"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
				from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor
class  _snake_case     (					a__		):






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		List[str]		   =     self.config_class(**self.inputs_dict    )
													self.parent.assertTrue(hasattr(UpperCAmelCase							,					"hidden_sizes"    )    )
													self.parent.assertTrue(hasattr(UpperCAmelCase							,					"neck_hidden_sizes"    )    )
													self.parent.assertTrue(hasattr(UpperCAmelCase							,					"num_attention_heads"    )    )

class  _snake_case     :






						def __init__(       self      :			int							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Dict=13							,					UpperCAmelCase      :			Any=32							,					UpperCAmelCase      :			List[str]=2							,					UpperCAmelCase      :			Optional[Any]=3							,					UpperCAmelCase      :			Dict=640							,					UpperCAmelCase      :			int=4							,					UpperCAmelCase      :			str="silu"							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			Optional[int]=32							,					UpperCAmelCase      :			List[Any]=0.1							,					UpperCAmelCase      :			List[str]=0.1							,					UpperCAmelCase      :			Optional[Any]=0.1							,					UpperCAmelCase      :			int=0.0_2							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			str=True							,					UpperCAmelCase      :			Union[str, Any]=10							,					UpperCAmelCase      :			List[Any]=None							,					):
													__lowerCamelCase					:		Tuple		   =     parent
													__lowerCamelCase					:		str		   =     batch_size
													__lowerCamelCase					:		str		   =     image_size
													__lowerCamelCase					:		Optional[Any]		   =     patch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Union[str, Any]		   =     last_hidden_size
													__lowerCamelCase					:		str		   =     num_attention_heads
													__lowerCamelCase					:		List[Any]		   =     hidden_act
													__lowerCamelCase					:		Any		   =     conv_kernel_size
													__lowerCamelCase					:		Any		   =     output_stride
													__lowerCamelCase					:		Any		   =     hidden_dropout_prob
													__lowerCamelCase					:		Dict		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		Union[str, Any]		   =     classifier_dropout_prob
													__lowerCamelCase					:		Optional[Any]		   =     use_labels
													__lowerCamelCase					:		Optional[int]		   =     is_training
													__lowerCamelCase					:		Any		   =     num_labels
													__lowerCamelCase					:		Optional[Any]		   =     initializer_range
													__lowerCamelCase					:		Tuple		   =     scope






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		List[str]		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													__lowerCamelCase					:		Optional[int]		   =     None
													__lowerCamelCase					:		str		   =     None
													if self.use_labels:
																				__lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size]							,					self.num_labels    )
																				__lowerCamelCase					:		Dict		   =     ids_tensor([self.batch_size, self.image_size, self.image_size]							,					self.num_labels    )

													__lowerCamelCase					:		Optional[int]		   =     self.get_config()

													return config, pixel_values, labels, pixel_labels






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													return MobileViTConfig(
													    image_size=self.image_size							,					patch_size=self.patch_size							,					num_channels=self.num_channels							,					num_attention_heads=self.num_attention_heads							,					hidden_act=self.hidden_act							,					conv_kernel_size=self.conv_kernel_size							,					output_stride=self.output_stride							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					classifier_dropout_prob=self.classifier_dropout_prob							,					initializer_range=self.initializer_range							,					)






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Dict    ):
													__lowerCamelCase					:		Union[str, Any]		   =     MobileViTModel(config=UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Optional[Any]		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(
													    result.last_hidden_state.shape							,					(
													        self.batch_size,
													        self.last_hidden_size,
													        self.image_size // self.output_stride,
													        self.image_size // self.output_stride,
													    )							,					)






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[int]    ):
													__lowerCamelCase					:		Tuple		   =     self.num_labels
													__lowerCamelCase					:		Any		   =     MobileViTForImageClassification(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		Any		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.num_labels
													__lowerCamelCase					:		Optional[int]		   =     MobileViTForSemanticSegmentation(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													__lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(
													    result.logits.shape							,					(
													        self.batch_size,
													        self.num_labels,
													        self.image_size // self.output_stride,
													        self.image_size // self.output_stride,
													    )							,					)
													__lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
													self.parent.assertEqual(
													    result.logits.shape							,					(
													        self.batch_size,
													        self.num_labels,
													        self.image_size // self.output_stride,
													        self.image_size // self.output_stride,
													    )							,					)







						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     self.prepare_config_and_inputs()
													__lowerCamelCase					:		Optional[Any]		   =     config_and_inputs
													__lowerCamelCase					:		str		   =     {"pixel_values": pixel_values}
													return config, inputs_dict

@require_torch
class  _snake_case     (					a__				, a__				, unittest.TestCase		):
						snake_case__			  =			(
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						snake_case__			  =			(
						    {
						        "feature-extraction": MobileViTModel,
						        "image-classification": MobileViTForImageClassification,
						        "image-segmentation": MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Any		   =     MobileViTModelTester(self    )
													__lowerCamelCase					:		Optional[int]		   =     MobileViTConfigTester(self							,					config_class=UpperCAmelCase							,					has_text_modality=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileViT does not use inputs_embeds"    )
						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						@unittest.skip(reason="MobileViT does not support input and output embeddings"    )
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													pass






						@unittest.skip(reason="MobileViT does not output attentions"    )
						def       lowerCamelCase__     (       self      :			str    ):
													pass






						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()

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

																				__lowerCamelCase					:		Optional[Any]		   =     ["pixel_values"]
																				self.assertListEqual(arg_names[:1]							,					UpperCAmelCase    )






						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
						def       lowerCamelCase__     (       self      :			List[str]    ):
													pass






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			int    ):
													def check_hidden_states_output(UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Any    ):
																				__lowerCamelCase					:		Union[str, Any]		   =     model_class(UpperCAmelCase    )
																				model.to(UpperCAmelCase    )
																				model.eval()

																				with torch.no_grad():
																											__lowerCamelCase					:		List[Any]		   =     model(**self._prepare_for_class(UpperCAmelCase							,					UpperCAmelCase    )    )

																				__lowerCamelCase					:		Dict		   =     outputs.hidden_states

																				__lowerCamelCase					:		Tuple		   =     5
																				self.assertEqual(len(UpperCAmelCase    )							,					UpperCAmelCase    )

																				# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																				# with the width and height being successively divided by 2.
																				__lowerCamelCase					:		Optional[int]		   =     2
																				for i in range(len(UpperCAmelCase    )    ):
																											self.assertListEqual(
																											    list(hidden_states[i].shape[-2:]    )							,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,					)
																											divisor *= 2

																				self.assertEqual(self.model_tester.output_stride							,					divisor // 2    )

													__lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowerCamelCase					:		List[Any]		   =     True
																				check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__lowerCamelCase					:		int		   =     True

																				check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Tuple		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		int		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase    )







						@slow
						def       lowerCamelCase__     (       self      :			int    ):
													for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase					:		Tuple		   =     MobileViTModel.from_pretrained(UpperCAmelCase    )
																				self.assertIsNotNone(UpperCAmelCase    )


def    lowercase_   (   ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
							return image







@require_torch
@require_vision
class  _snake_case     (					unittest.TestCase		):






						@cached_property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"    ) if is_vision_available() else None






						@slow
						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		str		   =     MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"    ).to(UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     self.default_image_processor
													__lowerCamelCase					:		List[Any]		   =     prepare_img()
													__lowerCamelCase					:		Union[str, Any]		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

													# forward pass
													with torch.no_grad():
																				__lowerCamelCase					:		Dict		   =     model(**UpperCAmelCase    )

													# verify the logits
													__lowerCamelCase					:		str		   =     torch.Size((1, 1000)    )
													self.assertEqual(outputs.logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]    ).to(UpperCAmelCase    )

													self.assertTrue(torch.allclose(outputs.logits[0, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )






						@slow
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		int		   =     MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )
													__lowerCamelCase					:		Dict		   =     model.to(UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

													__lowerCamelCase					:		Dict		   =     prepare_img()
													__lowerCamelCase					:		List[str]		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

													# forward pass
													with torch.no_grad():
																				__lowerCamelCase					:		int		   =     model(**UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     outputs.logits

													# verify the logits
													__lowerCamelCase					:		Any		   =     torch.Size((1, 21, 32, 32)    )
													self.assertEqual(logits.shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor(
													    [
													        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
													        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9_8_6_8, -9.7_1_3_2], [-11.0405, -11.0221, -10.7318]],
													        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
													    ]							,					device=UpperCAmelCase							,					)

													self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,					UpperCAmelCase							,					atol=1E-4    )    )







						@slow
						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Tuple		   =     MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )
													__lowerCamelCase					:		Dict		   =     model.to(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

													__lowerCamelCase					:		int		   =     prepare_img()
													__lowerCamelCase					:		Union[str, Any]		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

													# forward pass
													with torch.no_grad():
																				__lowerCamelCase					:		Any		   =     model(**UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     outputs.logits.detach().cpu()

													__lowerCamelCase					:		Union[str, Any]		   =     image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase							,					target_sizes=[(50, 60)]    )
													__lowerCamelCase					:		str		   =     torch.Size((50, 60)    )
													self.assertEqual(segmentation[0].shape							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     torch.Size((32, 32)    )
													self.assertEqual(segmentation[0].shape							,					UpperCAmelCase    )






"""simple docstring"""





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  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													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      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

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

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# 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+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

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

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (samples / 2 + 0.5).clamp(0							,					1    )

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

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )