




import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DeformableDetrImageProcessor


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Dict=3					, UpperCAmelCase__      :	List[Any]=30					, UpperCAmelCase__      :	Any=400					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Any=True					, UpperCAmelCase__      :	Optional[Any]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[int]=1 / 255					, UpperCAmelCase__      :	Optional[Any]=True					, )						->str:





           '''simple docstring'''

           A__       =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           A__       =    parent
           A__       =    batch_size
           A__       =    num_channels
           A__       =    min_resolution
           A__       =    max_resolution
           A__       =    do_resize
           A__       =    size
           A__       =    do_normalize
           A__       =    image_mean
           A__       =    image_std
           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_pad


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=False)						->Optional[Any]:





           '''simple docstring'''

           if not batched:
                  A__       =    image_inputs[0]
                  if isinstance(UpperCAmelCase__					, Image.Image):
                         A__ ,							A__       =    image.size
                  else:
                         A__ ,							A__       =    image.shape[1], image.shape[2]
                  if w < h:
                         A__       =    int(self.size['''shortest_edge'''] * h / w)
                         A__       =    self.size['''shortest_edge''']
                  elif w > h:
                         A__       =    self.size['''shortest_edge''']
                         A__       =    int(self.size['''shortest_edge'''] * w / h)
                  else:
                         A__       =    self.size['''shortest_edge''']
                         A__       =    self.size['''shortest_edge''']

           else:
                  A__       =    []
                  for image in image_inputs:
                         A__ ,							A__       =    self.get_expected_values([image])
                         expected_values.append((expected_height, expected_width))
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[0])[0]
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      DeformableDetrImageProcessor if is_vision_available() else None


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           A__       =    DeformableDetrImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''size'''))


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 18, '''longest_edge''': 1_333})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)

           A__       =    self.image_processing_class.from_dict(
               self.image_processor_dict					, size=42					, max_size=84					, pad_and_return_pixel_mask=UpperCAmelCase__)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 42, '''longest_edge''': 84})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, Image.Image)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__					, numpify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, np.ndarray)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__					, torchify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, torch.Tensor)

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

           self.assertEqual(
               encoded_images.shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					, )


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           A__       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''					, '''r''') as f:
                  A__       =    json.loads(f.read())

           A__       =    {'''image_id''': 39_769, '''annotations''': target}

           # encode them
           A__       =    DeformableDetrImageProcessor()
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[int]:





           '''simple docstring'''

           A__       =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''					, '''r''') as f:
                  A__       =    json.loads(f.read())

           A__       =    {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

           A__       =    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

           # encode them
           A__       =    DeformableDetrImageProcessor(format='''coco_panoptic''')
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, masks_path=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify masks
           A__       =    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					, UpperCAmelCase__)
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))




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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:Optional[int]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,):
												super().__init__()
												self.register_modules(
												    vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,unet=A__   ,scheduler=A__   ,safety_checker=A__   ,feature_extractor=A__   ,)




							def  A__      (				self   ,A__ = "auto"):
												if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	lowercase							=						self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(A__)




							def  A__      (				self):
												self.enable_attention_slicing(A__)





							@torch.no_grad()
							def __call__(				self   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 5_0   ,A__ = 7.5   ,A__ = None   ,A__ = 1   ,A__ = 0.0   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,A__ = None   ,A__ = 1   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,A__):
																	lowercase							=						1
												elif isinstance(A__   ,A__):
																	lowercase							=						len(A__)
												else:
																	raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')

												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(A__   ,A__) or callback_steps <= 0)
												):
																	raise ValueError(
																	    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    f' {type(A__)}.')

												# get prompt text embeddings
												lowercase							=						self.tokenizer(
												    A__   ,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]

												if text_embeddings is None:
																	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   ,A__   ,1)
												lowercase							=						text_embeddings.view(bs_embed * num_images_per_prompt   ,A__   ,-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							=						['''''']
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__   ,A__):
																						lowercase							=						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, 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(
																	    A__   ,padding='''max_length'''   ,max_length=A__   ,truncation=A__   ,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(A__   ,A__   ,1)
																	lowercase							=						uncond_embeddings.view(batch_size * num_images_per_prompt   ,A__   ,-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							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
												lowercase							=						text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(self.device)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device=self.device   ,dtype=A__)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device=self.device   ,dtype=A__)
												else:
																	if latents_reference.shape != latents_shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																	lowercase							=						latents_reference.to(self.device)
																	lowercase							=						latents.to(self.device)

												# This is the key part of the pipeline where we
												# try to ensure that the generated images w/ the same seed
												# but different sizes actually result in similar images
												lowercase							=						(latents_shape[3] - latents_shape_reference[3]) // 2
												lowercase							=						(latents_shape[2] - latents_shape_reference[2]) // 2
												lowercase							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
												lowercase							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
												lowercase							=						0 if dx < 0 else dx
												lowercase							=						0 if dy < 0 else dy
												lowercase							=						max(-dx   ,0)
												lowercase							=						max(-dy   ,0)
												# import pdb
												# pdb.set_trace()
												lowercase							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

												# set timesteps
												self.scheduler.set_timesteps(A__)

												# 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(A__)):
																	# 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(A__   ,A__)

																	# predict the noise residual
																	lowercase							=						self.unet(A__   ,A__   ,encoder_hidden_states=A__).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(A__   ,A__   ,A__   ,**A__).prev_sample

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

												lowercase							=						1 / 0.18215 * latents
												lowercase							=						self.vae.decode(A__).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 self.safety_checker is not None:
																	lowercase							=						self.feature_extractor(self.numpy_to_pil(A__)   ,return_tensors='''pt''').to(
																	    self.device)
																	lowercase	,						lowercase							=						self.safety_checker(
																	    images=A__   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
												else:
																	lowercase							=						None

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image, has_nsfw_concept)

												return StableDiffusionPipelineOutput(images=A__   ,nsfw_content_detected=A__)




