




"""simple docstring"""





import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def        _UpperCamelCase     (      UpperCamelCase				)      ->      int:


			"""simple docstring"""
			def wrapper(*UpperCamelCase					,							**UpperCamelCase				):
						__UpperCAmelCase						:			int												=       timeit.default_timer()
						__UpperCAmelCase						:			int												=       func(*UpperCamelCase					,							**UpperCamelCase				)
						__UpperCAmelCase						:			Any												=       timeit.default_timer() - starttime
						return delta

			__UpperCAmelCase						:			Optional[Any]												=       func.__name__

			return wrapper

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=100					,							UpperCamelCase=None				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       []
			__UpperCAmelCase						:			Optional[Any]												=       seq_shapes or {}
			for i in range(UpperCamelCase				):
						__UpperCAmelCase						:			Union[str, Any]												=       {}
						for col_id, (k, v) in enumerate(features.items()				):
									if isinstance(UpperCamelCase					,							_ArrayXD				):
												__UpperCAmelCase						:			List[str]												=       np.random.rand(*v.shape				).astype(v.dtype				)
									elif isinstance(UpperCamelCase					,							datasets.Value				):
												if v.dtype == "string":
															__UpperCAmelCase						:			Optional[int]												=       "The small grey turtle was surprisingly fast when challenged."
												else:
															__UpperCAmelCase						:			int												=       np.random.randint(10					,							size=1				).astype(v.dtype				).item()
									elif isinstance(UpperCamelCase					,							datasets.Sequence				):
												while isinstance(UpperCamelCase					,							datasets.Sequence				):
															__UpperCAmelCase						:			List[Any]												=       v.feature
												__UpperCAmelCase						:			Optional[int]												=       seq_shapes[k]
												__UpperCAmelCase						:			Tuple												=       np.random.rand(*UpperCamelCase				).astype(v.dtype				)
									__UpperCAmelCase						:			Any												=       data

						dummy_data.append((i, example)				)

			return dummy_data





def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=100					,							UpperCamelCase=None				)      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       generate_examples(UpperCamelCase					,							num_examples=UpperCamelCase					,							seq_shapes=UpperCamelCase				)

			with ArrowWriter(features=UpperCamelCase					,							path=UpperCamelCase				) as writer:
						for key, record in dummy_data:
									__UpperCAmelCase						:			Union[str, Any]												=       features.encode_example(UpperCamelCase				)
									writer.write(UpperCamelCase				)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."				)

			__UpperCAmelCase						:			int												=       datasets.Dataset.from_file(filename=UpperCamelCase					,							info=datasets.DatasetInfo(features=UpperCamelCase				)				)

			return dataset






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 		_A		(		_lowercase					,  _lowercase=0 )					-> Dict:


     """simple docstring"""


     return sorted(_lowercase					,  key=lambda _lowercase : x[column] )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> List[Any]:


     """simple docstring"""


     for i in range(points_counts - 1 ):
          for j in range(i + 1					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> Tuple:


     """simple docstring"""


     for i in range(min(6					,  points_counts - 1 )					,  _lowercase ):
          for j in range(max(0					,  i - 6 )					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase					,  _lowercase )

     # recursion
     __UpperCamelCase =			points_counts // 2
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[:mid]					,  _lowercase )
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[mid:]					,  points_counts - mid )
     __UpperCamelCase =			min(_lowercase					,  _lowercase )
     __UpperCamelCase =			[]
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
               cross_strip.append(_lowercase )

     __UpperCamelCase =			dis_between_closest_in_strip(
         _lowercase					,  len(_lowercase )					,  _lowercase )
     return min(_lowercase					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			column_based_sort(_lowercase					,  column=0 )
     __UpperCamelCase =			column_based_sort(_lowercase					,  column=1 )
     return (
         closest_pair_of_points_sqr(
             _lowercase					,  _lowercase					,  _lowercase )
     ) ** 0.5


if __name__ == "__main__":
    __snake_case   							=						[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
    print('''Distance:''', closest_pair_of_points(points, len(points)))


