




"""simple docstring"""



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

from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						):
 A__	=    nn.functional.normalize(lowercase_						)
 A__	=    nn.functional.normalize(lowercase_						)
 return torch.mm(lowercase_      ,			normalized_text_embeds.t()						)






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				CLIPConfig

    lowercase__						 =				['''CLIPEncoderLayer''']




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	CLIPConfig				)				->							Tuple:




     '''simple docstring'''

     super().__init__(snake_case_				)

     A__	=    CLIPVisionModel(config.vision_config				)
     A__	=    nn.Linear(config.vision_config.hidden_size     ,			config.projection_dim     ,			bias=snake_case_				)

     A__	=    nn.Parameter(torch.ones(17     ,			config.projection_dim				)     ,			requires_grad=snake_case_				)
     A__	=    nn.Parameter(torch.ones(3     ,			config.projection_dim				)     ,			requires_grad=snake_case_				)

     A__	=    nn.Parameter(torch.ones(17				)     ,			requires_grad=snake_case_				)
     A__	=    nn.Parameter(torch.ones(3				)     ,			requires_grad=snake_case_				)




    @torch.no_grad()
    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.vision_model(snake_case_				)[1]  # pooled_output
     A__	=    self.visual_projection(snake_case_				)

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

     A__	=    []
     A__	=    image_embeds.shape[0]
     for i in range(snake_case_				):
      A__	=    {"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
      A__	=    0.0

      for concept_idx in range(len(special_cos_dist[0]				)				):
       A__	=    special_cos_dist[i][concept_idx]
       A__	=    self.special_care_embeds_weights[concept_idx].item()
       A__	=    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]}				)
        A__	=    0.01

      for concept_idx in range(len(cos_dist[0]				)				):
       A__	=    cos_dist[i][concept_idx]
       A__	=    self.concept_embeds_weights[concept_idx].item()
       A__	=    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_				)

     A__	=    [len(res["bad_concepts"]				) > 0 for res in result]

     return images, has_nsfw_concepts




    @torch.no_grad()
    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	torch.FloatTensor     ,			snake_case_						:	torch.FloatTensor				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.vision_model(snake_case_				)[1]  # pooled_output
     A__	=    self.visual_projection(snake_case_				)

     A__	=    cosine_distance(snake_case_     ,			self.special_care_embeds				)
     A__	=    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
     A__	=    0.0

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

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

     return images, has_nsfw_concepts











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





