


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


SCREAMING_SNAKE_CASE       :		Any         =				logging.get_logger(__name__)

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






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	Optional[Any]									="""biogpt"""
   def __init__(     self   ,							UpperCamelCase_=4_2384   ,							UpperCamelCase_=1024   ,							UpperCamelCase_=24   ,							UpperCamelCase_=16   ,							UpperCamelCase_=4096   ,							UpperCamelCase_="gelu"   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=1024   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=1E-12   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=1   ,							UpperCamelCase_=0   ,							UpperCamelCase_=2   ,							**UpperCamelCase_   ,							):
        lowercase_       :List[Any]     =       vocab_size
        lowercase_       :Tuple     =       max_position_embeddings
        lowercase_       :Union[str, Any]     =       hidden_size
        lowercase_       :List[Any]     =       num_hidden_layers
        lowercase_       :Tuple     =       num_attention_heads
        lowercase_       :Tuple     =       intermediate_size
        lowercase_       :Union[str, Any]     =       hidden_act
        lowercase_       :int     =       hidden_dropout_prob
        lowercase_       :int     =       attention_probs_dropout_prob
        lowercase_       :Optional[Any]     =       initializer_range
        lowercase_       :Optional[int]     =       layer_norm_eps
        lowercase_       :Tuple     =       scale_embedding
        lowercase_       :Optional[int]     =       use_cache
        lowercase_       :List[str]     =       layerdrop
        lowercase_       :Optional[Any]     =       activation_dropout
        super().__init__(pad_token_id=UpperCamelCase_   ,							bos_token_id=UpperCamelCase_   ,							eos_token_id=UpperCamelCase_   ,							**UpperCamelCase_		)






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

from ...utils import logging


SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=CLIPConfig

   lowercase					:	Tuple									=["""CLIPEncoderLayer"""]
   def __init__(     self   ,							UpperCamelCase_		):
        super().__init__(UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       CLIPVisionModelWithProjection(config.vision_config		)

        lowercase_       :str     =       nn.Linear(config.vision_config.projection_dim   ,							1		)
        lowercase_       :Optional[int]     =       nn.Linear(config.vision_config.projection_dim   ,							1		)




   @torch.no_grad()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=0.5   ,							UpperCamelCase_=0.5		):
        lowercase_       :Optional[int]     =       self.vision_model(UpperCamelCase_		)[0]

        lowercase_       :Optional[int]     =       self.p_head(UpperCamelCase_		)
        lowercase_       :List[Any]     =       nsfw_detected.flatten()
        lowercase_       :List[str]     =       nsfw_detected > p_threshold
        lowercase_       :Optional[int]     =       nsfw_detected.tolist()

        if any(UpperCamelCase_		):
             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(UpperCamelCase_		):
             if nsfw_detected_:
                  lowercase_       :str     =       np.zeros(images[idx].shape		)

        lowercase_       :int     =       self.w_head(UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       watermark_detected.flatten()
        lowercase_       :int     =       watermark_detected > w_threshold
        lowercase_       :int     =       watermark_detected.tolist()

        if any(UpperCamelCase_		):
             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(UpperCamelCase_		):
             if watermark_detected_:
                  lowercase_       :Dict     =       np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected

