
'''simple docstring'''






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

from ...utils import logging


A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Any):
				lowerCamelCase					:			str									= nn.functional.normalize(UpperCAmelCase__)
				lowerCamelCase					:			int									= nn.functional.normalize(UpperCAmelCase__)
				return torch.mm(UpperCAmelCase__       ,							normalized_text_embeds.t())




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	CLIPConfig

					_lowerCAmelCase			     =	['''CLIPEncoderLayer''']
					def __init__(     self,    A      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Optional[Any]									= CLIPVisionModel(config.vision_config      )
									lowerCamelCase					:			str									= nn.Linear(config.vision_config.hidden_size,    config.projection_dim,    bias=A      )

									lowerCamelCase					:			str									= nn.Parameter(torch.ones(17,    config.projection_dim      ),    requires_grad=A      )
									lowerCamelCase					:			List[Any]									= nn.Parameter(torch.ones(3,    config.projection_dim      ),    requires_grad=A      )

									lowerCamelCase					:			Dict									= nn.Parameter(torch.ones(17      ),    requires_grad=A      )
									lowerCamelCase					:			Dict									= nn.Parameter(torch.ones(3      ),    requires_grad=A      )
					@torch.no_grad()
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.vision_model(A      )[1]  # pooled_output
									lowerCamelCase					:			int									= self.visual_projection(A      )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									lowerCamelCase					:			List[str]									= cosine_distance(A,    self.special_care_embeds      ).cpu().float().numpy()
									lowerCamelCase					:			Tuple									= cosine_distance(A,    self.concept_embeds      ).cpu().float().numpy()

									lowerCamelCase					:			int									= []
									lowerCamelCase					:			str									= image_embeds.shape[0]
									for i in range(A      ):
													lowerCamelCase					:			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
													lowerCamelCase					:			Tuple									= 0.0

													for concept_idx in range(len(special_cos_dist[0]      )      ):
																	lowerCamelCase					:			Union[str, Any]									= special_cos_dist[i][concept_idx]
																	lowerCamelCase					:			str									= self.special_care_embeds_weights[concept_idx].item()
																	lowerCamelCase					:			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]}      )
																					lowerCamelCase					:			int									= 0.01

													for concept_idx in range(len(cos_dist[0]      )      ):
																	lowerCamelCase					:			int									= cos_dist[i][concept_idx]
																	lowerCamelCase					:			Union[str, Any]									= self.concept_embeds_weights[concept_idx].item()
																	lowerCamelCase					:			Dict									= round(concept_cos - concept_threshold + adjustment,    3      )
																	if result_img["concept_scores"][concept_idx] > 0:
																					result_img["bad_concepts"].append(A      )

													result.append(A      )

									lowerCamelCase					:			int									= [len(res['bad_concepts']      ) > 0 for res in result]

									return images, has_nsfw_concepts




					@torch.no_grad()
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.vision_model(A      )[1]  # pooled_output
									lowerCamelCase					:			Optional[int]									= self.visual_projection(A      )

									lowerCamelCase					:			Dict									= cosine_distance(A,    self.special_care_embeds      )
									lowerCamelCase					:			str									= cosine_distance(A,    self.concept_embeds      )

									# increase this value to create a stronger `nsfw` filter
									# at the cost of increasing the possibility of filtering benign images
									lowerCamelCase					:			Optional[int]									= 0.0

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

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

									return images, has_nsfw_concepts


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
