code: 
  
  
  import numpy as np
  import torch
  import torch.nn as nn
  from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
  
  from ...utils import logging
  
  
  __lowerCamelCase       :       List[str]				   =			logging.get_logger(__name__)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Any:
  				A__     :							List[str]   				=nn.functional.normalize(snake_case_	)
  				A__     :							str   				=nn.functional.normalize(snake_case_	)
  				return torch.mm(snake_case_,	normalized_text_embeds.t()	)
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		CLIPConfig
  
  		__lowercase													=		["""CLIPEncoderLayer"""]
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						super().__init__(__UpperCamelCase							)
  
  						A__     :							Optional[int]   				=CLIPVisionModel(config.vision_config							)
  						A__     :							Union[str, Any]   				=nn.Linear(config.vision_config.hidden_size	,     config.projection_dim	,     bias=__UpperCamelCase							)
  
  						A__     :							int   				=nn.Parameter(torch.ones(17	,     config.projection_dim							)	,     requires_grad=__UpperCamelCase							)
  						A__     :							Dict   				=nn.Parameter(torch.ones(3	,     config.projection_dim							)	,     requires_grad=__UpperCamelCase							)
  
  						A__     :							Optional[Any]   				=nn.Parameter(torch.ones(17							)	,     requires_grad=__UpperCamelCase							)
  						A__     :							Any   				=nn.Parameter(torch.ones(3							)	,     requires_grad=__UpperCamelCase							)
  
  
  
  
  
  
  		@torch.no_grad()
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=self.vision_model(__UpperCamelCase							)[1]  # pooled_output
  						A__     :							Any   				=self.visual_projection(__UpperCamelCase							)
  
  						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
  						A__     :							Optional[Any]   				=cosine_distance(__UpperCamelCase	,     self.special_care_embeds							).cpu().float().numpy()
  						A__     :							Any   				=cosine_distance(__UpperCamelCase	,     self.concept_embeds							).cpu().float().numpy()
  
  						A__     :							Tuple   				=[]
  						A__     :							List[str]   				=image_embeds.shape[0]
  						for i in range(__UpperCamelCase							):
  										A__     :							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
  										A__     :							Union[str, Any]   				=0.0
  
  										for concept_idx in range(len(special_cos_dist[0]							)							):
  														A__     :							int   				=special_cos_dist[i][concept_idx]
  														A__     :							int   				=self.special_care_embeds_weights[concept_idx].item()
  														A__     :							int   				=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__     :							Dict   				=0.01
  
  										for concept_idx in range(len(cos_dist[0]							)							):
  														A__     :							Any   				=cos_dist[i][concept_idx]
  														A__     :							Any   				=self.concept_embeds_weights[concept_idx].item()
  														A__     :							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							)
  
  						A__     :							Optional[int]   				=[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__     :							Optional[int]   				=torch.zeros_like(images[idx]							)  # black image
  														else:
  																		A__     :							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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=self.vision_model(__UpperCamelCase							)[1]  # pooled_output
  						A__     :							int   				=self.visual_projection(__UpperCamelCase							)
  
  						A__     :							List[str]   				=cosine_distance(__UpperCamelCase	,     self.special_care_embeds							)
  						A__     :							Dict   				=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__     :							Union[str, Any]   				=0.0
  
  						A__     :							List[str]   				=special_cos_dist - self.special_care_embeds_weights + adjustment
  						# special_scores = special_scores.round(decimals=3)
  						A__     :							Dict   				=torch.any(special_scores > 0	,     dim=1							)
  						A__     :							Optional[int]   				=special_care * 0.01
  						A__     :							List[Any]   				=special_adjustment.unsqueeze(1							).expand(-1	,     cos_dist.shape[1]							)
  
  						A__     :							Optional[int]   				=(cos_dist - self.concept_embeds_weights) + special_adjustment
  						# concept_scores = concept_scores.round(decimals=3)
  						A__     :							Any   				=torch.any(concept_scores > 0	,     dim=1							)
  
  						A__     :							List[Any]   				=0.0  # black image
  
  						return images, has_nsfw_concepts
  
  
  
  
style_context: 
  
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Tuple:
  				A__     :							List[Any]   				=set()
  				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  								A__     :							Optional[int]   				=getattr(transformers.models.auto,	snake_case_,	snake_case_	)
  								if module is None:
  												continue
  								# all mappings in a single auto modeling file
  								A__     :							Optional[int]   				=[
  								    x
  								    for x in dir(snake_case_	)
  								    if x.endswith('''_MAPPING_NAMES'''	)
  								    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  								]
  								for name in mapping_names:
  												A__     :							Any   				=getattr(snake_case_,	snake_case_	)
  												if mapping is not None:
  																for v in mapping.values():
  																				if isinstance(snake_case_,	(list, tuple)	):
  																								model_names.update(snake_case_	)
  																				elif isinstance(snake_case_,	snake_case_	):
  																								model_names.add(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
  				# All model names defined in auto mappings
  				A__     :							int   				=set(get_all_model_names()	)
  
  				with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  								A__     :							Union[str, Any]   				=json.load(snake_case_	)
  				A__     :							int   				=set()
  				for model_base_name in tiny_model_info:
  								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  				A__     :							Optional[Any]   				=model_names.difference(snake_case_	)
  				for model_name in copy.copy(snake_case_	):
  								if not model_name.startswith('''TF'''	) and f'TF{model_name}' in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  								elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
  				A__     :							Dict   				=COMPOSITE_MODELS.values()
  
  				# All tiny model base names on Hub
  				A__     :							Union[str, Any]   				=get_all_model_names()
  				A__     :							Optional[Any]   				=hf_api.list_models(
  				    filter=ModelFilter(
  				        author='''hf-internal-testing''',	)	)
  				A__     :							str   				=set()
  				for x in models:
  								A__     :							List[Any]   				=x.modelId
  								A__				,  A__     :							List[str]   				=model.split('''/'''	)
  								if not model.startswith('''tiny-random-'''	):
  												continue
  								A__     :							str   				=model.replace('''tiny-random-''',	''''''	)
  								if not model[0].isupper():
  												continue
  								if model not in model_names and model not in special_models:
  												continue
  								_models.add(snake_case_	)
  
  				A__     :							Dict   				=sorted(_models	)
  				# All tiny model names on Hub
  				A__     :							Optional[int]   				={}
  				for model in models:
  								A__     :							Optional[int]   				=f'hf-internal-testing/tiny-random-{model}'
  								A__     :							str   				=model.split('''-'''	)[0]
  								try:
  												A__     :							int   				=hf_api.repo_info(snake_case_	)
  												A__     :							Optional[int]   				={
  												    '''tokenizer_classes''': set(),
  												    '''processor_classes''': set(),
  												    '''model_classes''': set(),
  												    '''sha''': repo_info.sha,
  												}
  								except Exception:
  												continue
  								try:
  												time.sleep(1	)
  												A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoTokenizer.from_pretrained(snake_case_,	use_fast=snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoImageProcessor.from_pretrained(snake_case_	)
  												content["processor_classes"].add(img_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Optional[int]   				=AutoFeatureExtractor.from_pretrained(snake_case_	)
  												if not isinstance(snake_case_,	snake_case_	):
  																content["processor_classes"].add(feat_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Tuple   				=getattr(snake_case_,	snake_case_	)
  												A__     :							Tuple   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=getattr(snake_case_,	f'TF{model}'	)
  												A__     :							Union[str, Any]   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  
  								A__     :							List[str]   				=sorted(content['''tokenizer_classes''']	)
  								A__     :							Tuple   				=sorted(content['''processor_classes''']	)
  								A__     :							Optional[Any]   				=sorted(content['''model_classes''']	)
  
  								A__     :							List[Any]   				=content
  								with open(os.path.join(snake_case_,	'''hub_tiny_model_summary.json'''	),	'''w'''	) as fp:
  												json.dump(snake_case_,	snake_case_,	ensure_ascii=snake_case_,	indent=4	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Any				   =			argparse.ArgumentParser()
  						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
  						__lowerCamelCase       :       int				   =			parser.parse_args()
  
  						# This has to be `spawn` to avoid hanging forever!
  						multiprocessing.set_start_method("spawn")
  
  						__lowerCamelCase       :       Any				   =			"tiny_models"
  						__lowerCamelCase       :       Optional[int]				   =			True
  						__lowerCamelCase       :       int				   =			None
  						__lowerCamelCase       :       str				   =			get_tiny_model_names_from_repo()
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       Optional[Any]				   =			"hf-internal-testing"
  
  						create_tiny_models(
  						    output_path,
  						    all,
  						    model_types,
  						    models_to_skip,
  						    no_check,
  						    upload,
  						    organization,
  						    token=os.environ.get("TOKEN", None),
  						    num_workers=args.num_workers,
  						)
  
  
  
  