






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

from ...utils import logging


UpperCamelCase        =				logging.get_logger(__name__)

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   Any        =			CLIPConfig

				_snake_case				:   List[Any]        =			["""CLIPEncoderLayer"""]







				def __init__( self     :Union[str, Any]  ,     lowerCamelCase__     :CLIPConfig     ):
					super().__init__(lowerCamelCase__     )

					UpperCamelCase__ :List[str]						=      CLIPVisionModelWithProjection(config.vision_config     )

					UpperCamelCase__ :Optional[int]						=      nn.Linear(config.vision_config.projection_dim  ,     1     )
					UpperCamelCase__ :Optional[Any]						=      nn.Linear(config.vision_config.projection_dim  ,     1     )







				@torch.no_grad()
				def 				__a   ( self     :Optional[Any]  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Optional[int]=0.5  ,     lowerCamelCase__     :Tuple=0.5     ):
					UpperCamelCase__ :Optional[int]						=      self.vision_model(lowerCamelCase__     )[0]

					UpperCamelCase__ :str						=      self.p_head(lowerCamelCase__     )
					UpperCamelCase__ :List[Any]						=      nsfw_detected.flatten()
					UpperCamelCase__ :Optional[int]						=      nsfw_detected > p_threshold
					UpperCamelCase__ :Dict						=      nsfw_detected.tolist()

					if any(lowerCamelCase__     ):
						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(lowerCamelCase__     ):
						if nsfw_detected_:
							UpperCamelCase__ :List[str]						=      np.zeros(images[idx].shape     )

					UpperCamelCase__ :Optional[Any]						=      self.w_head(lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      watermark_detected.flatten()
					UpperCamelCase__ :List[str]						=      watermark_detected > w_threshold
					UpperCamelCase__ :str						=      watermark_detected.tolist()

					if any(lowerCamelCase__     ):
						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(lowerCamelCase__     ):
						if watermark_detected_:
							UpperCamelCase__ :Any						=      np.zeros(images[idx].shape     )

					return images, nsfw_detected, watermark_detected


'''simple docstring'''

import os
from collections.abc import Iterator

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     for dir_path, dir_names, filenames in os.walk(lowercase__			):
          __lowercase      		=[d for d in dir_names if d != 'scripts' and d[0] not in '._']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(lowercase__			)[1] in (".py", ".ipynb"):
                    yield os.path.join(lowercase__,       lowercase__			).lstrip('./'			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     return F'''{i * "  "}*''' if i else "\n##"

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=old_path.split(os.sep			)
     for i, new_part in enumerate(new_path.split(os.sep			)			):
          if (i + 1 > len(lowercase__			) or old_parts[i] != new_part) and new_part:
               print(F'''{md_prefix(lowercase__			)} {new_part.replace("_",       " "			).title()}'''			)
     return new_path

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     __lowercase      		=''
     for filepath in sorted(good_file_paths(lowercase__			)			):
          __lowercase		,  __lowercase      		=os.path.split(lowercase__			)
          if filepath != old_path:
               __lowercase      		=print_path(lowercase__,       lowercase__			)
          __lowercase      		=(filepath.count(os.sep			) + 1) if filepath else 0
          __lowercase      		=F'''{filepath}/{filename}'''.replace(' ',       '%20'			)
          __lowercase      		=os.path.splitext(filename.replace('_',       ' '			).title()			)[0]
          print(F'''{md_prefix(lowercase__			)} [{filename}]({url})'''			)


if __name__ == "__main__":
       print_directory_md('''.''')


