




'''simple docstring'''




import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   Optional[Any]			)   ->   Union[str, Any]:
					assert isinstance(_A			,		_A			)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					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[Any]			,		lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   List[Any]			)   ->   Dict:
					A_ :     Any =							tmp_path / '''cache'''
					A_ :     Tuple =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										A_ :     Tuple =							CsvDatasetReader(_A			,		cache_dir=_A			,		keep_in_memory=_A			).read()
					_check_csv_dataset(_A			,		_A			)




@pytest.mark.parametrize(
    '''features'''			,		[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]			,		)
def 						snake_case__    ( lowerCamelCase__				:   str			,		lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   List[Any]			)   ->   int:
					A_ :     Any =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					A_ :     Any =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					A_ :     Optional[Any] =							features.copy() if features else default_expected_features
					A_ :     Optional[Any] =							(
					    Features({feature: Value(_A			) for feature, dtype in features.items()}			) if features is not None else None
					)
					A_ :     int =							CsvDatasetReader(_A			,		features=_A			,		cache_dir=_A			).read()
					_check_csv_dataset(_A			,		_A			)




@pytest.mark.parametrize('''split'''			,		[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Any			)   ->   int:
					A_ :     List[Any] =							tmp_path / '''cache'''
					A_ :     str =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					A_ :     Optional[int] =							CsvDatasetReader(_A			,		cache_dir=_A			,		split=_A			).read()
					_check_csv_dataset(_A			,		_A			)
					assert dataset.split == split if split else "train"




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


					if issubclass(_A			,		_A			):
										A_ :     Optional[Any] =							csv_path
					elif issubclass(_A			,		_A			):
										A_ :     List[Any] =							[csv_path]
					A_ :     Tuple =							tmp_path / '''cache'''
					A_ :     List[str] =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					A_ :     Optional[int] =							CsvDatasetReader(_A			,		cache_dir=_A			).read()
					_check_csv_dataset(_A			,		_A			)




def 						snake_case__    ( lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Union[str, Any]=("train",)			)   ->   Tuple:
					assert isinstance(_A			,		_A			)
					for split in splits:
										A_ :     List[str] =							dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 3
										assert dataset.column_names == ["col_1", "col_2", "col_3"]
										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[Any]			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Union[str, Any]			)   ->   Optional[Any]:
					A_ :     str =							tmp_path / '''cache'''
					A_ :     List[str] =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										A_ :     Dict =							CsvDatasetReader({'''train''': csv_path}			,		cache_dir=_A			,		keep_in_memory=_A			).read()
					_check_csv_datasetdict(_A			,		_A			)




@pytest.mark.parametrize(
    '''features'''			,		[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]			,		)
def 						snake_case__    ( lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   List[str]			)   ->   Optional[int]:
					A_ :     str =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					A_ :     Optional[Any] =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					A_ :     Union[str, Any] =							features.copy() if features else default_expected_features
					A_ :     str =							(
					    Features({feature: Value(_A			) for feature, dtype in features.items()}			) if features is not None else None
					)
					A_ :     int =							CsvDatasetReader({'''train''': csv_path}			,		features=_A			,		cache_dir=_A			).read()
					_check_csv_datasetdict(_A			,		_A			)




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


					if split:
										A_ :     Optional[int] =							{split: csv_path}
					else:
										A_ :     Tuple =							{'''train''': csv_path, '''test''': csv_path}
					A_ :     Optional[int] =							tmp_path / '''cache'''
					A_ :     Dict =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					A_ :     List[str] =							CsvDatasetReader(_A			,		cache_dir=_A			).read()
					_check_csv_datasetdict(_A			,		_A			,		splits=list(path.keys()			)			)
					assert all(dataset[split].split == split for split in path.keys()			)




def 						snake_case__    ( lowerCamelCase__				:   List[str]			)   ->   Union[str, Any]:


					with open(_A			,		encoding='''utf-8'''			) as csvfile:
										yield from csv.reader(_A			)




def 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   List[Any]			)   ->   Optional[Any]:
					A_ :     Tuple =							tmp_path / '''cache'''
					A_ :     List[str] =							os.path.join(_A			,		'''tmp.csv'''			)
					A_ :     List[Any] =							CsvDatasetReader({'''train''': csv_path}			,		cache_dir=_A			).read()
					CsvDatasetWriter(dataset['''train''']			,		_A			,		num_proc=1			).write()

					A_ :     Optional[int] =							iter_csv_file(_A			)
					A_ :     str =							iter_csv_file(_A			)

					for rowa, rowa in zip(_A			,		_A			):
										assert rowa == rowa




def 						snake_case__    ( lowerCamelCase__				:   str			,		lowerCamelCase__				:   Dict			)   ->   List[str]:
					A_ :     Any =							tmp_path / '''cache'''
					A_ :     Tuple =							os.path.join(_A			,		'''tmp.csv'''			)
					A_ :     Dict =							CsvDatasetReader({'''train''': csv_path}			,		cache_dir=_A			).read()
					CsvDatasetWriter(dataset['''train''']			,		_A			,		num_proc=2			).write()

					A_ :     Optional[int] =							iter_csv_file(_A			)
					A_ :     List[Any] =							iter_csv_file(_A			)

					for rowa, rowa in zip(_A			,		_A			):
										assert rowa == rowa





def 						snake_case__    ( lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   Tuple			)   ->   Dict:
					A_ :     List[str] =							tmp_path / '''cache'''
					A_ :     Tuple =							os.path.join(_A			,		'''tmp.csv'''			)
					A_ :     Dict =							CsvDatasetReader({'''train''': csv_path}			,		cache_dir=_A			).read()
					with pytest.raises(_A			):
										CsvDatasetWriter(dataset['''train''']			,		_A			,		num_proc=0			)









'''simple docstring'''




from __future__ import annotations

snake_case__          =						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Dict							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     int =							-1
											A_ :     int =							-1
											A_ :     list[int] =							[-1] * length
											A_ :     Node | None =							None
											A_ :     Node | None =							None







						def __repr__(     self      :       List[str]    ):





											"""simple docstring"""







											return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   Node | None:
					A_ :     Optional[Any] =							Node(len(lowerCamelCase__			)			)
					A_ ,A_ :     str =							min(lowerCamelCase__			), max(lowerCamelCase__			)
					# Leaf node case where the node contains only one unique value
					if root.minn == root.maxx:
										return root
					A_ :     Optional[int] =							(root.minn + root.maxx) // 2

					A_ :     list[int] =							[]
					A_ :     list[int] =							[]

					for index, num in enumerate(lowerCamelCase__			):
										if num <= pivot:
															left_arr.append(lowerCamelCase__			)
										else:
															right_arr.append(lowerCamelCase__			)
										A_ :     Union[str, Any] =							len(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					return root




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index < 0 or node is None:
										return 0
					# Leaf node cases
					if node.minn == node.maxx:
										return index + 1 if node.minn == num else 0
					A_ :     List[Any] =							(node.minn + node.maxx) // 2
					if num <= pivot:
										# go the left subtree and map index to the left subtree
										return rank_till_index(node.left			,		lowerCamelCase__			,		node.map_left[index] - 1			)
					else:
										# go to the right subtree and map index to the right subtree
										return rank_till_index(node.right			,		lowerCamelCase__			,		index - node.map_left[index]			)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if start > end:
										return 0
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		start - 1			)
					return rank_till_end - rank_before_start




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index > (end - start) or start > end or node is None:
										return -1
					# Leaf node case
					if node.minn == node.maxx:
										return node.minn
					# Number of elements in the left subtree in interval [start, end]
					A_ :     List[str] =							node.map_left[end] - (
					    node.map_left[start - 1] if start else 0
					)
					if num_elements_in_left_tree > index:
										return quantile(
										    node.left			,		lowerCamelCase__			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
					else:
										return quantile(
										    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if (
					    start > end
					    or node is None
					    or start_num > end_num
					    or node.minn > end_num
					    or node.maxx < start_num
					):
										return 0
					if start_num <= node.minn and node.maxx <= end_num:
										return end - start + 1
					A_ :     int =							range_counting(
					    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					A_ :     List[str] =							range_counting(
					    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



