


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


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


def    __lowerCamelCase   (      A__						: str						,						A__						: str					)     ->    Optional[Any]:
   lowerCamelCase_						:   Any		= RobertaPreLayerNormConfig.from_pretrained(
       A__						,						architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

   # convert state_dict
   lowerCamelCase_						:   Tuple		= torch.load(hf_hub_download(repo_id=A__						,						filename="""pytorch_model.bin"""					)					)
   lowerCamelCase_						:   Optional[Any]		= {}
   for tensor_key, tensor_value in original_state_dict.items():
      # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
      if tensor_key.startswith("""roberta."""					):
         lowerCamelCase_						:   Optional[int]		= """roberta_prelayernorm.""" + tensor_key[len("""roberta."""					) :]

      # The original implementation contains weights which are not used, remove them from the state_dict
      if tensor_key.endswith(""".self.LayerNorm.weight"""					) or tensor_key.endswith(""".self.LayerNorm.bias"""					):
         continue

      lowerCamelCase_						:   Dict		= tensor_value

   lowerCamelCase_						:   List[Any]		= RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=A__						,						config=A__						,						state_dict=A__					)
   model.save_pretrained(A__					)

   # convert tokenizer
   lowerCamelCase_						:   Dict		= AutoTokenizer.from_pretrained(A__					)
   tokenizer.save_pretrained(A__					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint-repo',
          default=None,
          type=str,
          required=True,
          help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      snake_case__				:	Tuple					       =					parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









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

from ...utils import logging


snake_case__				:	int					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							CLIPConfig

       _a   							=							["CLIPEncoderLayer"]



       def __init__(						self				:				Tuple		,				__a				:				CLIPConfig							)					->Union[str, Any]:
          super().__init__(__a							)

          lowerCamelCase_						:   List[Any]		= CLIPVisionModelWithProjection(config.vision_config							)

          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)
          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)




       @torch.no_grad()
       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				List[Any]		,				__a				:				Tuple		,				__a				:				int=0.5		,				__a				:				Optional[Any]=0.5							)					->Union[str, Any]:
          lowerCamelCase_						:   Dict		= self.vision_model(__a							)[0]

          lowerCamelCase_						:   str		= self.p_head(__a							)
          lowerCamelCase_						:   Union[str, Any]		= nsfw_detected.flatten()
          lowerCamelCase_						:   Tuple		= nsfw_detected > p_threshold
          lowerCamelCase_						:   Dict		= 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_:
                lowerCamelCase_						:   int		= np.zeros(images[idx].shape							)

          lowerCamelCase_						:   Union[str, Any]		= self.w_head(__a							)
          lowerCamelCase_						:   List[str]		= watermark_detected.flatten()
          lowerCamelCase_						:   Dict		= watermark_detected > w_threshold
          lowerCamelCase_						:   Optional[int]		= 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_:
                lowerCamelCase_						:   Tuple		= np.zeros(images[idx].shape							)

          return images, nsfw_detected, watermark_detected





