



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

a_			   =					TypeVar('''KEY''')
a_			   =					TypeVar('''VAL''')







@dataclass(frozen=UpperCamelCase_					,    slots=UpperCamelCase_)
class    __lowercase     (					Generic[KEY, VAL]):







						"""simple docstring"""







						_A		:							List[Any]							       =		42
						_A		:							int							       =		42







class    __lowercase     (					_Item):







						"""simple docstring"""







						def __init__(self       ):
											super().__init__(__A   ,		__A       )







						def __bool__(self       ):
											return False
a_			   =					_DeletedItem()







class    __lowercase     (					MutableMapping[KEY, VAL]):







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = 8   ,		lowercase__ = 0.75       ):
											snake_case_					:		Dict 					=					initial_block_size
											snake_case_					:		list[_Item | None] 					=					[None] * initial_block_size
											assert 0.0 < capacity_factor < 1.0
											snake_case_					:		Optional[Any] 					=					capacity_factor
											snake_case_					:		str 					=					0







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return hash(__A       ) % len(self._buckets       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return (ind + 1) % len(self._buckets       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					self._buckets[ind]
											if not stored:
																snake_case_					:		List[Any] 					=					_Item(__A   ,		__A       )
																self._len += 1
																return True
											elif stored.key == key:
																snake_case_					:		List[str] 					=					_Item(__A   ,		__A       )
																return True
											else:
																return False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					len(self._buckets       ) * self._capacity_factor
											return len(self       ) >= int(__A       )







						def 						__UpperCamelCase							(self       ):
											if len(self._buckets       ) <= self._initial_block_size:
																return False
											snake_case_					:		Optional[Any] 					=					len(self._buckets       ) * self._capacity_factor / 2
											return len(self       ) < limit







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					self._buckets
											snake_case_					:		Optional[int] 					=					[None] * new_size
											snake_case_					:		Any 					=					0
											for item in old_buckets:
																if item:
																					self._add_item(item.key   ,		item.val       )







						def 						__UpperCamelCase							(self       ):
											self._resize(len(self._buckets       ) * 2       )







						def 						__UpperCamelCase							(self       ):
											self._resize(len(self._buckets       ) // 2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		List[Any] 					=					self._get_bucket_index(__A       )
											for _ in range(len(self._buckets       )       ):
																yield ind
																snake_case_					:		int 					=					self._get_next_ind(__A       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											for ind in self._iterate_buckets(__A       ):
																if self._try_set(__A   ,		__A   ,		__A       ):
																					break







						def __setitem__(self   ,		lowercase__   ,		lowercase__       ):
											if self._is_full():
																self._size_up()

											self._add_item(__A   ,		__A       )







						def __delitem__(self   ,		lowercase__       ):
											for ind in self._iterate_buckets(__A       ):
																snake_case_					:		str 					=					self._buckets[ind]
																if item is None:
																					raise KeyError(__A       )
																if item is _deleted:
																					continue
																if item.key == key:
																					snake_case_					:		str 					=					_deleted
																					self._len -= 1
																					break
											if self._is_sparse():
																self._size_down()







						def __getitem__(self   ,		lowercase__       ):
											for ind in self._iterate_buckets(__A       ):
																snake_case_					:		Dict 					=					self._buckets[ind]
																if item is None:
																					break
																if item is _deleted:
																					continue
																if item.key == key:
																					return item.val
											raise KeyError(__A       )







						def __len__(self       ):
											return self._len







						def __iter__(self       ):
											yield from (item.key for item in self._buckets if item)







						def __repr__(self       ):
											snake_case_					:		Union[str, Any] 					=					" ,".join(
											    f'{item.key}: {item.val}' for item in self._buckets if item       )
											return f'HashMap({val_string})'









"""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()			)




