





'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[list[float]]   ) ->	list[list[float]]:
						UpperCAmelCase_			:      list[list[float]]     						=  []
						for data in source_data:
												for i, el in enumerate(SCREAMING_SNAKE_CASE__   ):
																		if len(SCREAMING_SNAKE_CASE__   ) < i + 1:
																								data_lists.append([]   )
																		data_lists[i].append(float(SCREAMING_SNAKE_CASE__   )   )
						return data_lists





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[list[float]],			SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[float]]:
						UpperCAmelCase_			:      list[list[float]]     						=  []
						for dlist, weight in zip(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
												UpperCAmelCase_			:      Union[str, Any]     						=  min(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Dict     						=  max(SCREAMING_SNAKE_CASE__   )

												UpperCAmelCase_			:      list[float]     						=  []
												# for weight 0 score is 1 - actual score
												if weight == 0:
																		for item in dlist:
																								try:
																														score.append(1 - ((item - mind) / (maxd - mind))   )
																								except ZeroDivisionError:
																														score.append(1   )

												elif weight == 1:
																		for item in dlist:
																								try:
																														score.append((item - mind) / (maxd - mind)   )
																								except ZeroDivisionError:
																														score.append(0   )

        # weight not 0 or 1
												else:
																		UpperCAmelCase_			:      Union[str, Any]     						=  F"""Invalid weight of {weight:f} provided"""
																		raise ValueError(SCREAMING_SNAKE_CASE__   )

												score_lists.append(SCREAMING_SNAKE_CASE__   )

						return score_lists





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[list[float]]   ) ->	list[float]:
						UpperCAmelCase_			:      list[float]     						=  [0 for i in range(len(score_lists[0]   )   )]

						for slist in score_lists:
												for j, ele in enumerate(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Optional[Any]     						=  final_scores[j] + ele

						return final_scores





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[list[float]],			SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[float]]:
						UpperCAmelCase_			:      Union[str, Any]     						=  get_data(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  calculate_each_score(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  generate_final_scores(SCREAMING_SNAKE_CASE__   )

						# append scores to source data
						for i, ele in enumerate(SCREAMING_SNAKE_CASE__   ):
												source_data[i].append(SCREAMING_SNAKE_CASE__   )

						return source_data












'''simple docstring'''







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  get_activation('''swish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  get_activation('''silu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  get_activation('''mish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.Mish   )

													self.assertEqual(act(torch.tensor(-2_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  get_activation('''gelu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.GELU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )





