
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A( a			):
				snake_case_						   =    (CMStochasticIterativeScheduler,)
				snake_case_						   =    1_0




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 201,
								    '''sigma_min''': 0.002,
								    '''sigma_max''': 80.0,
								}

								config.update(**_snake_case				)
								return config




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    10

								__a										=    self.get_scheduler_config()
								__a										=    self.scheduler_classes[0](**_snake_case				)

								scheduler.set_timesteps(_snake_case				)

								__a										=    scheduler.timesteps[0]
								__a										=    scheduler.timesteps[1]

								__a										=    self.dummy_sample
								__a										=    0.1 * sample

								__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				).prev_sample
								__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case				).prev_sample

								self.assertEqual(output_a.shape		,     sample.shape				)
								self.assertEqual(output_a.shape		,     output_a.shape				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for clip_denoised in [True, False]:
												self.check_over_configs(clip_denoised=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    1
								scheduler.set_timesteps(_snake_case				)
								__a										=    scheduler.timesteps

								__a										=    torch.manual_seed(0				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(_snake_case				):
												# 1. scale model input
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												# 2. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 3. predict previous sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 192.7614				) < 1E-2
								assert abs(result_mean.item() - 0.2510				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [106, 0]
								scheduler.set_timesteps(timesteps=_snake_case				)
								__a										=    scheduler.timesteps

								__a										=    torch.manual_seed(0				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
												# 1. scale model input
												__a										=    scheduler.scale_model_input(_snake_case		,     _snake_case				)

												# 2. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 3. predict previous sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 347.6357				) < 1E-2
								assert abs(result_mean.item() - 0.4527				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [39, 30, 12, 15, 0]

								with self.assertRaises(_snake_case		,     msg='''`timesteps` must be in descending order.'''				):
												scheduler.set_timesteps(timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [39, 30, 12, 1, 0]
								__a										=    len(_snake_case				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [scheduler.config.num_train_timesteps]

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






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
