



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     : int				=   {"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   [
							    """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """VisualBertForMultipleChoice""",
							    """VisualBertForPreTraining""",
							    """VisualBertForQuestionAnswering""",
							    """VisualBertForRegionToPhraseAlignment""",
							    """VisualBertForVisualReasoning""",
							    """VisualBertLayer""",
							    """VisualBertModel""",
							    """VisualBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_visual_bert import (
														    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    VisualBertForMultipleChoice,
														    VisualBertForPreTraining,
														    VisualBertForQuestionAnswering,
														    VisualBertForRegionToPhraseAlignment,
														    VisualBertForVisualReasoning,
														    VisualBertLayer,
														    VisualBertModel,
														    VisualBertPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''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
