



import flax.linen as nn
import jax
import jax.numpy as jnp
class 				__A					(nn.Module      ):
			UpperCamelCase					:int
			UpperCamelCase					:jnp.dtype     =			jnp.floataa





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		nn.Conv(
						    self.out_channels							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )





			def __call__(self							,    __magic_name__				):
						lowerCamelCase__ :      Optional[Any]								=		hidden_states.shape
						lowerCamelCase__ :      List[Any]								=		jax.image.resize(
						    lowerCamelCase__							,    shape=(batch, height * 2, width * 2, channels)							,    method="""nearest"""							,    )
						lowerCamelCase__ :      Any								=		self.conv(lowerCamelCase__				)
						return hidden_states







class 				__A					(nn.Module      ):
			UpperCamelCase					:int
			UpperCamelCase					:jnp.dtype     =			jnp.floataa





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		nn.Conv(
						    self.out_channels							,    kernel_size=(3, 3)							,    strides=(2, 2)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )





			def __call__(self							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		self.conv(lowerCamelCase__				)
						return hidden_states







class 				__A					(nn.Module      ):
			UpperCamelCase					:int
			UpperCamelCase					:int     =			None
			UpperCamelCase					:float     =			0.0
			UpperCamelCase					:bool     =			None
			UpperCamelCase					:jnp.dtype     =			jnp.floataa





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		self.in_channels if self.out_channels is None else self.out_channels

						lowerCamelCase__ :      Any								=		nn.GroupNorm(num_groups=32							,    epsilon=1E-5				)
						lowerCamelCase__ :      Tuple								=		nn.Conv(
						    lowerCamelCase__							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )

						lowerCamelCase__ :      Union[str, Any]								=		nn.Dense(lowerCamelCase__							,    dtype=self.dtype				)

						lowerCamelCase__ :      Optional[Any]								=		nn.GroupNorm(num_groups=32							,    epsilon=1E-5				)
						lowerCamelCase__ :      Tuple								=		nn.Dropout(self.dropout_prob				)
						lowerCamelCase__ :      List[str]								=		nn.Conv(
						    lowerCamelCase__							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )

						lowerCamelCase__ :      Optional[int]								=		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						lowerCamelCase__ :      Optional[Any]								=		None
						if use_nin_shortcut:
									lowerCamelCase__ :      List[str]								=		nn.Conv(
									    lowerCamelCase__							,    kernel_size=(1, 1)							,    strides=(1, 1)							,    padding="""VALID"""							,    dtype=self.dtype							,    )





			def __call__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__=True				):
						lowerCamelCase__ :      Optional[int]								=		hidden_states
						lowerCamelCase__ :      int								=		self.norma(lowerCamelCase__				)
						lowerCamelCase__ :      List[str]								=		nn.swish(lowerCamelCase__				)
						lowerCamelCase__ :      Tuple								=		self.conva(lowerCamelCase__				)

						lowerCamelCase__ :      List[str]								=		self.time_emb_proj(nn.swish(lowerCamelCase__				)				)
						lowerCamelCase__ :      str								=		jnp.expand_dims(jnp.expand_dims(lowerCamelCase__							,    1				)							,    1				)
						lowerCamelCase__ :      Tuple								=		hidden_states + temb

						lowerCamelCase__ :      Dict								=		self.norma(lowerCamelCase__				)
						lowerCamelCase__ :      int								=		nn.swish(lowerCamelCase__				)
						lowerCamelCase__ :      str								=		self.dropout(lowerCamelCase__							,    lowerCamelCase__				)
						lowerCamelCase__ :      int								=		self.conva(lowerCamelCase__				)

						if self.conv_shortcut is not None:
									lowerCamelCase__ :      str								=		self.conv_shortcut(lowerCamelCase__				)

						return hidden_states + residual








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



