





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     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Union[str, Any]      ,					a      :    List[Any]      ,					a      :    Dict=2      ,					a      :    List[Any]=32      ,					a      :    int=16      ,					a      :    Optional[Any]=3      ,					a      :    Optional[int]=True      ,					a      :    List[Any]=True      ,					a      :    Union[str, Any]=32      ,					a      :    List[Any]=4      ,					a      :    List[Any]=[0, 1, 2, 3]      ,					a      :    Union[str, Any]=4      ,					a      :    Tuple=37      ,					a      :    Dict="gelu"      ,					a      :    Dict=0.1      ,					a      :    int=0.1      ,					a      :    Dict=0.02      ,					a      :    Tuple=3      ,					a      :    List[str]=[1, 384, 24, 24]      ,					a      :    int=True      ,					a      :    List[str]=None      ,					)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  image_size
     lowercase__           =  patch_size
     lowercase__           =  num_channels
     lowercase__           =  is_training
     lowercase__           =  use_labels
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  backbone_out_indices
     lowercase__           =  num_attention_heads
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  initializer_range
     lowercase__           =  num_labels
     lowercase__           =  backbone_featmap_shape
     lowercase__           =  scope
     lowercase__           =  is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     lowercase__           =  (image_size // patch_size) ** 2
     lowercase__           =  num_patches + 1


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Any:





     """simple docstring"""







     lowercase__           =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

     lowercase__           =  self.get_config()

     return config, pixel_values, labels


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





     """simple docstring"""







     lowercase__           =  {
         'global_padding': 'same',
         'layer_type': 'bottleneck',
         'depths': [3, 4, 9],
         'out_features': ['stage1', 'stage2', 'stage3'],
         'embedding_dynamic_padding': True,
         'hidden_sizes': [96, 192, 384, 768],
         'num_groups': 2,
     }

     return DPTConfig(
         image_size=self.image_size      ,					patch_size=self.patch_size      ,					num_channels=self.num_channels      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					backbone_out_indices=self.backbone_out_indices      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					is_decoder=a      ,					initializer_range=self.initializer_range      ,					is_hybrid=self.is_hybrid      ,					backbone_config=a      ,					backbone_featmap_shape=self.backbone_featmap_shape      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str      ,					a      :    List[Any]      ,					a      :    List[Any]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  DPTModel(config=a	)
     model.to(a	)
     model.eval()
     lowercase__           =  model(a	)
     self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[str, Any]      ,					a      :    List[Any]      ,					a      :    int	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  self.num_labels
     lowercase__           =  DPTForDepthEstimation(a	)
     model.to(a	)
     model.eval()
     lowercase__           =  model(a	)
     self.parent.assertEqual(result.predicted_depth.shape      ,					(self.batch_size, self.image_size, self.image_size)	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Union[str, Any]      ,					a      :    str      ,					a      :    int	)->       Dict:





     """simple docstring"""







     lowercase__           =  self.num_labels
     lowercase__           =  DPTForSemanticSegmentation(a	)
     model.to(a	)
     model.eval()
     lowercase__           =  model(a      ,					labels=a	)
     self.parent.assertEqual(
         result.logits.shape      ,					(self.batch_size, self.num_labels, self.image_size, self.image_size)	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       str:





     """simple docstring"""







     lowercase__           =  self.prepare_config_and_inputs()
     lowercase__      ,   lowercase__      ,   lowercase__           =  config_and_inputs
     lowercase__           =  {'pixel_values': pixel_values}
     return config, inputs_dict






@require_torch
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		str			 =	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   _UpperCamelCase   :		List[str]			 =	(
       {
           'depth-estimation': DPTForDepthEstimation,
           'feature-extraction': DPTModel,
           'image-segmentation': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _UpperCamelCase   :		str			 =	False
   _UpperCamelCase   :		List[Any]			 =	False
   _UpperCamelCase   :		List[str]			 =	False


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





     """simple docstring"""







     lowercase__           =  DPTModelTester(self	)
     lowercase__           =  ConfigTester(self      ,					config_class=a      ,					has_text_modality=a      ,					hidden_size=37	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[str]:





     """simple docstring"""







     self.config_tester.run_common_tests()


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





     """simple docstring"""







     pass


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





     """simple docstring"""







     lowercase__      ,   lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Any:





     """simple docstring"""







     lowercase__      ,   lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*a	)


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





     """simple docstring"""







     lowercase__           =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       List[Any]:





     """simple docstring"""







     for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
         continue

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

       if model_class in get_values(a	):
         continue

       lowercase__           =  model_class(a	)
       model.to(a	)
       model.train()
       lowercase__           =  self._prepare_for_class(a      ,					a      ,					return_labels=a	)
       lowercase__           =  model(**a	).loss
       loss.backward()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       str:





     """simple docstring"""







     for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
         continue

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       int:





     """simple docstring"""







     lowercase__      ,   lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__           =  _config_zero_init(a	)
     for model_class in self.all_model_classes:
       lowercase__           =  model_class(config=a	)
       # Skip the check for the backbone
       lowercase__           =  []
       for name, module in model.named_modules():
         if module.__class__.__name__ == "DPTViTHybridEmbeddings":
           lowercase__           =  [f"""{name}.{key}""" for key in module.state_dict().keys()]
           break

       for name, param in model.named_parameters():
         if param.requires_grad:
           if name in backbone_params:
             continue
           self.assertIn(
               ((param.data.mean() * 1E9).round() / 1E9).item()      ,					[0.0, 1.0]      ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,					)


   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Any:





     """simple docstring"""







     pass


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Tuple:





     """simple docstring"""







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


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





     """simple docstring"""







     lowercase__      ,   lowercase__           =  self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__           =  'add'
     with self.assertRaises(a	):
       lowercase__           =  DPTForDepthEstimation(a	)






def       __UpperCamelCase  ()    ->       List[Any]:
  lowercase__           =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
  return image


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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       str:





     """simple docstring"""







     lowercase__           =  DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'	)
     lowercase__           =  DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'	).to(a	)

     lowercase__           =  prepare_img()
     lowercase__           =  image_processor(images=a      ,					return_tensors='pt'	).to(a	)

     # forward pass
     with torch.no_grad():
       lowercase__           =  model(**a	)
       lowercase__           =  outputs.predicted_depth

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

     lowercase__           =  torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]	).to(a	)

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









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


