






"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        _UpperCAmelCase          =   tempfile.mkdtemp()

        _UpperCAmelCase          =   [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """的""",
            """价""",
            """格""",
            """是""",
            """15""",
            """便""",
            """alex""",
            """##andra""",
            """，""",
            """。""",
            """-""",
            """t""",
            """shirt""",
        ]
        _UpperCAmelCase          =   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]     )
        with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""     ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

        _UpperCAmelCase          =   {
            """do_resize""": True,
            """size""": {"""height""": 2_24, """width""": 2_24},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
            """do_convert_rgb""": True,
        }
        _UpperCAmelCase          =   os.path.join(self.tmpdirname     ,					__UpperCamelCase     )
        with open(self.image_processor_file     ,					"""w"""     ,					encoding="""utf-8"""     ) as fp:
            json.dump(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					**__UpperCamelCase			:       Any     )		->List[Any]:






        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					**__UpperCamelCase			:       str     )		->Optional[int]:






        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					**__UpperCamelCase			:       Any     )		->Tuple:






        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







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






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname     )







    def      _snake_case					(			self			:       str     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   [np.random.randint(2_55     ,					size=(3, 30, 4_00)     ,					dtype=np.uinta     )]

        _UpperCAmelCase          =   [Image.fromarray(np.moveaxis(__UpperCamelCase     ,					0     ,					-1     )     ) for x in image_inputs]

        return image_inputs







    def      _snake_case					(			self			:       Tuple     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_tokenizer()
        _UpperCAmelCase          =   self.get_rust_tokenizer()
        _UpperCAmelCase          =   self.get_image_processor()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )
        processor_slow.save_pretrained(self.tmpdirname     )
        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,					use_fast=__UpperCamelCase     )

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )
        processor_fast.save_pretrained(self.tmpdirname     )
        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor_slow.tokenizer.get_vocab()     ,					tokenizer_slow.get_vocab()     )
        self.assertEqual(processor_fast.tokenizer.get_vocab()     ,					tokenizer_fast.get_vocab()     )
        self.assertEqual(tokenizer_slow.get_vocab()     ,					tokenizer_fast.get_vocab()     )
        self.assertIsInstance(processor_slow.tokenizer     ,					__UpperCamelCase     )
        self.assertIsInstance(processor_fast.tokenizer     ,					__UpperCamelCase     )

        self.assertEqual(processor_slow.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
        self.assertEqual(processor_fast.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
        self.assertIsInstance(processor_slow.image_processor     ,					__UpperCamelCase     )
        self.assertIsInstance(processor_fast.image_processor     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCAmelCase          =   self.get_tokenizer(cls_token="""(CLS)"""     ,					sep_token="""(SEP)"""     )
        _UpperCAmelCase          =   self.get_image_processor(do_normalize=__UpperCamelCase     )

        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname     ,					cls_token="""(CLS)"""     ,					sep_token="""(SEP)"""     ,					do_normalize=__UpperCamelCase     )

        self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer     ,					__UpperCamelCase     )

        self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   image_processor(__UpperCamelCase     ,					return_tensors="""np"""     )
        _UpperCAmelCase          =   processor(images=__UpperCamelCase     ,					return_tensors="""np"""     )

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2     )







    def      _snake_case					(			self			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     )

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]     )







    def      _snake_case					(			self			:       Any     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""
        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     ,					images=__UpperCamelCase     )

        self.assertListEqual(list(inputs.keys()     )     ,					["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

        # test if it raises when no input is passed
        with pytest.raises(__UpperCamelCase     ):
            processor()







    def      _snake_case					(			self			:       Union[str, Any]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase          =   processor.batch_decode(__UpperCamelCase     )
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )

        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""
        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     ,					images=__UpperCamelCase     )

        self.assertListEqual(list(inputs.keys()     )     ,					processor.model_input_names     )

"""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     )     )