



'''simple docstring'''



import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=3			,				UpperCamelCase=32			,				UpperCamelCase=3			,				UpperCamelCase=10			,				UpperCamelCase=[8, 16, 32, 64]			,				UpperCamelCase=[1, 1, 2, 1]			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase="relu"			,				UpperCamelCase=3			,				UpperCamelCase=None			,				UpperCamelCase=["stage2", "stage3", "stage4"]			,				UpperCamelCase=[2, 3, 4]			,				UpperCamelCase=1			,				)     ->			List[Any]:
						__lowerCAmelCase												=  parent
						__lowerCAmelCase												=  batch_size
						__lowerCAmelCase												=  image_size
						__lowerCAmelCase												=  num_channels
						__lowerCAmelCase												=  embeddings_size
						__lowerCAmelCase												=  hidden_sizes
						__lowerCAmelCase												=  depths
						__lowerCAmelCase												=  is_training
						__lowerCAmelCase												=  use_labels
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  num_labels
						__lowerCAmelCase												=  scope
						__lowerCAmelCase												=  len(UpperCamelCase     )
						__lowerCAmelCase												=  out_features
						__lowerCAmelCase												=  out_indices
						__lowerCAmelCase												=  num_groups



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

						__lowerCAmelCase												=  None
						if self.use_labels:
							__lowerCAmelCase												=  ids_tensor([self.batch_size]			,				self.num_labels     )

						__lowerCAmelCase												=  self.get_config()

						return config, pixel_values, labels



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return BitConfig(
						    num_channels=self.num_channels			,				embeddings_size=self.embeddings_size			,				hidden_sizes=self.hidden_sizes			,				depths=self.depths			,				hidden_act=self.hidden_act			,				num_labels=self.num_labels			,				out_features=self.out_features			,				out_indices=self.out_indices			,				num_groups=self.num_groups			,				)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  BitModel(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(UpperCamelCase     )
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,				)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						__lowerCAmelCase												=  self.num_labels
						__lowerCAmelCase												=  BitForImageClassification(UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(UpperCamelCase			,				labels=UpperCamelCase     )
						self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Dict:
						__lowerCAmelCase												=  BitBackbone(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(UpperCamelCase     )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps     )			,				len(config.out_features     )     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,				[self.batch_size, self.hidden_sizes[1], 4, 4]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )			,				len(config.out_features     )     )
						self.parent.assertListEqual(model.channels			,				config.hidden_sizes[1:]     )

						# verify backbone works with out_features=None
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  BitBackbone(config=UpperCamelCase     )
						model.to(UpperCamelCase     )
						model.eval()
						__lowerCAmelCase												=  model(UpperCamelCase     )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps     )			,				1     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,				[self.batch_size, self.hidden_sizes[-1], 1, 1]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )			,				1     )
						self.parent.assertListEqual(model.channels			,				[config.hidden_sizes[-1]]     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.prepare_config_and_inputs()
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  config_and_inputs
						__lowerCAmelCase												=  {"pixel_values": pixel_values}
						return config, inputs_dict






@require_torch
class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__							,       unittest.TestCase ):
					a      :     Any        =							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
					a      :     int        =							(
					    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
					    if is_torch_available()
					    else {}
					)

					a      :     Dict        =							False
					a      :     Optional[Any]        =							False
					a      :     str        =							False
					a      :     List[Any]        =							False
					a      :     str        =							False



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  BitModelTester(self     )
						__lowerCAmelCase												=  ConfigTester(self			,				config_class=UpperCamelCase			,				has_text_modality=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return



					@unittest.skip(reason="Bit does not output attentions"     )
					def        UpperCAmelCase_ (    self     )     ->			int:
						pass



					@unittest.skip(reason="Bit does not use inputs_embeds"     )
					def        UpperCAmelCase_ (    self     )     ->			int:
						pass



					@unittest.skip(reason="Bit does not support input and output embeddings"     )
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						pass



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

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

							__lowerCAmelCase												=  ["pixel_values"]
							self.assertListEqual(arg_names[:1]			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							__lowerCAmelCase												=  model_class(config=UpperCamelCase     )
							for name, module in model.named_modules():
								if isinstance(UpperCamelCase			,				(nn.BatchNormad, nn.GroupNorm)     ):
									self.assertTrue(
									    torch.all(module.weight == 1     )			,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,				)
									self.assertTrue(
									    torch.all(module.bias == 0     )			,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,				)



					def        UpperCAmelCase_ (    self     )     ->			int:
						def check_hidden_states_output(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ):
							__lowerCAmelCase												=  model_class(UpperCamelCase     )
							model.to(UpperCamelCase     )
							model.eval()

							with torch.no_grad():
								__lowerCAmelCase												=  model(**self._prepare_for_class(UpperCamelCase			,				UpperCamelCase     )     )

							__lowerCAmelCase												=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							__lowerCAmelCase												=  self.model_tester.num_stages
							self.assertEqual(len(UpperCamelCase     )			,				expected_num_stages + 1     )

							# Bit's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]     )			,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,				)

						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase												=  ["preactivation", "bottleneck"]
						for model_class in self.all_model_classes:
							for layer_type in layers_type:
								__lowerCAmelCase												=  layer_type
								__lowerCAmelCase												=  True
								check_hidden_states_output(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								__lowerCAmelCase												=  True

								check_hidden_states_output(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )



					@unittest.skip(reason="Bit does not use feedforward chunking"     )
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						pass



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCamelCase     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase												=  BitModel.from_pretrained(UpperCamelCase     )
							self.assertIsNotNone(UpperCamelCase     )



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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






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



					@cached_property
					def        UpperCAmelCase_ (    self     )     ->			str:
						return (
						    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ) if is_vision_available() else None
						)



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(UpperCamelCase     )

						__lowerCAmelCase												=  self.default_image_processor
						__lowerCAmelCase												=  prepare_img()
						__lowerCAmelCase												=  image_processor(images=UpperCamelCase			,				return_tensors="pt"     ).to(UpperCamelCase     )

						# forward pass
						with torch.no_grad():
							__lowerCAmelCase												=  model(**UpperCamelCase     )

						# verify the logits
						__lowerCAmelCase												=  torch.Size((1, 1000)     )
						self.assertEqual(outputs.logits.shape			,				UpperCamelCase     )

						__lowerCAmelCase												=  torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]     ).to(UpperCamelCase     )

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






@require_torch
class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     Any        =							(BitBackbone,) if is_torch_available() else ()
					a      :     str        =							BitConfig

					a      :     Optional[Any]        =							False



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  BitModelTester(self     )







'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     List[Any]        =							KandinskyImgaImgPipeline
					a      :     Union[str, Any]        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					a      :     List[Any]        =							[
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					a      :     Any        =							[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					a      :     Union[str, Any]        =							False



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return self.time_input_dim



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return self.time_input_dim * 4



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 100



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
						return tokenizer



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  MCLIPConfig(
						    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1005			,				)

						__lowerCAmelCase												=  MultilingualCLIP(UpperCamelCase     )
						__lowerCAmelCase												=  text_encoder.eval()

						return text_encoder



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						torch.manual_seed(0     )

						__lowerCAmelCase												=  {
						    "in_channels": 4,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "text_image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "text_image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__lowerCAmelCase												=  UNetaDConditionModel(**UpperCamelCase     )
						return model



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  VQModel(**self.dummy_movq_kwargs     )
						return model



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.dummy_text_encoder
						__lowerCAmelCase												=  self.dummy_tokenizer
						__lowerCAmelCase												=  self.dummy_unet
						__lowerCAmelCase												=  self.dummy_movq

						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "beta_schedule": "linear",
						    "beta_start": 0.0_00_85,
						    "beta_end": 0.0_12,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__lowerCAmelCase												=  DDIMScheduler(**UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			Optional[Any]:
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1     )     ).to(UpperCamelCase     )
						# create init_image
						__lowerCAmelCase												=  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(UpperCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )

						if str(UpperCamelCase     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(UpperCamelCase     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
						__lowerCAmelCase												=  {
						    "prompt": "horse",
						    "image": init_image,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 64,
						    "width": 64,
						    "num_inference_steps": 10,
						    "guidance_scale": 7.0,
						    "strength": 0.2,
						    "output_type": "np",
						}
						return inputs



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

						__lowerCAmelCase												=  self.pipeline_class(**UpperCamelCase     )
						__lowerCAmelCase												=  pipe.to(UpperCamelCase     )

						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  pipe(**self.get_dummy_inputs(UpperCamelCase     )     )
						__lowerCAmelCase												=  output.images

						__lowerCAmelCase												=  pipe(
						    **self.get_dummy_inputs(UpperCamelCase     )			,				return_dict=UpperCamelCase			,				)[0]

						__lowerCAmelCase												=  image[0, -3:, -3:, -1]
						__lowerCAmelCase												=  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase												=  np.array(
						    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]     )
						assert (
						    np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_img2img_frog.npy"     )

						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
						__lowerCAmelCase												=  "A red cartoon frog, 4k"

						__lowerCAmelCase												=  KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa     )
						pipe_prior.to(UpperCamelCase     )

						__lowerCAmelCase												=  KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"			,				torch_dtype=torch.floataa     )
						__lowerCAmelCase												=  pipeline.to(UpperCamelCase     )

						pipeline.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  torch.Generator(device="cpu"     ).manual_seed(0     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  pipe_prior(
						    UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

						__lowerCAmelCase												=  pipeline(
						    UpperCamelCase			,				image=UpperCamelCase			,				image_embeds=UpperCamelCase			,				negative_image_embeds=UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=100			,				height=768			,				width=768			,				strength=0.2			,				output_type="np"			,				)

						__lowerCAmelCase												=  output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(UpperCamelCase			,				UpperCamelCase     )