






'''simple docstring'''

import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

A:      int  				=				collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
A:      int  				=				"https://storage.googleapis.com/cvdf-datasets/mnist/"






def 	_UpperCAmelCase    (  a		:	List[Any]							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   str        =			numpy.dtype(numpy.uintaa							).newbyteorder('>'							)
						return numpy.frombuffer(bytestream.read(4							)						,			dtype=a							)[0]






@deprecated(a						,			'Please use tf.data to implement this functionality.'							)
def 	_UpperCAmelCase    (  a		:	List[str]							)			->			Optional[int]:





						"""simple docstring"""
						print('Extracting'						,			f.name							)
						with gzip.GzipFile(fileobj=a							) as bytestream:
												lowercase_      :   List[str]        =			_readaa(a							)
												if magic != 2_0_5_1:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)							)
												lowercase_      :   Optional[int]        =			_readaa(a							)
												lowercase_      :   Tuple        =			_readaa(a							)
												lowercase_      :   Optional[Any]        =			_readaa(a							)
												lowercase_      :   Tuple        =			bytestream.read(rows * cols * num_images							)
												lowercase_      :   Optional[int]        =			numpy.frombuffer(a						,			dtype=numpy.uinta							)
												lowercase_      :   Optional[Any]        =			data.reshape(a						,			a						,			a						,			1							)
												return data






@deprecated(a						,			'Please use tf.one_hot on tensors.'							)
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	List[str]							)			->			Any:





						"""simple docstring"""
						lowercase_      :   Dict        =			labels_dense.shape[0]
						lowercase_      :   List[Any]        =			numpy.arange(a							) * num_classes
						lowercase_      :   Union[str, Any]        =			numpy.zeros((num_labels, num_classes)							)
						lowercase_      :   List[str]        =			1
						return labels_one_hot






@deprecated(a						,			'Please use tf.data to implement this functionality.'							)
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	str=False						,			a		:	Tuple=1_0							)			->			str:





						"""simple docstring"""
						print('Extracting'						,			f.name							)
						with gzip.GzipFile(fileobj=a							) as bytestream:
												lowercase_      :   int        =			_readaa(a							)
												if magic != 2_0_4_9:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)							)
												lowercase_      :   Tuple        =			_readaa(a							)
												lowercase_      :   Union[str, Any]        =			bytestream.read(a							)
												lowercase_      :   int        =			numpy.frombuffer(a						,			dtype=numpy.uinta							)
												if one_hot:
																		return _dense_to_one_hot(a						,			a							)
												return labels






class 							__magic_name__     :







	"""simple docstring"""



	@deprecated(
	    _lowercase  ,      'Please use alternatives such as official/mnist/_DataSet.py'
	    ' from tensorflow/models.'  ,      )
	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=dtypes.floataa  ,      _lowercase=True  ,      _lowercase=None  ,      )						->  int:
							lowercase_      :   Union[str, Any]        =			random_seed.get_seed(_lowercase       )
							# If op level seed is not set, use whatever graph level seed is returned
							numpy.random.seed(seeda if seed is None else seeda       )
							lowercase_      :   Optional[int]        =			dtypes.as_dtype(_lowercase       ).base_dtype
							if dtype not in (dtypes.uinta, dtypes.floataa):
													raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype       )
							if fake_data:
													lowercase_      :   Optional[int]        =			1_0000
													lowercase_      :   int        =			one_hot
							else:
													assert (
													    images.shape[0] == labels.shape[0]
													), f"images.shape: {images.shape} labels.shape: {labels.shape}"
													lowercase_      :   Any        =			images.shape[0]

													# Convert shape from [num examples, rows, columns, depth]
													# to [num examples, rows*columns] (assuming depth == 1)
													if reshape:
																			assert images.shape[3] == 1
																			lowercase_      :   Optional[Any]        =			images.reshape(
																			    images.shape[0]  ,      images.shape[1] * images.shape[2]       )
													if dtype == dtypes.floataa:
																			# Convert from [0, 255] -> [0.0, 1.0].
																			lowercase_      :   Union[str, Any]        =			images.astype(numpy.floataa       )
																			lowercase_      :   str        =			numpy.multiply(_lowercase  ,      1.0 / 255.0       )
							lowercase_      :   Dict        =			images
							lowercase_      :   Union[str, Any]        =			labels
							lowercase_      :   int        =			0
							lowercase_      :   List[Any]        =			0
	@property
	def        lowerCamelCase__		(							self       )						->  Tuple:
							return self._images
	@property
	def        lowerCamelCase__		(							self       )						->  Tuple:
							return self._labels
	@property
	def        lowerCamelCase__		(							self       )						->  Dict:
							return self._num_examples
	@property
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							return self._epochs_completed







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=False  ,      _lowercase=True       )						->  int:
							if fake_data:
													lowercase_      :   str        =			[1] * 784
													lowercase_      :   Union[str, Any]        =			[1] + [0] * 9 if self.one_hot else 0
													return (
													    [fake_image for _ in range(_lowercase       )],
													    [fake_label for _ in range(_lowercase       )],
													)
							lowercase_      :   str        =			self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
													lowercase_      :   List[Any]        =			numpy.arange(self._num_examples       )
													numpy.random.shuffle(_lowercase       )
													lowercase_      :   Optional[Any]        =			self.images[perma]
													lowercase_      :   Optional[Any]        =			self.labels[perma]
							# Go to the next epoch
							if start + batch_size > self._num_examples:
													# Finished epoch
													self._epochs_completed += 1
													# Get the rest examples in this epoch
													lowercase_      :   List[str]        =			self._num_examples - start
													lowercase_      :   List[str]        =			self._images[start : self._num_examples]
													lowercase_      :   Union[str, Any]        =			self._labels[start : self._num_examples]
													# Shuffle the data
													if shuffle:
																			lowercase_      :   int        =			numpy.arange(self._num_examples       )
																			numpy.random.shuffle(_lowercase       )
																			lowercase_      :   List[str]        =			self.images[perm]
																			lowercase_      :   Dict        =			self.labels[perm]
													# Start next epoch
													lowercase_      :   List[str]        =			0
													lowercase_      :   int        =			batch_size - rest_num_examples
													lowercase_      :   Dict        =			self._index_in_epoch
													lowercase_      :   Union[str, Any]        =			self._images[start:end]
													lowercase_      :   List[Any]        =			self._labels[start:end]
													return (
													    numpy.concatenate((images_rest_part, images_new_part)  ,      axis=0       ),
													    numpy.concatenate((labels_rest_part, labels_new_part)  ,      axis=0       ),
													)
							else:
													self._index_in_epoch += batch_size
													lowercase_      :   Any        =			self._index_in_epoch
													return self._images[start:end], self._labels[start:end]






@deprecated(a						,			'Please write your own downloading logic.'							)
def 	_UpperCAmelCase    (  a		:	Dict						,			a		:	Any						,			a		:	str							)			->			List[str]:





						"""simple docstring"""
						if not gfile.Exists(a							):
												gfile.MakeDirs(a							)
						lowercase_      :   Tuple        =			os.path.join(a						,			a							)
						if not gfile.Exists(a							):
												urllib.request.urlretrieve(a						,			a							)  # noqa: S310
												with gfile.GFile(a							) as f:
																		lowercase_      :   str        =			f.size()
												print('Successfully downloaded'						,			a						,			a						,			'bytes.'							)
						return filepath






@deprecated(
    a						,			'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'							)
def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	int=False						,			a		:	Dict=False						,			a		:	List[Any]=dtypes.floataa						,			a		:	List[str]=True						,			a		:	Tuple=5_0_0_0						,			a		:	Any=None						,			a		:	Optional[int]=DEFAULT_SOURCE_URL						,			)			->			Optional[int]:





						"""simple docstring"""
						if fake_data:

												def fake():
																		return _DataSet(
																		    []						,			[]						,			fake_data=a						,			one_hot=a						,			dtype=a						,			seed=a							)

												lowercase_      :   Tuple        =			fake()
												lowercase_      :   Tuple        =			fake()
												lowercase_      :   Tuple        =			fake()
												return _Datasets(train=a						,			validation=a						,			test=a							)

						if not source_url:  # empty string check
												lowercase_      :   Dict        =			DEFAULT_SOURCE_URL

						lowercase_      :   List[Any]        =			'train-images-idx3-ubyte.gz'
						lowercase_      :   str        =			'train-labels-idx1-ubyte.gz'
						lowercase_      :   Optional[int]        =			't10k-images-idx3-ubyte.gz'
						lowercase_      :   Optional[Any]        =			't10k-labels-idx1-ubyte.gz'

						lowercase_      :   Tuple        =			_maybe_download(
						    a						,			a						,			source_url + train_images_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[int]        =			_extract_images(a							)

						lowercase_      :   Optional[int]        =			_maybe_download(
						    a						,			a						,			source_url + train_labels_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[Any]        =			_extract_labels(a						,			one_hot=a							)

						lowercase_      :   Dict        =			_maybe_download(
						    a						,			a						,			source_url + test_images_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[Any]        =			_extract_images(a							)

						lowercase_      :   List[str]        =			_maybe_download(
						    a						,			a						,			source_url + test_labels_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   int        =			_extract_labels(a						,			one_hot=a							)

						if not 0 <= validation_size <= len(a							):
												lowercase_      :   List[str]        =			(
												    'Validation size should be between 0 and '
												    f"{len(a							)}. Received: {validation_size}."
												)
												raise ValueError(a							)

						lowercase_      :   List[Any]        =			train_images[:validation_size]
						lowercase_      :   List[str]        =			train_labels[:validation_size]
						lowercase_      :   Any        =			train_images[validation_size:]
						lowercase_      :   Optional[int]        =			train_labels[validation_size:]

						lowercase_      :   Optional[int]        =			{'dtype': dtype, 'reshape': reshape, 'seed': seed}

						lowercase_      :   Optional[int]        =			_DataSet(a						,			a						,			**a							)
						lowercase_      :   List[Any]        =			_DataSet(a						,			a						,			**a							)
						lowercase_      :   Optional[Any]        =			_DataSet(a						,			a						,			**a							)

						return _Datasets(train=a						,			validation=a						,			test=a							)












'''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       )
