






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase     (    _lowercase		):
							a    				=	(PNDMScheduler,)
							a    				=	(("""num_inference_steps""", 50),)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							**UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     int	  =	{
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														}

														config.update(**UpperCamelCase__					)
														return config




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Any=0      ,							**UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Dict	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Tuple	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	self.dummy_sample
														lowerCamelCase__ :     Union[str, Any]	  =	0.1 * sample
														lowerCamelCase__ :     Tuple	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Optional[int]	  =	self.get_scheduler_config(**UpperCamelCase__					)
																					lowerCamelCase__ :     Any	  =	scheduler_class(**UpperCamelCase__					)
																					scheduler.set_timesteps(UpperCamelCase__					)
																					# copy over dummy past residuals
																					lowerCamelCase__ :     int	  =	dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__					)
																												lowerCamelCase__ :     str	  =	scheduler_class.from_pretrained(UpperCamelCase__					)
																												new_scheduler.set_timesteps(UpperCamelCase__					)
																												# copy over dummy past residuals
																												lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     List[Any]	  =	new_scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     int	  =	new_scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"




							def 		lowerCamelCase_    (							self:    List[Any]					):
														pass




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Dict=0      ,							**UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Any	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	self.dummy_sample
														lowerCamelCase__ :     int	  =	0.1 * sample
														lowerCamelCase__ :     Optional[Any]	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     List[Any]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     List[str]	  =	scheduler_class(**UpperCamelCase__					)
																					scheduler.set_timesteps(UpperCamelCase__					)

																					# copy over dummy past residuals (must be after setting timesteps)
																					lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__					)
																												lowerCamelCase__ :     Tuple	  =	scheduler_class.from_pretrained(UpperCamelCase__					)
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(UpperCamelCase__					)

																												# copy over dummy past residual (must be after setting timesteps)
																												lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Union[str, Any]	  =	new_scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																					lowerCamelCase__ :     str	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Optional[Any]	  =	new_scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							**UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     int	  =	self.scheduler_classes[0]
														lowerCamelCase__ :     str	  =	self.get_scheduler_config(**UpperCamelCase__					)
														lowerCamelCase__ :     List[str]	  =	scheduler_class(**UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	10
														lowerCamelCase__ :     List[Any]	  =	self.dummy_model()
														lowerCamelCase__ :     List[str]	  =	self.dummy_sample_deter
														scheduler.set_timesteps(UpperCamelCase__					)

														for i, t in enumerate(scheduler.prk_timesteps					):
																					lowerCamelCase__ :     Union[str, Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														for i, t in enumerate(scheduler.plms_timesteps					):
																					lowerCamelCase__ :     List[Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     Optional[Any]	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														return sample




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	dict(self.forward_default_kwargs					)

														lowerCamelCase__ :     str	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Optional[int]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     str	  =	scheduler_class(**UpperCamelCase__					)

																					lowerCamelCase__ :     int	  =	self.dummy_sample
																					lowerCamelCase__ :     Tuple	  =	0.1 * sample

																					if num_inference_steps is not None and hasattr(UpperCamelCase__      ,							"""set_timesteps"""					):
																												scheduler.set_timesteps(UpperCamelCase__					)
																					elif num_inference_steps is not None and not hasattr(UpperCamelCase__      ,							"""set_timesteps"""					):
																												lowerCamelCase__ :     List[Any]	  =	num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					lowerCamelCase__ :     Dict	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																					lowerCamelCase__ :     Tuple	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							0      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     List[Any]	  =	scheduler.step_prk(UpperCamelCase__      ,							1      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

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

																					lowerCamelCase__ :     int	  =	scheduler.step_plms(UpperCamelCase__      ,							0      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Optional[int]	  =	scheduler.step_plms(UpperCamelCase__      ,							1      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

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




							def 		lowerCamelCase_    (							self:    Any					):
														for timesteps in [100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str					):
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	self.scheduler_classes[0]
														lowerCamelCase__ :     Optional[Any]	  =	self.get_scheduler_config(steps_offset=1					)
														lowerCamelCase__ :     Dict	  =	scheduler_class(**UpperCamelCase__					)
														scheduler.set_timesteps(10					)
														assert torch.equal(
														    scheduler.timesteps      ,							torch.LongTensor(
														        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)      ,							)




							def 		lowerCamelCase_    (							self:    int					):
														for beta_start, beta_end in zip([0.0_001, 0.001]      ,							[0.002, 0.02]					):
																					self.check_over_configs(beta_start=UpperCamelCase__      ,							beta_end=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict					):
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any					):
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														for t in [1, 5, 10]:
																					self.check_over_forward(time_step=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														for t, num_inference_steps in zip([1, 5, 10]      ,							[10, 50, 100]					):
																					self.check_over_forward(num_inference_steps=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict					):
														# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
														lowerCamelCase__ :     Any	  =	27

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Any	  =	self.dummy_sample
																					lowerCamelCase__ :     int	  =	0.1 * sample

																					lowerCamelCase__ :     str	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     Tuple	  =	scheduler_class(**UpperCamelCase__					)

																					scheduler.set_timesteps(UpperCamelCase__					)

																					# before power of 3 fix, would error on first step, so we only need to do two
																					for i, t in enumerate(scheduler.prk_timesteps[:2]					):
																												lowerCamelCase__ :     str	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample




							def 		lowerCamelCase_    (							self:    str					):
														with self.assertRaises(UpperCamelCase__					):
																					lowerCamelCase__ :     str	  =	self.scheduler_classes[0]
																					lowerCamelCase__ :     Optional[Any]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     List[Any]	  =	scheduler_class(**UpperCamelCase__					)

																					scheduler.step_plms(self.dummy_sample      ,							1      ,							self.dummy_sample					).prev_sample




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.full_loop()
														lowerCamelCase__ :     Optional[Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     int	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 198.1_318					) < 1e-2
														assert abs(result_mean.item() - 0.2_580					) < 1e-3




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     int	  =	self.full_loop(prediction_type="""v_prediction"""					)
														lowerCamelCase__ :     Tuple	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     Optional[int]	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 67.3_986					) < 1e-2
														assert abs(result_mean.item() - 0.0_878					) < 1e-3




							def 		lowerCamelCase_    (							self:    Tuple					):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__ :     str	  =	self.full_loop(set_alpha_to_one=UpperCamelCase__      ,							beta_start=0.01					)
														lowerCamelCase__ :     Union[str, Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     int	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 230.0_399					) < 1e-2
														assert abs(result_mean.item() - 0.2_995					) < 1e-3




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__ :     Dict	  =	self.full_loop(set_alpha_to_one=UpperCamelCase__      ,							beta_start=0.01					)
														lowerCamelCase__ :     List[Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     str	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 186.9_482					) < 1e-2
														assert abs(result_mean.item() - 0.2_434					) < 1e-3









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=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.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=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)

