



def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any] = 1_0			):



					"""simple docstring"""





					if not isinstance(lowerCAmelCase_				,    lowerCAmelCase_			) or n < 0:
										raise ValueError("""Invalid input"""			)
					snake_case_					:		Tuple 					=					1_0**n
					snake_case_					:		Any 					=					2_8_4_3_3 * (pow(2				,    7_8_3_0_4_5_7				,    lowerCAmelCase_			)) + 1
					return str(number % modulus			)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			print(F'''{solution(10) = }''')









"""simple docstring"""

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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[int] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Tuple 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""path_type"""				,    [str, list]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					if issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		List[str] 					=					text_path
					elif issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		str 					=					[text_path]
					snake_case_					:		List[str] 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]=("train",)			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for split in splits:
										snake_case_					:		Dict 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





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







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





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







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





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




