


"""simple docstring"""







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=1024  ,						lowerCAmelCase_=1024  ,						lowerCAmelCase_=False  ,						**lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    AutoTokenizer.from_pretrained(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    SeqaSeqDataset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						type_path="train"  ,						**lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    tok.pad_token_id

					def get_lens(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE						       =    tqdm(
										    DataLoader(lowerCAmelCase_  ,						batch_size=512  ,						num_workers=8  ,						shuffle=lowerCAmelCase_  ,						collate_fn=ds.collate_fn       )  ,						desc=str(ds.len_file       )  ,						)
										__SCREAMING_SNAKE_CASE						       =    []
										for batch in dl:
															__SCREAMING_SNAKE_CASE						       =    batch["input_ids"].ne(lowerCAmelCase_       ).sum(1       ).tolist()
															__SCREAMING_SNAKE_CASE						       =    batch["labels"].ne(lowerCAmelCase_       ).sum(1       ).tolist()
															if consider_target:
																				for src, tgt in zip(lowerCAmelCase_  ,						lowerCAmelCase_       ):
																									max_lens.append(max(lowerCAmelCase_  ,						lowerCAmelCase_       )       )
															else:
																				max_lens.extend(lowerCAmelCase_       )
										return max_lens

					__SCREAMING_SNAKE_CASE						       =    get_lens(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    SeqaSeqDataset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						type_path="val"  ,						**lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    get_lens(lowerCAmelCase_       )
					pickle_save(lowerCAmelCase_  ,						train_ds.len_file       )
					pickle_save(lowerCAmelCase_  ,						val_ds.len_file       )


if __name__ == "__main__":
		fire.Fire(save_len_file)




"""simple docstring"""







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

from ...utils import logging


a__    :							Optional[Any]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  int    =      CLIPConfig

	snake_case__    :  str    =      ["CLIPEncoderLayer"]




	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    CLIPConfig       )	->     Dict:
						super().__init__(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    CLIPVisionModelWithProjection(config.vision_config       )

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




	@torch.no_grad()
	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int=0.5      ,      UpperCAmelCase__      :    Optional[int]=0.5       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.vision_model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    self.p_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected > p_threshold
						__SCREAMING_SNAKE_CASE						       =    nsfw_detected.tolist()

						if any(UpperCAmelCase__       ):
											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(UpperCAmelCase__       ):
											if nsfw_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						__SCREAMING_SNAKE_CASE						       =    self.w_head(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.flatten()
						__SCREAMING_SNAKE_CASE						       =    watermark_detected > w_threshold
						__SCREAMING_SNAKE_CASE						       =    watermark_detected.tolist()

						if any(UpperCAmelCase__       ):
											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(UpperCAmelCase__       ):
											if watermark_detected_:
																__SCREAMING_SNAKE_CASE						       =    np.zeros(images[idx].shape       )

						return images, nsfw_detected, watermark_detected
