





import functools





def    __UpperCamelCase      (    a,     a) ->int:

					# Validation
					if not isinstance(a,     a) or not all(isinstance(a,     a) for day in days):
										raise ValueError("The parameter days should be a list of integers")

					if len(a) != 3 or not all(isinstance(a,     a) for cost in costs):
										raise ValueError("The parameter costs should be a list of three integers")

					if len(a) == 0:
										return 0

					if min(a) <= 0:
										raise ValueError("All days elements should be greater than 0")

					if max(a) >= 366:
										raise ValueError("All days elements should be less than 366")

					lowerCamelCase__       =       set(a)

					@functools.cache
					def dynamic_programming(a) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1)

										return min(
										    costs[0] + dynamic_programming(index + 1),     costs[1] + dynamic_programming(index + 7),     costs[2] + dynamic_programming(index + 30),     )

					return dynamic_programming(1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()








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





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

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

class SCREAMING_SNAKE_CASE_ (					datasets.BeamBasedBuilder   ):





					"""simple docstring"""


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





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										import apache_beam as beam

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





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





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







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       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"	)}	)	)
															lowerCamelCase__       =       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 					__magic_name__ ( self	):
										import apache_beam as beam

										lowerCamelCase__       =       beam.io.parquetio.WriteToParquet

										lowerCamelCase__       =       len(get_test_dummy_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	,  beam_runner="DirectRunner"	)
															with patch("apache_beam.io.parquetio.WriteToParquet"	) as write_parquet_mock:
																				lowerCamelCase__       =       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"	)}	)	)
															lowerCamelCase__       =       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 					__magic_name__ ( self	):
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       DummyBeamDataset(cache_dir=_lowerCAmelCase	)
															self.assertRaises(datasets.builder.MissingBeamOptions	,  builder.download_and_prepare	)






					@require_beam
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       len(get_test_nested_examples()	)
										with tempfile.TemporaryDirectory() as tmp_cache_dir:
															lowerCamelCase__       =       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"	)}	)}	)	)
															lowerCamelCase__       =       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

