





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

from ...utils import logging


UpperCAmelCase_					:   Optional[int] 							=						logging.get_logger(__name__)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  CLIPConfig

  __UpperCamelCase	     =  ["""CLIPEncoderLayer"""]







  def __init__(	self    ,						_lowercase						):
       super().__init__(_lowercase						)

       lowerCAmelCase_					:       List[Any]    =		CLIPVisionModelWithProjection(config.vision_config						)

       lowerCAmelCase_					:       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    ,						_lowercase    ,						_lowercase    ,						_lowercase=0.5    ,						_lowercase=0.5						):
       lowerCAmelCase_					:       Union[str, Any]    =		self.vision_model(_lowercase						)[0]

       lowerCAmelCase_					:       List[str]    =		self.p_head(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		nsfw_detected.flatten()
       lowerCAmelCase_					:       List[Any]    =		nsfw_detected > p_threshold
       lowerCAmelCase_					:       Optional[int]    =		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_:
                 lowerCAmelCase_					:       List[Any]    =		np.zeros(images[idx].shape						)

       lowerCAmelCase_					:       List[str]    =		self.w_head(_lowercase						)
       lowerCAmelCase_					:       str    =		watermark_detected.flatten()
       lowerCAmelCase_					:       Any    =		watermark_detected > w_threshold
       lowerCAmelCase_					:       Union[str, Any]    =		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_:
                 lowerCAmelCase_					:       Optional[Any]    =		np.zeros(images[idx].shape						)

       return images, nsfw_detected, watermark_detected







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Tuple 							=						{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Tuple 							=						["""VivitImageProcessor"""]


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Any 							=						[
          """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VivitModel""",
          """VivitPreTrainedModel""",
          """VivitForVideoClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vivit import VivitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vivit import (
                VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VivitForVideoClassification,
                VivitModel,
                VivitPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Dict 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
