


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


snake_case			:      Optional[Any] 							=   logging.get_logger(__name__)  # pylint: disable=invalid-name


def 				SCREAMING_SNAKE_CASE (     self   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = image.to(device=UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = image  # take image as image_embeddings
				_SCREAMING_SNAKE_CASE      = image_embeddings.unsqueeze(1							)

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = image_embeddings.shape
				_SCREAMING_SNAKE_CASE      = image_embeddings.repeat(1   ,UpperCAmelCase__   ,1							)
				_SCREAMING_SNAKE_CASE      = image_embeddings.view(bs_embed * num_images_per_prompt   ,UpperCAmelCase__   ,-1							)

				if do_classifier_free_guidance:
								_SCREAMING_SNAKE_CASE      = torch.zeros_like(UpperCAmelCase__							)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								_SCREAMING_SNAKE_CASE      = torch.cat([uncond_embeddings, image_embeddings]							)

				return image_embeddings



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_				, A_				, A_				, A_ = None				, )->	str:
											super().__init__()

											_SCREAMING_SNAKE_CASE      = {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

											_SCREAMING_SNAKE_CASE      = decoder_pipe_kwargs.get('torch_dtype'				, A_	) or prior.dtype

											_SCREAMING_SNAKE_CASE      = StableDiffusionImageVariationPipeline.from_pretrained(
											    'lambdalabs/sd-image-variations-diffusers'				, **A_	)

											# replace `_encode_image` method
											_SCREAMING_SNAKE_CASE      = types.MethodType(_encode_image				, self.decoder_pipe	)

											self.register_modules(
											    prior=A_				, tokenizer=A_				, text_encoder=A_				, prior_scheduler=A_				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_ = None				, A_ = None				, )->	str:
											if text_model_output is None:
															_SCREAMING_SNAKE_CASE      = len(A_	) if isinstance(A_				, A_	) else 1
															# get prompt text embeddings
															_SCREAMING_SNAKE_CASE      = self.tokenizer(
															    A_				, padding='max_length'				, max_length=self.tokenizer.model_max_length				, return_tensors='pt'				, )
															_SCREAMING_SNAKE_CASE      = text_inputs.input_ids
															_SCREAMING_SNAKE_CASE      = text_inputs.attention_mask.bool().to(A_	)

															if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			_SCREAMING_SNAKE_CASE      = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
																			logger.warning(
																			    'The following part of your input was truncated because CLIP can only handle sequences up to'
																			    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''	)
																			_SCREAMING_SNAKE_CASE      = text_input_ids[:, : self.tokenizer.model_max_length]

															_SCREAMING_SNAKE_CASE      = self.text_encoder(text_input_ids.to(A_	)	)

															_SCREAMING_SNAKE_CASE      = text_encoder_output.text_embeds
															_SCREAMING_SNAKE_CASE      = text_encoder_output.last_hidden_state

											else:
															_SCREAMING_SNAKE_CASE      = text_model_output[0].shape[0]
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = text_model_output[0], text_model_output[1]
															_SCREAMING_SNAKE_CASE      = text_attention_mask

											_SCREAMING_SNAKE_CASE      = text_embeddings.repeat_interleave(A_				, dim=0	)
											_SCREAMING_SNAKE_CASE      = text_encoder_hidden_states.repeat_interleave(A_				, dim=0	)
											_SCREAMING_SNAKE_CASE      = text_mask.repeat_interleave(A_				, dim=0	)

											if do_classifier_free_guidance:
															_SCREAMING_SNAKE_CASE      = [''] * batch_size

															_SCREAMING_SNAKE_CASE      = self.tokenizer(
															    A_				, padding='max_length'				, max_length=self.tokenizer.model_max_length				, truncation=A_				, return_tensors='pt'				, )
															_SCREAMING_SNAKE_CASE      = uncond_input.attention_mask.bool().to(A_	)
															_SCREAMING_SNAKE_CASE      = self.text_encoder(uncond_input.input_ids.to(A_	)	)

															_SCREAMING_SNAKE_CASE      = uncond_embeddings_text_encoder_output.text_embeds
															_SCREAMING_SNAKE_CASE      = uncond_embeddings_text_encoder_output.last_hidden_state

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

															_SCREAMING_SNAKE_CASE      = uncond_embeddings.shape[1]
															_SCREAMING_SNAKE_CASE      = uncond_embeddings.repeat(1				, A_	)
															_SCREAMING_SNAKE_CASE      = uncond_embeddings.view(batch_size * num_images_per_prompt				, A_	)

															_SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.shape[1]
															_SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.repeat(1				, A_				, 1	)
															_SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt				, A_				, -1	)
															_SCREAMING_SNAKE_CASE      = uncond_text_mask.repeat_interleave(A_				, dim=0	)

															# done duplicates

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															_SCREAMING_SNAKE_CASE      = torch.cat([uncond_embeddings, text_embeddings]	)
															_SCREAMING_SNAKE_CASE      = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

															_SCREAMING_SNAKE_CASE      = torch.cat([uncond_text_mask, text_mask]	)

											return text_embeddings, text_encoder_hidden_states, text_mask





							@property
							def 		__magic_name__			(    self	)->	Dict:
											if self.device != torch.device('meta'	) or not hasattr(self.prior				, '_hf_hook'	):
															return self.device
											for module in self.prior.modules():
															if (
															    hasattr(A_				, '_hf_hook'	)
															    and hasattr(module._hf_hook				, 'execution_device'	)
															    and module._hf_hook.execution_device is not None
															):
																			return torch.device(module._hf_hook.execution_device	)
											return self.device





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_	)->	List[Any]:
											if latents is None:
															_SCREAMING_SNAKE_CASE      = randn_tensor(A_				, generator=A_				, device=A_				, dtype=A_	)
											else:
															if latents.shape != shape:
																			raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
															_SCREAMING_SNAKE_CASE      = latents.to(A_	)

											_SCREAMING_SNAKE_CASE      = latents * scheduler.init_noise_sigma
											return latents





							def 		__magic_name__			(    self				, A_ = None	)->	int:
											self.decoder_pipe.to(A_	)
											super().to(A_	)





							@torch.no_grad()
							def __call__(    self				, A_ = None				, A_ = None				, A_ = None				, A_ = 1				, A_ = 25				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = 4.0				, A_ = 8.0				, A_ = 50				, A_ = 1				, A_ = 0.0				, A_ = "pil"				, A_ = True				, )->	Optional[Any]:
											if prompt is not None:
															if isinstance(A_				, A_	):
																			_SCREAMING_SNAKE_CASE      = 1
															elif isinstance(A_				, A_	):
																			_SCREAMING_SNAKE_CASE      = len(A_	)
															else:
																			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_	)}'''	)
											else:
															_SCREAMING_SNAKE_CASE      = text_model_output[0].shape[0]

											_SCREAMING_SNAKE_CASE      = self._execution_device

											_SCREAMING_SNAKE_CASE      = batch_size * num_images_per_prompt

											_SCREAMING_SNAKE_CASE      = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._encode_prompt(
											    A_				, A_				, A_				, A_				, A_				, A_	)

											# prior

											self.prior_scheduler.set_timesteps(A_				, device=A_	)
											_SCREAMING_SNAKE_CASE      = self.prior_scheduler.timesteps

											_SCREAMING_SNAKE_CASE      = self.prior.config.embedding_dim

											_SCREAMING_SNAKE_CASE      = self.prepare_latents(
											    (batch_size, embedding_dim)				, text_embeddings.dtype				, A_				, A_				, A_				, self.prior_scheduler				, )

											for i, t in enumerate(self.progress_bar(A_	)	):
															# expand the latents if we are doing classifier free guidance
															_SCREAMING_SNAKE_CASE      = torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents

															_SCREAMING_SNAKE_CASE      = self.prior(
															    A_				, timestep=A_				, proj_embedding=A_				, encoder_hidden_states=A_				, attention_mask=A_				, ).predicted_image_embedding

															if do_classifier_free_guidance:
																			_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = predicted_image_embedding.chunk(2	)
																			_SCREAMING_SNAKE_CASE      = predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

															if i + 1 == prior_timesteps_tensor.shape[0]:
																			_SCREAMING_SNAKE_CASE      = None
															else:
																			_SCREAMING_SNAKE_CASE      = prior_timesteps_tensor[i + 1]

															_SCREAMING_SNAKE_CASE      = self.prior_scheduler.step(
															    A_				, timestep=A_				, sample=A_				, generator=A_				, prev_timestep=A_				, ).prev_sample

											_SCREAMING_SNAKE_CASE      = self.prior.post_process_latents(A_	)

											_SCREAMING_SNAKE_CASE      = prior_latents

											_SCREAMING_SNAKE_CASE      = self.decoder_pipe(
											    image=A_				, height=A_				, width=A_				, num_inference_steps=A_				, guidance_scale=A_				, generator=A_				, output_type=A_				, return_dict=A_				, num_images_per_prompt=A_				, eta=A_				, )
											return output




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				, A_				, A_=2				, A_=32				, A_=16				, A_=3				, A_=True				, A_=True				, A_=32				, A_=4				, A_=[0, 1, 2, 3]				, A_=4				, A_=37				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=0.02				, A_=3				, A_=False				, A_=None				, )->	int:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = image_size
											_SCREAMING_SNAKE_CASE      = patch_size
											_SCREAMING_SNAKE_CASE      = num_channels
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = backbone_out_indices
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = initializer_range
											_SCREAMING_SNAKE_CASE      = num_labels
											_SCREAMING_SNAKE_CASE      = scope
											_SCREAMING_SNAKE_CASE      = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											_SCREAMING_SNAKE_CASE      = (image_size // patch_size) ** 2
											_SCREAMING_SNAKE_CASE      = num_patches + 1





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

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

											_SCREAMING_SNAKE_CASE      = self.get_config()

											return config, pixel_values, labels





							def 		__magic_name__			(    self	)->	Optional[int]:
											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=A_				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DPTModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, labels=A_	)
											self.parent.assertEqual(
											    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = config_and_inputs
											_SCREAMING_SNAKE_CASE      = {'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE   :					Any        =						(
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE   :					Tuple        =						False
							SCREAMING_SNAKE_CASE   :					str        =						False
							SCREAMING_SNAKE_CASE   :					Dict        =						False





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = DPTModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, has_text_modality=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	str:
											self.config_tester.run_common_tests()





							@unittest.skip(reason='DPT does not use inputs_embeds'	)
							def 		__magic_name__			(    self	)->	Optional[Any]:
											pass





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE      = ['pixel_values']
															self.assertListEqual(arg_names[:1]				, A_	)





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





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*A_	)





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

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

															if model_class in get_values(A_	):
																			continue

															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	Any:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

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





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE      = _config_zero_init(A_	)
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(config=A_	)
															# Skip the check for the backbone
															_SCREAMING_SNAKE_CASE      = []
															for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																							_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self	)->	List[Any]:
											pass





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = DPTModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)
															_SCREAMING_SNAKE_CASE      = outputs.predicted_depth

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

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(A_	)

											self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				, A_				, atol=1e-4	)	)





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											# verify the logits
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 150, 480, 480)	)
											self.assertEqual(outputs.logits.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											_SCREAMING_SNAKE_CASE      = outputs.logits.detach().cpu()

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_				, target_sizes=[(500, 300)]	)
											_SCREAMING_SNAKE_CASE      = torch.Size((500, 300)	)
											self.assertEqual(segmentation[0].shape				, A_	)

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_	)
											_SCREAMING_SNAKE_CASE      = torch.Size((480, 480)	)
											self.assertEqual(segmentation[0].shape				, A_	)
