



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
					from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Optional[int]=2						,  __UpperCAmelCase     :						str=32						,  __UpperCAmelCase     :						Dict=16						,  __UpperCAmelCase     :						Optional[Any]=3						,  __UpperCAmelCase     :						Optional[int]=True						,  __UpperCAmelCase     :						List[str]=True						,  __UpperCAmelCase     :						Optional[Any]=32						,  __UpperCAmelCase     :						int=4						,  __UpperCAmelCase     :						Optional[int]=[0, 1, 2, 3]						,  __UpperCAmelCase     :						Union[str, Any]=4						,  __UpperCAmelCase     :						Optional[int]=37						,  __UpperCAmelCase     :						Optional[Any]="gelu"						,  __UpperCAmelCase     :						Tuple=0.1						,  __UpperCAmelCase     :						Optional[int]=0.1						,  __UpperCAmelCase     :						List[str]=0.02						,  __UpperCAmelCase     :						Any=3						,  __UpperCAmelCase     :						Tuple=[1, 384, 24, 24]						,  __UpperCAmelCase     :						Any=True						,  __UpperCAmelCase     :						List[Any]=None						,  )      ->			Optional[Any]:
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       patch_size
							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       is_training
							UpperCAmelCase_=       use_labels
							UpperCAmelCase_=       hidden_size
							UpperCAmelCase_=       num_hidden_layers
							UpperCAmelCase_=       backbone_out_indices
							UpperCAmelCase_=       num_attention_heads
							UpperCAmelCase_=       intermediate_size
							UpperCAmelCase_=       hidden_act
							UpperCAmelCase_=       hidden_dropout_prob
							UpperCAmelCase_=       attention_probs_dropout_prob
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       num_labels
							UpperCAmelCase_=       backbone_featmap_shape
							UpperCAmelCase_=       scope
							UpperCAmelCase_=       is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							UpperCAmelCase_=       (image_size // patch_size) ** 2
							UpperCAmelCase_=       num_patches + 1




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

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

							UpperCAmelCase_=       self.get_config()

							return config, pixel_values, labels




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			int:
							UpperCAmelCase_=       {
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [96, 192, 384, 768],
							    """num_groups""": 2,
							}

							return DPTConfig(
							    image_size=self.image_size						,  patch_size=self.patch_size						,  num_channels=self.num_channels						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  backbone_out_indices=self.backbone_out_indices						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  is_decoder=UpperCamelCase_						,  initializer_range=self.initializer_range						,  is_hybrid=self.is_hybrid						,  backbone_config=UpperCamelCase_						,  backbone_featmap_shape=self.backbone_featmap_shape						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						str					)      ->			Union[str, Any]:
							UpperCAmelCase_=       DPTModel(config=UpperCamelCase_					)
							model.to(UpperCamelCase_					)
							model.eval()
							UpperCAmelCase_=       model(UpperCamelCase_					)
							self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       self.num_labels
							UpperCAmelCase_=       DPTForDepthEstimation(UpperCamelCase_					)
							model.to(UpperCamelCase_					)
							model.eval()
							UpperCAmelCase_=       model(UpperCamelCase_					)
							self.parent.assertEqual(result.predicted_depth.shape						,  (self.batch_size, self.image_size, self.image_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]					)      ->			List[Any]:
							UpperCAmelCase_=       self.num_labels
							UpperCAmelCase_=       DPTForSemanticSegmentation(UpperCamelCase_					)
							model.to(UpperCamelCase_					)
							model.eval()
							UpperCAmelCase_=       model(UpperCamelCase_						,  labels=UpperCamelCase_					)
							self.parent.assertEqual(
							    result.logits.shape						,  (self.batch_size, self.num_labels, self.image_size, self.image_size)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Dict:
							UpperCAmelCase_=       self.prepare_config_and_inputs()
							UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       config_and_inputs
							UpperCAmelCase_=       {"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 		lowercase						(			__lowerCamelCase			,   __lowerCamelCase			,   unittest.TestCase):






	"""simple docstring"""


	a__				: List[str]		       =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	a__				: Dict		       =							(
	    {
	        'depth-estimation': DPTForDepthEstimation,
	        'feature-extraction': DPTModel,
	        'image-segmentation': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a__				: Optional[int]		       =							False
	a__				: str		       =							False
	a__				: List[Any]		       =							False




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							UpperCAmelCase_=       DPTModelTester(self					)
							UpperCAmelCase_=       ConfigTester(self						,  config_class=UpperCamelCase_						,  has_text_modality=UpperCamelCase_						,  hidden_size=37					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Dict:
							self.config_tester.run_common_tests()




	@unittest.skip(reason="""DPT does not use inputs_embeds"""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[Any]:
							pass




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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Optional[int]:
							UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase_=       ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]						,  UpperCamelCase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

													UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()
													UpperCAmelCase_=       True

													if model_class in get_values(UpperCamelCase_					):
																			continue

													UpperCAmelCase_=       model_class(UpperCamelCase_					)
													model.to(UpperCamelCase_					)
													model.train()
													UpperCAmelCase_=       self._prepare_for_class(UpperCamelCase_						,  UpperCamelCase_						,  return_labels=UpperCamelCase_					)
													UpperCAmelCase_=       model(**UpperCamelCase_					).loss
													loss.backward()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Union[str, Any]:
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

													UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()
													UpperCAmelCase_=       False
													UpperCAmelCase_=       True

													if model_class in get_values(UpperCamelCase_					) or not model_class.supports_gradient_checkpointing:
																			continue
													UpperCAmelCase_=       model_class(UpperCamelCase_					)
													model.to(UpperCamelCase_					)
													model.gradient_checkpointing_enable()
													model.train()
													UpperCAmelCase_=       self._prepare_for_class(UpperCamelCase_						,  UpperCamelCase_						,  return_labels=UpperCamelCase_					)
													UpperCAmelCase_=       model(**UpperCamelCase_					).loss
													loss.backward()




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

							UpperCAmelCase_=       _config_zero_init(UpperCamelCase_					)
							for model_class in self.all_model_classes:
													UpperCAmelCase_=       model_class(config=UpperCamelCase_					)
													# Skip the check for the backbone
													UpperCAmelCase_=       []
													for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																									UpperCAmelCase_=       [F"""{name}.{key}""" for key in module.state_dict().keys()]
																									break

													for name, param in model.named_parameters():
																			if param.requires_grad:
																									if name in backbone_params:
																															continue
																									self.assertIn(
																									    ((param.data.mean() * 1E9).round() / 1E9).item()						,  [0.0, 1.0]						,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""						,  )




	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							pass




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Union[str, Any]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													UpperCAmelCase_=       DPTModel.from_pretrained(UpperCamelCase_					)
													self.assertIsNotNone(UpperCamelCase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							UpperCAmelCase_,       UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=       """add"""
							with self.assertRaises(UpperCamelCase_					):
													UpperCAmelCase_=       DPTForDepthEstimation(UpperCamelCase_					)



def 	__a					(   )				->			List[Any]:






						'''simple docstring'''



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






@require_torch
@require_vision
@slow
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""					)
							UpperCAmelCase_=       DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""					).to(UpperCamelCase_					)

							UpperCAmelCase_=       prepare_img()
							UpperCAmelCase_=       image_processor(images=UpperCamelCase_						,  return_tensors="""pt"""					).to(UpperCamelCase_					)

							# forward pass
							with torch.no_grad():
													UpperCAmelCase_=       model(**UpperCamelCase_					)
													UpperCAmelCase_=       outputs.predicted_depth

							# verify the predicted depth
							UpperCAmelCase_=       torch.Size((1, 384, 384)					)
							self.assertEqual(predicted_depth.shape						,  UpperCamelCase_					)

							UpperCAmelCase_=       torch.tensor(
							    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]					).to(UpperCamelCase_					)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						,  UpperCamelCase_						,  atol=1E-4					)					)









import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							KandinskyInpaintPipeline
	a__				: Tuple		       =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__				: str		       =							[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__				: List[Any]		       =							[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "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   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							return self.time_input_dim




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							return self.time_input_dim * 4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							return 100




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
							return tokenizer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       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=1_005						,  )

							UpperCAmelCase_=       MultilingualCLIP(__UpperCAmelCase					)
							UpperCAmelCase_=       text_encoder.eval()

							return text_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							torch.manual_seed(0					)

							UpperCAmelCase_=       {
							    """in_channels""": 9,
							    # 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,
							}

							UpperCAmelCase_=       UNetaDConditionModel(**__UpperCAmelCase					)
							return model




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       VQModel(**self.dummy_movq_kwargs					)
							return model




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.dummy_text_encoder
							UpperCAmelCase_=       self.dummy_tokenizer
							UpperCAmelCase_=       self.dummy_unet
							UpperCAmelCase_=       self.dummy_movq

							UpperCAmelCase_=       DDIMScheduler(
							    num_train_timesteps=1_000						,  beta_schedule="""linear"""						,  beta_start=0.00_085						,  beta_end=0.012						,  clip_sample=__UpperCAmelCase						,  set_alpha_to_one=__UpperCAmelCase						,  steps_offset=1						,  prediction_type="""epsilon"""						,  thresholding=__UpperCAmelCase						,  )

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

							return components




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict=0					)      ->			Dict:
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(seed + 1					)					).to(__UpperCAmelCase					)
							# create init_image
							UpperCAmelCase_=       floats_tensor((1, 3, 64, 64)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
							# create mask
							UpperCAmelCase_=       np.ones((64, 64)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       self.get_dummy_components()

							UpperCAmelCase_=       self.pipeline_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)

							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
							UpperCAmelCase_=       output.images

							UpperCAmelCase_=       pipe(
							    **self.get_dummy_inputs(__UpperCAmelCase					)						,  return_dict=__UpperCAmelCase						,  )[0]

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

							print(F"""image.shape {image.shape}"""					)

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

							UpperCAmelCase_=       np.array(
							    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

							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()}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






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






	"""simple docstring"""


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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
							UpperCAmelCase_=       np.ones((768, 768)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							UpperCAmelCase_=       """a hat"""

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

							UpperCAmelCase_=       KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipeline.to(__UpperCAmelCase					)
							pipeline.set_progress_bar_config(disable=__UpperCAmelCase					)

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

							UpperCAmelCase_=       pipeline(
							    __UpperCAmelCase						,  image=__UpperCAmelCase						,  mask_image=__UpperCAmelCase						,  image_embeds=__UpperCAmelCase						,  negative_image_embeds=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=100						,  height=768						,  width=768						,  output_type="""np"""						,  )

							UpperCAmelCase_=       output.images[0]

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

							assert_mean_pixel_difference(__UpperCAmelCase						,  __UpperCAmelCase					)




