



"""simple docstring"""

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

     if is_vision_available():
          from transformers import MaskFormerImageProcessor

if is_vision_available():
     from PIL import Image




class        _UpperCAmelCase       :

 def __init__(     self		:       Dict  ,     lowercase_		:       Any  ,     lowercase_		:       Any=2  ,     lowercase_		:       Dict=True  ,     lowercase_		:       int=False  ,     lowercase_		:       Optional[Any]=10  ,     lowercase_		:       Optional[Any]=3  ,     lowercase_		:       List[Any]=32 * 4  ,     lowercase_		:       Optional[int]=32 * 6  ,     lowercase_		:       int=4  ,     lowercase_		:       int=32  ,     ):
     snake_case_ :     Tuple				=	parent
     snake_case_ :     Any				=	batch_size
     snake_case_ :     Tuple				=	is_training
     snake_case_ :     Tuple				=	use_auxiliary_loss
     snake_case_ :     Dict				=	num_queries
     snake_case_ :     Any				=	num_channels
     snake_case_ :     List[str]				=	min_size
     snake_case_ :     int				=	max_size
     snake_case_ :     List[str]				=	num_labels
     snake_case_ :     List[str]				=	mask_feature_size

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     int				=	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
         lowercase_				)

     snake_case_ :     str				=	torch.ones([self.batch_size, self.min_size, self.max_size]  ,     device=lowercase_				)

     snake_case_ :     List[Any]				=	(
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,     device=lowercase_				) > 0.5
     ).float()
     snake_case_ :     Optional[int]				=	(torch.rand((self.batch_size, self.num_labels)  ,     device=lowercase_				) > 0.5).long()

     snake_case_ :     Optional[int]				=	self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels

 def 						_snake_case					(     self		:       int				):
     return MaskFormerConfig.from_backbone_and_decoder_configs(
         backbone_config=SwinConfig(
             depths=[1, 1, 1, 1]  ,     )  ,     decoder_config=DetrConfig(
             decoder_ffn_dim=128  ,     num_queries=self.num_queries  ,     decoder_attention_heads=2  ,     d_model=self.mask_feature_size  ,     )  ,     mask_feature_size=self.mask_feature_size  ,     fpn_feature_size=self.mask_feature_size  ,     num_channels=self.num_channels  ,     num_labels=self.num_labels  ,     )

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     str				=	self.prepare_config_and_inputs()
     snake_case_ :     str				=	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
     return config, inputs_dict

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       int  ,     lowercase_		:       Optional[int]				):
     snake_case_ :     Tuple				=	output.encoder_hidden_states
     snake_case_ :     Optional[Any]				=	output.pixel_decoder_hidden_states
     snake_case_ :     Any				=	output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(lowercase_				)  ,     len(config.backbone_config.depths				)				)
     self.parent.assertTrue(len(lowercase_				)  ,     len(config.backbone_config.depths				)				)
     self.parent.assertTrue(len(lowercase_				)  ,     config.decoder_config.decoder_layers				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       Dict  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       str  ,     lowercase_		:       int=False				):
     with torch.no_grad():
         snake_case_ :     Optional[Any]				=	MaskFormerModel(config=lowercase_				)
         model.to(lowercase_				)
         model.eval()

         snake_case_ :     List[Any]				=	model(pixel_values=lowercase_  ,     pixel_mask=lowercase_				)
         snake_case_ :     Optional[Any]				=	model(lowercase_  ,     output_hidden_states=lowercase_				)
     # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
     # encoder and pixel decoder
     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape  ,     (self.batch_size, self.num_queries, self.mask_feature_size)  ,     )
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
     self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

     if output_hidden_states:
         self.check_output_hidden_state(lowercase_  ,     lowercase_				)






 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Tuple  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int				):
     snake_case_ :     Tuple				=	MaskFormerForInstanceSegmentation(config=lowercase_				)
     model.to(lowercase_				)
     model.eval()

     def comm_check_on_output(lowercase_		:       Union[str, Any]				):
         # let's still check that all the required stuff is there
         self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
         self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
         self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
         # okay, now we need to check the logits shape
         # due to the encoder compression, masks have a //4 spatial size
         self.parent.assertEqual(
             result.masks_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,     )
         # + 1 for null class
         self.parent.assertEqual(
             result.class_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.num_labels + 1)				)

     with torch.no_grad():
         snake_case_ :     Any				=	model(pixel_values=lowercase_  ,     pixel_mask=lowercase_				)
         snake_case_ :     Union[str, Any]				=	model(lowercase_				)

         comm_check_on_output(lowercase_				)

         snake_case_ :     int				=	model(
             pixel_values=lowercase_  ,     pixel_mask=lowercase_  ,     mask_labels=lowercase_  ,     class_labels=lowercase_				)

     comm_check_on_output(lowercase_				)

     self.parent.assertTrue(result.loss is not None				)
     self.parent.assertEqual(result.loss.shape  ,     torch.Size([1]				)				)





@require_torch
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			List[Any]							=		(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 _lowerCAmelCase   :			Optional[int]							=		(
     {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase   :			str							=		False
 _lowerCAmelCase   :			List[str]							=		False
 _lowerCAmelCase   :			Any							=		False
 _lowerCAmelCase   :			Any							=		False

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     str				=	MaskFormerModelTester(self				)
     snake_case_ :     str				=	ConfigTester(self  ,     config_class=lowercase_  ,     has_text_modality=lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     self.config_tester.run_common_tests()

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_,	snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_  ,     **lowercase_  ,     output_hidden_states=lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Any				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase_				)

 @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''				)
 def 						_snake_case					(     self		:       Tuple				):
     pass

 @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''				)
 def 						_snake_case					(     self		:       Optional[Any]				):
     pass

 @unittest.skip(reason='''MaskFormer is not a generative model'''				)
 def 						_snake_case					(     self		:       str				):
     pass

 @unittest.skip(reason='''MaskFormer does not use token embeddings'''				)
 def 						_snake_case					(     self		:       List[Any]				):
     pass

 @require_torch_multi_gpu
 @unittest.skip(
     reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
 def 						_snake_case					(     self		:       int				):
     pass

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

 def 						_snake_case					(     self		:       Any				):
     snake_case_,	snake_case_ :     str				=	self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_ :     Tuple				=	['''pixel_values''']
         self.assertListEqual(arg_names[:1]  ,     lowercase_				)

 @slow
 def 						_snake_case					(     self		:       str				):
     for model_name in ["facebook/maskformer-swin-small-coco"]:
         snake_case_ :     str				=	MaskFormerModel.from_pretrained(lowercase_				)
         self.assertIsNotNone(lowercase_				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Optional[Any]				=	(self.model_tester.min_size,) * 2
     snake_case_ :     int				=	{
         '''pixel_values''': torch.randn((2, 3, *size)  ,     device=lowercase_				),
         '''mask_labels''': torch.randn((2, 10, *size)  ,     device=lowercase_				),
         '''class_labels''': torch.zeros(2  ,     10  ,     device=lowercase_				).long(),
     }

     snake_case_ :     List[str]				=	MaskFormerForInstanceSegmentation(MaskFormerConfig()				).to(lowercase_				)
     snake_case_ :     Dict				=	model(**lowercase_				)
     self.assertTrue(outputs.loss is not None				)

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_,	snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskformer_model(lowercase_  ,     **lowercase_  ,     output_hidden_states=lowercase_				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_,	snake_case_ :     Any				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_ :     Optional[Any]				=	model_class(lowercase_				).to(lowercase_				)
         snake_case_ :     List[str]				=	model(**lowercase_  ,     output_attentions=lowercase_				)
         self.assertTrue(outputs.attentions is not None				)

 def 						_snake_case					(     self		:       int				):
     if not self.model_tester.is_training:
         return
     # only MaskFormerForInstanceSegmentation has the loss
     snake_case_ :     int				=	self.all_model_classes[1]
     snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     str				=	self.model_tester.prepare_config_and_inputs()

     snake_case_ :     List[Any]				=	model_class(lowercase_				)
     model.to(lowercase_				)
     model.train()

     snake_case_ :     Optional[Any]				=	model(lowercase_  ,     mask_labels=lowercase_  ,     class_labels=lowercase_				).loss
     loss.backward()






 def 						_snake_case					(     self		:       Optional[Any]				):
     # only MaskFormerForInstanceSegmentation has the loss
     snake_case_ :     List[str]				=	self.all_model_classes[1]
     snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     Union[str, Any]				=	self.model_tester.prepare_config_and_inputs()
     snake_case_ :     Union[str, Any]				=	True
     snake_case_ :     List[Any]				=	True

     snake_case_ :     Union[str, Any]				=	model_class(lowercase_				)
     model.to(lowercase_				)
     model.train()

     snake_case_ :     str				=	model(lowercase_  ,     mask_labels=lowercase_  ,     class_labels=lowercase_				)

     snake_case_ :     List[str]				=	outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     snake_case_ :     Optional[int]				=	outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()
     # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
     snake_case_ :     int				=	outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     snake_case_ :     Optional[Any]				=	outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=lowercase_				)

     self.assertIsNotNone(encoder_hidden_states.grad				)
     self.assertIsNotNone(pixel_decoder_hidden_states.grad				)
     self.assertIsNotNone(transformer_decoder_hidden_states.grad				)
     self.assertIsNotNone(attentions.grad				)





lowercase__    :  Tuple							    =	1e-4




def __lowercase			(							):
    snake_case_ :     int				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
    return image






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

 @cached_property
 def 						_snake_case					(     self		:       int				):
     return (
         MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
         if is_vision_available()
         else None
     )

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Optional[int]				=	MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''				).to(lowercase_				)
     snake_case_ :     List[str]				=	self.default_image_processor
     snake_case_ :     List[Any]				=	prepare_img()
     snake_case_ :     Any				=	image_processor(lowercase_  ,     return_tensors='''pt'''				).to(lowercase_				)
     snake_case_ :     Tuple				=	inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
     # check size
     self.assertEqual(lowercase_  ,     (1, 3, 800, 1088)				)

     with torch.no_grad():
         snake_case_ :     Tuple				=	model(**lowercase_				)

     snake_case_ :     Union[str, Any]				=	torch.tensor(
         [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]				).to(lowercase_				)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)

     snake_case_ :     Optional[int]				=	torch.tensor(
         [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]				).to(lowercase_				)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)

     snake_case_ :     List[str]				=	torch.tensor(
         [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]				).to(lowercase_				)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     List[str]				=	(
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
         .to(lowercase_				)
         .eval()
     )
     snake_case_ :     Any				=	self.default_image_processor
     snake_case_ :     Union[str, Any]				=	prepare_img()
     snake_case_ :     Union[str, Any]				=	image_processor(lowercase_  ,     return_tensors='''pt'''				).to(lowercase_				)
     snake_case_ :     List[str]				=	inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
     # check size
     self.assertEqual(lowercase_  ,     (1, 3, 800, 1088)				)

     with torch.no_grad():
         snake_case_ :     Tuple				=	model(**lowercase_				)
     # masks_queries_logits
     snake_case_ :     Any				=	outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
     snake_case_ :     Tuple				=	[
         [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
         [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
         [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
     ]
     snake_case_ :     str				=	torch.tensor(lowercase_				).to(lowercase_				)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)
     # class_queries_logits
     snake_case_ :     Union[str, Any]				=	outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
     snake_case_ :     Tuple				=	torch.tensor(
         [
             [1.6512E00, -5.2572E00, -3.3519E00],
             [3.6169E-02, -5.9025E00, -2.9313E00],
             [1.0766E-04, -7.7630E00, -5.1263E00],
         ]				).to(lowercase_				)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Optional[int]				=	(
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''				)
         .to(lowercase_				)
         .eval()
     )
     snake_case_ :     Tuple				=	self.default_image_processor
     snake_case_ :     Optional[int]				=	prepare_img()
     snake_case_ :     List[Any]				=	image_processor(lowercase_  ,     return_tensors='''pt'''				).to(lowercase_				)
     snake_case_ :     Dict				=	inputs['''pixel_values'''].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0				)
     # check size
     self.assertEqual(lowercase_  ,     (1, 3, 800, 1088)				)

     with torch.no_grad():
         snake_case_ :     Dict				=	model(**lowercase_				)
     # masks_queries_logits
     snake_case_ :     Tuple				=	outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
     snake_case_ :     str				=	[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
     snake_case_ :     Optional[Any]				=	torch.tensor(lowercase_				).to(lowercase_				)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)
     # class_queries_logits
     snake_case_ :     int				=	outputs.class_queries_logits
     self.assertEqual(
         class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)				)
     snake_case_ :     List[Any]				=	torch.tensor(
         [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]				).to(lowercase_				)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     lowercase_  ,     atol=lowercase_				)				)






 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Tuple				=	(
         MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''				)
         .to(lowercase_				)
         .eval()
     )
     snake_case_ :     Tuple				=	self.default_image_processor

     snake_case_ :     int				=	image_processor(
         [np.zeros((3, 800, 1333)				), np.zeros((3, 800, 1333)				)]  ,     segmentation_maps=[np.zeros((384, 384)				).astype(np.floataa				), np.zeros((384, 384)				).astype(np.floataa				)]  ,     return_tensors='''pt'''  ,     )

     snake_case_ :     Union[str, Any]				=	inputs['''pixel_values'''].to(lowercase_				)
     snake_case_ :     Dict				=	[el.to(lowercase_				) for el in inputs['''mask_labels''']]
     snake_case_ :     Dict				=	[el.to(lowercase_				) for el in inputs['''class_labels''']]

     with torch.no_grad():
         snake_case_ :     List[Any]				=	model(**lowercase_				)

     self.assertTrue(outputs.loss is not None				)










"""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        _UpperCAmelCase       (			lowerCAmelCase__):

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

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

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

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

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

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

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

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

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

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

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

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

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





