
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


lowercase__	:List[str]										=					{
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Optional[int]										=					[
							    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ErnieForCausalLM",
							    "ErnieForMaskedLM",
							    "ErnieForMultipleChoice",
							    "ErnieForNextSentencePrediction",
							    "ErnieForPreTraining",
							    "ErnieForQuestionAnswering",
							    "ErnieForSequenceClassification",
							    "ErnieForTokenClassification",
							    "ErnieModel",
							    "ErniePreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							lowercase__	:str										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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




