
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)

lowercase__	:int										=					{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : int       ='''biogpt'''




							def __init__(       self     ,A__=4_2_3_8_4     ,A__=1_0_2_4     ,A__=2_4     ,A__=1_6     ,A__=4_0_9_6     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=1_0_2_4     ,A__=0.02     ,A__=1E-12     ,A__=True     ,A__=True     ,A__=0.0     ,A__=0.0     ,A__=1     ,A__=0     ,A__=2     ,**A__     ,):
												lowercase					=		vocab_size
												lowercase					=		max_position_embeddings
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size
												lowercase					=		hidden_act
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		initializer_range
												lowercase					=		layer_norm_eps
												lowercase					=		scale_embedding
												lowercase					=		use_cache
												lowercase					=		layerdrop
												lowercase					=		activation_dropout
												super().__init__(pad_token_id=A__     ,bos_token_id=A__     ,eos_token_id=A__     ,**A__)






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

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




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

												lowercase					=		CLIPVisionModelWithProjection(config.vision_config)

												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)
												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)





							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												lowercase					=		self.vision_model(A__)[0]

												lowercase					=		self.p_head(A__)
												lowercase					=		nsfw_detected.flatten()
												lowercase					=		nsfw_detected > p_threshold
												lowercase					=		nsfw_detected.tolist()

												if any(A__):
																	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.''')

												for idx, nsfw_detected_ in enumerate(A__):
																	if nsfw_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												lowercase					=		self.w_head(A__)
												lowercase					=		watermark_detected.flatten()
												lowercase					=		watermark_detected > w_threshold
												lowercase					=		watermark_detected.tolist()

												if any(A__):
																	logger.warning(
																	    '''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
																	    ''' Try again with a different prompt and/or seed.''')

												for idx, watermark_detected_ in enumerate(A__):
																	if watermark_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												return images, nsfw_detected, watermark_detected




