




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

from ...utils import logging


a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    nn.functional.normalize(lowercase_   )
    __UpperCamelCase			=    nn.functional.normalize(lowercase_   )
    return torch.mm(lowercase_       ,  normalized_text_embeds.t()   )


class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       CLIPConfig

      lowerCAmelCase__      :			Dict     =       ["CLIPEncoderLayer"]







      def __init__(  self       :			List[str]			,							snake_case       :			CLIPConfig					):
          super().__init__(snake_case					)

          __UpperCamelCase			=    CLIPVisionModel(config.vision_config					)
          __UpperCamelCase			=    nn.Linear(config.vision_config.hidden_size			,							config.projection_dim			,							bias=snake_case					)

          __UpperCamelCase			=    nn.Parameter(torch.ones(17			,							config.projection_dim					)			,							requires_grad=snake_case					)
          __UpperCamelCase			=    nn.Parameter(torch.ones(3			,							config.projection_dim					)			,							requires_grad=snake_case					)

          __UpperCamelCase			=    nn.Parameter(torch.ones(17					)			,							requires_grad=snake_case					)
          __UpperCamelCase			=    nn.Parameter(torch.ones(3					)			,							requires_grad=snake_case					)







      @torch.no_grad()
      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Tuple			,							snake_case       :			List[str]					):
          __UpperCamelCase			=    self.vision_model(snake_case					)[1]  # pooled_output
          __UpperCamelCase			=    self.visual_projection(snake_case					)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          __UpperCamelCase			=    cosine_distance(snake_case			,							self.special_care_embeds					).cpu().float().numpy()
          __UpperCamelCase			=    cosine_distance(snake_case			,							self.concept_embeds					).cpu().float().numpy()

          __UpperCamelCase			=    []
          __UpperCamelCase			=    image_embeds.shape[0]
          for i in range(snake_case					):
              __UpperCamelCase			=    {'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

              # increase this value to create a stronger `nfsw` filter
              # at the cost of increasing the possibility of filtering benign images
              __UpperCamelCase			=    0.0

              for concept_idx in range(len(special_cos_dist[0]					)					):
                  __UpperCamelCase			=    special_cos_dist[i][concept_idx]
                  __UpperCamelCase			=    self.special_care_embeds_weights[concept_idx].item()
                  __UpperCamelCase			=    round(concept_cos - concept_threshold + adjustment			,							3					)
                  if result_img["special_scores"][concept_idx] > 0:
                      result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}					)
                      __UpperCamelCase			=    0.01

              for concept_idx in range(len(cos_dist[0]					)					):
                  __UpperCamelCase			=    cos_dist[i][concept_idx]
                  __UpperCamelCase			=    self.concept_embeds_weights[concept_idx].item()
                  __UpperCamelCase			=    round(concept_cos - concept_threshold + adjustment			,							3					)
                  if result_img["concept_scores"][concept_idx] > 0:
                      result_img["bad_concepts"].append(snake_case					)

              result.append(snake_case					)

          __UpperCamelCase			=    [len(res['''bad_concepts''']					) > 0 for res in result]

          for idx, has_nsfw_concept in enumerate(snake_case					):
              if has_nsfw_concept:
                  if torch.is_tensor(snake_case					) or torch.is_tensor(images[0]					):
                      __UpperCamelCase			=    torch.zeros_like(images[idx]					)  # black image
                  else:
                      __UpperCamelCase			=    np.zeros(images[idx].shape					)  # black image

          if any(snake_case					):
              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.'''					)

          return images, has_nsfw_concepts







      @torch.no_grad()
      def    snake_case						(  self       :			int			,							snake_case       :			torch.FloatTensor			,							snake_case       :			torch.FloatTensor					):
          __UpperCamelCase			=    self.vision_model(snake_case					)[1]  # pooled_output
          __UpperCamelCase			=    self.visual_projection(snake_case					)

          __UpperCamelCase			=    cosine_distance(snake_case			,							self.special_care_embeds					)
          __UpperCamelCase			=    cosine_distance(snake_case			,							self.concept_embeds					)

          # increase this value to create a stronger `nsfw` filter
          # at the cost of increasing the possibility of filtering benign images
          __UpperCamelCase			=    0.0

          __UpperCamelCase			=    special_cos_dist - self.special_care_embeds_weights + adjustment
          # special_scores = special_scores.round(decimals=3)
          __UpperCamelCase			=    torch.any(special_scores > 0			,							dim=1					)
          __UpperCamelCase			=    special_care * 0.01
          __UpperCamelCase			=    special_adjustment.unsqueeze(1					).expand(-1			,							cos_dist.shape[1]					)

          __UpperCamelCase			=    (cos_dist - self.concept_embeds_weights) + special_adjustment
          # concept_scores = concept_scores.round(decimals=3)
          __UpperCamelCase			=    torch.any(concept_scores > 0			,							dim=1					)

          __UpperCamelCase			=    0.0  # black image

          return images, has_nsfw_concepts








"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_  					= {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DetrForObjectDetection',
      'DetrForSegmentation',
      'DetrModel',
      'DetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_detr import DetrFeatureExtractor
    from .image_processing_detr import DetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_detr import (
        DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetrForObjectDetection,
        DetrForSegmentation,
        DetrModel,
        DetrPreTrainedModel,
    )

else:
  import sys

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

