


"""simple docstring"""
import matplotlib.pyplot as plt
import numpy as np






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Any	,  _snake_case		: str	)	->List[Any]:
	"""simple docstring"""





	__snake_case      :		str         =							len(_snake_case	)  # Number of training samples
	__snake_case      :		List[Any]         =							np.eye(_snake_case	)  # Initialize weights as identity matrix
	for j in range(_snake_case	):
		__snake_case      :		Tuple         =							point - x_train[j]
		__snake_case      :		Optional[int]         =							np.exp(diff @ diff.T / (-2.0 * tau**2)	)

	return weights






def 						lowercase				(	_snake_case		: Any	,  _snake_case		: int	,  _snake_case		: Dict	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		int         =							weight_matrix(_snake_case	,  _snake_case	,  _snake_case	)
	__snake_case      :		str         =							np.linalg.inv(x_train.T @ weight_mat @ x_train	) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Optional[int]	,  _snake_case		: str	)	->str:
	"""simple docstring"""





	__snake_case      :		int         =							np.zeros(len(_snake_case	)	)  # Initialize array of predictions
	for i, item in enumerate(_snake_case	):
		__snake_case      :		Dict         =							item @ local_weight(_snake_case	,  _snake_case	,  _snake_case	,  _snake_case	)

	return y_pred






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Dict	,  _snake_case		: Union[str, Any]	)	->str:
	"""simple docstring"""





	import seaborn as sns

	__snake_case      :		Union[str, Any]         =							sns.load_dataset(_snake_case	)
	__snake_case      :		Tuple         =							np.array(data[x_name]	)
	__snake_case      :		Optional[int]         =							np.array(data[y_name]	)

	__snake_case      :		str         =							np.ones(len(_snake_case	)	)

	# pairing elements of one and x_data
	__snake_case      :		Optional[int]         =							np.column_stack((one, x_data)	)

	return x_train, x_data, y_data






def 						lowercase				(	_snake_case		: Optional[int]	,  _snake_case		: Union[str, Any]	,  _snake_case		: List[str]	,  _snake_case		: List[str]	,  _snake_case		: str	,  _snake_case		: Optional[Any]	,  )	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							np.sort(_snake_case	,  axis=0	)
	plt.scatter(_snake_case	,  _snake_case	,  color='''blue'''	)
	plt.plot(
	    x_train_sorted[:, 1]	,  preds[x_train[:, 1].argsort(0	)]	,  color='''yellow'''	,  linewidth=5	,  )
	plt.title('''Local Weighted Regression'''	)
	plt.xlabel(_snake_case	)
	plt.ylabel(_snake_case	)
	plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# Demo with a dataset from the seaborn module
			SCREAMING_SNAKE_CASE					:       str							   =							load_data("""tips""", """total_bill""", """tip""")
			SCREAMING_SNAKE_CASE					:       str							   =							local_weight_regression(training_data_x, tip, 5)
			plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")










'''simple docstring'''







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

from ...utils import logging


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.functional.normalize(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      nn.functional.normalize(SCREAMING_SNAKE_CASE     )
		return torch.mm(SCREAMING_SNAKE_CASE ,		normalized_text_embeds.t()     )





class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		int		=      CLIPConfig

			__snake_case  :		Optional[Any]		=      ['CLIPEncoderLayer']






			def __init__(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							CLIPConfig	):




					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      CLIPVisionModel(config.vision_config	)
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.Linear(config.vision_config.hidden_size      ,       config.projection_dim      ,       bias=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.Parameter(torch.ones(17      ,       config.projection_dim	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.Parameter(torch.ones(3      ,       config.projection_dim	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      nn.Parameter(torch.ones(17	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      nn.Parameter(torch.ones(3	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)






			@torch.no_grad()
			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.vision_model(SCREAMING_SNAKE_CASE	)[1]  # pooled_output
					SCREAMING_SNAKE_CASE_						:Dict										=      self.visual_projection(SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:str										=      []
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image_embeds.shape[0]
					for i in range(SCREAMING_SNAKE_CASE	):
							SCREAMING_SNAKE_CASE_						:List[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
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      0.0

							for concept_idx in range(len(special_cos_dist[0]	)	):
									SCREAMING_SNAKE_CASE_						:Dict										=      special_cos_dist[i][concept_idx]
									SCREAMING_SNAKE_CASE_						:Tuple										=      self.special_care_embeds_weights[concept_idx].item()
									SCREAMING_SNAKE_CASE_						:Any										=      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]}	)
											SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      0.01

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

							result.append(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      [len(res['bad_concepts']	) > 0 for res in result]

					for idx, has_nsfw_concept in enumerate(SCREAMING_SNAKE_CASE	):
							if has_nsfw_concept:
									if torch.is_tensor(SCREAMING_SNAKE_CASE	) or torch.is_tensor(images[0]	):
											SCREAMING_SNAKE_CASE_						:Tuple										=      torch.zeros_like(images[idx]	)  # black image
									else:
											SCREAMING_SNAKE_CASE_						:str										=      np.zeros(images[idx].shape	)  # black image

					if any(SCREAMING_SNAKE_CASE	):
							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 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.vision_model(SCREAMING_SNAKE_CASE	)[1]  # pooled_output
					SCREAMING_SNAKE_CASE_						:Tuple										=      self.visual_projection(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      cosine_distance(SCREAMING_SNAKE_CASE      ,       self.special_care_embeds	)
					SCREAMING_SNAKE_CASE_						:int										=      cosine_distance(SCREAMING_SNAKE_CASE      ,       self.concept_embeds	)

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

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

					SCREAMING_SNAKE_CASE_						:str										=      (cos_dist - self.concept_embeds_weights) + special_adjustment
					# concept_scores = concept_scores.round(decimals=3)
					SCREAMING_SNAKE_CASE_						:str										=      torch.any(concept_scores > 0      ,       dim=1	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0.0  # black image

					return images, has_nsfw_concepts
