"""simple docstring"""

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """shi-labs/oneformer_ade20k_swin_tiny""": (
        """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}






class 		_lowercase (						_SCREAMING_SNAKE_CASE      ):



						"""simple docstring"""




						lowercase__       =  '''oneformer'''
						lowercase__       =  {'''hidden_size''': '''hidden_dim'''}





						def __init__(				self							:    List[str]     , UpperCamelCase__							:    Tuple = None     , UpperCamelCase__							:    Optional[Any] = 255     , UpperCamelCase__							:    Dict = 150     , UpperCamelCase__							:    Dict = 0.1     , UpperCamelCase__							:    Dict = 2.0     , UpperCamelCase__							:    Any = 5.0     , UpperCamelCase__							:    int = 5.0     , UpperCamelCase__							:    Any = 0.5     , UpperCamelCase__							:    Optional[Any] = 0.07     , UpperCamelCase__							:    List[Any] = 12544     , UpperCamelCase__							:    Tuple = 3.0     , UpperCamelCase__							:    Tuple = 0.75     , UpperCamelCase__							:    Optional[Any] = 0.02     , UpperCamelCase__							:    List[str] = 1.0     , UpperCamelCase__							:    Optional[int] = 1E-05     , UpperCamelCase__							:    str = False     , UpperCamelCase__							:    int = True     , UpperCamelCase__							:    Union[str, Any] = True     , UpperCamelCase__							:    Dict = [4, 8, 16, 32]     , UpperCamelCase__							:    Optional[int] = 77     , UpperCamelCase__							:    List[str] = 256     , UpperCamelCase__							:    str = 77     , UpperCamelCase__							:    Any = 6     , UpperCamelCase__							:    List[Any] = 49408     , UpperCamelCase__							:    Tuple = 2     , UpperCamelCase__							:    Optional[int] = 16     , UpperCamelCase__							:    Optional[int] = 256     , UpperCamelCase__							:    Any = 256     , UpperCamelCase__							:    Any = 256     , UpperCamelCase__							:    Tuple = 1024     , UpperCamelCase__							:    Tuple = "GN"     , UpperCamelCase__							:    str = 6     , UpperCamelCase__							:    str = 10     , UpperCamelCase__							:    Union[str, Any] = True     , UpperCamelCase__							:    Dict = 8     , UpperCamelCase__							:    str = 0.1     , UpperCamelCase__							:    Optional[Any] = 2048     , UpperCamelCase__							:    Dict = False     , UpperCamelCase__							:    Dict = False     , UpperCamelCase__							:    int = 2     , UpperCamelCase__							:    Optional[int] = 4     , **UpperCamelCase__							:    Optional[Any]     , )				->				Optional[int]:
													'''simple docstring'''



													if backbone_config is None:
																				logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''				)
																				__UpperCamelCase							=CONFIG_MAPPING['''swin'''](
																				    image_size=224     , in_channels=3     , patch_size=4     , embed_dim=96     , depths=[2, 2, 6, 2]     , num_heads=[3, 6, 12, 24]     , window_size=7     , drop_path_rate=0.3     , use_absolute_embeddings=__A     , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     , )
													elif isinstance(__A     , __A				):
																				__UpperCamelCase							=backbone_config.get('''model_type'''				)
																				__UpperCamelCase							=CONFIG_MAPPING[backbone_model_type]
																				__UpperCamelCase							=config_class.from_dict(__A				)

													__UpperCamelCase							=backbone_config

													__UpperCamelCase							=ignore_value
													__UpperCamelCase							=num_queries
													__UpperCamelCase							=no_object_weight
													__UpperCamelCase							=class_weight
													__UpperCamelCase							=mask_weight
													__UpperCamelCase							=dice_weight
													__UpperCamelCase							=contrastive_weight
													__UpperCamelCase							=contrastive_temperature
													__UpperCamelCase							=train_num_points
													__UpperCamelCase							=oversample_ratio
													__UpperCamelCase							=importance_sample_ratio
													__UpperCamelCase							=init_std
													__UpperCamelCase							=init_xavier_std
													__UpperCamelCase							=layer_norm_eps
													__UpperCamelCase							=is_training
													__UpperCamelCase							=use_auxiliary_loss
													__UpperCamelCase							=output_auxiliary_logits
													__UpperCamelCase							=strides
													__UpperCamelCase							=task_seq_len
													__UpperCamelCase							=text_encoder_width
													__UpperCamelCase							=text_encoder_context_length
													__UpperCamelCase							=text_encoder_num_layers
													__UpperCamelCase							=text_encoder_vocab_size
													__UpperCamelCase							=text_encoder_proj_layers
													__UpperCamelCase							=text_encoder_n_ctx
													__UpperCamelCase							=conv_dim
													__UpperCamelCase							=mask_dim
													__UpperCamelCase							=hidden_dim
													__UpperCamelCase							=encoder_feedforward_dim
													__UpperCamelCase							=norm
													__UpperCamelCase							=encoder_layers
													__UpperCamelCase							=decoder_layers
													__UpperCamelCase							=use_task_norm
													__UpperCamelCase							=num_attention_heads
													__UpperCamelCase							=dropout
													__UpperCamelCase							=dim_feedforward
													__UpperCamelCase							=pre_norm
													__UpperCamelCase							=enforce_input_proj
													__UpperCamelCase							=query_dec_layers
													__UpperCamelCase							=common_stride
													__UpperCamelCase							=decoder_layers

													super().__init__(**__A				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Dict[str, any]:
													'''simple docstring'''



													__UpperCamelCase							=copy.deepcopy(self.__dict__				)
													__UpperCamelCase							=self.backbone_config.to_dict()
													__UpperCamelCase							=self.__class__.model_type
													return output







"""simple docstring"""







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

from ...utils import logging


lowerCamelCase_  :  Tuple     		=    logging.get_logger(__name__)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=nn.functional.normalize(lowercase						)
    a											=nn.functional.normalize(lowercase						)
    return torch.mm(lowercase			,       normalized_text_embeds.t()						)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    CLIPConfig

 __lowerCAmelCase					   =    ["CLIPEncoderLayer"]


 def __init__(						self					,       __A		)   ->	int:
     super().__init__(__A		)

     a											=CLIPVisionModel(config.vision_config		)
     a											=nn.Linear(config.vision_config.hidden_size					,       config.projection_dim					,       bias=__A		)

     a											=nn.Parameter(torch.ones(17					,       config.projection_dim		)					,       requires_grad=__A		)
     a											=nn.Parameter(torch.ones(3					,       config.projection_dim		)					,       requires_grad=__A		)

     a											=nn.Parameter(torch.ones(17		)					,       requires_grad=__A		)
     a											=nn.Parameter(torch.ones(3		)					,       requires_grad=__A		)


 @torch.no_grad()
 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	Union[str, Any]:
     a											=self.vision_model(__A		)[1]  # pooled_output
     a											=self.visual_projection(__A		)

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

     a											=[]
     a											=image_embeds.shape[0]
     for i in range(__A		):
         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(__A		)

         result.append(__A		)

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

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

     if any(__A		):
         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  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.vision_model(__A		)[1]  # pooled_output
     a											=self.visual_projection(__A		)

     a											=cosine_distance(__A					,       self.special_care_embeds		)
     a											=cosine_distance(__A					,       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		)

     a											=0.0  # black image

     return images, has_nsfw_concepts