






'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
						import torch

						from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase="resnet50"  ,      _lowercase=3  ,      _lowercase=32  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=True  ,      )						->  List[str]:
							lowercase_      :   int        =			parent
							lowercase_      :   List[Any]        =			out_indices if out_indices is not None else [4]
							lowercase_      :   List[Any]        =			stage_names
							lowercase_      :   Any        =			out_features
							lowercase_      :   Optional[int]        =			backbone
							lowercase_      :   Optional[int]        =			batch_size
							lowercase_      :   Any        =			image_size
							lowercase_      :   Any        =			num_channels
							lowercase_      :   Optional[int]        =			use_pretrained_backbone
							lowercase_      :   Optional[int]        =			is_training
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   List[Any]        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
							lowercase_      :   Union[str, Any]        =			self.get_config()

							return config, pixel_values
	def        lowerCamelCase__		(							self       )						->  str:
							return TimmBackboneConfig(
							    image_size=self.image_size  ,      num_channels=self.num_channels  ,      out_features=self.out_features  ,      out_indices=self.out_indices  ,      stage_names=self.stage_names  ,      use_pretrained_backbone=self.use_pretrained_backbone  ,      backbone=self.backbone  ,      )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Any:
							lowercase_      :   int        =			TimmBackbone(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							with torch.no_grad():
													lowercase_      :   Dict        =			model(_lowercase       )
							self.parent.assertEqual(
							    result.feature_map[-1].shape  ,      (self.batch_size, model.channels[-1], 14, 14)  ,      )







	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Tuple        =			self.prepare_config_and_inputs()
							lowercase_      :   int        =			config_and_inputs
							lowercase_      :   Optional[Any]        =			{"pixel_values": pixel_values}
							return config, inputs_dict






@require_torch
@require_timm
class 							__magic_name__     (	lowercase__,						lowercase__,						lowercase__,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      int        =  (TimmBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  {'feature-extraction': TimmBackbone} if is_torch_available() else {}
	SCREAMING_SNAKE_CASE_					:      List[str]        =  False
	SCREAMING_SNAKE_CASE_					:      List[str]        =  False
	SCREAMING_SNAKE_CASE_					:      Tuple        =  False
	SCREAMING_SNAKE_CASE_					:      int        =  False
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   int        =			TimmBackboneModelTester(self       )
							lowercase_      :   Optional[int]        =			ConfigTester(self  ,      config_class=_lowercase  ,      has_text_modality=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   List[Any]        =			"resnet18"
							lowercase_      :   Optional[int]        =			"microsoft/resnet-18"

							lowercase_      :   Tuple        =			AutoBackbone.from_pretrained(_lowercase  ,      use_timm_backbone=_lowercase       )
							lowercase_      :   Dict        =			AutoBackbone.from_pretrained(_lowercase       )

							self.assertEqual(len(timm_model.out_features       )  ,      len(transformers_model.out_features       )       )
							self.assertEqual(len(timm_model.stage_names       )  ,      len(transformers_model.stage_names       )       )
							self.assertEqual(timm_model.channels  ,      transformers_model.channels       )
							# Out indices are set to the last layer by default. For timm models, we don't know
							# the number of layers in advance, so we set it to (-1,), whereas for transformers
							# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
							self.assertEqual(timm_model.out_indices  ,      (-1,)       )
							self.assertEqual(transformers_model.out_indices  ,      [len(timm_model.stage_names       ) - 1]       )

							lowercase_      :   str        =			AutoBackbone.from_pretrained(_lowercase  ,      use_timm_backbone=_lowercase  ,      out_indices=[1, 2, 3]       )
							lowercase_      :   Any        =			AutoBackbone.from_pretrained(_lowercase  ,      out_indices=[1, 2, 3]       )

							self.assertEqual(timm_model.out_indices  ,      transformers_model.out_indices       )
							self.assertEqual(len(timm_model.out_features       )  ,      len(transformers_model.out_features       )       )
							self.assertEqual(timm_model.channels  ,      transformers_model.channels       )
	@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							pass
	@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							pass
	@unittest.skip('TimmBackbone initialization is managed on the timm side'       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							pass
	@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							pass
	@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'       )
	def        lowerCamelCase__		(							self       )						->  str:
							pass
	@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'       )
	def        lowerCamelCase__		(							self       )						->  Any:
							pass
	@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'       )
	def        lowerCamelCase__		(							self       )						->  int:
							pass
	@unittest.skip('model weights aren\'t tied in TimmBackbone.'       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							pass
	@unittest.skip('model weights aren\'t tied in TimmBackbone.'       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							pass
	@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							pass
	@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							pass
	@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							pass
	@unittest.skip('TimmBackbone doesn\'t support output_attentions.'       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							pass
	@unittest.skip('Safetensors is not supported by timm.'       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							pass
	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							pass
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   str        =			model_class(_lowercase       )
													lowercase_      :   Tuple        =			inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase_      :   Optional[int]        =			[*signature.parameters.keys()]

													lowercase_      :   int        =			["pixel_values"]
													self.assertListEqual(arg_names[:1]  ,      _lowercase       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   Any        =			self.model_tester.prepare_config_and_inputs_for_common()
							lowercase_      :   Any        =			True
							lowercase_      :   Optional[int]        =			self.has_attentions

							# no need to test all models as different heads yield the same functionality
							lowercase_      :   str        =			self.all_model_classes[0]
							lowercase_      :   str        =			model_class(_lowercase       )
							model.to(_lowercase       )

							lowercase_      :   Any        =			self._prepare_for_class(_lowercase  ,      _lowercase       )
							lowercase_      :   int        =			model(**_lowercase       )
							lowercase_      :   int        =			outputs[0][-1]

							# Encoder-/Decoder-only models
							lowercase_      :   Any        =			outputs.hidden_states[0]
							hidden_states.retain_grad()

							if self.has_attentions:
													lowercase_      :   List[Any]        =			outputs.attentions[0]
													attentions.retain_grad()

							output.flatten()[0].backward(retain_graph=_lowercase       )

							self.assertIsNotNone(hidden_states.grad       )

							if self.has_attentions:
													self.assertIsNotNone(attentions.grad       )







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Dict        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   Dict        =			model_class(_lowercase       )
													model.to(_lowercase       )
													model.eval()
													lowercase_      :   int        =			model(**_lowercase       )

													self.assertEqual(len(result.feature_maps       )  ,      len(config.out_indices       )       )
													self.assertEqual(len(model.channels       )  ,      len(config.out_indices       )       )

													# Check output of last stage is taken if out_features=None, out_indices=None
													lowercase_      :   Optional[int]        =			copy.deepcopy(_lowercase       )
													lowercase_      :   str        =			None
													lowercase_      :   Tuple        =			model_class(_lowercase       )
													model.to(_lowercase       )
													model.eval()
													lowercase_      :   Union[str, Any]        =			model(**_lowercase       )

													self.assertEqual(len(result.feature_maps       )  ,      1       )
													self.assertEqual(len(model.channels       )  ,      1       )

													# Check backbone can be initialized with fresh weights
													lowercase_      :   Optional[Any]        =			copy.deepcopy(_lowercase       )
													lowercase_      :   Optional[Any]        =			False
													lowercase_      :   Any        =			model_class(_lowercase       )
													model.to(_lowercase       )
													model.eval()
													lowercase_      :   List[Any]        =			model(**_lowercase       )












'''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       )
