






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				@property
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			1
								lowercase__    =			3
								lowercase__    =			(32, 32)

								lowercase__    =			floats_tensor((batch_size, num_channels) + sizes,  rng=random.Random(0  )  ).to(lowerCamelCase  )
								return image




				@property
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			UNetaDConditionModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=4,  out_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),  cross_attention_dim=32,  )
								return model




				@property
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			AutoencoderKL(
								    block_out_channels=[32, 64],  in_channels=3,  out_channels=3,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],  latent_channels=4,  )
								return model




				@property
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			RobertaSeriesConfig(
								    hidden_size=32,  project_dim=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=5_006,  )
								return RobertaSeriesModelWithTransformation(lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								def extract(*lowerCamelCase						:    Tuple,  **lowerCamelCase						:    Optional[Any]  ):
												class       _UpperCAmelCase					:

																"""simple docstring"""




																def __init__(     self						:    Union[str, Any]  ):

																				'''simple docstring'''
																				lowercase__    =			torch.ones([0]  )




																def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[Any]  ):

																				'''simple docstring'''
																				self.pixel_values.to(lowerCamelCase  )
																				return self



												return Out()

								return extract




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.dummy_cond_unet
								lowercase__    =			PNDMScheduler(skip_prk_steps=lowerCamelCase  )
								lowercase__    =			self.dummy_vae
								lowercase__    =			self.dummy_text_encoder
								lowercase__    =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
								lowercase__    =			77

								lowercase__    =			self.dummy_image.to(lowerCamelCase  )
								lowercase__    =			init_image / 2 + 0.5

								# make sure here that pndm scheduler skips prk
								lowercase__    =			AltDiffusionImgaImgPipeline(
								    unet=lowerCamelCase,  scheduler=lowerCamelCase,  vae=lowerCamelCase,  text_encoder=lowerCamelCase,  tokenizer=lowerCamelCase,  safety_checker=lowerCamelCase,  feature_extractor=self.dummy_extractor,  )
								lowercase__    =			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,  do_normalize=lowerCamelCase  )
								lowercase__    =			alt_pipe.to(lowerCamelCase  )
								alt_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			'''A painting of a squirrel eating a burger'''
								lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(0  )
								lowercase__    =			alt_pipe(
								    [prompt],  generator=lowerCamelCase,  guidance_scale=6.0,  num_inference_steps=2,  output_type='''np''',  image=lowerCamelCase,  )

								lowercase__    =			output.images

								lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(0  )
								lowercase__    =			alt_pipe(
								    [prompt],  generator=lowerCamelCase,  guidance_scale=6.0,  num_inference_steps=2,  output_type='''np''',  image=lowerCamelCase,  return_dict=lowerCamelCase,  )[0]

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

								assert image.shape == (1, 32, 32, 3)
								lowercase__    =			np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 5E-3




				@unittest.skipIf(torch_device != '''cuda''',  '''This test requires a GPU'''  )
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.dummy_cond_unet
								lowercase__    =			PNDMScheduler(skip_prk_steps=lowerCamelCase  )
								lowercase__    =			self.dummy_vae
								lowercase__    =			self.dummy_text_encoder
								lowercase__    =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
								lowercase__    =			77

								lowercase__    =			self.dummy_image.to(lowerCamelCase  )

								# put models in fp16
								lowercase__    =			unet.half()
								lowercase__    =			vae.half()
								lowercase__    =			bert.half()

								# make sure here that pndm scheduler skips prk
								lowercase__    =			AltDiffusionImgaImgPipeline(
								    unet=lowerCamelCase,  scheduler=lowerCamelCase,  vae=lowerCamelCase,  text_encoder=lowerCamelCase,  tokenizer=lowerCamelCase,  safety_checker=lowerCamelCase,  feature_extractor=self.dummy_extractor,  )
								lowercase__    =			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,  do_normalize=lowerCamelCase  )
								lowercase__    =			alt_pipe.to(lowerCamelCase  )
								alt_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			'''A painting of a squirrel eating a burger'''
								lowercase__    =			torch.manual_seed(0  )
								lowercase__    =			alt_pipe(
								    [prompt],  generator=lowerCamelCase,  num_inference_steps=2,  output_type='''np''',  image=lowerCamelCase,  ).images

								assert image.shape == (1, 32, 32, 3)




				@unittest.skipIf(torch_device != '''cuda''',  '''This test requires a GPU'''  )
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/img2img/sketch-mountains-input.jpg'''  )
								# resize to resolution that is divisible by 8 but not 16 or 32
								lowercase__    =			init_image.resize((760, 504)  )

								lowercase__    =			'''BAAI/AltDiffusion'''
								lowercase__    =			AltDiffusionImgaImgPipeline.from_pretrained(
								    lowerCamelCase,  safety_checker=lowerCamelCase,  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			'''A fantasy landscape, trending on artstation'''

								lowercase__    =			torch.manual_seed(0  )
								lowercase__    =			pipe(
								    prompt=lowerCamelCase,  image=lowerCamelCase,  strength=0.75,  guidance_scale=7.5,  generator=lowerCamelCase,  output_type='''np''',  )
								lowercase__    =			output.images[0]

								lowercase__    =			image[255:258, 383:386, -1]

								assert image.shape == (504, 760, 3)
								lowercase__    =			np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]  )

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



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

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/img2img/sketch-mountains-input.jpg'''  )
								lowercase__    =			init_image.resize((768, 512)  )
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''  )

								lowercase__    =			'''BAAI/AltDiffusion'''
								lowercase__    =			AltDiffusionImgaImgPipeline.from_pretrained(
								    lowerCamelCase,  safety_checker=lowerCamelCase,  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			'''A fantasy landscape, trending on artstation'''

								lowercase__    =			torch.manual_seed(0  )
								lowercase__    =			pipe(
								    prompt=lowerCamelCase,  image=lowerCamelCase,  strength=0.75,  guidance_scale=7.5,  generator=lowerCamelCase,  output_type='''np''',  )
								lowercase__    =			output.images[0]

								assert image.shape == (512, 768, 3)
								# img2img is flaky across GPUs even in fp32, so using MAE here
								assert np.abs(expected_image - image  ).max() < 1E-2












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       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    str,  lowerCamelCase						:    Dict,  lowerCamelCase						:    List[str]=2,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Tuple=16,  lowerCamelCase						:    List[Any]=3,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Tuple=32,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    Dict=[0, 1, 2, 3],  lowerCamelCase						:    Union[str, Any]=4,  lowerCamelCase						:    Optional[int]=37,  lowerCamelCase						:    Optional[Any]="gelu",  lowerCamelCase						:    List[Any]=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    Optional[int]=0.02,  lowerCamelCase						:    str=3,  lowerCamelCase						:    str=[1, 384, 24, 24],  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    List[str]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			image_size
								lowercase__    =			patch_size
								lowercase__    =			num_channels
								lowercase__    =			is_training
								lowercase__    =			use_labels
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			backbone_out_indices
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			backbone_featmap_shape
								lowercase__    =			scope
								lowercase__    =			is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								lowercase__    =			(image_size // patch_size) ** 2
								lowercase__    =			num_patches + 1




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowercase__    =			self.get_config()

								return config, pixel_values, labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			{
								    '''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=lowerCamelCase,  initializer_range=self.initializer_range,  is_hybrid=self.is_hybrid,  backbone_config=lowerCamelCase,  backbone_featmap_shape=self.backbone_featmap_shape,  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			DPTForDepthEstimation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.predicted_depth.shape,  (self.batch_size, self.image_size, self.image_size)  )




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			DPTForSemanticSegmentation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,  (self.batch_size, self.num_labels, self.image_size, self.image_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()
								lowercase__ ,						lowercase__ ,						lowercase__    =			config_and_inputs
								lowercase__    =			{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """depth-estimation""": DPTForDepthEstimation,
				        """feature-extraction""": DPTModel,
				        """image-segmentation""": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				@unittest.skip(reason='''DPT does not use inputs_embeds'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

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




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase__    =			['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase  )




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

												if model_class in get_values(lowerCamelCase  ):
																continue

												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

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




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__    =			_config_zero_init(lowerCamelCase  )
								for model_class in self.all_model_classes:
												lowercase__    =			model_class(config=lowerCamelCase  )
												# Skip the check for the backbone
												lowercase__    =			[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				lowercase__    =			[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 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												lowercase__    =			DPTModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__    =			'''add'''
								with self.assertRaises(lowerCamelCase  ):
												lowercase__    =			DPTForDepthEstimation(lowerCamelCase  )






def   a			(						):






				'''simple docstring'''
				lowercase__    =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




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

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''  )
								lowercase__    =			DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''  ).to(lowerCamelCase  )

								lowercase__    =			prepare_img()
								lowercase__    =			image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__    =			model(**lowerCamelCase  )
												lowercase__    =			outputs.predicted_depth

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

								lowercase__    =			torch.tensor(
								    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]  ).to(lowerCamelCase  )

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




