


'''simple docstring'''

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

from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[str]				)      ->     Dict:







      '''simple docstring'''






      _a						=		nn.functional.normalize(__lowerCamelCase				)
      _a						=		nn.functional.normalize(__lowerCamelCase				)
      return torch.mm(__lowerCamelCase      ,  normalized_text_embeds.t()				)






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				CLIPConfig

     UpperCAmelCase 					=				['''CLIPEncoderLayer''']






     def __init__(			self     ,							__UpperCamelCase		)       ->    Any:
           super().__init__(__UpperCamelCase		)

           _a						=		CLIPVisionModel(config.vision_config		)
           _a						=		nn.Linear(config.vision_config.hidden_size     ,							config.projection_dim     ,							bias=__UpperCamelCase		)

           _a						=		nn.Parameter(torch.ones(17     ,							config.projection_dim		)     ,							requires_grad=__UpperCamelCase		)
           _a						=		nn.Parameter(torch.ones(3     ,							config.projection_dim		)     ,							requires_grad=__UpperCamelCase		)

           _a						=		nn.Parameter(torch.ones(17		)     ,							requires_grad=__UpperCamelCase		)
           _a						=		nn.Parameter(torch.ones(3		)     ,							requires_grad=__UpperCamelCase		)






     @torch.no_grad()
     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Any:
           _a						=		self.vision_model(__UpperCamelCase		)[1]  # pooled_output
           _a						=		self.visual_projection(__UpperCamelCase		)

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

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

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

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

                 result.append(__UpperCamelCase		)

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

           return images, has_nsfw_concepts






     @torch.no_grad()
     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
           _a						=		self.vision_model(__UpperCamelCase		)[1]  # pooled_output
           _a						=		self.visual_projection(__UpperCamelCase		)

           _a						=		cosine_distance(__UpperCamelCase     ,							self.special_care_embeds		)
           _a						=		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
           _a						=		0.0

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

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

           return images, has_nsfw_concepts





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

