from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase_  : Any								=					8







def       A__   (						snake_case_			:		Union[str, Any]							,					snake_case_			:		int=BITS   ):
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   x.device

 SCREAMING_SNAKE_CASE__: Tuple=   (x * 255).int().clamp(0							,					255   )

 SCREAMING_SNAKE_CASE__: Any=   2 ** torch.arange(bits - 1							,					-1							,					-1							,					device=A__   )
 SCREAMING_SNAKE_CASE__: Any=   rearrange(A__							,					'''d -> d 1 1'''   )
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   rearrange(A__							,					'''b c h w -> b c 1 h w'''   )

 SCREAMING_SNAKE_CASE__: Optional[Any]=   ((x & mask) != 0).float()
 SCREAMING_SNAKE_CASE__: Optional[int]=   rearrange(A__							,					'''b c d h w -> b (c d) h w'''   )
 SCREAMING_SNAKE_CASE__: List[str]=   bits * 2 - 1
 return bits







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		Optional[int]=BITS   ):
 SCREAMING_SNAKE_CASE__: List[str]=   x.device

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   (x > 0).int()
 SCREAMING_SNAKE_CASE__: Tuple=   2 ** torch.arange(bits - 1							,					-1							,					-1							,					device=A__							,					dtype=torch.intaa   )

 SCREAMING_SNAKE_CASE__: Optional[int]=   rearrange(A__							,					'''d -> d 1 1'''   )
 SCREAMING_SNAKE_CASE__: str=   rearrange(A__							,					'''b (c d) h w -> b c d h w'''							,					d=8   )
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   reduce(x * mask							,					'''b c d h w -> b c h w'''							,					'''sum'''   )
 return (dec / 255).clamp(0.0							,					1.0   )







def       A__   (						self			:		Optional[Any]							,					snake_case_			:		torch.FloatTensor							,					snake_case_			:		int							,					snake_case_			:		torch.FloatTensor							,					snake_case_			:		float = 0.0							,					snake_case_			:		bool = True							,					snake_case_			:		List[Any]=None							,					snake_case_			:		bool = True							,					):
 if self.num_inference_steps is None:
  raise ValueError(
      '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

 # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
 # Ideally, read DDIM paper in-detail understanding

 # Notation (<variable name> -> <name in paper>
 # - pred_noise_t -> e_theta(x_t, t)
 # - pred_original_sample -> f_theta(x_t, t) or x_0
 # - std_dev_t -> sigma_t
 # - eta -> η
 # - pred_sample_direction -> "direction pointing to x_t"
 # - pred_prev_sample -> "x_t-1"

 # 1. get previous step value (=t-1)
 SCREAMING_SNAKE_CASE__: Optional[Any]=   timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 SCREAMING_SNAKE_CASE__: Dict=   self.alphas_cumprod[timestep]
 SCREAMING_SNAKE_CASE__: List[str]=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   1 - alpha_prod_t

 # 3. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 SCREAMING_SNAKE_CASE__: Optional[int]=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 SCREAMING_SNAKE_CASE__: List[Any]=   self.bit_scale
 if self.config.clip_sample:
  SCREAMING_SNAKE_CASE__: List[Any]=   torch.clamp(A__							,					-scale							,					A__   )

 # 5. compute variance: "sigma_t(η)" -> see formula (16)
 # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
 SCREAMING_SNAKE_CASE__: Optional[int]=   self._get_variance(A__							,					A__   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  SCREAMING_SNAKE_CASE__: Any=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

 # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 SCREAMING_SNAKE_CASE__: Tuple=   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

 # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
 SCREAMING_SNAKE_CASE__: Optional[int]=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

 if eta > 0:
  # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
  SCREAMING_SNAKE_CASE__: int=   model_output.device if torch.is_tensor(A__   ) else """cpu"""
  SCREAMING_SNAKE_CASE__: Optional[Any]=   torch.randn(model_output.shape							,					dtype=model_output.dtype							,					generator=A__   ).to(A__   )
  SCREAMING_SNAKE_CASE__: Any=   self._get_variance(A__							,					A__   ) ** 0.5 * eta * noise

  SCREAMING_SNAKE_CASE__: Optional[int]=   prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=A__							,					pred_original_sample=A__   )







def       A__   (						self			:		Any							,					snake_case_			:		torch.FloatTensor							,					snake_case_			:		int							,					snake_case_			:		torch.FloatTensor							,					snake_case_			:		List[Any]="epsilon"							,					snake_case_			:		Any=None							,					snake_case_			:		bool = True							,					):
 SCREAMING_SNAKE_CASE__: Optional[int]=   timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  SCREAMING_SNAKE_CASE__: str=   torch.split(A__							,					sample.shape[1]							,					dim=1   )
 else:
  SCREAMING_SNAKE_CASE__: Tuple=   None

 # 1. compute alphas, betas
 SCREAMING_SNAKE_CASE__: Tuple=   self.alphas_cumprod[t]
 SCREAMING_SNAKE_CASE__: Tuple=   self.alphas_cumprod[t - 1] if t > 0 else self.one
 SCREAMING_SNAKE_CASE__: Optional[int]=   1 - alpha_prod_t
 SCREAMING_SNAKE_CASE__: Optional[int]=   1 - alpha_prod_t_prev

 # 2. compute predicted original sample from predicted noise also called
 # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
 if prediction_type == "epsilon":
  SCREAMING_SNAKE_CASE__: Tuple=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   model_output
 else:
  raise ValueError(F'Unsupported prediction_type {prediction_type}.'   )

 # 3. Clip "predicted x_0"
 SCREAMING_SNAKE_CASE__: Tuple=   self.bit_scale
 if self.config.clip_sample:
  SCREAMING_SNAKE_CASE__: Tuple=   torch.clamp(A__							,					-scale							,					A__   )

 # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 SCREAMING_SNAKE_CASE__: Any=   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 SCREAMING_SNAKE_CASE__: Optional[int]=   self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

 # 5. Compute predicted previous sample µ_t
 # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

 # 6. Add noise
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   0
 if t > 0:
  SCREAMING_SNAKE_CASE__: str=   torch.randn(
      model_output.size()							,					dtype=model_output.dtype							,					layout=model_output.layout							,					generator=A__   ).to(model_output.device   )
  SCREAMING_SNAKE_CASE__: Dict=   (self._get_variance(A__							,					predicted_variance=A__   ) ** 0.5) * noise

 SCREAMING_SNAKE_CASE__: Optional[int]=   pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=A__							,					pred_original_sample=A__   )






class 		_lowerCamelCase		( a__     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = 1.0			,			)							->       Any:
     super().__init__()
     SCREAMING_SNAKE_CASE__: str=   bit_scale
     SCREAMING_SNAKE_CASE__: Any=   (
         ddim_bit_scheduler_step if isinstance(__a			,			__a ) else ddpm_bit_scheduler_step
     )

     self.register_modules(unet=__a			,			scheduler=__a )



    @torch.no_grad()
    def __call__(	self			,			lowerCAmelCase = 256			,			lowerCAmelCase = 256			,			lowerCAmelCase = 50			,			lowerCAmelCase = None			,			lowerCAmelCase = 1			,			lowerCAmelCase = "pil"			,			lowerCAmelCase = True			,			**lowerCAmelCase			,			)							->       Union[Tuple, ImagePipelineOutput]:
     SCREAMING_SNAKE_CASE__: List[str]=   torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)			,			generator=__a			,			)
     SCREAMING_SNAKE_CASE__: List[str]=   decimal_to_bits(__a ) * self.bit_scale
     SCREAMING_SNAKE_CASE__: Any=   latents.to(self.device )

     self.scheduler.set_timesteps(__a )

     for t in self.progress_bar(self.scheduler.timesteps ):
      # predict the noise residual
      SCREAMING_SNAKE_CASE__: Any=   self.unet(__a			,			__a ).sample

      # compute the previous noisy sample x_t -> x_t-1
      SCREAMING_SNAKE_CASE__: Any=   self.scheduler.step(__a			,			__a			,			__a ).prev_sample

     SCREAMING_SNAKE_CASE__: List[str]=   bits_to_decimal(__a )

     if output_type == "pil":
      SCREAMING_SNAKE_CASE__: List[str]=   self.numpy_to_pil(__a )

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=__a )





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





