'''simple docstring'''

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam



class       UpperCAmelCase  (						tf.keras.optimizers.schedules.LearningRateSchedule ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = None     ,       )  ->  Union[str, Any]:
        super().__init__()
        lowercase__    :   Any						= initial_learning_rate
        lowercase__    :   str						= warmup_steps
        lowercase__    :   Dict						= power
        lowercase__    :   int						= decay_schedule_fn
        lowercase__    :   Dict						= name

       def __call__(						self     ,       __lowerCAmelCase			)  ->  int:
        with tf.name_scope(self.name or '''WarmUp'''			) as name:
         # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
         # learning rate will be `global_step/num_warmup_steps * init_lr`.
         lowercase__    :   Union[str, Any]						= tf.cast(__lowerCAmelCase     ,       tf.floataa			)
         lowercase__    :   Optional[int]						= tf.cast(self.warmup_steps     ,       tf.floataa			)
         lowercase__    :   Dict						= global_step_float / warmup_steps_float
         lowercase__    :   Dict						= self.initial_learning_rate * tf.math.pow(__lowerCAmelCase     ,       self.power			)
         return tf.cond(
             global_step_float < warmup_steps_float     ,       lambda: warmup_learning_rate     ,       lambda: self.decay_schedule_fn(step - self.warmup_steps			)     ,       name=__lowerCAmelCase     ,       )


       def _lowerCAmelCase(						self			)  ->  List[str]:
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 0.9		,					UpperCAmelCase = 0.9_9_9		,					UpperCAmelCase = 1E-8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = None		,					):
 lowercase__    :   List[Any]						= tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=UpperCAmelCase		,					)
 if num_warmup_steps:
  lowercase__    :   Optional[Any]						= WarmUp(
      initial_learning_rate=UpperCAmelCase		,					decay_schedule_fn=UpperCAmelCase		,					warmup_steps=UpperCAmelCase		,					)
 if weight_decay_rate > 0.0:
  lowercase__    :   str						= AdamWeightDecay(
      learning_rate=UpperCAmelCase		,					weight_decay_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=UpperCAmelCase		,					)
 else:
  lowercase__    :   Any						= tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					)
 # We return the optimizer and the LR scheduler in order to better track the
 # evolution of the LR independently of the optimizer.
 return optimizer, lr_schedule



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase = 0.0_0_1     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = 0.9_9_9     ,       __lowerCAmelCase = 1E-7     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = 0.0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "AdamWeightDecay"     ,       **__lowerCAmelCase     ,       )  ->  str:
        super().__init__(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   int						= weight_decay_rate
        lowercase__    :   Dict						= include_in_weight_decay
        lowercase__    :   List[str]						= exclude_from_weight_decay

       @classmethod
       def _lowerCAmelCase(						cls     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= {'''WarmUp''': WarmUp}
        return super(__lowerCAmelCase     ,       cls			).from_config(__lowerCAmelCase     ,       custom_objects=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        super(__lowerCAmelCase     ,       self			)._prepare_local(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= tf.constant(
            self.weight_decay_rate     ,       name='''adam_weight_decay_rate'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   str						= self._do_use_weight_decay(var.name			)
        if do_decay:
         return var.assign_sub(
             learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']     ,       use_locking=self._use_locking     ,       )
        return tf.no_op()

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Any						= list(zip(*__lowerCAmelCase			)			)
        return super(__lowerCAmelCase     ,       self			).apply_gradients(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       name=__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

        lowercase__    :   Union[str, Any]						= apply_state or {}
        lowercase__    :   Optional[Any]						= apply_state.get((var_device, var_dtype)			)
        if coefficients is None:
         lowercase__    :   Union[str, Any]						= self._fallback_apply_state(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Optional[int]						= coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_dense(__lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_sparse(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}			)
        return config


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        if self.weight_decay_rate == 0:
         return False

        if self._include_in_weight_decay:
         for r in self._include_in_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return True

        if self._exclude_from_weight_decay:
         for r in self._exclude_from_weight_decay:
          if re.search(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return False
        return True



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= []
        lowercase__    :   Optional[int]						= None

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if self._accum_steps is None:
         lowercase__    :   int						= tf.Variable(
             tf.constant(0     ,       dtype=tf.intaa			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )

        return self._accum_steps.value()

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        if not self._gradients:
         raise ValueError('''The accumulator should be called first to initialize the gradients'''			)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

       def __call__(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self._gradients:
         lowercase__    :   str						= self.step  # Create the step variable.
         self._gradients.extend(
             [
                 tf.Variable(
                     tf.zeros_like(__lowerCAmelCase			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )
                 if gradient is not None
                 else gradient
                 for gradient in gradients
             ]			)
        if len(__lowerCAmelCase			) != len(self._gradients			):
         raise ValueError(F"""Expected {len(self._gradients			)} gradients, but got {len(__lowerCAmelCase			)}"""			)

        for accum_gradient, gradient in zip(self._gradients     ,       __lowerCAmelCase			):
         if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(__lowerCAmelCase			)

        self._accum_steps.assign_add(1			)


       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if not self._gradients:
         return
        self._accum_steps.assign(0			)
        for gradient in self._gradients:
         if gradient is not None:
          gradient.assign(tf.zeros_like(__lowerCAmelCase			)			)


'''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       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=2     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=16     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=[0, 1, 2, 3]     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=37     ,       __lowerCAmelCase="gelu"     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.0_2     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=[1, 384, 24, 24]     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       )  ->  Dict:
        lowercase__    :   str						= parent
        lowercase__    :   List[Any]						= batch_size
        lowercase__    :   Dict						= image_size
        lowercase__    :   Tuple						= patch_size
        lowercase__    :   str						= num_channels
        lowercase__    :   Dict						= is_training
        lowercase__    :   Optional[int]						= use_labels
        lowercase__    :   List[Any]						= hidden_size
        lowercase__    :   int						= num_hidden_layers
        lowercase__    :   int						= backbone_out_indices
        lowercase__    :   List[str]						= num_attention_heads
        lowercase__    :   Union[str, Any]						= intermediate_size
        lowercase__    :   List[Any]						= hidden_act
        lowercase__    :   str						= hidden_dropout_prob
        lowercase__    :   Tuple						= attention_probs_dropout_prob
        lowercase__    :   List[Any]						= initializer_range
        lowercase__    :   Optional[int]						= num_labels
        lowercase__    :   Optional[int]						= backbone_featmap_shape
        lowercase__    :   int						= scope
        lowercase__    :   List[str]						= is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase__    :   List[str]						= (image_size // patch_size) ** 2
        lowercase__    :   Tuple						= num_patches + 1

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

        lowercase__    :   Union[str, Any]						= self.get_config()

        return config, pixel_values, labels

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= {
            '''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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= DPTModel(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Dict						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Union[str, Any]						= self.num_labels
        lowercase__    :   str						= DPTForDepthEstimation(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   List[str]						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.predicted_depth.shape     ,       (self.batch_size, self.image_size, self.image_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   str						= self.num_labels
        lowercase__    :   Tuple						= DPTForSemanticSegmentation(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Dict						= model(__lowerCAmelCase     ,       labels=__lowerCAmelCase			)
        self.parent.assertEqual(
            result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)			)


       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   List[str]						= self.prepare_config_and_inputs()
        lowercase__			,							lowercase__			,							lowercase__    :   Dict						= config_and_inputs
        lowercase__    :   Tuple						= {'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase  (						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



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

       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   str						= DPTModelTester(self			)
        lowercase__    :   int						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       has_text_modality=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        self.config_tester.run_common_tests()

       @unittest.skip(reason='''DPT does not use inputs_embeds'''			)
       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__			,							lowercase__    :   Union[str, Any]						= self.model_tester.prepare_config_and_inputs_for_common()

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

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

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

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

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Any						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        for model_class in self.all_model_classes:
         if model_class.__name__ == "DPTForDepthEstimation":
          continue

         lowercase__			,							lowercase__    :   str						= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__    :   Dict						= True

         if model_class in get_values(__lowerCAmelCase			):
          continue

         lowercase__    :   List[str]						= model_class(__lowerCAmelCase			)
         model.to(__lowerCAmelCase			)
         model.train()
         lowercase__    :   Tuple						= self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase     ,       return_labels=__lowerCAmelCase			)
         lowercase__    :   str						= model(**__lowerCAmelCase			).loss
         loss.backward()

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

         lowercase__			,							lowercase__    :   Optional[int]						= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__    :   Any						= False
         lowercase__    :   str						= True

         if model_class in get_values(__lowerCAmelCase			) or not model_class.supports_gradient_checkpointing:
          continue
         lowercase__    :   List[str]						= model_class(__lowerCAmelCase			)
         model.to(__lowerCAmelCase			)
         model.gradient_checkpointing_enable()
         model.train()
         lowercase__    :   str						= self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase     ,       return_labels=__lowerCAmelCase			)
         lowercase__    :   List[Any]						= model(**__lowerCAmelCase			).loss
         loss.backward()

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

        lowercase__    :   Union[str, Any]						= _config_zero_init(__lowerCAmelCase			)
        for model_class in self.all_model_classes:
         lowercase__    :   Dict						= model_class(config=__lowerCAmelCase			)
         # Skip the check for the backbone
         lowercase__    :   Union[str, Any]						= []
         for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
           lowercase__    :   List[Any]						= [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 _lowerCAmelCase(						self			)  ->  List[str]:
        pass

       @slow
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
         lowercase__    :   Dict						= DPTModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)


       def _lowerCAmelCase(						self			)  ->  str:
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        lowercase__			,							lowercase__    :   Any						= self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__    :   List[str]						= '''add'''
        with self.assertRaises(__lowerCAmelCase			):
         lowercase__    :   Tuple						= DPTForDepthEstimation(__lowerCAmelCase			)


def       __UpperCamelCase	(       ):
 lowercase__    :   Optional[int]						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
 return image





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





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Optional[int]						= DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''			)
        lowercase__    :   List[Any]						= DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''			).to(__lowerCAmelCase			)

        lowercase__    :   Optional[Any]						= prepare_img()
        lowercase__    :   Optional[Any]						= image_processor(images=__lowerCAmelCase     ,       return_tensors='''pt'''			).to(__lowerCAmelCase			)

        # forward pass
        with torch.no_grad():
         lowercase__    :   Optional[Any]						= model(**__lowerCAmelCase			)
         lowercase__    :   str						= outputs.predicted_depth

        # verify the predicted depth
        lowercase__    :   Optional[Any]						= torch.Size((1, 384, 384)			)
        self.assertEqual(predicted_depth.shape     ,       __lowerCAmelCase			)

        lowercase__    :   str						= 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(__lowerCAmelCase			)

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

