


import random



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 0.01     ,      _UpperCAmelCase = 1000     ,      _UpperCAmelCase = -1     ,      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      sample
										if len(self.sample      ) == 0:
																raise ValueError('Sample data can not be empty'      )
										__magic_name__				:							Tuple				=      target
										if len(self.target      ) == 0:
																raise ValueError('Target data can not be empty'      )
										if len(self.sample      ) != len(self.target      ):
																raise ValueError('Sample data and Target data do not have matching lengths'      )
										__magic_name__				:							List[Any]				=      learning_rate
										__magic_name__				:							Union[str, Any]				=      epoch_number
										__magic_name__				:							Any				=      bias
										__magic_name__				:							str				=      len(_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      len(sample[0]      )  # number of columns in dataset
										__magic_name__				:							list				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										for sample in self.sample:
																sample.insert(0     ,      self.bias      )

										for _ in range(self.col_sample      ):
																self.weight.append(random.random()      )

										self.weight.insert(0     ,      self.bias      )

										__magic_name__				:							Tuple				=      0

										while True:
																__magic_name__				:							Tuple				=      False
																for i in range(self.number_sample      ):
																						__magic_name__				:							Optional[int]				=      0
																						for j in range(self.col_sample + 1      ):
																												__magic_name__				:							int				=      u + self.weight[j] * self.sample[i][j]
																						__magic_name__				:							Tuple				=      self.sign(_UpperCAmelCase      )
																						if y != self.target[i]:
																												for j in range(self.col_sample + 1      ):
																																		__magic_name__				:							int				=      (
																																		    self.weight[j]
																																		    + self.learning_rate
																																		    * (self.target[i] - y)
																																		    * self.sample[i][j]
																																		)
																												__magic_name__				:							Union[str, Any]				=      True
            # print('Epoch: \n',epoch_count)
																__magic_name__				:							int				=      epoch_count + 1
																# if you want control the epoch or just by error
																if not has_misclassified:
																						print(('\nEpoch:\n', epoch_count)      )
																						print('------------------------\n'      )
																						# if epoch_count > self.epoch_number or not error:
																						break
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if len(self.sample      ) == 0:
																raise ValueError('Sample data can not be empty'      )
										sample.insert(0     ,      self.bias      )
										__magic_name__				:							str				=      0
										for i in range(self.col_sample + 1      ):
																__magic_name__				:							Optional[Any]				=      u + self.weight[i] * sample[i]

										__magic_name__				:							Optional[int]				=      self.sign(_UpperCAmelCase      )

										if y == -1:
																print(('Sample: ', sample)      )
																print('classification: P1'      )
										else:
																print(('Sample: ', sample)      )
																print('classification: P2'      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return 1 if u >= 0 else -1






snake_case__									= [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

snake_case__									= [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			snake_case__									= Perceptron(
			    sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
			)
			network.training()
			print('Finished training perceptron')
			print('Enter values to predict or q to exit')
			while True:
						snake_case__									= []
						for i in range(len(samples[0])):
									snake_case__									= input('value: ').strip()
									if user_input == "q":
												break
									snake_case__									= float(user_input)
									sample.insert(i, observation)
						network.sort(sample)






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


snake_case__     							= logging.get_logger(__name__)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :List[str]				)       ->  Tuple:
						__magic_name__				:							List[str]				=      nn.functional.normalize(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      nn.functional.normalize(lowercase__				)
						return torch.mm(lowercase__      ,						normalized_text_embeds.t()				)



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      CLIPConfig

				__lowerCAmelCase		=      ['''CLIPEncoderLayer''']
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										super().__init__(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      CLIPVisionModel(config.vision_config      )
										__magic_name__				:							Tuple				=      nn.Linear(config.vision_config.hidden_size     ,      config.projection_dim     ,      bias=_UpperCAmelCase      )

										__magic_name__				:							str				=      nn.Parameter(torch.ones(17     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(3     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )

										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(17      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							str				=      nn.Parameter(torch.ones(3      )     ,      requires_grad=_UpperCAmelCase      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							Any				=      self.visual_projection(_UpperCAmelCase      )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      ).cpu().float().numpy()
										__magic_name__				:							int				=      cosine_distance(_UpperCAmelCase     ,      self.concept_embeds      ).cpu().float().numpy()

										__magic_name__				:							str				=      []
										__magic_name__				:							List[Any]				=      image_embeds.shape[0]
										for i in range(_UpperCAmelCase      ):
																__magic_name__				:							Optional[Any]				=      {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}

																# increase this value to create a stronger `nfsw` filter
																# at the cost of increasing the possibility of filtering benign images
																__magic_name__				:							Optional[int]				=      0.0

																for concept_idx in range(len(special_cos_dist[0]      )      ):
																						__magic_name__				:							Any				=      special_cos_dist[i][concept_idx]
																						__magic_name__				:							Tuple				=      self.special_care_embeds_weights[concept_idx].item()
																						__magic_name__				:							Dict				=      round(concept_cos - concept_threshold + adjustment     ,      3      )
																						if result_img["special_scores"][concept_idx] > 0:
																												result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]}      )
																												__magic_name__				:							str				=      0.01

																for concept_idx in range(len(cos_dist[0]      )      ):
																						__magic_name__				:							Optional[Any]				=      cos_dist[i][concept_idx]
																						__magic_name__				:							Tuple				=      self.concept_embeds_weights[concept_idx].item()
																						__magic_name__				:							Optional[int]				=      round(concept_cos - concept_threshold + adjustment     ,      3      )
																						if result_img["concept_scores"][concept_idx] > 0:
																												result_img["bad_concepts"].append(_UpperCAmelCase      )

																result.append(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      [len(res['bad_concepts']      ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(_UpperCAmelCase      ):
																if has_nsfw_concept:
																						if torch.is_tensor(_UpperCAmelCase      ) or torch.is_tensor(images[0]      ):
																												__magic_name__				:							Union[str, Any]				=      torch.zeros_like(images[idx]      )  # black image
																						else:
																												__magic_name__				:							Optional[int]				=      np.zeros(images[idx].shape      )  # black image

										if any(_UpperCAmelCase      ):
																logger.warning(
																    'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
																    ' Try again with a different prompt and/or seed.'      )

										return images, has_nsfw_concepts





				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							List[str]				=      self.visual_projection(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      )
										__magic_name__				:							Optional[int]				=      cosine_distance(_UpperCAmelCase     ,      self.concept_embeds      )

										# increase this value to create a stronger `nsfw` filter
										# at the cost of increasing the possibility of filtering benign images
										__magic_name__				:							int				=      0.0

										__magic_name__				:							Optional[int]				=      special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										__magic_name__				:							int				=      torch.any(special_scores > 0     ,      dim=1      )
										__magic_name__				:							Optional[Any]				=      special_care * 0.01
										__magic_name__				:							Any				=      special_adjustment.unsqueeze(1      ).expand(-1     ,      cos_dist.shape[1]      )

										__magic_name__				:							Optional[int]				=      (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__magic_name__				:							Union[str, Any]				=      torch.any(concept_scores > 0     ,      dim=1      )

										__magic_name__				:							Dict				=      0.0  # black image

										return images, has_nsfw_concepts


