
"""simple docstring"""


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

from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      SCREAMING_SNAKE_CASE_    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    CLIPConfig

						_lowerCamelCase:    Optional[int]      =    ['''CLIPEncoderLayer''']


						def __init__( self     :      List[str]  ,A_     :      Any      )     ->   List[Any]:
							super().__init__(UpperCamelCase__      )

							A						   =   CLIPVisionModelWithProjection(config.vision_config      )

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




						@torch.no_grad()
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Optional[Any]=0.5  ,A_     :      Optional[Any]=0.5      )     ->   Tuple:
							A						   =   self.vision_model(UpperCamelCase__      )[0]

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

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

							return images, nsfw_detected, watermark_detected

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')