code: 
  
  
  
  
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
  
  from ...utils import logging
  
  
  A_    :					Optional[Any]             =logging.get_logger(__name__)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		nn.functional.normalize(UpperCAmelCase__      )
  						a_					=		nn.functional.normalize(UpperCAmelCase__      )
  						return torch.mm(UpperCAmelCase__       ,    normalized_text_embeds.t()      )
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		CLIPConfig
  
  							snake_case_        =		['''CLIPEncoderLayer''']
  
  
  							def __init__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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_					=		0.0_1
  
  																			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]
  
  													for idx, has_nsfw_concept in enumerate(_UpperCAmelCase		):
  																			if has_nsfw_concept:
  																									if torch.is_tensor(_UpperCAmelCase		) or torch.is_tensor(images[0]		):
  																															a_					=		torch.zeros_like(images[idx]		)  # black image
  																									else:
  																															a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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 * 0.0_1
  													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		)
  
  													a_					=		0.0  # black image
  
  													return images, has_nsfw_concepts
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)