






def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase=0 ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=float('''inf''' ) ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=float('''inf''' ) ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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


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














import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



