

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (PNDMScheduler,)
	SCREAMING_SNAKE_CASE__    				=     (('num_inference_steps', 50),)




	def       __A					(		self	:						Tuple		,	**lowerCAmelCase	:						int							):


		'''simple docstring'''







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

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						str		,	lowerCAmelCase	:						Optional[Any]=0		,	**lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dict(self.forward_default_kwargs							)
		UpperCAmelCase_       				=			kwargs.pop("num_inference_steps"		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			self.dummy_sample
		UpperCAmelCase_       				=			0.1 * sample
		UpperCAmelCase_       				=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

		for scheduler_class in self.scheduler_classes:
			UpperCAmelCase_       				=			self.get_scheduler_config(**lowerCAmelCase							)
			UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)
			scheduler.set_timesteps(lowerCAmelCase							)
			# copy over dummy past residuals
			UpperCAmelCase_       				=			dummy_past_residuals[:]

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

			UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			new_scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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

			UpperCAmelCase_       				=			scheduler.step_plms(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			new_scheduler.step_plms(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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




	def       __A					(		self	:						int							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Any=0		,	**lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dict(self.forward_default_kwargs							)
		UpperCAmelCase_       				=			kwargs.pop("num_inference_steps"		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			self.dummy_sample
		UpperCAmelCase_       				=			0.1 * sample
		UpperCAmelCase_       				=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

		for scheduler_class in self.scheduler_classes:
			UpperCAmelCase_       				=			self.get_scheduler_config()
			UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)
			scheduler.set_timesteps(lowerCAmelCase							)

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

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

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

			UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			new_scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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

			UpperCAmelCase_       				=			scheduler.step_plms(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			new_scheduler.step_plms(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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




	def       __A					(		self	:						Tuple		,	**lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			10
		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase							)

		for i, t in enumerate(scheduler.prk_timesteps							):
			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							).prev_sample

		for i, t in enumerate(scheduler.plms_timesteps							):
			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			scheduler.step_plms(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							).prev_sample

		return sample




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dict(self.forward_default_kwargs							)

		UpperCAmelCase_       				=			kwargs.pop("num_inference_steps"		,	lowerCAmelCase							)

		for scheduler_class in self.scheduler_classes:
			UpperCAmelCase_       				=			self.get_scheduler_config()
			UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

			UpperCAmelCase_       				=			self.dummy_sample
			UpperCAmelCase_       				=			0.1 * sample

			if num_inference_steps is not None and hasattr(lowerCAmelCase		,	"set_timesteps"							):
				scheduler.set_timesteps(lowerCAmelCase							)
			elif num_inference_steps is not None and not hasattr(lowerCAmelCase		,	"set_timesteps"							):
				UpperCAmelCase_       				=			num_inference_steps

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

			UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	0		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	1		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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

			UpperCAmelCase_       				=			scheduler.step_plms(lowerCAmelCase		,	0		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample
			UpperCAmelCase_       				=			scheduler.step_plms(lowerCAmelCase		,	1		,	lowerCAmelCase		,	**lowerCAmelCase							).prev_sample

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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		for timesteps in [100, 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(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       __A					(		self	:						Any							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.0_001, 0.001]		,	[0.002, 0.02]							):
			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	:						int							):


		'''simple docstring'''







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




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		for t in [1, 5, 10]:
			self.check_over_forward(time_step=lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]							):
			self.check_over_forward(num_inference_steps=lowerCAmelCase							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			27

		for scheduler_class in self.scheduler_classes:
			UpperCAmelCase_       				=			self.dummy_sample
			UpperCAmelCase_       				=			0.1 * sample

			UpperCAmelCase_       				=			self.get_scheduler_config()
			UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

			scheduler.set_timesteps(lowerCAmelCase							)

			# 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]							):
				UpperCAmelCase_       				=			scheduler.step_prk(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							).prev_sample




	def       __A					(		self	:						str							):


		'''simple docstring'''







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

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




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







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

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




	def       __A					(		self	:						List[str]							):


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




	def       __A					(		self	:						int							):


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




	def       __A					(		self	:						int							):


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