


def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :int      ,						lowercase__  :Optional[int]      ,						lowercase__  :Tuple				)       ->  Any:
						if start == end:
												return start if lst[start] > item else start + 1
						if start > end:
												return start

						__magic_name__				:							Optional[Any]				=      (start + end) // 2
						if lst[mid] < item:
												return binary_search(lowercase__      ,						lowercase__      ,						mid + 1      ,						lowercase__				)
						elif lst[mid] > item:
												return binary_search(lowercase__      ,						lowercase__      ,						lowercase__      ,						mid - 1				)
						else:
												return mid
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str				)       ->  Union[str, Any]:
						__magic_name__				:							int				=      len(lowercase__				)

						for index in range(1      ,						lowercase__				):
												__magic_name__				:							Tuple				=      lst[index]
												__magic_name__				:							Optional[int]				=      binary_search(lowercase__      ,						lowercase__      ,						0      ,						index - 1				)
												__magic_name__				:							int				=      lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

						return lst
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Any				)       ->  Optional[int]:
						if not left:
												return right

						if not right:
												return left

						if left[0] < right[0]:
												return [left[0], *merge(left[1:]      ,						lowercase__				)]

						return [right[0], *merge(lowercase__      ,						right[1:]				)]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict				)       ->  List[str]:
						__magic_name__				:							str				=      len(lowercase__				)
						__magic_name__		, __magic_name__				:							List[str]				=      [], []
						__magic_name__				:							Optional[Any]				=      [lst[0]]
						__magic_name__				:							Optional[int]				=      []
						__magic_name__				:							Union[str, Any]				=      1
						while i < length:
												if lst[i] < lst[i - 1]:
																		runs.append(lowercase__				)
																		__magic_name__				:							Optional[int]				=      [lst[i]]
												else:
																		new_run.append(lst[i]				)
												i += 1
						runs.append(lowercase__				)

						for run in runs:
												sorted_runs.append(insertion_sort(lowercase__				)				)
						for run in sorted_runs:
												__magic_name__				:							str				=      merge(lowercase__      ,						lowercase__				)

						return sorted_array
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						__magic_name__				:							int				=      [5, 9, 1_0, 3, -4, 5, 1_7_8, 9_2, 4_6, -1_8, 0, 7]
						__magic_name__				:							str				=      tim_sort(lowercase__				)
						print(lowercase__				)


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


snake_case__     							= logging.get_logger(__name__)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :List[str]				)       ->  Tuple:
						__magic_name__				:							List[str]				=      nn.functional.normalize(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      nn.functional.normalize(lowercase__				)
						return torch.mm(lowercase__      ,						normalized_text_embeds.t()				)



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      CLIPConfig

				__lowerCAmelCase		=      ['''CLIPEncoderLayer''']
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										super().__init__(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      CLIPVisionModel(config.vision_config      )
										__magic_name__				:							Tuple				=      nn.Linear(config.vision_config.hidden_size     ,      config.projection_dim     ,      bias=_UpperCAmelCase      )

										__magic_name__				:							str				=      nn.Parameter(torch.ones(17     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(3     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )

										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(17      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							str				=      nn.Parameter(torch.ones(3      )     ,      requires_grad=_UpperCAmelCase      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							Any				=      self.visual_projection(_UpperCAmelCase      )

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

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

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

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

																result.append(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      [len(res['bad_concepts']      ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(_UpperCAmelCase      ):
																if has_nsfw_concept:
																						if torch.is_tensor(_UpperCAmelCase      ) or torch.is_tensor(images[0]      ):
																												__magic_name__				:							Union[str, Any]				=      torch.zeros_like(images[idx]      )  # black image
																						else:
																												__magic_name__				:							Optional[int]				=      np.zeros(images[idx].shape      )  # black image

										if any(_UpperCAmelCase      ):
																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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							List[str]				=      self.visual_projection(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      )
										__magic_name__				:							Optional[int]				=      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
										__magic_name__				:							int				=      0.0

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

										__magic_name__				:							Optional[int]				=      (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__magic_name__				:							Union[str, Any]				=      torch.any(concept_scores > 0     ,      dim=1      )

										__magic_name__				:							Dict				=      0.0  # black image

										return images, has_nsfw_concepts


