






'''simple docstring'''

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      int        =  (DPMSolverSinglestepScheduler,)
	SCREAMING_SNAKE_CASE_					:      Any        =  (('num_inference_steps', 2_5),)
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  Tuple:
							lowercase_      :   Union[str, Any]        =			{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							    'prediction_type': 'epsilon',
							    'thresholding': False,
							    'sample_max_value': 1.0,
							    'algorithm_type': 'dpmsolver++',
							    'solver_type': 'midpoint',
							    'lambda_min_clipped': -float('inf'       ),
							    'variance_type': None,
							}

							config.update(**_lowercase       )
							return config
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  Dict:
							lowercase_      :   Union[str, Any]        =			dict(self.forward_default_kwargs       )
							lowercase_      :   List[Any]        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   List[str]        =			self.dummy_sample
							lowercase_      :   Dict        =			0.1 * sample
							lowercase_      :   List[Any]        =			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													lowercase_      :   Optional[int]        =			self.get_scheduler_config(**_lowercase       )
													lowercase_      :   List[str]        =			scheduler_class(**_lowercase       )
													scheduler.set_timesteps(_lowercase       )
													# copy over dummy past residuals
													lowercase_      :   Any        =			dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowercase       )
																			lowercase_      :   int        =			scheduler_class.from_pretrained(_lowercase       )
																			new_scheduler.set_timesteps(_lowercase       )
																			# copy over dummy past residuals
																			lowercase_      :   Union[str, Any]        =			dummy_past_residuals[: new_scheduler.config.solver_order]

													lowercase_  ,      lowercase_      :   Any        =			sample, sample
													for t in range(_lowercase  ,      time_step + scheduler.config.solver_order + 1       ):
																			lowercase_      :   Optional[int]        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
																			lowercase_      :   List[Any]        =			new_scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

																			assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self       )						->  List[str]:
							pass
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  List[str]:
							lowercase_      :   int        =			dict(self.forward_default_kwargs       )
							lowercase_      :   int        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   Any        =			self.dummy_sample
							lowercase_      :   Optional[Any]        =			0.1 * sample
							lowercase_      :   Dict        =			[residual + 0.2, residual + 0.15, residual + 0.10]

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

													# copy over dummy past residuals (must be after setting timesteps)
													lowercase_      :   Dict        =			dummy_past_residuals[: scheduler.config.solver_order]

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

																			# copy over dummy past residual (must be after setting timesteps)
																			lowercase_      :   Tuple        =			dummy_past_residuals[: new_scheduler.config.solver_order]

													lowercase_      :   Dict        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Union[str, Any]        =			new_scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

													assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self  ,      _lowercase=None  ,      **_lowercase       )						->  Optional[Any]:
							if scheduler is None:
													lowercase_      :   Union[str, Any]        =			self.scheduler_classes[0]
													lowercase_      :   Optional[Any]        =			self.get_scheduler_config(**_lowercase       )
													lowercase_      :   str        =			scheduler_class(**_lowercase       )

							lowercase_      :   Optional[Any]        =			self.scheduler_classes[0]
							lowercase_      :   Optional[Any]        =			self.get_scheduler_config(**_lowercase       )
							lowercase_      :   int        =			scheduler_class(**_lowercase       )

							lowercase_      :   Tuple        =			10
							lowercase_      :   List[Any]        =			self.dummy_model()
							lowercase_      :   Any        =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowercase       )

							for i, t in enumerate(scheduler.timesteps       ):
													lowercase_      :   str        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   Dict        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							return sample
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Optional[Any]        =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
							lowercase_      :   Tuple        =			50
							lowercase_      :   int        =			self.dummy_model()
							lowercase_      :   List[str]        =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowercase       )

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]       ):
													lowercase_      :   int        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   Any        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							lowercase_      :   Any        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.25_74       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Dict:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							lowercase_      :   str        =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()       )
							lowercase_      :   List[str]        =			self.full_loop(scheduler=_lowercase       )
							lowercase_      :   Tuple        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3

							lowercase_      :   Optional[Any]        =			DEISMultistepScheduler.from_config(scheduler.config       )
							lowercase_      :   Tuple        =			DPMSolverMultistepScheduler.from_config(scheduler.config       )
							lowercase_      :   int        =			UniPCMultistepScheduler.from_config(scheduler.config       )
							lowercase_      :   Dict        =			DPMSolverSinglestepScheduler.from_config(scheduler.config       )

							lowercase_      :   Any        =			self.full_loop(scheduler=_lowercase       )
							lowercase_      :   Tuple        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Dict:
							self.check_over_configs(thresholding=_lowercase       )
							for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=_lowercase  ,      prediction_type=_lowercase  ,      sample_max_value=_lowercase  ,      algorithm_type='dpmsolver++'  ,      solver_order=_lowercase  ,      solver_type=_lowercase  ,      )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=_lowercase  ,      solver_type=_lowercase  ,      prediction_type=_lowercase  ,      algorithm_type=_lowercase  ,      )
																															lowercase_      :   Union[str, Any]        =			self.full_loop(
																															    solver_order=_lowercase  ,      solver_type=_lowercase  ,      prediction_type=_lowercase  ,      algorithm_type=_lowercase  ,      )
																															assert not torch.isnan(_lowercase       ).any(), "Samples have nan numbers"
	def        lowerCamelCase__		(							self       )						->  int:
							self.check_over_configs(lower_order_final=_lowercase       )
							self.check_over_configs(lower_order_final=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							self.check_over_configs(lambda_min_clipped=-float('inf'       )       )
							self.check_over_configs(lambda_min_clipped=-5.1       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							self.check_over_configs(variance_type=_lowercase       )
							self.check_over_configs(variance_type='learned_range'       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=_lowercase  ,      time_step=0       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Tuple        =			self.full_loop()
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.27_91       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Optional[Any]        =			self.full_loop(use_karras_sigmas=_lowercase       )
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.22_48       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   Union[str, Any]        =			self.full_loop(prediction_type='v_prediction'       )
							lowercase_      :   str        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.14_53       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Tuple        =			self.full_loop(prediction_type='v_prediction'  ,      use_karras_sigmas=_lowercase       )
							lowercase_      :   List[str]        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_mean.item() - 0.06_49       ) < 1E-3







	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Optional[int]        =			self.scheduler_classes[0]
							lowercase_      :   List[str]        =			self.get_scheduler_config(thresholding=_lowercase  ,      dynamic_thresholding_ratio=0       )
							lowercase_      :   Optional[int]        =			scheduler_class(**_lowercase       )

							lowercase_      :   str        =			10
							lowercase_      :   List[str]        =			self.dummy_model()
							lowercase_      :   int        =			self.dummy_sample_deter.half()
							scheduler.set_timesteps(_lowercase       )

							for i, t in enumerate(scheduler.timesteps       ):
													lowercase_      :   Optional[int]        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   int        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							assert sample.dtype == torch.floataa








'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A:      List[Any]  				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

A:      Union[str, Any]  				=				"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Union[str, Any]						,			a		:	List[Any]=8							)			->			Dict:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase_      :   List[str]        =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Dict=5_1_2						,			a		:	Optional[Any]=5_1_2							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   int        =			pil_image.resize((w, h)						,			resample=Image.BICUBIC						,			reducing_gap=1							)
						lowercase_      :   int        =			np.array(pil_image.convert('RGB'							)							)
						lowercase_      :   Optional[int]        =			arr.astype(np.floataa							) / 1_27.5 - 1
						lowercase_      :   Any        =			np.transpose(a						,			[2, 0, 1]							)
						lowercase_      :   Any        =			torch.from_numpy(a							).unsqueeze(0							)
						return image






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  List[Any]:
							super().__init__()

							self.register_modules(
							    unet=_lowercase  ,      scheduler=_lowercase  ,      movq=_lowercase  ,      )
							lowercase_      :   Dict        =			2 ** (len(self.movq.config.block_out_channels       ) - 1)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							# get the original timestep using init_timestep
							lowercase_      :   List[Any]        =			min(int(num_inference_steps * strength       )  ,      _lowercase       )

							lowercase_      :   Tuple        =			max(num_inference_steps - init_timestep  ,      0       )
							lowercase_      :   Optional[Any]        =			self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Any:
							if not isinstance(_lowercase  ,      (torch.Tensor, PIL.Image.Image, list)       ):
													raise ValueError(
													    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase       )}"       )

							lowercase_      :   Dict        =			image.to(device=_lowercase  ,      dtype=_lowercase       )

							lowercase_      :   Dict        =			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
													lowercase_      :   str        =			image

							else:
													if isinstance(_lowercase  ,      _lowercase       ) and len(_lowercase       ) != batch_size:
																			raise ValueError(
																			    f"You have passed a list of generators of length {len(_lowercase       )}, but requested an effective batch"
																			    f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

													elif isinstance(_lowercase  ,      _lowercase       ):
																			lowercase_      :   List[Any]        =			[
																			    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
																			]
																			lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
													else:
																			lowercase_      :   Union[str, Any]        =			self.movq.encode(_lowercase       ).latent_dist.sample(_lowercase       )

													lowercase_      :   str        =			self.movq.config.scaling_factor * init_latents

							lowercase_      :   int        =			torch.cat([init_latents]  ,      dim=0       )

							lowercase_      :   Dict        =			init_latents.shape
							lowercase_      :   Dict        =			randn_tensor(_lowercase  ,      generator=_lowercase  ,      device=_lowercase  ,      dtype=_lowercase       )

							# get latents
							lowercase_      :   List[str]        =			self.scheduler.add_noise(_lowercase  ,      _lowercase  ,      _lowercase       )

							lowercase_      :   Optional[Any]        =			init_latents

							return latents
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('Please install accelerate via `pip install accelerate`'       )

							lowercase_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

							lowercase_      :   Optional[Any]        =			[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(_lowercase  ,      _lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							if is_accelerate_available() and is_accelerate_version('>='  ,      '0.17.0.dev0'       ):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'       )

							lowercase_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

							if self.device.type != "cpu":
													self.to('cpu'  ,      silence_dtype_warnings=_lowercase       )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowercase_      :   Tuple        =			None
							for cpu_offloaded_model in [self.unet, self.movq]:
													lowercase_  ,      lowercase_      :   Dict        =			cpu_offload_with_hook(_lowercase  ,      _lowercase  ,      prev_module_hook=_lowercase       )

							# We'll offload the last model manually.
							lowercase_      :   List[str]        =			hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        lowerCamelCase__		(							self       )						->  List[str]:
							if not hasattr(self.unet  ,      '_hf_hook'       ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(_lowercase  ,      '_hf_hook'       )
													    and hasattr(module._hf_hook  ,      'execution_device'       )
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device       )
							return self.device







	@torch.no_grad()
	@replace_example_docstring(_lowercase       )
	def __call__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = 512  ,      _lowercase = 512  ,      _lowercase = 100  ,      _lowercase = 4.0  ,      _lowercase = 0.3  ,      _lowercase = 1  ,      _lowercase = None  ,      _lowercase = "pil"  ,      _lowercase = True  ,      )						->  str:
							lowercase_      :   List[Any]        =			self._execution_device

							lowercase_      :   List[Any]        =			guidance_scale > 1.0

							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
							lowercase_      :   Optional[Any]        =			image_embeds.shape[0]
							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   List[str]        =			torch.cat(_lowercase  ,      dim=0       )

							if do_classifier_free_guidance:
													lowercase_      :   List[str]        =			image_embeds.repeat_interleave(_lowercase  ,      dim=0       )
													lowercase_      :   Dict        =			negative_image_embeds.repeat_interleave(_lowercase  ,      dim=0       )

							lowercase_      :   Dict        =			torch.cat([negative_image_embeds, image_embeds]  ,      dim=0       ).to(dtype=self.unet.dtype  ,      device=_lowercase       )

							if not isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			[image]
							if not all(isinstance(_lowercase  ,      (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
													raise ValueError(
													    f"Input is in incorrect format: {[type(_lowercase       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"       )

							lowercase_      :   List[Any]        =			torch.cat([prepare_image(_lowercase  ,      _lowercase  ,      _lowercase       ) for i in image]  ,      dim=0       )
							lowercase_      :   Dict        =			image.to(dtype=image_embeds.dtype  ,      device=_lowercase       )

							lowercase_      :   Dict        =			self.movq.encode(_lowercase       )['latents']
							lowercase_      :   Optional[Any]        =			latents.repeat_interleave(_lowercase  ,      dim=0       )
							self.scheduler.set_timesteps(_lowercase  ,      device=_lowercase       )
							lowercase_  ,      lowercase_      :   str        =			self.get_timesteps(_lowercase  ,      _lowercase  ,      _lowercase       )
							lowercase_      :   int        =			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
							lowercase_  ,      lowercase_      :   Union[str, Any]        =			downscale_height_and_width(_lowercase  ,      _lowercase  ,      self.movq_scale_factor       )
							lowercase_      :   List[str]        =			self.prepare_latents(
							    _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      image_embeds.dtype  ,      _lowercase  ,      _lowercase       )
							for i, t in enumerate(self.progress_bar(_lowercase       )       ):
													# expand the latents if we are doing classifier free guidance
													lowercase_      :   int        =			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

													lowercase_      :   str        =			{'image_embeds': image_embeds}
													lowercase_      :   str        =			self.unet(
													    sample=_lowercase  ,      timestep=_lowercase  ,      encoder_hidden_states=_lowercase  ,      added_cond_kwargs=_lowercase  ,      return_dict=_lowercase  ,      )[0]

													if do_classifier_free_guidance:
																			lowercase_  ,      lowercase_      :   Dict        =			noise_pred.split(latents.shape[1]  ,      dim=1       )
																			lowercase_  ,      lowercase_      :   Optional[int]        =			noise_pred.chunk(2       )
																			lowercase_  ,      lowercase_      :   Tuple        =			variance_pred.chunk(2       )
																			lowercase_      :   List[str]        =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			lowercase_      :   Tuple        =			torch.cat([noise_pred, variance_pred_text]  ,      dim=1       )

													if not (
													    hasattr(self.scheduler.config  ,      'variance_type'       )
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																			lowercase_  ,      lowercase_      :   List[str]        =			noise_pred.split(latents.shape[1]  ,      dim=1       )

													# compute the previous noisy sample x_t -> x_t-1
													lowercase_      :   Dict        =			self.scheduler.step(
													    _lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase  ,      )[0]

							# post-processing
							lowercase_      :   Any        =			self.movq.decode(_lowercase  ,      force_not_quantize=_lowercase       )['sample']

							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"       )

							if output_type in ["np", "pil"]:
													lowercase_      :   Dict        =			image * 0.5 + 0.5
													lowercase_      :   Dict        =			image.clamp(0  ,      1       )
													lowercase_      :   int        =			image.cpu().permute(0  ,      2  ,      3  ,      1       ).float().numpy()

							if output_type == "pil":
													lowercase_      :   int        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )
