

"""simple docstring"""





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

from ...utils import logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     CLIPConfig

 __UpperCAmelCase     : Optional[int]       		=     ["CLIPEncoderLayer"]


 def __init__(      self							:      List[Any], UpperCAmelCase__							:      CLIPConfig     ):
    super().__init__(UpperCAmelCase__     )

    __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  _lowercase			(      self							:      str, UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Optional[Any], UpperCAmelCase__							:      List[str]=0.5, UpperCAmelCase__							:      Any=0.5     ):
    __lowercase								=						self.vision_model(UpperCAmelCase__     )[0]

    __lowercase								=						self.p_head(UpperCAmelCase__     )
    __lowercase								=						nsfw_detected.flatten()
    __lowercase								=						nsfw_detected > p_threshold
    __lowercase								=						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_:
          __lowercase								=						np.zeros(images[idx].shape     )

    __lowercase								=						self.w_head(UpperCAmelCase__     )
    __lowercase								=						watermark_detected.flatten()
    __lowercase								=						watermark_detected > w_threshold
    __lowercase								=						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_:
          __lowercase								=						np.zeros(images[idx].shape     )

    return images, nsfw_detected, watermark_detected







"""simple docstring"""





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
   # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
   # setup instructions, if using on-demand hardware

   # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
   # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
   # Throw an error if user passes both BYO and on-demand cluster args
   # Otherwise, use default values
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--user', type=str, default='ubuntu')
   parser.add_argument('--host', type=str, default='localhost')
   parser.add_argument('--key_path', type=str, default=None)
   parser.add_argument('--instance', type=str, default='V100:1')
   parser.add_argument('--provider', type=str, default='cheapest')
   parser.add_argument('--use_spot', type=bool, default=False)
   parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
   _a				,						_a							=							parser.parse_known_args()
   if args.host != "localhost":
      if args.instance != "V100:1" or args.provider != "cheapest":
         raise ValueError('Cannot specify both BYO and on-demand cluster args')
      _a							=							rh.cluster(
          name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
      )
   else:
      _a							=							rh.cluster(
          name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
      )
   _a							=							args.example.rsplit('/', 1)[0]

   # Set up remote environment
   cluster.install_packages(['pip:./'])  # Installs transformers from local source
   # Note transformers is copied into the home directory on the remote machine, so we can install from there
   cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
   cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

   # Run example. You can bypass the CLI wrapper and paste your own code here.
   cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

   # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
   # from my_script... import train
   # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
   # launch_train_gpu = rh.function(fn=train,
   #                                system=gpu,
   #                                reqs=reqs,
   #                                name='train_bert_glue')
   #
   # We can pass in arguments just like we would to a function:
   # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
   #                  stream_logs=True)




