
from collections import OrderedDict
from typing import Mapping

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


lowercase__	:str										=					logging.get_logger(__name__)

lowercase__	:List[Any]										=					{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : str       ='''distilbert'''
							lowercase_ : Union[str, Any]       ={
							    '''hidden_size''': '''dim''',
							    '''num_attention_heads''': '''n_heads''',
							    '''num_hidden_layers''': '''n_layers''',
							}




							def __init__(       self     ,A__=3_0_5_2_2     ,A__=5_1_2     ,A__=False     ,A__=6     ,A__=1_2     ,A__=7_6_8     ,A__=4 * 7_6_8     ,A__=0.1     ,A__=0.1     ,A__="gelu"     ,A__=0.02     ,A__=0.1     ,A__=0.2     ,A__=0     ,**A__     ,):
												lowercase					=		vocab_size
												lowercase					=		max_position_embeddings
												lowercase					=		sinusoidal_pos_embds
												lowercase					=		n_layers
												lowercase					=		n_heads
												lowercase					=		dim
												lowercase					=		hidden_dim
												lowercase					=		dropout
												lowercase					=		attention_dropout
												lowercase					=		activation
												lowercase					=		initializer_range
												lowercase					=		qa_dropout
												lowercase					=		seq_classif_dropout
												super().__init__(**A__     ,pad_token_id=A__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							@property
							def 				A__   (       self):
												if self.task == "multiple-choice":
																	lowercase					=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	lowercase					=		{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])






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




