
"""simple docstring"""



from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['vqvae']







    def __init__(			self			:       str     ,					__UpperCamelCase			:       AutoencoderKL     ,					__UpperCamelCase			:       UNetaDConditionModel     ,					__UpperCamelCase			:       Mel     ,					__UpperCamelCase			:       Union[DDIMScheduler, DDPMScheduler]     ,					)		->int:






        '''simple docstring'''






        super().__init__()
        self.register_modules(unet=__UpperCamelCase     ,					scheduler=__UpperCamelCase     ,					mel=__UpperCamelCase     ,					vqvae=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     )		->int:






        '''simple docstring'''






        return 50 if isinstance(self.scheduler     ,					__UpperCamelCase     ) else 10_00







    @torch.no_grad()
    def __call__(			self			:       Optional[Any]     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       str = None     ,					__UpperCamelCase			:       np.ndarray = None     ,					__UpperCamelCase			:       int = 0     ,					__UpperCamelCase			:       int = 0     ,					__UpperCamelCase			:       int = None     ,					__UpperCamelCase			:       torch.Generator = None     ,					__UpperCamelCase			:       float = 0     ,					__UpperCamelCase			:       float = 0     ,					__UpperCamelCase			:       torch.Generator = None     ,					__UpperCamelCase			:       float = 0     ,					__UpperCamelCase			:       torch.Tensor = None     ,					__UpperCamelCase			:       torch.Tensor = None     ,					__UpperCamelCase			:       Any=True     ,					)		->Union[
        Union[AudioPipelineOutput, ImagePipelineOutput],
        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
    ]:






        '''simple docstring'''






        _UpperCAmelCase          =   steps or self.get_default_steps()
        self.scheduler.set_timesteps(__UpperCamelCase     )
        _UpperCAmelCase          =   step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size     ) == int:
            _UpperCAmelCase          =   (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
            _UpperCAmelCase          =   randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )     ,					generator=__UpperCamelCase     ,					device=self.device     ,					)
        _UpperCAmelCase          =   noise
        _UpperCAmelCase          =   None

        if audio_file is not None or raw_audio is not None:
            self.mel.load_audio(__UpperCamelCase     ,					__UpperCamelCase     )
            _UpperCAmelCase          =   self.mel.audio_slice_to_image(__UpperCamelCase     )
            _UpperCAmelCase          =   np.frombuffer(input_image.tobytes()     ,					dtype="""uint8"""     ).reshape(
                (input_image.height, input_image.width)     )
            _UpperCAmelCase          =   (input_image / 2_55) * 2 - 1
            _UpperCAmelCase          =   torch.tensor(input_image[np.newaxis, :, :]     ,					dtype=torch.float     ).to(self.device     )

            if self.vqvae is not None:
                _UpperCAmelCase          =   self.vqvae.encode(torch.unsqueeze(__UpperCamelCase     ,					0     )     ).latent_dist.sample(
                    generator=__UpperCamelCase     )[0]
                _UpperCAmelCase          =   self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
                _UpperCAmelCase          =   self.scheduler.add_noise(__UpperCamelCase     ,					__UpperCamelCase     ,					self.scheduler.timesteps[start_step - 1]     )

            _UpperCAmelCase          =   (
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            _UpperCAmelCase          =   int(mask_start_secs * pixels_per_second     )
            _UpperCAmelCase          =   int(mask_end_secs * pixels_per_second     )
            _UpperCAmelCase          =   self.scheduler.add_noise(__UpperCamelCase     ,					__UpperCamelCase     ,					torch.tensor(self.scheduler.timesteps[start_step:]     )     )

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]     )     ):
            if isinstance(self.unet     ,					__UpperCamelCase     ):
                _UpperCAmelCase          =   self.unet(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )["""sample"""]
            else:
                _UpperCAmelCase          =   self.unet(__UpperCamelCase     ,					__UpperCamelCase     )["""sample"""]

            if isinstance(self.scheduler     ,					__UpperCamelCase     ):
                _UpperCAmelCase          =   self.scheduler.step(
                    model_output=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					sample=__UpperCamelCase     ,					eta=__UpperCamelCase     ,					generator=__UpperCamelCase     ,					)["""prev_sample"""]
            else:
                _UpperCAmelCase          =   self.scheduler.step(
                    model_output=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					sample=__UpperCamelCase     ,					generator=__UpperCamelCase     ,					)["""prev_sample"""]

            if mask is not None:
                if mask_start > 0:
                    _UpperCAmelCase          =   mask[:, step, :, :mask_start]
                if mask_end > 0:
                    _UpperCAmelCase          =   mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            _UpperCAmelCase          =   1 / self.vqvae.config.scaling_factor * images
            _UpperCAmelCase          =   self.vqvae.decode(__UpperCamelCase     )["""sample"""]

        _UpperCAmelCase          =   (images / 2 + 0.5).clamp(0     ,					1     )
        _UpperCAmelCase          =   images.cpu().permute(0     ,					2     ,					3     ,					1     ).numpy()
        _UpperCAmelCase          =   (images * 2_55).round().astype("""uint8"""     )
        _UpperCAmelCase          =   list(
            (Image.fromarray(_[:, :, 0]     ) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(__UpperCamelCase     ,					mode="""RGB"""     ).convert("""L"""     ) for _ in images)     )

        _UpperCAmelCase          =   [self.mel.image_to_audio(__UpperCamelCase     ) for _ in images]
        if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(__UpperCamelCase     )[:, np.newaxis, :]     )     ,					**ImagePipelineOutput(__UpperCamelCase     )     )







    @torch.no_grad()
    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       List[Image.Image]     ,					__UpperCamelCase			:       int = 50     )		->np.ndarray:






        '''simple docstring'''






        assert isinstance(self.scheduler     ,					__UpperCamelCase     )
        self.scheduler.set_timesteps(__UpperCamelCase     )
        _UpperCAmelCase          =   np.array(
            [np.frombuffer(image.tobytes()     ,					dtype="""uint8"""     ).reshape((1, image.height, image.width)     ) for image in images]     )
        _UpperCAmelCase          =   (sample / 2_55) * 2 - 1
        _UpperCAmelCase          =   torch.Tensor(__UpperCamelCase     ).to(self.device     )

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,					(0,)     )     ):
            _UpperCAmelCase          =   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            _UpperCAmelCase          =   self.scheduler.alphas_cumprod[t]
            _UpperCAmelCase          =   (
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            _UpperCAmelCase          =   1 - alpha_prod_t
            _UpperCAmelCase          =   self.unet(__UpperCamelCase     ,					__UpperCamelCase     )["""sample"""]
            _UpperCAmelCase          =   (1 - alpha_prod_t_prev) ** 0.5 * model_output
            _UpperCAmelCase          =   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            _UpperCAmelCase          =   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







    @staticmethod
    def      _snake_case					(			__UpperCamelCase			:       torch.Tensor     ,					__UpperCamelCase			:       torch.Tensor     ,					__UpperCamelCase			:       float     )		->torch.Tensor:






        '''simple docstring'''






        _UpperCAmelCase          =   acos(torch.dot(torch.flatten(__UpperCamelCase     )     ,					torch.flatten(__UpperCamelCase     )     ) / torch.norm(__UpperCamelCase     ) / torch.norm(__UpperCamelCase     )     )
        return sin((1 - alpha) * theta     ) * xa / sin(__UpperCamelCase     ) + sin(alpha * theta     ) * xa / sin(__UpperCamelCase     )

"""simple docstring"""



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 		a_						:







    def __init__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]=2     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Tuple=16     ,					__UpperCamelCase			:       Dict=3     ,					__UpperCamelCase			:       Dict=True     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[Any]=32     ,					__UpperCamelCase			:       Any=4     ,					__UpperCamelCase			:       Optional[int]=[0, 1, 2, 3]     ,					__UpperCamelCase			:       str=4     ,					__UpperCamelCase			:       Optional[Any]=37     ,					__UpperCamelCase			:       str="gelu"     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       Any=0.1     ,					__UpperCamelCase			:       Any=0.0_2     ,					__UpperCamelCase			:       Optional[int]=3     ,					__UpperCamelCase			:       int=[1, 3_84, 24, 24]     ,					__UpperCamelCase			:       Union[str, Any]=True     ,					__UpperCamelCase			:       Any=None     ,					)		->Optional[Any]:






        '''simple docstring'''






        _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      _snake_case					(			self			:       str     )		->int:






        '''simple docstring'''






        _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      _snake_case					(			self			:       List[str]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """global_padding""": """same""",
            """layer_type""": """bottleneck""",
            """depths""": [3, 4, 9],
            """out_features""": ["""stage1""", """stage2""", """stage3"""],
            """embedding_dynamic_padding""": True,
            """hidden_sizes""": [96, 1_92, 3_84, 7_68],
            """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      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        _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      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       List[Any]     )		->int:






        '''simple docstring'''






        _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      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        _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      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   config_and_inputs
        _UpperCAmelCase          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Dict     =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a      :				int     =				(
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

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







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   DPTModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					has_text_modality=__UpperCamelCase     ,					hidden_size=37     )







    def      _snake_case					(			self			:       Optional[int]     )		->Any:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    @unittest.skip(reason="""DPT does not use inputs_embeds"""     )
    def      _snake_case					(			self			:       Tuple     )		->Tuple:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        _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      _snake_case					(			self			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        _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      _snake_case					(			self			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        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      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        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      _snake_case					(			self			:       Tuple     )		->List[str]:






        '''simple docstring'''






        _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      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        pass







    @slow
    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
            _UpperCAmelCase          =   DPTModel.from_pretrained(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase          =   """add"""
        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   DPTForDepthEstimation(__UpperCamelCase     )







def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

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







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







    def      _snake_case					(			self			:       Any     )		->Optional[Any]:






        '''simple docstring'''






        _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, 3_84, 3_84)     )
        self.assertEqual(predicted_depth.shape     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]     ).to(__UpperCamelCase     )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00     ,					__UpperCamelCase     ,					atol=1e-4     )     )