
"""simple docstring"""

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

from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Any				   =						CLIPConfig

      __SCREAMING_SNAKE_CASE :				str				   =						["CLIPEncoderLayer"]




      def __init__(				self ,							lowercase_				)    ->       Optional[Any]:
          super().__init__(lowercase_				)

          UpperCAmelCase          =       CLIPVisionModelWithProjection(config.vision_config				)

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




      @torch.no_grad()
      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=0.5 ,							lowercase_=0.5				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.vision_model(lowercase_				)[0]

          UpperCAmelCase          =       self.p_head(lowercase_				)
          UpperCAmelCase          =       nsfw_detected.flatten()
          UpperCAmelCase          =       nsfw_detected > p_threshold
          UpperCAmelCase          =       nsfw_detected.tolist()

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

          UpperCAmelCase          =       self.w_head(lowercase_				)
          UpperCAmelCase          =       watermark_detected.flatten()
          UpperCAmelCase          =       watermark_detected > w_threshold
          UpperCAmelCase          =       watermark_detected.tolist()

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

          return images, nsfw_detected, watermark_detected





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



