


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :		Tuple         =				{
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Any         =				["LongformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		List[Any]         =				[
       "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LongformerForMaskedLM",
       "LongformerForMultipleChoice",
       "LongformerForQuestionAnswering",
       "LongformerForSequenceClassification",
       "LongformerForTokenClassification",
       "LongformerModel",
       "LongformerPreTrainedModel",
       "LongformerSelfAttention",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		List[Any]         =				[
       "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLongformerForMaskedLM",
       "TFLongformerForMultipleChoice",
       "TFLongformerForQuestionAnswering",
       "TFLongformerForSequenceClassification",
       "TFLongformerForTokenClassification",
       "TFLongformerModel",
       "TFLongformerPreTrainedModel",
       "TFLongformerSelfAttention",
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				_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


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

