



import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

							from transformers import ViTMAEForPreTraining, ViTMAEModel
							from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					List[str]=13,						lowerCamelCase		:					int=30,						lowerCamelCase		:					int=2,						lowerCamelCase		:					List[str]=3,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=True,						lowerCamelCase		:					str=32,						lowerCamelCase		:					Dict=5,						lowerCamelCase		:					int=4,						lowerCamelCase		:					Dict=37,						lowerCamelCase		:					Optional[int]="gelu",						lowerCamelCase		:					Tuple=0.1,						lowerCamelCase		:					Union[str, Any]=0.1,						lowerCamelCase		:					List[Any]=10,						lowerCamelCase		:					Optional[int]=0.02,						lowerCamelCase		:					Union[str, Any]=3,						lowerCamelCase		:					str=0.6,						lowerCamelCase		:					Optional[Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    image_size
								lowercase__     				=    patch_size
								lowercase__     				=    num_channels
								lowercase__     				=    is_training
								lowercase__     				=    use_labels
								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    intermediate_size
								lowercase__     				=    hidden_act
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    type_sequence_label_size
								lowercase__     				=    initializer_range
								lowercase__     				=    mask_ratio
								lowercase__     				=    scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								lowercase__     				=    (image_size // patch_size) ** 2
								lowercase__     				=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								lowercase__     				=    None
								if self.use_labels:
												lowercase__     				=    ids_tensor([self.batch_size],						self.type_sequence_label_size						)

								lowercase__     				=    self.get_config()

								return config, pixel_values, labels




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return ViTMAEConfig(
								    image_size=self.image_size,						patch_size=self.patch_size,						num_channels=self.num_channels,						hidden_size=self.hidden_size,						num_hidden_layers=self.num_hidden_layers,						num_attention_heads=self.num_attention_heads,						intermediate_size=self.intermediate_size,						hidden_act=self.hidden_act,						hidden_dropout_prob=self.hidden_dropout_prob,						attention_probs_dropout_prob=self.attention_probs_dropout_prob,						is_decoder=snake_case__,						initializer_range=self.initializer_range,						mask_ratio=self.mask_ratio,						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    ViTMAEModel(config=snake_case__						)
								model.to(snake_case__						)
								model.eval()
								lowercase__     				=    model(snake_case__						)
								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    ViTMAEForPreTraining(snake_case__						)
								model.to(snake_case__						)
								model.eval()
								lowercase__     				=    model(snake_case__						)
								lowercase__     				=    (self.image_size // self.patch_size) ** 2
								lowercase__     				=    self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, num_patches, expected_num_channels)						)

								# test greyscale images
								lowercase__     				=    1
								lowercase__     				=    ViTMAEForPreTraining(snake_case__						)
								model.to(snake_case__						)
								model.eval()
								lowercase__     				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								lowercase__     				=    model(snake_case__						)
								lowercase__     				=    self.patch_size**2
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, num_patches, expected_num_channels)						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								lowercase__				,		lowercase__				,		lowercase__     				=    config_and_inputs
								lowercase__     				=    {'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class    _UpperCAmelCase      ( __a	,__a	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				lowercase__       							= {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    ViTMAEModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=snake_case__,						has_text_modality=snake_case__,						hidden_size=37						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''						)
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    model_class(snake_case__						)
												self.assertIsInstance(model.get_input_embeddings(),						(nn.Module)						)
												lowercase__     				=    model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(snake_case__,						nn.Linear						)						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    model_class(snake_case__						)
												lowercase__     				=    inspect.signature(model.forward						)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowercase__     				=    [*signature.parameters.keys()]

												lowercase__     				=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1],						snake_case__						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*snake_case__						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					int,						lowerCamelCase		:					Any,						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								# make masks reproducible
								np.random.seed(2						)

								lowercase__     				=    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2						)
								lowercase__     				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
								lowercase__     				=    torch.from_numpy(snake_case__						)

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								lowercase__     				=    pt_noise

								super().check_pt_tf_models(snake_case__,						snake_case__,						snake_case__						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    model_class(snake_case__						)
												model.to(snake_case__						)
												model.eval()
												# make random mask reproducible
												torch.manual_seed(2						)
												with torch.no_grad():
																lowercase__     				=    model(**self._prepare_for_class(snake_case__,						snake_case__						)						)

												lowercase__     				=    outputs[0].cpu().numpy()
												lowercase__     				=    0

												with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(snake_case__						)
																lowercase__     				=    model_class.from_pretrained(snake_case__						)
																model.to(snake_case__						)
																# make random mask reproducible
																torch.manual_seed(2						)
																with torch.no_grad():
																				lowercase__     				=    model(**self._prepare_for_class(snake_case__,						snake_case__						)						)

																# Make sure we don't have nans
																lowercase__     				=    after_outputs[0].cpu().numpy()
																lowercase__     				=    0
																lowercase__     				=    np.amax(np.abs(out_a - out_a						)						)
																self.assertLessEqual(snake_case__,						1E-5						)




				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								pass




				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								pass




				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''						)
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								pass




				@slow
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    ViTMAEModel.from_pretrained(snake_case__						)
												self.assertIsNotNone(snake_case__						)






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image




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

				"""simple docstring"""




				@cached_property
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''						) if is_vision_available() else None




				@slow
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								# make random mask reproducible across the PT and TF model
								np.random.seed(2						)

								lowercase__     				=    ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''						).to(snake_case__						)

								lowercase__     				=    self.default_image_processor
								lowercase__     				=    prepare_img()
								lowercase__     				=    image_processor(images=snake_case__,						return_tensors='''pt'''						).to(snake_case__						)

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								lowercase__     				=    ViTMAEConfig()
								lowercase__     				=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
								lowercase__     				=    np.random.uniform(size=(1, num_patches)						)

								# forward pass
								with torch.no_grad():
												lowercase__     				=    model(**snake_case__,						noise=torch.from_numpy(snake_case__						).to(device=snake_case__						)						)

								# verify the logits
								lowercase__     				=    torch.Size((1, 196, 768)						)
								self.assertEqual(outputs.logits.shape,						snake_case__						)

								lowercase__     				=    torch.tensor(
								    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]						)

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












import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A__			:    Any         =				logging.get_logger(__name__)  # pylint: disable=invalid-name
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					WhisperForConditionalGeneration,						lowerCamelCase		:					WhisperProcessor,						lowerCamelCase		:					AutoencoderKL,						lowerCamelCase		:					CLIPTextModel,						lowerCamelCase		:					CLIPTokenizer,						lowerCamelCase		:					UNetaDConditionModel,						lowerCamelCase		:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],						lowerCamelCase		:					StableDiffusionSafetyChecker,						lowerCamelCase		:					CLIPImageProcessor,						):

								'''simple docstring'''
								super().__init__()

								if safety_checker is None:
												logger.warning(
												    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
												    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
												    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
												    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
												    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
												    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''						)

								self.register_modules(
								    speech_model=lowerCamelCase,						speech_processor=lowerCamelCase,						vae=lowerCamelCase,						text_encoder=lowerCamelCase,						tokenizer=lowerCamelCase,						unet=lowerCamelCase,						scheduler=lowerCamelCase,						feature_extractor=lowerCamelCase,						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[Union[str, int]] = "auto"						):

								'''simple docstring'''
								if slice_size == "auto":
												lowercase__     				=    self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.enable_attention_slicing(lowerCamelCase						)




				@torch.no_grad()
				def __call__(  self		:					Any,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Optional[Any]=16_000,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 50,						lowerCamelCase		:					float = 7.5,						lowerCamelCase		:					Optional[Union[str, List[str]]] = None,						lowerCamelCase		:					Optional[int] = 1,						lowerCamelCase		:					float = 0.0,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					Optional[torch.FloatTensor] = None,						lowerCamelCase		:					Optional[str] = "pil",						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None,						lowerCamelCase		:					int = 1,						**lowerCamelCase		:					Optional[Any],						):

								'''simple docstring'''
								lowercase__     				=    self.speech_processor.feature_extractor(
								    lowerCamelCase,						return_tensors='''pt''',						sampling_rate=lowerCamelCase						).input_features.to(self.device						)
								lowercase__     				=    self.speech_model.generate(lowerCamelCase,						max_length=480_000						)

								lowercase__     				=    self.speech_processor.tokenizer.batch_decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase,						normalize=lowerCamelCase						)[
								    0
								]

								if isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    1
								elif isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    len(lowerCamelCase						)
								else:
												raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase						)}"""						)

								if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""						)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(lowerCamelCase,						lowerCamelCase						) or callback_steps <= 0)
								):
												raise ValueError(
												    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
												    F""" {type(lowerCamelCase						)}."""						)

								# get prompt text embeddings
								lowercase__     				=    self.tokenizer(
								    lowerCamelCase,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						return_tensors='''pt''',						)
								lowercase__     				=    text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												lowercase__     				=    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
												logger.warning(
												    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
												    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""						)
												lowercase__     				=    text_input_ids[:, : self.tokenizer.model_max_length]
								lowercase__     				=    self.text_encoder(text_input_ids.to(self.device						)						)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowercase__				,		lowercase__				,		lowercase__     				=    text_embeddings.shape
								lowercase__     				=    text_embeddings.repeat(1,						lowerCamelCase,						1						)
								lowercase__     				=    text_embeddings.view(bs_embed * num_images_per_prompt,						lowerCamelCase,						-1						)

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								lowercase__     				=    guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												lowercase__     				=    42
												if negative_prompt is None:
																lowercase__     				=    [''''''] * batch_size
												elif type(lowerCamelCase						) is not type(lowerCamelCase						):
																raise TypeError(
																    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase						)} !="""
																    F""" {type(lowerCamelCase						)}."""						)
												elif isinstance(lowerCamelCase,						lowerCamelCase						):
																lowercase__     				=    [negative_prompt]
												elif batch_size != len(lowerCamelCase						):
																raise ValueError(
																    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase						)}, but `prompt`:"""
																    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																    ''' the batch size of `prompt`.'''						)
												else:
																lowercase__     				=    negative_prompt

												lowercase__     				=    text_input_ids.shape[-1]
												lowercase__     				=    self.tokenizer(
												    lowerCamelCase,						padding='''max_length''',						max_length=lowerCamelCase,						truncation=lowerCamelCase,						return_tensors='''pt''',						)
												lowercase__     				=    self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												lowercase__     				=    uncond_embeddings.shape[1]
												lowercase__     				=    uncond_embeddings.repeat(1,						lowerCamelCase,						1						)
												lowercase__     				=    uncond_embeddings.view(batch_size * num_images_per_prompt,						lowerCamelCase,						-1						)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												lowercase__     				=    torch.cat([uncond_embeddings, text_embeddings]						)

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								lowercase__     				=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowercase__     				=    text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device='''cpu''',						dtype=lowerCamelCase						).to(
																    self.device						)
												else:
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device=self.device,						dtype=lowerCamelCase						)
								else:
												if latents.shape != latents_shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
												lowercase__     				=    latents.to(self.device						)

								# set timesteps
								self.scheduler.set_timesteps(lowerCamelCase						)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								lowercase__     				=    self.scheduler.timesteps.to(self.device						)

								# scale the initial noise by the standard deviation required by the scheduler
								lowercase__     				=    latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								lowercase__     				=    '''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
								lowercase__     				=    {}
								if accepts_eta:
												lowercase__     				=    eta

								for i, t in enumerate(self.progress_bar(lowerCamelCase						)						):
												# expand the latents if we are doing classifier free guidance
												lowercase__     				=    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
												lowercase__     				=    self.scheduler.scale_model_input(lowerCamelCase,						lowerCamelCase						)

												# predict the noise residual
												lowercase__     				=    self.unet(lowerCamelCase,						lowerCamelCase,						encoder_hidden_states=lowerCamelCase						).sample

												# perform guidance
												if do_classifier_free_guidance:
																lowercase__				,		lowercase__     				=    noise_pred.chunk(2						)
																lowercase__     				=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												lowercase__     				=    self.scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																callback(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    1 / 0.18215 * latents
								lowercase__     				=    self.vae.decode(lowerCamelCase						).sample

								lowercase__     				=    (image / 2 + 0.5).clamp(0,						1						)

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

								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=lowerCamelCase,						nsfw_content_detected=lowerCamelCase						)




