



import numpy as np
import requests
def 	UpperCamelCase			(     )			->  Dict:
   UpperCamelCase					:						Union[str, Any]					=			requests.get(
       'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
       'master/Week1/ADRvsRating.csv'			)
   UpperCamelCase					:						Optional[Any]					=			response.text.splitlines()
   UpperCamelCase					:						Optional[Any]					=			[]
   for item in lines:
      UpperCamelCase					:						Any					=			item.split(','			)
      data.append(snake_case__			)
   data.pop(0			)  # This is for removing the labels from the list
   UpperCamelCase					:						Tuple					=			np.matrix(snake_case__			)
   return dataset
def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					List[Any] ,  snake_case__ :					Any ,  snake_case__ :					Optional[Any] ,  snake_case__ :					Union[str, Any]			)			->  List[Any]:
   UpperCamelCase					:						List[Any]					=			len_data

   UpperCamelCase					:						Union[str, Any]					=			np.dot(snake_case__ ,  data_x.transpose()			)
   prod -= data_y.transpose()
   UpperCamelCase					:						List[str]					=			np.dot(snake_case__ ,  snake_case__			)
   UpperCamelCase					:						str					=			theta - (alpha / n) * sum_grad
   return theta
def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					List[Any] ,  snake_case__ :					Any ,  snake_case__ :					List[Any]			)			->  Optional[int]:
   UpperCamelCase					:						Union[str, Any]					=			np.dot(snake_case__ ,  data_x.transpose()			)
   prod -= data_y.transpose()
   UpperCamelCase					:						Any					=			np.sum(np.square(snake_case__			)			)
   UpperCamelCase					:						Tuple					=			sum_elem / (2 * len_data)
   return error
def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[str]			)			->  Union[str, Any]:
   UpperCamelCase					:						Tuple					=			100000
   UpperCamelCase					:						Dict					=			0.0001550

   UpperCamelCase					:						str					=			data_x.shape[1]
   UpperCamelCase					:						List[Any]					=			data_x.shape[0] - 1

   UpperCamelCase					:						Optional[int]					=			np.zeros((1, no_features)			)

   for i in range(0 ,  snake_case__			):
      UpperCamelCase					:						Optional[int]					=			run_steep_gradient_descent(snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__			)
      UpperCamelCase					:						str					=			sum_of_square_error(snake_case__ ,  snake_case__ ,  snake_case__ ,  snake_case__			)
      print(F"""At Iteration {i + 1} - Error is {error:.5f}"""			)

   return theta
def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[Any]			)			->  Union[str, Any]:
   UpperCamelCase					:						Any					=			sum(abs(y - predicted_y[i]			) for i, y in enumerate(snake_case__			)			)
   return total / len(snake_case__			)
def 	UpperCamelCase			(     )			->  Tuple:
   UpperCamelCase					:						List[Any]					=			collect_dataset()

   UpperCamelCase					:						Dict					=			data.shape[0]
   UpperCamelCase					:						Optional[int]					=			np.c_[np.ones(snake_case__			), data[:, :-1]].astype(snake_case__			)
   UpperCamelCase					:						Dict					=			data[:, -1].astype(snake_case__			)

   UpperCamelCase					:						int					=			run_linear_regression(snake_case__ ,  snake_case__			)
   UpperCamelCase					:						List[Any]					=			theta.shape[1]
   print('Resultant Feature vector : '			)
   for i in range(0 ,  snake_case__			):
      print(F"""{theta[0, i]:.5f}"""			)


if __name__ == "__main__":
    main()








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

from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)
def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Dict:
   UpperCamelCase					:						int					=			nn.functional.normalize(snake_case__			)
   UpperCamelCase					:						Union[str, Any]					=			nn.functional.normalize(snake_case__			)
   return torch.mm(snake_case__ ,  normalized_text_embeds.t()			)



class        lowerCAmelCase_	(						a__							):
       UpperCAmelCase__       :      int       =						CLIPConfig

       UpperCAmelCase__       :      Union[str, Any]       =						["CLIPEncoderLayer"]
       def __init__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						int					=			CLIPVisionModel(config.vision_config )
          UpperCamelCase					:						int					=			nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=SCREAMING_SNAKE_CASE_ )

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

          UpperCamelCase					:						Dict					=			nn.Parameter(torch.ones(17 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					:						Optional[Any]					=			nn.Parameter(torch.ones(3 ),    requires_grad=SCREAMING_SNAKE_CASE_ )
       @torch.no_grad()
       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
          UpperCamelCase					:						Optional[int]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
          UpperCamelCase					:						int					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )

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

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

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

             for concept_idx in range(len(cos_dist[0] ) ):
                UpperCamelCase					:						List[Any]					=			cos_dist[i][concept_idx]
                UpperCamelCase					:						Optional[int]					=			self.concept_embeds_weights[concept_idx].item()
                UpperCamelCase					:						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_ )

          UpperCamelCase					:						int					=			[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] ):
                   UpperCamelCase					:						List[str]					=			torch.zeros_like(images[idx] )  # black image
                else:
                   UpperCamelCase					:						int					=			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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
          UpperCamelCase					:						Union[str, Any]					=			self.vision_model(SCREAMING_SNAKE_CASE_ )[1]  # pooled_output
          UpperCamelCase					:						str					=			self.visual_projection(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase					:						str					=			cosine_distance(SCREAMING_SNAKE_CASE_,    self.special_care_embeds )
          UpperCamelCase					:						Any					=			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
          UpperCamelCase					:						Dict					=			0.0

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

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

          UpperCamelCase					:						Any					=			0.0  # black image

          return images, has_nsfw_concepts



