

'''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      __magic_name__							(			A       , A				)      ->		str:
	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      __magic_name__							(			A       , A       , A				)      ->		Tuple:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case							=					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      __magic_name__							(			A       , A       , A				)      ->		List[Any]:
	snake_case							=					tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							=					features.copy() if features else default_expected_features
	snake_case							=					(
	    Features({feature: Value(A				) for feature, dtype in features.items()}				) if features is not None else None
	)
	snake_case							=					CsvDatasetReader(A       , features=A       , cache_dir=A				).read()
	_check_csv_dataset(A       , A				)

@pytest.mark.parametrize('split'       , [None, NamedSplit('train'				), 'train', 'test']				)
def      __magic_name__							(			A       , A       , A				)      ->		int:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							=					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      __magic_name__							(			A       , A       , A				)      ->		str:
	if issubclass(A       , A				):
		snake_case							=					csv_path
	elif issubclass(A       , A				):
		snake_case							=					[csv_path]
	snake_case							=					tmp_path / 'cache'
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							=					CsvDatasetReader(A       , cache_dir=A				).read()
	_check_csv_dataset(A       , A				)

def      __magic_name__							(			A       , A       , A=("train",)				)      ->		Optional[int]:
	assert isinstance(A       , A				)
	for split in splits:
		snake_case							=					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      __magic_name__							(			A       , A       , A				)      ->		Dict:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case							=					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      __magic_name__							(			A       , A       , A				)      ->		Any:
	snake_case							=					tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							=					features.copy() if features else default_expected_features
	snake_case							=					(
	    Features({feature: Value(A				) for feature, dtype in features.items()}				) if features is not None else None
	)
	snake_case							=					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      __magic_name__							(			A       , A       , A				)      ->		Any:
	if split:
		snake_case							=					{split: csv_path}
	else:
		snake_case							=					{'train': csv_path, 'test': csv_path}
	snake_case							=					tmp_path / 'cache'
	snake_case							=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							=					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      __magic_name__							(			A				)      ->		str:
	with open(A       , encoding='utf-8'				) as csvfile:
		yield from csv.reader(A				)

def      __magic_name__							(			A       , A				)      ->		Any:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					os.path.join(A       , 'tmp.csv'				)
	snake_case							=					CsvDatasetReader({'train': csv_path}       , cache_dir=A				).read()
	CsvDatasetWriter(dataset['train']       , A       , num_proc=1				).write()

	snake_case							=					iter_csv_file(A				)
	snake_case							=					iter_csv_file(A				)

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

def      __magic_name__							(			A       , A				)      ->		Tuple:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					os.path.join(A       , 'tmp.csv'				)
	snake_case							=					CsvDatasetReader({'train': csv_path}       , cache_dir=A				).read()
	CsvDatasetWriter(dataset['train']       , A       , num_proc=2				).write()

	snake_case							=					iter_csv_file(A				)
	snake_case							=					iter_csv_file(A				)

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


def      __magic_name__							(			A       , A				)      ->		List[Any]:
	snake_case							=					tmp_path / 'cache'
	snake_case							=					os.path.join(A       , 'tmp.csv'				)
	snake_case							=					CsvDatasetReader({'train': csv_path}       , cache_dir=A				).read()
	with pytest.raises(A				):
		CsvDatasetWriter(dataset['train']       , A       , num_proc=0				)









"""simple docstring"""







def 	lowercase     (							A_    ,  A_			)-> list[tuple[int, int, int]]:
  '''simple docstring'''







  a		:						Union[str, Any] 				=     sorted(A_    ,  key=lambda A_			: edge[2]			)

  a		:						List[Any] 				=     list(range(A_			)			)

  def find_parent(A_			):
    if i != parent[i]:
      a		:						Dict 				=     find_parent(parent[i]			)
    return parent[i]

  a		:						Optional[Any] 				=     0
  a		:						Optional[int] 				=     []

  for edge in edges:
    a		:						List[str] 				=     find_parent(edge[0]			)
    a		:						int 				=     find_parent(edge[1]			)
    if parent_a != parent_b:
      minimum_spanning_tree_cost += edge[2]
      minimum_spanning_tree.append(A_			)
      a		:						Tuple 				=     parent_b

  return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
 __lowercase   ,				__lowercase          = list(map(int, input().strip().split()))
 __lowercase          = []

 for _ in range(num_edges):
  __lowercase   ,				__lowercase   ,				__lowercase          = (int(x) for x in input().strip().split())
  edges.append((nodea, nodea, cost))

 kruskal(num_nodes, edges)






