



"""simple docstring"""







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase_   						=   8



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=BITS	)							->      Optional[Any]:
		lowercase__   :    List[Any]				=			x.device

		lowercase__   :    List[Any]				=			(x * 2_55).int().clamp(0						,     2_55	)

		lowercase__   :    List[Any]				=			2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=__lowerCamelCase	)
		lowercase__   :    Tuple				=			rearrange(__lowerCamelCase						,     '''d -> d 1 1'''	)
		lowercase__   :    Optional[int]				=			rearrange(__lowerCamelCase						,     '''b c h w -> b c 1 h w'''	)

		lowercase__   :    Tuple				=			((x & mask) != 0).float()
		lowercase__   :    Dict				=			rearrange(__lowerCamelCase						,     '''b c d h w -> b (c d) h w'''	)
		lowercase__   :    Optional[Any]				=			bits * 2 - 1
		return bits



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=BITS	)							->      List[Any]:
		lowercase__   :    str				=			x.device

		lowercase__   :    Tuple				=			(x > 0).int()
		lowercase__   :    Optional[Any]				=			2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=__lowerCamelCase						,     dtype=torch.intaa	)

		lowercase__   :    List[Any]				=			rearrange(__lowerCamelCase						,     '''d -> d 1 1'''	)
		lowercase__   :    Tuple				=			rearrange(__lowerCamelCase						,     '''b (c d) h w -> b c d h w'''						,     d=8	)
		lowercase__   :    Union[str, Any]				=			reduce(x * mask						,     '''b c d h w -> b c h w'''						,     '''sum'''	)
		return (dec / 2_55).clamp(0.0						,     1.0	)



def      __UpperCAmelCase      (    self						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = 0.0						,     __lowerCamelCase = True						,     __lowerCamelCase=None						,     __lowerCamelCase = True						,     )							->      Union[DDIMSchedulerOutput, Tuple]:
		if self.num_inference_steps is None:
				raise ValueError(
				    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		lowercase__   :    Union[str, Any]				=			timestep - self.config.num_train_timesteps // self.num_inference_steps

		# 2. compute alphas, betas
		lowercase__   :    Tuple				=			self.alphas_cumprod[timestep]
		lowercase__   :    Tuple				=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

		lowercase__   :    str				=			1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		lowercase__   :    Optional[int]				=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

		# 4. Clip "predicted x_0"
		lowercase__   :    Dict				=			self.bit_scale
		if self.config.clip_sample:
				lowercase__   :    Union[str, Any]				=			torch.clamp(__lowerCamelCase						,     -scale						,     __lowerCamelCase	)

		# 5. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		lowercase__   :    Optional[Any]				=			self._get_variance(__lowerCamelCase						,     __lowerCamelCase	)
		lowercase__   :    Optional[int]				=			eta * variance ** 0.5

		if use_clipped_model_output:
				# the model_output is always re-derived from the clipped x_0 in Glide
				lowercase__   :    Any				=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

		# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		lowercase__   :    Dict				=			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

		# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		lowercase__   :    Tuple				=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if eta > 0:
				# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
				lowercase__   :    Union[str, Any]				=			model_output.device if torch.is_tensor(__lowerCamelCase	) else '''cpu'''
				lowercase__   :    Union[str, Any]				=			torch.randn(model_output.shape						,     dtype=model_output.dtype						,     generator=__lowerCamelCase	).to(__lowerCamelCase	)
				lowercase__   :    Dict				=			self._get_variance(__lowerCamelCase						,     __lowerCamelCase	) ** 0.5 * eta * noise

				lowercase__   :    List[Any]				=			prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=__lowerCamelCase						,     pred_original_sample=__lowerCamelCase	)



def      __UpperCAmelCase      (    self						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase="epsilon"						,     __lowerCamelCase=None						,     __lowerCamelCase = True						,     )							->      Union[DDPMSchedulerOutput, Tuple]:
		lowercase__   :    List[str]				=			timestep

		if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
				lowercase__		,			lowercase__   :    Optional[Any]				=			torch.split(__lowerCamelCase						,     sample.shape[1]						,     dim=1	)
		else:
				lowercase__   :    Optional[int]				=			None

		# 1. compute alphas, betas
		lowercase__   :    Tuple				=			self.alphas_cumprod[t]
		lowercase__   :    Tuple				=			self.alphas_cumprod[t - 1] if t > 0 else self.one
		lowercase__   :    Optional[Any]				=			1 - alpha_prod_t
		lowercase__   :    Union[str, Any]				=			1 - alpha_prod_t_prev

		# 2. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
		if prediction_type == "epsilon":
				lowercase__   :    Dict				=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif prediction_type == "sample":
				lowercase__   :    Optional[int]				=			model_output
		else:
				raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

		# 3. Clip "predicted x_0"
		lowercase__   :    Tuple				=			self.bit_scale
		if self.config.clip_sample:
				lowercase__   :    Union[str, Any]				=			torch.clamp(__lowerCamelCase						,     -scale						,     __lowerCamelCase	)

		# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		lowercase__   :    Any				=			(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
		lowercase__   :    int				=			self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

		# 5. Compute predicted previous sample µ_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		lowercase__   :    List[str]				=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		lowercase__   :    Any				=			0
		if t > 0:
				lowercase__   :    List[Any]				=			torch.randn(
				    model_output.size()						,     dtype=model_output.dtype						,     layout=model_output.layout						,     generator=__lowerCamelCase	).to(model_output.device	)
				lowercase__   :    Dict				=			(self._get_variance(__lowerCamelCase						,     predicted_variance=__lowerCamelCase	) ** 0.5) * noise

		lowercase__   :    str				=			pred_prev_sample + variance

		if not return_dict:
				return (pred_prev_sample,)

		return DDPMSchedulerOutput(prev_sample=__lowerCamelCase						,     pred_original_sample=__lowerCamelCase	)





class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  str			,_snake_case						:  UNetaDConditionModel			,_snake_case						:  Union[DDIMScheduler, DDPMScheduler]			,_snake_case						:  Optional[float] = 1.0			,)					->		Optional[Any]:






				"""simple docstring"""



				super().__init__()
				lowercase__   :    Optional[Any]				=			bit_scale
				lowercase__   :    Dict				=			(
				    ddim_bit_scheduler_step if isinstance(_snake_case			,_snake_case						) else ddpm_bit_scheduler_step
				)

				self.register_modules(unet=_snake_case			,scheduler=_snake_case						)







		@torch.no_grad()
		def __call__(	self						:  Optional[Any]			,_snake_case						:  Optional[int] = 256			,_snake_case						:  Optional[int] = 256			,_snake_case						:  Optional[int] = 50			,_snake_case						:  Optional[torch.Generator] = None			,_snake_case						:  Optional[int] = 1			,_snake_case						:  Optional[str] = "pil"			,_snake_case						:  bool = True			,**_snake_case						:  Optional[int]			,)					->		Union[Tuple, ImagePipelineOutput]:






				"""simple docstring"""



				lowercase__   :    Dict				=			torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)			,generator=_snake_case			,)
				lowercase__   :    Dict				=			decimal_to_bits(_snake_case						) * self.bit_scale
				lowercase__   :    Optional[Any]				=			latents.to(self.device						)

				self.scheduler.set_timesteps(_snake_case						)

				for t in self.progress_bar(self.scheduler.timesteps						):
						# predict the noise residual
						lowercase__   :    List[Any]				=			self.unet(_snake_case			,_snake_case						).sample

						# compute the previous noisy sample x_t -> x_t-1
						lowercase__   :    Any				=			self.scheduler.step(_snake_case			,_snake_case			,_snake_case						).prev_sample

				lowercase__   :    str				=			bits_to_decimal(_snake_case						)

				if output_type == "pil":
						lowercase__   :    str				=			self.numpy_to_pil(_snake_case						)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=_snake_case						)








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  )  )




