
import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      (DPMSolverSDEScheduler,)
	a     :Dict       	=      10
	def 		_lowercase    (			self    :     Dict			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Dict:
					lowercase_	    =   {
					    '''num_train_timesteps''': 1_1_0_0,
					    '''beta_start''': 0.00_01,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''noise_sampler_seed''': 0,
					}

					config.update(**SCREAMING_SNAKE_CASE_			)
					return config
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	str:
					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,				[0.00_02, 0.0_02, 0.02]			):
									self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,				beta_end=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	str:
					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for i, t in enumerate(scheduler.timesteps			):
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75			) < 1e-2
									assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06			) < 1e-2
									assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62			) < 1e-2
									assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26			) < 1e-3
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(prediction_type='''v_prediction'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for i, t in enumerate(scheduler.timesteps			):
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53			) < 1e-2
									assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03			) < 1e-2
									assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25			) < 1e-2
									assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21			) < 1e-3
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			,				device=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38			) < 1e-2
									assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12			) < 1e-2
									assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62			) < 1e-2
									assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26			) < 1e-3




	def 		_lowercase    (			self    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			,				use_karras_sigmas=SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			,				device=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for t in scheduler.timesteps:
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2
					else:
									assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2









'''simple docstring'''





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Optional[Any]       			=					1.5
  __lowerCamelCase			:			Tuple       			=					int(factor * num_class_images			)
  __lowerCamelCase			:			Tuple       			=					ClipClient(
      url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			)

  os.makedirs(F'{class_data_dir}/images'			,exist_ok=_lowerCAmelCase			)
  if len(list(Path(F'{class_data_dir}/images'			).iterdir()			)			) >= num_class_images:
    return

  while True:
    __lowerCamelCase			:			Dict       			=					client.query(text=_lowerCAmelCase			)
    if len(_lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
      break
    else:
      __lowerCamelCase			:			Union[str, Any]       			=					int(factor * num_images			)
      __lowerCamelCase			:			List[str]       			=					ClipClient(
          url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			,)

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  with open(F'{class_data_dir}/caption.txt'			,'w'			) as fa, open(F'{class_data_dir}/urls.txt'			,'w'			) as fa, open(
      F'{class_data_dir}/images.txt'			,'w'			) as fa:
    while total < num_class_images:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			str       			=					Image.open(BytesIO(img.content			)			)
          with open(F'{class_data_dir}/images/{total}.jpg'			,'wb'			) as f:
            f.write(img.content			)
          fa.write(images['caption'] + '\n'			)
          fa.write(images['url'] + '\n'			)
          fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n'			)
          total += 1
          pbar.update(1			)
        else:
          continue
      except Exception:
        continue
  return

def 			a_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


if __name__ == "__main__":
   _UpperCamelCase											=		parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






