


"""simple docstring"""






from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase			:			Tuple										=						datasets.load_iris()

_lowercase			:			Any										=						np.array(data["data"])
_lowercase			:			Tuple										=						np.array(data["target"])
_lowercase			:			Optional[int]										=						data["target_names"]

_lowercase			:			Union[str, Any]										=						train_test_split(X, y)






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Any=5     ):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[Any]		     =zip(__lowerCamelCase							,		__lowerCamelCase     )
			# List of distances of all points from the point to be classified
			lowerCamelCase__		:		List[Any]		     =[]
			for data_point in data:
						lowerCamelCase__		:		Optional[Any]		     =euclidean_distance(data_point[0]							,		__lowerCamelCase     )
						distances.append((distance, data_point[1])     )
			# Choosing 'k' points with the least distances.
			lowerCamelCase__		:		List[str]		     =[i[1] for i in sorted(__lowerCamelCase     )[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			lowerCamelCase__		:		Tuple		     =Counter(__lowerCamelCase     ).most_common(1     )[0][0]
			return classes[result]


if __name__ == "__main__":
		print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))









"""simple docstring"""






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

from ...utils import logging


_lowercase			:			int										=						logging.get_logger(__name__)

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							CLIPConfig

					_a             =							['CLIPEncoderLayer']



					def __init__(	self							:  int,  lowerCamelCase							:  CLIPConfig					)-> List[Any]:
								super().__init__(lowerCamelCase					)

								lowerCamelCase__		:		Dict		     =CLIPVisionModelWithProjection(config.vision_config					)

								lowerCamelCase__		:		Dict		     =nn.Linear(config.vision_config.projection_dim,  1					)
								lowerCamelCase__		:		Union[str, Any]		     =nn.Linear(config.vision_config.projection_dim,  1					)




					@torch.no_grad()
					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  List[Any]=0.5,  lowerCamelCase							:  Optional[Any]=0.5					)-> Optional[int]:
								lowerCamelCase__		:		Dict		     =self.vision_model(lowerCamelCase					)[0]

								lowerCamelCase__		:		List[Any]		     =self.p_head(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =nsfw_detected.flatten()
								lowerCamelCase__		:		Any		     =nsfw_detected > p_threshold
								lowerCamelCase__		:		Optional[Any]		     =nsfw_detected.tolist()

								if any(lowerCamelCase					):
											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.'''					)

								for idx, nsfw_detected_ in enumerate(lowerCamelCase					):
											if nsfw_detected_:
														lowerCamelCase__		:		Tuple		     =np.zeros(images[idx].shape					)

								lowerCamelCase__		:		str		     =self.w_head(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =watermark_detected.flatten()
								lowerCamelCase__		:		Optional[Any]		     =watermark_detected > w_threshold
								lowerCamelCase__		:		int		     =watermark_detected.tolist()

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

								for idx, watermark_detected_ in enumerate(lowerCamelCase					):
											if watermark_detected_:
														lowerCamelCase__		:		Optional[int]		     =np.zeros(images[idx].shape					)

								return images, nsfw_detected, watermark_detected




