
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__	:int										=					logging.get_logger(__name__)

lowercase__	:Optional[int]										=					{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[Any]       ='''table-transformer'''
							lowercase_ : Any       =['''past_key_values''']
							lowercase_ : Tuple       ={
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}




							def __init__(       self     ,A__=True     ,A__=None     ,A__=3     ,A__=1_0_0     ,A__=6     ,A__=2_0_4_8     ,A__=8     ,A__=6     ,A__=2_0_4_8     ,A__=8     ,A__=0.0     ,A__=0.0     ,A__=True     ,A__="relu"     ,A__=2_5_6     ,A__=0.1     ,A__=0.0     ,A__=0.0     ,A__=0.02     ,A__=1.0     ,A__=False     ,A__="sine"     ,A__="resnet50"     ,A__=True     ,A__=False     ,A__=1     ,A__=5     ,A__=2     ,A__=1     ,A__=1     ,A__=5     ,A__=2     ,A__=0.1     ,**A__     ,):
												if backbone_config is not None and use_timm_backbone:
																	raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

												if not use_timm_backbone:
																	if backbone_config is None:
																						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
																						lowercase					=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
																	elif isinstance(A__     ,A__):
																						lowercase					=		backbone_config.get('''model_type''')
																						lowercase					=		CONFIG_MAPPING[backbone_model_type]
																						lowercase					=		config_class.from_dict(A__)
																	# set timm attributes to None
																	lowercase			,		lowercase			,		lowercase					=		None, None, None

												lowercase					=		use_timm_backbone
												lowercase					=		backbone_config
												lowercase					=		num_channels
												lowercase					=		num_queries
												lowercase					=		d_model
												lowercase					=		encoder_ffn_dim
												lowercase					=		encoder_layers
												lowercase					=		encoder_attention_heads
												lowercase					=		decoder_ffn_dim
												lowercase					=		decoder_layers
												lowercase					=		decoder_attention_heads
												lowercase					=		dropout
												lowercase					=		attention_dropout
												lowercase					=		activation_dropout
												lowercase					=		activation_function
												lowercase					=		init_std
												lowercase					=		init_xavier_std
												lowercase					=		encoder_layerdrop
												lowercase					=		decoder_layerdrop
												lowercase					=		encoder_layers
												lowercase					=		auxiliary_loss
												lowercase					=		position_embedding_type
												lowercase					=		backbone
												lowercase					=		use_pretrained_backbone
												lowercase					=		dilation
												# Hungarian matcher
												lowercase					=		class_cost
												lowercase					=		bbox_cost
												lowercase					=		giou_cost
												# Loss coefficients
												lowercase					=		mask_loss_coefficient
												lowercase					=		dice_loss_coefficient
												lowercase					=		bbox_loss_coefficient
												lowercase					=		giou_loss_coefficient
												lowercase					=		eos_coefficient
												super().__init__(is_encoder_decoder=A__     ,**A__)




							@property
							def 				A__   (       self):
												return self.encoder_attention_heads





							@property
							def 				A__   (       self):
												return self.d_model







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : str       =version.parse('''1.11'''	)




							@property
							def 				A__   (       self):
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												        ('''pixel_mask''', {0: '''batch'''}),
												    ])




							@property
							def 				A__   (       self):
												return 1E-5





							@property
							def 				A__   (       self):
												return 1_2






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

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




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

												lowercase					=		CLIPVisionModelWithProjection(config.vision_config)

												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)
												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)





							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												lowercase					=		self.vision_model(A__)[0]

												lowercase					=		self.p_head(A__)
												lowercase					=		nsfw_detected.flatten()
												lowercase					=		nsfw_detected > p_threshold
												lowercase					=		nsfw_detected.tolist()

												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.''')

												for idx, nsfw_detected_ in enumerate(A__):
																	if nsfw_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												lowercase					=		self.w_head(A__)
												lowercase					=		watermark_detected.flatten()
												lowercase					=		watermark_detected > w_threshold
												lowercase					=		watermark_detected.tolist()

												if any(A__):
																	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(A__):
																	if watermark_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												return images, nsfw_detected, watermark_detected




