

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   __UpperCAmelCase					(      __a :     str						,__a :     Optional[int]      )     ->	str:

					"""simple docstring"""

					assert isinstance(__a						,__a      )
					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   __UpperCAmelCase					(      __a :     Any						,__a :     Union[str, Any]						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

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



@pytest.mark.parametrize(
    '''features'''						,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]						,)
def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Optional[Any]						,__a :     int      )     ->	List[str]:

					"""simple docstring"""

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



@pytest.mark.parametrize('''split'''						,[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def   __UpperCAmelCase					(      __a :     str						,__a :     int						,__a :     List[str]      )     ->	List[Any]:

					"""simple docstring"""

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



@pytest.mark.parametrize('''path_type'''						,[str, list]      )
def   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     str      )     ->	Optional[Any]:

					"""simple docstring"""

					if issubclass(__a						,__a      ):
										_a      :       Tuple   =						text_path
					elif issubclass(__a						,__a      ):
										_a      :       Tuple   =						[text_path]
					_a      :       Union[str, Any]   =						tmp_path / '''cache'''
					_a      :       Any   =						{'''text''': '''string'''}
					_a      :       int   =						TextDatasetReader(__a						,cache_dir=__a      ).read()
					_check_text_dataset(__a						,__a      )



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Union[str, Any]						,__a :     Optional[int]=("train",)      )     ->	List[Any]:

					"""simple docstring"""

					assert isinstance(__a						,__a      )
					for split in splits:
										_a      :       Union[str, Any]   =						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   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Optional[Any]      )     ->	Any:

					"""simple docstring"""

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



@pytest.mark.parametrize(
    '''features'''						,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]						,)
def   __UpperCAmelCase					(      __a :     Dict						,__a :     List[str]						,__a :     Optional[int]      )     ->	Tuple:

					"""simple docstring"""

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







@pytest.mark.parametrize('''split'''						,[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

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





from __future__ import annotations

lowerCamelCase__						=				"#"







class 							lowerCAmelCase__   :

						def __init__(      self					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						{}

						def 			A_					(      self							,				a					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in text:
										if char not in trie:
												_UpperCamelCase         =						{}
										_UpperCamelCase         =						trie[char]
								_UpperCamelCase         =						True

						def 			A_					(      self							,				a					)       ->   tuple | list:


								'''simple docstring'''

								_UpperCamelCase         =						self._trie
								for char in prefix:
										if char in trie:
												_UpperCamelCase         =						trie[char]
										else:
												return []
								return self._elements(a					)







						def 			A_					(      self							,				a					)       ->   tuple:


								'''simple docstring'''

								_UpperCamelCase         =						[]
								for c, v in d.items():
										_UpperCamelCase         =						[""" """] if c == END else [(c + s) for s in self._elements(a					)]
										result.extend(a					)
								return tuple(a					)




lowerCamelCase__						=				Trie()
lowerCamelCase__						=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
		trie.insert_word(word)


def 				__A(lowerCAmelCase			)		->					tuple:

		"""simple docstring"""






		_UpperCamelCase         =						trie.find_word(lowerCAmelCase			)
		return tuple(string + word for word in suffixes			)


def 				__A()		->					None:

		"""simple docstring"""






		print(autocomplete_using_trie("""de"""			)			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



