






'''simple docstring'''







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       (DDIMParallelScheduler,)
		_UpperCamelCase						=       (('eta', 0.0), ('num_inference_steps', 50))






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								lowerCamelCase__								=	{
								    """num_train_timesteps""": 10_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """clip_sample""": True,
								}

								config.update(**_lowerCAmelCase							)
								return config






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config(**_lowerCAmelCase							)
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								lowerCamelCase__							,      lowerCamelCase__								=	10, 0.0

								lowerCamelCase__								=	self.dummy_model()
								lowerCamelCase__								=	self.dummy_sample_deter

								scheduler.set_timesteps(_lowerCAmelCase							)

								for t in scheduler.timesteps:
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							)
														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							).prev_sample

								return sample






		def       UpperCamelCase_       (   self							):
								for timesteps in [1_00, 5_00, 10_00]:
														self.check_over_configs(num_train_timesteps=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=_lowerCAmelCase							)

								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config(steps_offset=1							)
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)
								scheduler.set_timesteps(5							)
								assert torch.equal(scheduler.timesteps ,torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]							)							)






		def       UpperCamelCase_       (   self							):
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2]							):
														self.check_over_configs(beta_start=_lowerCAmelCase ,beta_end=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								self.check_over_configs(thresholding=_lowerCAmelCase							)
								for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(
																				    thresholding=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,sample_max_value=_lowerCAmelCase ,)






		def       UpperCamelCase_       (   self							):
								for t in [1, 10, 49]:
														self.check_over_forward(time_step=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for t, num_inference_steps in zip([1, 10, 50] ,[10, 50, 5_00]							):
														self.check_over_forward(time_step=_lowerCAmelCase ,num_inference_steps=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for t, eta in zip([1, 10, 49] ,[0.0, 0.5, 1.0]							):
														self.check_over_forward(time_step=_lowerCAmelCase ,eta=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config()
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								assert torch.sum(torch.abs(scheduler._get_variance(0 ,0							) - 0.0							)							) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_20 ,4_00							) - 0.1_4771							)							) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_80 ,9_60							) - 0.3_2460							)							) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0 ,0							) - 0.0							)							) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87 ,4_86							) - 0.0_0979							)							) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99 ,9_98							) - 0.02							)							) < 1E-5






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config()
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								lowerCamelCase__							,      lowerCamelCase__								=	10, 0.0
								scheduler.set_timesteps(_lowerCAmelCase							)

								lowerCamelCase__								=	self.dummy_model()
								lowerCamelCase__								=	self.dummy_sample_deter
								lowerCamelCase__								=	self.dummy_sample_deter + 0.1
								lowerCamelCase__								=	self.dummy_sample_deter - 0.1

								lowerCamelCase__								=	samplea.shape[0]
								lowerCamelCase__								=	torch.stack([samplea, samplea, samplea] ,dim=0							)
								lowerCamelCase__								=	torch.arange(_lowerCAmelCase							)[0:3, None].repeat(1 ,_lowerCAmelCase							)

								lowerCamelCase__								=	model(samples.flatten(0 ,1							) ,timesteps.flatten(0 ,1							)							)
								lowerCamelCase__								=	scheduler.batch_step_no_noise(_lowerCAmelCase ,timesteps.flatten(0 ,1							) ,samples.flatten(0 ,1							) ,_lowerCAmelCase							)

								lowerCamelCase__								=	torch.sum(torch.abs(_lowerCAmelCase							)							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_sum.item() - 1147.7904							) < 1E-2
								assert abs(result_mean.item() - 0.4982							) < 1E-3






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.full_loop()

								lowerCamelCase__								=	torch.sum(torch.abs(_lowerCAmelCase							)							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_sum.item() - 172.0067							) < 1E-2
								assert abs(result_mean.item() - 0.22_3967							) < 1E-3






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.full_loop(prediction_type="""v_prediction"""							)

								lowerCamelCase__								=	torch.sum(torch.abs(_lowerCAmelCase							)							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_sum.item() - 52.5302							) < 1E-2
								assert abs(result_mean.item() - 0.0684							) < 1E-3






		def       UpperCamelCase_       (   self							):
								# We specify different beta, so that the first alpha is 0.99
								lowerCamelCase__								=	self.full_loop(set_alpha_to_one=_lowerCAmelCase ,beta_start=0.01							)
								lowerCamelCase__								=	torch.sum(torch.abs(_lowerCAmelCase							)							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_sum.item() - 149.8295							) < 1E-2
								assert abs(result_mean.item() - 0.1951							) < 1E-3






		def       UpperCamelCase_       (   self							):
								# We specify different beta, so that the first alpha is 0.99
								lowerCamelCase__								=	self.full_loop(set_alpha_to_one=_lowerCAmelCase ,beta_start=0.01							)
								lowerCamelCase__								=	torch.sum(torch.abs(_lowerCAmelCase							)							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_sum.item() - 149.0784							) < 1E-2
								assert abs(result_mean.item() - 0.1941							) < 1E-3







import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=OmegaConf.load(lowerCamelCase )
				if display:
								print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None ):
				if conf_path is None:
								__magic_name__						:						List[str]										="""./model_checkpoints/vqgan_only.yaml"""
				__magic_name__						:						Dict										=load_config(lowerCamelCase						,  display=lowerCamelCase )
				__magic_name__						:						Tuple										=VQModel(**config.model.params )
				if ckpt_path is None:
								__magic_name__						:						Optional[Any]										="""./model_checkpoints/vqgan_only.pt"""
				__magic_name__						:						Tuple										=torch.load(lowerCamelCase						,  map_location=lowerCamelCase )
				if ".ckpt" in ckpt_path:
								__magic_name__						:						Any										=sd["""state_dict"""]
				model.load_state_dict(lowerCamelCase						,  strict=lowerCamelCase )
				model.to(lowerCamelCase )
				del sd
				return model




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=model.encode(lowerCamelCase )
				print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
				__magic_name__						:						List[Any]										=model.decode(lowerCamelCase )
				return xrec




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__					,			__magic_name__						:						Optional[int]										=string.rsplit("""."""						,  1 )
				if reload:
								__magic_name__						:						Optional[int]										=importlib.import_module(lowerCamelCase )
								importlib.reload(lowerCamelCase )
				return getattr(importlib.import_module(lowerCamelCase						,  package=lowerCamelCase )						,  cls )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if "target" not in config:
								raise KeyError("""Expected key `target` to instantiate.""" )
				return get_obj_from_str(config["""target"""] )(**config.get("""params"""						,  {} ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True						,  lowerCamelCase=True ):
				__magic_name__						:						str										=instantiate_from_config(lowerCamelCase )
				if sd is not None:
								model.load_state_dict(lowerCamelCase )
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# load the specified checkpoint
				if ckpt:
								__magic_name__						:						str										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
								__magic_name__						:						Any										=pl_sd["""global_step"""]
								print(F"loaded model from global step {global_step}." )
				else:
								__magic_name__						:						List[Any]										={"""state_dict""": None}
								__magic_name__						:						Optional[Any]										=None
				__magic_name__						:						Tuple										=load_model_from_config(config.model						,  pl_sd["""state_dict"""]						,  gpu=lowerCamelCase						,  eval_mode=lowerCamelCase )["""model"""]
				return model, global_step




