


'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=0      ):
		return sorted(__lowerCAmelCase					,       key=lambda __lowerCAmelCase      : x[column]      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=float("inf"      )      ):

		for i in range(points_counts - 1      ):
				for j in range(i + 1					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					euclidean_distance_sqr(points[i]					,       points[j]      )
						if current_dis < min_dis:
								_UpperCAmelCase						:				Any	    =					current_dis
		return min_dis






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=float("inf"      )      ):

		for i in range(min(6					,       points_counts - 1      )					,       __lowerCAmelCase      ):
				for j in range(max(0					,       i - 6      )					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				Tuple	    =					euclidean_distance_sqr(points[i]					,       points[j]      )
						if current_dis < min_dis:
								_UpperCAmelCase						:				Union[str, Any]	    =					current_dis
		return min_dis






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):

		# base case
		if points_counts <= 3:
				return dis_between_closest_pair(__lowerCAmelCase					,       __lowerCAmelCase      )

		# recursion
		_UpperCAmelCase						:				Optional[Any]	    =					points_counts // 2
		_UpperCAmelCase						:				str	    =					closest_pair_of_points_sqr(
		    __lowerCAmelCase					,       points_sorted_on_y[:mid]					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				List[Any]	    =					closest_pair_of_points_sqr(
		    __lowerCAmelCase					,       points_sorted_on_y[mid:]					,       points_counts - mid      )
		_UpperCAmelCase						:				Dict	    =					min(__lowerCAmelCase					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[int]	    =					[]
		for point in points_sorted_on_x:
				if abs(point[0] - points_sorted_on_x[mid][0]      ) < closest_pair_dis:
						cross_strip.append(__lowerCAmelCase      )

		_UpperCAmelCase						:				Optional[Any]	    =					dis_between_closest_in_strip(
		    __lowerCAmelCase					,       len(__lowerCAmelCase      )					,       __lowerCAmelCase      )
		return min(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[str]	    =					column_based_sort(__lowerCAmelCase					,       column=0      )
		_UpperCAmelCase						:				Union[str, Any]	    =					column_based_sort(__lowerCAmelCase					,       column=1      )
		return (
		    closest_pair_of_points_sqr(
		        __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
		) ** 0.5


if __name__ == "__main__":
						lowerCamelCase__					=   [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
						print('Distance:', closest_pair_of_points(points, len(points)))







'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				int	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Any	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[Any]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Union[str, Any]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Dict	    =					{"text": "string"}
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       split=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					,       [str, list]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					text_path
		elif issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				int	    =					[text_path]
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Union[str, Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[int]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=("train",)      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		for split in splits:
				_UpperCAmelCase						:				List[str]	    =					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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Tuple	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Any	    =					TextDatasetReader({"train": text_path}					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCAmelCase						:				List[Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				List[str]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Optional[int]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Tuple	    =					TextDatasetReader({"train": text_path}					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if split:
				_UpperCAmelCase						:				int	    =					{split: text_path}
		else:
				_UpperCAmelCase						:				Tuple	    =					"train"
				_UpperCAmelCase						:				List[str]	    =					{"train": text_path, "test": text_path}
		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Optional[int]	    =					{"text": "string"}
		_UpperCAmelCase						:				int	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase					,       splits=list(path.keys()      )      )
		assert all(dataset[split].split == split for split in path.keys()      )



