


"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     List[Any]			) ->      Optional[Any]:
		return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     List[str]=0			) ->      str:
		return sorted(lowerCAmelCase__    ,					key=lambda lowerCAmelCase__			: x[column]			)

def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     List[str]=float('''inf'''			)			) ->      Any:

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

def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Union[str, Any]=float('''inf'''			)			) ->      Union[str, Any]:

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

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     List[str]			) ->      List[str]:

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

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

		__a           =			dis_between_closest_in_strip(
		    lowerCAmelCase__    ,					len(lowerCAmelCase__			)    ,					lowerCAmelCase__			)
		return min(lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[Any]			) ->      List[Any]:
		__a           =			column_based_sort(lowerCAmelCase__    ,					column=0			)
		__a           =			column_based_sort(lowerCAmelCase__    ,					column=1			)
		return (
		    closest_pair_of_points_sqr(
		        lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		) ** 0.5


if __name__ == "__main__":
				lowercase_			     =  [(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)))










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






