




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

from ...utils import logging


_SCREAMING_SNAKE_CASE							:       str						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				nn.functional.normalize(__lowerCAmelCase					)
							snake_case_      =				nn.functional.normalize(__lowerCAmelCase					)
							return torch.mm(__lowerCAmelCase							,    normalized_text_embeds.t()					)




class 			_snake_case		(     lowerCamelCase__							):
			lowerCAmelCase_					:				List[str]							=   CLIPConfig

			lowerCAmelCase_					:				Optional[Any]							=   ["""CLIPEncoderLayer"""]




			def __init__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										super().__init__(snake_case__							)

										snake_case_      =				CLIPVisionModel(config.vision_config							)
										snake_case_      =				nn.Linear(config.vision_config.hidden_size    ,							config.projection_dim    ,							bias=snake_case__							)

										snake_case_      =				nn.Parameter(torch.ones(17    ,							config.projection_dim							)    ,							requires_grad=snake_case__							)
										snake_case_      =				nn.Parameter(torch.ones(3    ,							config.projection_dim							)    ,							requires_grad=snake_case__							)

										snake_case_      =				nn.Parameter(torch.ones(17							)    ,							requires_grad=snake_case__							)
										snake_case_      =				nn.Parameter(torch.ones(3							)    ,							requires_grad=snake_case__							)




			@torch.no_grad()
			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self.vision_model(snake_case__							)[1]  # pooled_output
										snake_case_      =				self.visual_projection(snake_case__							)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										snake_case_      =				cosine_distance(snake_case__    ,							self.special_care_embeds							).cpu().float().numpy()
										snake_case_      =				cosine_distance(snake_case__    ,							self.concept_embeds							).cpu().float().numpy()

										snake_case_      =				[]
										snake_case_      =				image_embeds.shape[0]
										for i in range(snake_case__							):
																	snake_case_      =				{'''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
																	snake_case_      =				0.0

																	for concept_idx in range(len(special_cos_dist[0]							)							):
																								snake_case_      =				special_cos_dist[i][concept_idx]
																								snake_case_      =				self.special_care_embeds_weights[concept_idx].item()
																								snake_case_      =				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]}							)
																															snake_case_      =				0.0_1

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

																	result.append(snake_case__							)

										snake_case_      =				[len(res["bad_concepts"]							) > 0 for res in result]

										return images, has_nsfw_concepts




			@torch.no_grad()
			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				self.vision_model(snake_case__							)[1]  # pooled_output
										snake_case_      =				self.visual_projection(snake_case__							)

										snake_case_      =				cosine_distance(snake_case__    ,							self.special_care_embeds							)
										snake_case_      =				cosine_distance(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
										snake_case_      =				0.0

										snake_case_      =				special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										snake_case_      =				torch.any(special_scores > 0    ,							dim=1							)
										snake_case_      =				special_care * 0.0_1
										snake_case_      =				special_adjustment.unsqueeze(1							).expand(-1    ,							cos_dist.shape[1]							)

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

										return images, has_nsfw_concepts










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

							snake_case_      =				Stack()
							snake_case_      =				Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(snake_case					)					)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(snake_case					)
														elif i == ")":
																					# RULE 4
																					snake_case_      =				operator_stack.peek()
																					operator_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




