



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 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					List[str]=2			,						__lowerCAmelCase					:					Tuple=32			,						__lowerCAmelCase					:					Tuple=16			,						__lowerCAmelCase					:					Any=3			,						__lowerCAmelCase					:					str=True			,						__lowerCAmelCase					:					List[str]=True			,						__lowerCAmelCase					:					Optional[int]=32			,						__lowerCAmelCase					:					List[Any]=4			,						__lowerCAmelCase					:					Union[str, Any]=[0, 1, 2, 3]			,						__lowerCAmelCase					:					List[str]=4			,						__lowerCAmelCase					:					str=37			,						__lowerCAmelCase					:					Optional[int]="gelu"			,						__lowerCAmelCase					:					List[Any]=0.1			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					Optional[Any]=0.02			,						__lowerCAmelCase					:					Union[str, Any]=3			,						__lowerCAmelCase					:					Union[str, Any]=[1, 3_84, 24, 24]			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Optional[int]=None			,						)   ->	List[Any]:
    _A      =       parent
    _A      =       batch_size
    _A      =       image_size
    _A      =       patch_size
    _A      =       num_channels
    _A      =       is_training
    _A      =       use_labels
    _A      =       hidden_size
    _A      =       num_hidden_layers
    _A      =       backbone_out_indices
    _A      =       num_attention_heads
    _A      =       intermediate_size
    _A      =       hidden_act
    _A      =       hidden_dropout_prob
    _A      =       attention_probs_dropout_prob
    _A      =       initializer_range
    _A      =       num_labels
    _A      =       backbone_featmap_shape
    _A      =       scope
    _A      =       is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    _A      =       (image_size // patch_size) ** 2
    _A      =       num_patches + 1

 def     snake_case_      (    self					:					str      )   ->	str:
    _A      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

    _A      =       self.get_config()

    return config, pixel_values, labels

 def     snake_case_      (    self					:					Any      )   ->	int:
    _A      =       {
        '''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=__lowerCAmelCase			,						initializer_range=self.initializer_range			,						is_hybrid=self.is_hybrid			,						backbone_config=__lowerCAmelCase			,						backbone_featmap_shape=self.backbone_featmap_shape			,						)

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					int      )   ->	List[str]:
    _A      =       DPTModel(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )
    self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)      )

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

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Any      )   ->	Tuple:
    _A      =       self.num_labels
    _A      =       DPTForSemanticSegmentation(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase			,						labels=__lowerCAmelCase      )
    self.parent.assertEqual(
        result.logits.shape			,						(self.batch_size, self.num_labels, self.image_size, self.image_size)      )

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.prepare_config_and_inputs()
    _A  ,    _A  ,    _A      =       config_and_inputs
    _A      =       {'''pixel_values''': pixel_values}
    return config, inputs_dict


@require_torch
class 	lowerCamelCase__	(       _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

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

 a__					: Optional[Any] =  False
 a__					: Any =  False
 a__					: List[str] =  False

 def     snake_case_      (    self					:					List[Any]      )   ->	Any:
    _A      =       DPTModelTester(self      )
    _A      =       ConfigTester(self			,						config_class=__lowerCAmelCase			,						has_text_modality=__lowerCAmelCase			,						hidden_size=37      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    self.config_tester.run_common_tests()

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

 def     snake_case_      (    self					:					str      )   ->	Any:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

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

 def     snake_case_      (    self					:					Dict      )   ->	Union[str, Any]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

 def     snake_case_      (    self					:					Any      )   ->	str:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase      )

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

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

       if model_class in get_values(__lowerCAmelCase      ):
          continue

       _A      =       model_class(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.train()
       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase      )
       _A      =       model(**__lowerCAmelCase      ).loss
       loss.backward()

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
          continue

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

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

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

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

 @slow
 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       _A      =       DPTModel.from_pretrained(__lowerCAmelCase      )
       self.assertIsNotNone(__lowerCAmelCase      )

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

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


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



 """simple docstring"""

 def     snake_case_      (    self					:					Dict      )   ->	str:
    _A      =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
    _A      =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(__lowerCAmelCase      )

    _A      =       prepare_img()
    _A      =       image_processor(images=__lowerCAmelCase			,						return_tensors='''pt'''      ).to(__lowerCAmelCase      )

    # forward pass
    with torch.no_grad():
       _A      =       model(**__lowerCAmelCase      )
       _A      =       outputs.predicted_depth

    # verify the predicted depth
    _A      =       torch.Size((1, 3_84, 3_84)      )
    self.assertEqual(predicted_depth.shape			,						__lowerCAmelCase      )

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

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








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     bool:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   if len(_snake_case				) == 1:
      return True
   _A      =       series[1] - series[0]
   for index in range(len(_snake_case				) - 1				):
      if series[index + 1] - series[index] != common_diff:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     float:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   _A      =       0
   for val in series:
      answer += val
   return answer / len(_snake_case				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



