and 9 < mod < 3_6:
						A__  				=       ALPHABET_VALUES[str(__a   )]
				else:
						A__  				=       str(__a   )
				new_value += actual_value
				A__  				=       num // base
				A__  				=       div
				if div == 0:
						return str(new_value[::-1]   )
				elif div == 1:
						new_value += str(__a   )
						return str(new_value[::-1]   )

		return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 3_7):
										for num in range(1_0_0_0):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)



