"""simple docstring"""



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

from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    List[str]														= nn.functional.normalize(lowerCAmelCase       )
							A__					:    List[str]														= nn.functional.normalize(lowerCAmelCase       )
							return torch.mm(lowerCAmelCase ,     normalized_text_embeds.t()       )



class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							CLIPConfig

			_UpperCamelCase     :	List[str]				    =							['CLIPEncoderLayer']





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										super().__init__(snake_case_       )

										A__					:    Optional[int]														= CLIPVisionModel(config.vision_config       )
										A__					:    List[str]														= nn.Linear(config.vision_config.hidden_size      ,			config.projection_dim      ,			bias=snake_case_       )

										A__					:    Optional[Any]														= nn.Parameter(torch.ones(17      ,			config.projection_dim       )      ,			requires_grad=snake_case_       )
										A__					:    Dict														= nn.Parameter(torch.ones(3      ,			config.projection_dim       )      ,			requires_grad=snake_case_       )

										A__					:    str														= nn.Parameter(torch.ones(17       )      ,			requires_grad=snake_case_       )
										A__					:    Dict														= nn.Parameter(torch.ones(3       )      ,			requires_grad=snake_case_       )





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= self.vision_model(snake_case_       )[1]  # pooled_output
										A__					:    Dict														= self.visual_projection(snake_case_       )

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

										A__					:    Optional[Any]														= []
										A__					:    List[str]														= image_embeds.shape[0]
										for i in range(snake_case_       ):
																	A__					:    str														= {"""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__					:    Optional[int]														= 0.0

																	for concept_idx in range(len(special_cos_dist[0]       )       ):
																								A__					:    int														= special_cos_dist[i][concept_idx]
																								A__					:    List[Any]														= self.special_care_embeds_weights[concept_idx].item()
																								A__					:    List[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]}       )
																															A__					:    Optional[Any]														= 0.01

																	for concept_idx in range(len(cos_dist[0]       )       ):
																								A__					:    Tuple														= cos_dist[i][concept_idx]
																								A__					:    int														= self.concept_embeds_weights[concept_idx].item()
																								A__					:    Any														= 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_       )

										A__					:    Union[str, Any]														= [len(res["""bad_concepts"""]       ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(snake_case_       ):
																	if has_nsfw_concept:
																								if torch.is_tensor(snake_case_       ) or torch.is_tensor(images[0]       ):
																															A__					:    Dict														= torch.zeros_like(images[idx]       )  # black image
																								else:
																															A__					:    Dict														= np.zeros(images[idx].shape       )  # black image

										if any(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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.vision_model(snake_case_       )[1]  # pooled_output
										A__					:    Dict														= self.visual_projection(snake_case_       )

										A__					:    Optional[Any]														= cosine_distance(snake_case_      ,			self.special_care_embeds       )
										A__					:    List[str]														= 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
										A__					:    Union[str, Any]														= 0.0

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

										A__					:    str														= (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										A__					:    Union[str, Any]														= torch.any(concept_scores > 0      ,			dim=1       )

										A__					:    int														= 0.0  # black image

										return images, has_nsfw_concepts






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





