






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	(DDPMParallelScheduler,)


     def 				_A       (				self       :	Dict					, **UpperCAmelCase_       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    {
           "num_train_timesteps": 1000,
           "beta_start": 0.0_001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "variance_type": "fixed_small",
           "clip_sample": True,
       }

       config.update(**UpperCAmelCase_      )
       return config


     def 				_A       (				self       :	List[Any]      ):
       for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )


     def 				_A       (				self       :	Dict      ):
       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					, [0.002, 0.02, 0.2, 2]      ):
         self.check_over_configs(beta_start=UpperCAmelCase_					, beta_end=UpperCAmelCase_      )


     def 				_A       (				self       :	Any      ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[int]      ):
       for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=UpperCAmelCase_      )


     def 				_A       (				self       :	List[str]      ):
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=UpperCAmelCase_      )


     def 				_A       (				self       :	Any      ):
       self.check_over_configs(thresholding=UpperCAmelCase_      )
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(
               thresholding=UpperCAmelCase_					, prediction_type=UpperCAmelCase_					, sample_max_value=UpperCAmelCase_					, )


     def 				_A       (				self       :	Optional[Any]      ):
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=UpperCAmelCase_      )


     def 				_A       (				self       :	List[str]      ):
       for t in [0, 500, 999]:
         self.check_over_forward(time_step=UpperCAmelCase_      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler_class(**UpperCAmelCase_      )

       assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.00_979      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1E-5


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      int							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    len(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    self.dummy_model()
       SCREAMING_SNAKE_CASE       :      Any							=    self.dummy_sample_deter
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.dummy_sample_deter + 0.1
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.dummy_sample_deter - 0.1

       SCREAMING_SNAKE_CASE       :      List[str]							=    samplea.shape[0]
       SCREAMING_SNAKE_CASE       :      Any							=    torch.stack([samplea, samplea, samplea]					, dim=0      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.arange(UpperCAmelCase_      )[0:3, None].repeat(1					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(samples.flatten(0					, 1      )					, timesteps.flatten(0					, 1      )      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    scheduler.batch_step_no_noise(UpperCAmelCase_					, timesteps.flatten(0					, 1      )					, samples.flatten(0					, 1      )      )

       SCREAMING_SNAKE_CASE       :      Dict							=    torch.sum(torch.abs(UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      int							=    torch.mean(torch.abs(UpperCAmelCase_      )      )

       assert abs(result_sum.item() - 1_153.1_833      ) < 1E-2
       assert abs(result_mean.item() - 0.5_005      ) < 1E-3


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    len(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    self.dummy_model()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.dummy_sample_deter
       SCREAMING_SNAKE_CASE       :      int							=    torch.manual_seed(0      )

       for t in reversed(range(UpperCAmelCase_      )      ):
         # 1. predict noise residual
         SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, UpperCAmelCase_      )

         # 2. predict previous mean of sample x_t-1
         SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_      ).prev_sample

         SCREAMING_SNAKE_CASE       :      Any							=    pred_prev_sample

       SCREAMING_SNAKE_CASE       :      Any							=    torch.sum(torch.abs(UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.mean(torch.abs(UpperCAmelCase_      )      )

       assert abs(result_sum.item() - 258.9_606      ) < 1E-2
       assert abs(result_mean.item() - 0.3_372      ) < 1E-3


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.get_scheduler_config(prediction_type="v_prediction"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    len(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    self.dummy_model()
       SCREAMING_SNAKE_CASE       :      Any							=    self.dummy_sample_deter
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.manual_seed(0      )

       for t in reversed(range(UpperCAmelCase_      )      ):
         # 1. predict noise residual
         SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, UpperCAmelCase_      )

         # 2. predict previous mean of sample x_t-1
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_      ).prev_sample

         SCREAMING_SNAKE_CASE       :      str							=    pred_prev_sample

       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.sum(torch.abs(UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.mean(torch.abs(UpperCAmelCase_      )      )

       assert abs(result_sum.item() - 202.0_296      ) < 1E-2
       assert abs(result_mean.item() - 0.2_631      ) < 1E-3


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Dict							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      List[str]							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    [100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    scheduler.timesteps

       for i, timestep in enumerate(UpperCAmelCase_      ):
         if i == len(UpperCAmelCase_      ) - 1:
           SCREAMING_SNAKE_CASE       :      List[str]							=    -1
         else:
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    timesteps[i + 1]

         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    scheduler.previous_timestep(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    prev_t.item()

         self.assertEqual(UpperCAmelCase_					, UpperCAmelCase_      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Tuple							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    [100, 87, 50, 51, 0]

       with self.assertRaises(UpperCAmelCase_					, msg="`custom_timesteps` must be in descending order."      ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      str							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      Any							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    [100, 87, 50, 1, 0]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    len(UpperCAmelCase_      )

       with self.assertRaises(UpperCAmelCase_					, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."      ):
         scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_					, timesteps=UpperCAmelCase_      )






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE       :      int							=    self.get_scheduler_config()
       SCREAMING_SNAKE_CASE       :      str							=    scheduler_class(**UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCAmelCase_					, msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"					, ):
         scheduler.set_timesteps(timesteps=UpperCAmelCase_      )














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

from ...utils import logging


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	CLIPConfig

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


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

       SCREAMING_SNAKE_CASE       :      Any							=    CLIPVisionModelWithProjection(config.vision_config      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.Linear(config.vision_config.projection_dim					, 1      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.Linear(config.vision_config.projection_dim					, 1      )






     @torch.no_grad()
     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]=0.5					, UpperCAmelCase_       :	Optional[Any]=0.5      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.vision_model(UpperCAmelCase_      )[0]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.p_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected.flatten()
       SCREAMING_SNAKE_CASE       :      List[str]							=    nsfw_detected > p_threshold
       SCREAMING_SNAKE_CASE       :      List[Any]							=    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_:
           SCREAMING_SNAKE_CASE       :      Dict							=    np.zeros(images[idx].shape      )

       SCREAMING_SNAKE_CASE       :      Any							=    self.w_head(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    watermark_detected.flatten()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    watermark_detected > w_threshold
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    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_:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.zeros(images[idx].shape      )

       return images, nsfw_detected, watermark_detected






