


'''simple docstring'''
__snake_case							      =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= 0
					while number:
										# Increased Speed Slightly by checking every 5 digits together.
										sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
										number //= 100000

					return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
__snake_case							      =     [None] * 10000000
__snake_case							      =     True
__snake_case							      =     False


def 			a      (			__a   )  ->	bool:



					'''simple docstring'''






					if CHAINS[number - 1] is not None:
										return CHAINS[number - 1]  # type: ignore

					UpperCamelCase__   :Dict												= chain(next_number(__a   )   )
					UpperCamelCase__   :int												= number_chain

					while number < 10000000:
										UpperCamelCase__   :Optional[int]												= number_chain
										number *= 10

					return number_chain


def 			a      (			__a = 10000000   )  ->	int:



					'''simple docstring'''






					for i in range(1 ,					__a   ):
										if CHAINS[i] is None:
															chain(i + 1   )

					return CHAINS[:number].count(__a   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{solution() = }""")
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("keep_in_memory"   , [False, True]	)
def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase   , keep_in_memory=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize(
    "features"   , [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   , )
def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TextDatasetReader(lowerCAmelCase   , features=lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize("split"   , [None, NamedSplit("train"	), "train", "test"]	)
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase   , split=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)
		assert dataset.split == split if split else "train"




@pytest.mark.parametrize("path_type"   , [str, list]	)
def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    text_path
		elif issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [text_path]
		SCREAMING_SNAKE_CASE_  :		int				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]=("train",)	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		for split in splits:
				SCREAMING_SNAKE_CASE_  :		int				      =    dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("keep_in_memory"   , [False, True]	)
def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TextDatasetReader({"train": text_path}   , cache_dir=lowerCAmelCase   , keep_in_memory=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize(
    "features"   , [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   , )
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		Dict				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    TextDatasetReader({"train": text_path}   , features=lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase	)







@pytest.mark.parametrize("split"   , [None, NamedSplit("train"	), "train", "test"]	)
def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		if split:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {split: text_path}
		else:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "train"
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"train": text_path, "test": text_path}
		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		str				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase   , splits=list(path.keys()	)	)
		assert all(dataset[split].split == split for split in path.keys()	)
