



from __future__ import annotations





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		order

           # a_{0} ... a_{k}
           lowerCamelCase								=		[1.0] + [0.0] * order
           # b_{0} ... b_{k}
           lowerCamelCase								=		[1.0] + [0.0] * order

           # x[n-1] ... x[n-k]
           lowerCamelCase								=		[0.0] * self.order
           # y[n-1] ... y[n-k]
           lowerCamelCase								=		[0.0] * self.order







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           if len(__a    ) < self.order:
                 lowerCamelCase								=		[1.0, *a_coeffs]

           if len(__a    ) != self.order + 1:
                 lowerCamelCase								=		(
                     F"""Expected a_coeffs to have {self.order + 1} elements """
                     F"""for {self.order}-order filter, got {len(__a    )}"""
                 )
                 raise ValueError(__a    )

           if len(__a    ) != self.order + 1:
                 lowerCamelCase								=		(
                     F"""Expected b_coeffs to have {self.order + 1} elements """
                     F"""for {self.order}-order filter, got {len(__a    )}"""
                 )
                 raise ValueError(__a    )

           lowerCamelCase								=		a_coeffs
           lowerCamelCase								=		b_coeffs







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		0.0

           # Start at index 1 and do index 0 at the end.
           for i in range(1				,			self.order + 1    ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

           lowerCamelCase								=		(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

           lowerCamelCase								=		self.input_history[:-1]
           lowerCamelCase								=		self.output_history[:-1]

           lowerCamelCase								=		sample
           lowerCamelCase								=		result

           return result




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

from ...utils import logging


a_						:     Tuple	     =		logging.get_logger(__name__)





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			CLIPConfig

     _A       =			['CLIPEncoderLayer']







     def __init__(self				,			__a    ):






           '''simple docstring'''



           super().__init__(__a    )

           lowerCamelCase								=		CLIPVisionModelWithProjection(config.vision_config    )

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







     @torch.no_grad()
     def 						_a      (self				,			__a				,			__a				,			__a=0.5				,			__a=0.5    ):






           '''simple docstring'''



           lowerCamelCase								=		self.vision_model(__a    )[0]

           lowerCamelCase								=		self.p_head(__a    )
           lowerCamelCase								=		nsfw_detected.flatten()
           lowerCamelCase								=		nsfw_detected > p_threshold
           lowerCamelCase								=		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								=		np.zeros(images[idx].shape    )

           lowerCamelCase								=		self.w_head(__a    )
           lowerCamelCase								=		watermark_detected.flatten()
           lowerCamelCase								=		watermark_detected > w_threshold
           lowerCamelCase								=		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								=		np.zeros(images[idx].shape    )

           return images, nsfw_detected, watermark_detected