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

from ...utils import logging


_lowerCamelCase     =logging.get_logger(__name__)





class 				A__						(      a_):
      _UpperCAmelCase							:       List[str]       			=     CLIPConfig

      _UpperCAmelCase							:       int       			=     ["CLIPEncoderLayer"]
      def __init__( self       ,    __magic_name__ ):
       super().__init__(__magic_name__ )

       lowerCamelCase						:      Tuple							     = CLIPVisionModelWithProjection(config.vision_config )

       lowerCamelCase						:      Optional[Any]							     = nn.Linear(config.vision_config.projection_dim       ,    1 )
       lowerCamelCase						:      Tuple							     = nn.Linear(config.vision_config.projection_dim       ,    1 )






      @torch.no_grad()
      def 		UpperCamelCase__				( self       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__=0.5       ,    __magic_name__=0.5 ):
       lowerCamelCase						:      Optional[Any]							     = self.vision_model(__magic_name__ )[0]

       lowerCamelCase						:      Union[str, Any]							     = self.p_head(__magic_name__ )
       lowerCamelCase						:      Tuple							     = nsfw_detected.flatten()
       lowerCamelCase						:      Union[str, Any]							     = nsfw_detected > p_threshold
       lowerCamelCase						:      List[str]							     = nsfw_detected.tolist()

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

       lowerCamelCase						:      Union[str, Any]							     = self.w_head(__magic_name__ )
       lowerCamelCase						:      Optional[Any]							     = watermark_detected.flatten()
       lowerCamelCase						:      str							     = watermark_detected > w_threshold
       lowerCamelCase						:      str							     = watermark_detected.tolist()

       if any(__magic_name__ ):
        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(__magic_name__ ):
        if watermark_detected_:
         lowerCamelCase						:      Optional[int]							     = np.zeros(images[idx].shape )

       return images, nsfw_detected, watermark_detected








from typing import TYPE_CHECKING

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


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

