

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (DDIMParallelScheduler,)
	SCREAMING_SNAKE_CASE__    				=     (('eta', 0.0), ('num_inference_steps', 50))




	def       __A					(		self	:						int		,	**lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_000,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		    "clip_sample": True,
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						Dict		,	**lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(**lowerCAmelCase							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			10, 0.0

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter

		scheduler.set_timesteps(lowerCAmelCase							)

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							).prev_sample

		return sample




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for timesteps in [100, 500, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(steps_offset=1							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)
		scheduler.set_timesteps(5							)
		assert torch.equal(scheduler.timesteps		,	torch.LongTensor([801, 601, 401, 201, 1]							)							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		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=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						int							):


		'''simple docstring'''







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		for rescale_betas_zero_snr in [True, False]:
			self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		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       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		for t, num_inference_steps in zip([1, 10, 50]		,	[10, 50, 500]							):
			self.check_over_forward(time_step=lowerCAmelCase		,	num_inference_steps=lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			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(420		,	400							) - 0.14_771							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(980		,	960							) - 0.32_460							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(0		,	0							) - 0.0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487		,	486							) - 0.00_979							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999		,	998							) - 0.02							)							) < 1e-5




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			10, 0.0
		scheduler.set_timesteps(lowerCAmelCase							)

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

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

		UpperCAmelCase_       				=			model(samples.flatten(0		,	1							)		,	timesteps.flatten(0		,	1							)							)
		UpperCAmelCase_       				=			scheduler.batch_step_no_noise(lowerCAmelCase		,	timesteps.flatten(0		,	1							)		,	samples.flatten(0		,	1							)		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		assert abs(result_sum.item() - 1_147.7_904							) < 1e-2
		assert abs(result_mean.item() - 0.4_982							) < 1e-3




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.full_loop()

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		assert abs(result_sum.item() - 172.0_067							) < 1e-2
		assert abs(result_mean.item() - 0.223_967							) < 1e-3




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.full_loop(prediction_type="v_prediction"							)

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		assert abs(result_sum.item() - 52.5_302							) < 1e-2
		assert abs(result_mean.item() - 0.0_684							) < 1e-3




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.full_loop(set_alpha_to_one=lowerCAmelCase		,	beta_start=0.01							)
		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		assert abs(result_sum.item() - 149.8_295							) < 1e-2
		assert abs(result_mean.item() - 0.1_951							) < 1e-3




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.full_loop(set_alpha_to_one=lowerCAmelCase		,	beta_start=0.01							)
		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		assert abs(result_sum.item() - 149.0_784							) < 1e-2
		assert abs(result_mean.item() - 0.1_941							) < 1e-3


import importlib

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



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



def       __lowerCAmelCase      (						A       ,     A=None       ,     A=None    ):
	if conf_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.yaml"
	UpperCAmelCase_       				=			load_config(A       ,     display=A    )
	UpperCAmelCase_       				=			VQModel(**config.model.params    )
	if ckpt_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.pt"
	UpperCAmelCase_       				=			torch.load(A       ,     map_location=A    )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_       				=			sd["state_dict"]
	model.load_state_dict(A       ,     strict=A    )
	model.to(A    )
	del sd
	return model



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



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



def       __lowerCAmelCase      (						A       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			instantiate_from_config(A    )
	if sd is not None:
		model.load_state_dict(A    )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}




def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     map_location="cpu"    )
		UpperCAmelCase_       				=			pl_sd["global_step"]
		print(F"loaded model from global step {global_step}."    )
	else:
		UpperCAmelCase_       				=			{"state_dict": None}
		UpperCAmelCase_       				=			None
	UpperCAmelCase_       				=			load_model_from_config(config.model       ,     pl_sd["state_dict"]       ,     gpu=A       ,     eval_mode=A    )["model"]
	return model, global_step