
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :str   ,     lowercase      :TransformeraDModel   ,     lowercase      :AutoencoderKL   ,     lowercase      :KarrasDiffusionSchedulers   ,     lowercase      :Optional[Dict[int, str]] = None   ,     )		->      int:







				"""simple docstring"""






				super().__init__()
				self.register_modules(transformer=lowercase   ,     vae=lowercase   ,     scheduler=lowercase			)

				# create a imagenet -> id dictionary for easier use
				SCREAMING_SNAKE_CASE     						= {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(''','''			):
													SCREAMING_SNAKE_CASE     						= int(lowercase			)
							SCREAMING_SNAKE_CASE     						= dict(sorted(self.labels.items()			)			)







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :Union[str, List[str]]			)		->      List[int]:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= list(lowercase			)

				for l in label:
							if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""			)

				return [self.labels[l] for l in label]







	@torch.no_grad()
	def __call__(     self      :Union[str, Any]   ,     lowercase      :List[int]   ,     lowercase      :float = 4.0   ,     lowercase      :Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,     lowercase      :int = 5_0   ,     lowercase      :Optional[str] = "pil"   ,     lowercase      :bool = True   ,     )		->      Union[ImagePipelineOutput, Tuple]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= len(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.transformer.config.sample_size
				SCREAMING_SNAKE_CASE     						= self.transformer.config.in_channels

				SCREAMING_SNAKE_CASE     						= randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)   ,     generator=lowercase   ,     device=self.device   ,     dtype=self.transformer.dtype   ,     )
				SCREAMING_SNAKE_CASE     						= torch.cat([latents] * 2			) if guidance_scale > 1 else latents

				SCREAMING_SNAKE_CASE     						= torch.tensor(lowercase   ,     device=self.device			).reshape(-1			)
				SCREAMING_SNAKE_CASE     						= torch.tensor([1_0_0_0] * batch_size   ,     device=self.device			)
				SCREAMING_SNAKE_CASE     						= torch.cat([class_labels, class_null]   ,     0			) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowercase			)

				for t in self.progress_bar(self.scheduler.timesteps			):
							if guidance_scale > 1:
										SCREAMING_SNAKE_CASE     						= latent_model_input[: len(lowercase			) // 2]
										SCREAMING_SNAKE_CASE     						= torch.cat([half, half]   ,     dim=0			)
							SCREAMING_SNAKE_CASE     						= self.scheduler.scale_model_input(lowercase   ,     lowercase			)

							SCREAMING_SNAKE_CASE     						= t
							if not torch.is_tensor(lowercase			):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										SCREAMING_SNAKE_CASE     						= latent_model_input.device.type == '''mps'''
										if isinstance(lowercase   ,     lowercase			):
													SCREAMING_SNAKE_CASE     						= torch.floataa if is_mps else torch.floataa
										else:
													SCREAMING_SNAKE_CASE     						= torch.intaa if is_mps else torch.intaa
										SCREAMING_SNAKE_CASE     						= torch.tensor([timesteps]   ,     dtype=lowercase   ,     device=latent_model_input.device			)
							elif len(timesteps.shape			) == 0:
										SCREAMING_SNAKE_CASE     						= timesteps[None].to(latent_model_input.device			)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							SCREAMING_SNAKE_CASE     						= timesteps.expand(latent_model_input.shape[0]			)
							# predict noise model_output
							SCREAMING_SNAKE_CASE     						= self.transformer(
							    lowercase   ,     timestep=lowercase   ,     class_labels=lowercase			).sample

							# perform guidance
							if guidance_scale > 1:
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= torch.split(lowercase   ,     len(lowercase			) // 2   ,     dim=0			)

										SCREAMING_SNAKE_CASE     						= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										SCREAMING_SNAKE_CASE     						= torch.cat([half_eps, half_eps]   ,     dim=0			)

										SCREAMING_SNAKE_CASE     						= torch.cat([eps, rest]   ,     dim=1			)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= torch.split(lowercase   ,     lowercase   ,     dim=1			)
							else:
										SCREAMING_SNAKE_CASE     						= noise_pred

							# compute previous image: x_t -> x_t-1
							SCREAMING_SNAKE_CASE     						= self.scheduler.step(lowercase   ,     lowercase   ,     lowercase			).prev_sample

				if guidance_scale > 1:
							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= latent_model_input.chunk(2   ,     dim=0			)
				else:
							SCREAMING_SNAKE_CASE     						= latent_model_input

				SCREAMING_SNAKE_CASE     						= 1 / self.vae.config.scaling_factor * latents
				SCREAMING_SNAKE_CASE     						= self.vae.decode(lowercase			).sample

				SCREAMING_SNAKE_CASE     						= (samples / 2 + 0.5).clamp(0   ,     1			)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				SCREAMING_SNAKE_CASE     						= samples.cpu().permute(0   ,     2   ,     3   ,     1			).float().numpy()

				if output_type == "pil":
							SCREAMING_SNAKE_CASE     						= self.numpy_to_pil(lowercase			)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowercase			)

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

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







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

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