



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

from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Optional[int] )  ->Tuple:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		nn.functional.normalize(UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		nn.functional.normalize(UpperCamelCase )
			return torch.mm(UpperCamelCase      ,			normalized_text_embeds.t() )
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			CLIPConfig

			UpperCamelCase					:Optional[Any]     =			['''CLIPEncoderLayer''']





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)

						lowerCamelCase__ :      Union[str, Any]								=		CLIPVisionModel(config.vision_config				)
						lowerCamelCase__ :      Any								=		nn.Linear(config.vision_config.hidden_size							,    config.projection_dim							,    bias=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17							,    config.projection_dim				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		nn.Parameter(torch.ones(3							,    config.projection_dim				)							,    requires_grad=__magic_name__				)

						lowerCamelCase__ :      Any								=		nn.Parameter(torch.ones(17				)							,    requires_grad=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		nn.Parameter(torch.ones(3				)							,    requires_grad=__magic_name__				)





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      Tuple								=		self.visual_projection(__magic_name__				)

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

						lowerCamelCase__ :      int								=		[]
						lowerCamelCase__ :      str								=		image_embeds.shape[0]
						for i in range(__magic_name__				):
									lowerCamelCase__ :      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
									lowerCamelCase__ :      Optional[Any]								=		0.0

									for concept_idx in range(len(special_cos_dist[0]				)				):
												lowerCamelCase__ :      List[str]								=		special_cos_dist[i][concept_idx]
												lowerCamelCase__ :      Tuple								=		self.special_care_embeds_weights[concept_idx].item()
												lowerCamelCase__ :      str								=		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__ :      List[str]								=		0.01

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

									result.append(__magic_name__				)

						lowerCamelCase__ :      Tuple								=		[len(res["""bad_concepts"""]				) > 0 for res in result]

						return images, has_nsfw_concepts





			@torch.no_grad()
			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Union[str, Any]								=		self.vision_model(__magic_name__				)[1]  # pooled_output
						lowerCamelCase__ :      str								=		self.visual_projection(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		cosine_distance(__magic_name__							,    self.special_care_embeds				)
						lowerCamelCase__ :      Dict								=		cosine_distance(__magic_name__							,    self.concept_embeds				)

						# increase this value to create a stronger `nsfw` filter
						# at the cost of increasing the possibility of filtering benign images
						lowerCamelCase__ :      Any								=		0.0

						lowerCamelCase__ :      Dict								=		special_cos_dist - self.special_care_embeds_weights + adjustment
						# special_scores = special_scores.round(decimals=3)
						lowerCamelCase__ :      Union[str, Any]								=		torch.any(special_scores > 0							,    dim=1				)
						lowerCamelCase__ :      Optional[Any]								=		special_care * 0.01
						lowerCamelCase__ :      Dict								=		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__ :      Any								=		torch.any(concept_scores > 0							,    dim=1				)

						return images, has_nsfw_concepts








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



