



'''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 	__magic_name__						:







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







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      List[str]        =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
									    _UpperCAmelCase		)

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

									_a     :      Any        =							(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,device=_UpperCAmelCase		) > 0.5
									).float()
									_a     :      Any        =							(torch.rand((self.batch_size, self.num_labels)	,device=_UpperCAmelCase		) > 0.5).long()

									_a     :      str        =							self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels







							def 							__lowercase			(							self					:       List[str]		):
									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 							__lowercase			(							self					:       Optional[int]		):
									_a       ,					_a       ,					_a       ,					_a       ,					_a     :      Union[str, Any]        =							self.prepare_config_and_inputs()
									_a     :      str        =							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
									return config, inputs_dict







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Dict        =							output.encoder_hidden_states
									_a     :      Tuple        =							output.pixel_decoder_hidden_states
									_a     :      Optional[Any]        =							output.transformer_decoder_hidden_states

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







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Any=False		):
									with torch.no_grad():
											_a     :      List[str]        =							MaskFormerModel(config=_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

											_a     :      int        =							model(pixel_values=_UpperCAmelCase	,pixel_mask=_UpperCAmelCase		)
											_a     :      int        =							model(_UpperCAmelCase	,output_hidden_states=_UpperCAmelCase		)
									# 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(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Dict		):
									_a     :      List[str]        =							MaskFormerForInstanceSegmentation(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

									def comm_check_on_output(_UpperCAmelCase					:       List[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():
											_a     :      Dict        =							model(pixel_values=_UpperCAmelCase	,pixel_mask=_UpperCAmelCase		)
											_a     :      List[Any]        =							model(_UpperCAmelCase		)

											comm_check_on_output(_UpperCAmelCase		)

											_a     :      Optional[int]        =							model(
											    pixel_values=_UpperCAmelCase	,pixel_mask=_UpperCAmelCase	,mask_labels=_UpperCAmelCase	,class_labels=_UpperCAmelCase		)

									comm_check_on_output(_UpperCAmelCase		)

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

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    List[Any]   				=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							lowerCAmelCase       :    Any   				=					(
							    {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    List[Any]   				=					False
							lowerCAmelCase       :    List[str]   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False
							lowerCAmelCase       :    Tuple   				=					False







							def 							__lowercase			(							self					:       int		):
									_a     :      Any        =							MaskFormerModelTester(self		)
									_a     :      List[Any]        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]		):
									self.config_tester.run_common_tests()







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase	,**_UpperCAmelCase	,output_hidden_states=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_UpperCAmelCase		)







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







							@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'		)
							def 							__lowercase			(							self					:       List[str]		):
									pass







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







							@unittest.skip(reason='MaskFormer does not use token embeddings'		)
							def 							__lowercase			(							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 							__lowercase			(							self					:       Dict		):
									pass







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







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      Tuple        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[Any]		):
									for model_name in ["facebook/maskformer-swin-small-coco"]:
											_a     :      List[Any]        =							MaskFormerModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[int]        =							(self.model_tester.min_size,) * 2
									_a     :      Optional[Any]        =							{
									    'pixel_values': torch.randn((2, 3, *size)	,device=_UpperCAmelCase		),
									    'mask_labels': torch.randn((2, 10, *size)	,device=_UpperCAmelCase		),
									    'class_labels': torch.zeros(2	,10	,device=_UpperCAmelCase		).long(),
									}

									_a     :      str        =							MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(_UpperCAmelCase		)
									_a     :      Any        =							model(**_UpperCAmelCase		)
									self.assertTrue(outputs.loss is not None		)







							def 							__lowercase			(							self					:       Tuple		):
									_a       ,					_a     :      Dict        =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase	,**_UpperCAmelCase	,output_hidden_states=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									_a       ,					_a     :      int        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      Union[str, Any]        =							model_class(_UpperCAmelCase		).to(_UpperCAmelCase		)
											_a     :      Any        =							model(**_UpperCAmelCase	,output_attentions=_UpperCAmelCase		)
											self.assertTrue(outputs.attentions is not None		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									if not self.model_tester.is_training:
											return
									# only MaskFormerForInstanceSegmentation has the loss
									_a     :      List[Any]        =							self.all_model_classes[1]
									_a       ,					_a       ,					_a       ,					_a       ,					_a     :      Tuple        =							self.model_tester.prepare_config_and_inputs()

									_a     :      Optional[Any]        =							model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.train()

									_a     :      Optional[Any]        =							model(_UpperCAmelCase	,mask_labels=_UpperCAmelCase	,class_labels=_UpperCAmelCase		).loss
									loss.backward()







							def 							__lowercase			(							self					:       str		):
									# only MaskFormerForInstanceSegmentation has the loss
									_a     :      Tuple        =							self.all_model_classes[1]
									_a       ,					_a       ,					_a       ,					_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs()
									_a     :      Any        =							True
									_a     :      str        =							True

									_a     :      Optional[int]        =							model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.train()

									_a     :      List[str]        =							model(_UpperCAmelCase	,mask_labels=_UpperCAmelCase	,class_labels=_UpperCAmelCase		)

									_a     :      int        =							outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									_a     :      Optional[Any]        =							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
									_a     :      List[str]        =							outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									_a     :      Optional[int]        =							outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=_UpperCAmelCase		)

									self.assertIsNotNone(encoder_hidden_states.grad		)
									self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
									self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
									self.assertIsNotNone(attentions.grad		)
__lowerCAmelCase									=   1e-4



def      __lowerCamelCase     (      )			->      int:
		_a     :      Any        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







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







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Any        =							MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'		).to(_UpperCAmelCase		)
									_a     :      Optional[int]        =							self.default_image_processor
									_a     :      List[str]        =							prepare_img()
									_a     :      Optional[int]        =							image_processor(_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							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(_UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      List[str]        =							model(**_UpperCAmelCase		)

									_a     :      str        =							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(_UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)

									_a     :      Optional[Any]        =							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(_UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)

									_a     :      Optional[Any]        =							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(_UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Dict        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'		)
									    .to(_UpperCAmelCase		)
									    .eval()
									)
									_a     :      Union[str, Any]        =							self.default_image_processor
									_a     :      Tuple        =							prepare_img()
									_a     :      str        =							image_processor(_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)
									_a     :      Any        =							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(_UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      Any        =							model(**_UpperCAmelCase		)
									# masks_queries_logits
									_a     :      List[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)	,)
									_a     :      Union[str, Any]        =							[
									    [-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],
									]
									_a     :      int        =							torch.tensor(_UpperCAmelCase		).to(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)
									# class_queries_logits
									_a     :      str        =							outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
									_a     :      List[str]        =							torch.tensor(
									    [
									        [1.65_12E00, -5.25_72E00, -3.35_19E00],
									        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
									        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
									    ]		).to(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[Any]        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'		)
									    .to(_UpperCAmelCase		)
									    .eval()
									)
									_a     :      Union[str, Any]        =							self.default_image_processor
									_a     :      Dict        =							prepare_img()
									_a     :      Any        =							image_processor(_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)
									_a     :      int        =							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(_UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      Dict        =							model(**_UpperCAmelCase		)
									# masks_queries_logits
									_a     :      Union[str, 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)	,)
									_a     :      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]]
									_a     :      Optional[Any]        =							torch.tensor(_UpperCAmelCase		).to(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)
									# class_queries_logits
									_a     :      Dict        =							outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
									_a     :      Optional[int]        =							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(_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,_UpperCAmelCase	,atol=_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Dict        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'		)
									    .to(_UpperCAmelCase		)
									    .eval()
									)
									_a     :      Optional[Any]        =							self.default_image_processor

									_a     :      List[Any]        =							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'	,)

									_a     :      str        =							inputs['pixel_values'].to(_UpperCAmelCase		)
									_a     :      int        =							[el.to(_UpperCAmelCase		) for el in inputs['mask_labels']]
									_a     :      Optional[Any]        =							[el.to(_UpperCAmelCase		) for el in inputs['class_labels']]

									with torch.no_grad():
											_a     :      List[Any]        =							model(**_UpperCAmelCase		)

									self.assertTrue(outputs.loss is not None		)








import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



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




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[str]		     =					UNetaDModel(
             block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



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




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







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




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2




