



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

from ...utils import logging


UpperCAmelCase :				Any							=logging.get_logger(__name__)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= nn.functional.normalize(_lowerCAmelCase)
					UpperCamelCase_			= nn.functional.normalize(_lowerCAmelCase)
					return torch.mm(_lowerCAmelCase ,						normalized_text_embeds.t())






class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    CLIPConfig

				lowercase__     =    ["""CLIPEncoderLayer"""]

				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									super().__init__(snake_case__     )

									UpperCamelCase_			= CLIPVisionModel(config.vision_config     )
									UpperCamelCase_			= nn.Linear(config.vision_config.hidden_size  ,			config.projection_dim  ,			bias=snake_case__     )

									UpperCamelCase_			= nn.Parameter(torch.ones(17  ,			config.projection_dim     )  ,			requires_grad=snake_case__     )
									UpperCamelCase_			= nn.Parameter(torch.ones(3  ,			config.projection_dim     )  ,			requires_grad=snake_case__     )

									UpperCamelCase_			= nn.Parameter(torch.ones(17     )  ,			requires_grad=snake_case__     )
									UpperCamelCase_			= nn.Parameter(torch.ones(3     )  ,			requires_grad=snake_case__     )

				@torch.no_grad()
				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.vision_model(snake_case__     )[1]  # pooled_output
									UpperCamelCase_			= self.visual_projection(snake_case__     )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									UpperCamelCase_			= cosine_distance(snake_case__  ,			self.special_care_embeds     ).cpu().float().numpy()
									UpperCamelCase_			= cosine_distance(snake_case__  ,			self.concept_embeds     ).cpu().float().numpy()

									UpperCamelCase_			= []
									UpperCamelCase_			= image_embeds.shape[0]
									for i in range(snake_case__     ):
														UpperCamelCase_			= {"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
														UpperCamelCase_			= 0.0

														for concept_idx in range(len(special_cos_dist[0]     )     ):
																			UpperCamelCase_			= special_cos_dist[i][concept_idx]
																			UpperCamelCase_			= self.special_care_embeds_weights[concept_idx].item()
																			UpperCamelCase_			= 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]}     )
																								UpperCamelCase_			= 0.01

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

														result.append(snake_case__     )

									UpperCamelCase_			= [len(res["bad_concepts"]     ) > 0 for res in result]

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

									if any(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    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.vision_model(snake_case__     )[1]  # pooled_output
									UpperCamelCase_			= self.visual_projection(snake_case__     )

									UpperCamelCase_			= cosine_distance(snake_case__  ,			self.special_care_embeds     )
									UpperCamelCase_			= cosine_distance(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
									UpperCamelCase_			= 0.0

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

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

									UpperCamelCase_			= 0.0  # black image

									return images, has_nsfw_concepts




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)


