

def       a__					(		UpperCAmelCase							:      int      ):

  if isinstance(__a    ,			__a      ):
    raise TypeError('''\'float\' object cannot be interpreted as an integer'''      )
  if isinstance(__a    ,			__a      ):
    raise TypeError('''\'str\' object cannot be interpreted as an integer'''      )

  if num == 0:
    return "0b0"

  UpperCAmelCase	:							Optional[Any]						=			False

  if num < 0:
    UpperCAmelCase	:							Optional[Any]						=			True
    UpperCAmelCase	:							int						=			-num

  UpperCAmelCase	:							Optional[Any]						=			[]
  while num > 0:
    binary.insert(0    ,			num % 2      )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a      ) for e in binary      )

  return "0b" + "".join(str(__a      ) for e in binary      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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					:



   def __init__(							self							:					Any,		__A							:					List[Any],		__A							:					Optional[Any]=2,		__A							:					List[Any]=3_2,		__A							:					Tuple=1_6,		__A							:					int=3,		__A							:					Any=True,		__A							:					List[Any]=True,		__A							:					List[Any]=3_2,		__A							:					List[Any]=4,		__A							:					Union[str, Any]=[0, 1, 2, 3],		__A							:					List[Any]=4,		__A							:					Optional[int]=3_7,		__A							:					int="gelu",		__A							:					Any=0.1,		__A							:					Tuple=0.1,		__A							:					Any=0.0_2,		__A							:					List[str]=3,		__A							:					int=[1, 3_8_4, 2_4, 2_4],		__A							:					Any=True,		__A							:					List[str]=None,		):
     UpperCAmelCase	:							List[str]						=			parent
     UpperCAmelCase	:							List[Any]						=			batch_size
     UpperCAmelCase	:							Tuple						=			image_size
     UpperCAmelCase	:							Dict						=			patch_size
     UpperCAmelCase	:							str						=			num_channels
     UpperCAmelCase	:							Tuple						=			is_training
     UpperCAmelCase	:							Optional[Any]						=			use_labels
     UpperCAmelCase	:							Dict						=			hidden_size
     UpperCAmelCase	:							Optional[int]						=			num_hidden_layers
     UpperCAmelCase	:							str						=			backbone_out_indices
     UpperCAmelCase	:							Dict						=			num_attention_heads
     UpperCAmelCase	:							Dict						=			intermediate_size
     UpperCAmelCase	:							Union[str, Any]						=			hidden_act
     UpperCAmelCase	:							Optional[Any]						=			hidden_dropout_prob
     UpperCAmelCase	:							Tuple						=			attention_probs_dropout_prob
     UpperCAmelCase	:							str						=			initializer_range
     UpperCAmelCase	:							Optional[int]						=			num_labels
     UpperCAmelCase	:							int						=			backbone_featmap_shape
     UpperCAmelCase	:							Union[str, Any]						=			scope
     UpperCAmelCase	:							int						=			is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase	:							Any						=			(image_size // patch_size) ** 2
     UpperCAmelCase	:							Optional[Any]						=			num_patches + 1



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Optional[Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     UpperCAmelCase	:							Union[str, Any]						=			None
     if self.use_labels:
       UpperCAmelCase	:							List[Any]						=			ids_tensor([self.batch_size, self.image_size, self.image_size],		self.num_labels						)

     UpperCAmelCase	:							Tuple						=			self.get_config()

     return config, pixel_values, labels



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[Any]						=			{
         '''global_padding''': '''same''',
         '''layer_type''': '''bottleneck''',
         '''depths''': [3, 4, 9],
         '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
         '''embedding_dynamic_padding''': True,
         '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
         '''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 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any],		__A							:					Union[str, Any],		__A							:					Tuple						):
     UpperCAmelCase	:							Optional[Any]						=			DPTModel(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							int						=			model(__A						)
     self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Any,		__A							:					Dict,		__A							:					Optional[int]						):
     UpperCAmelCase	:							Optional[Any]						=			self.num_labels
     UpperCAmelCase	:							List[Any]						=			DPTForDepthEstimation(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Tuple						=			model(__A						)
     self.parent.assertEqual(result.predicted_depth.shape,		(self.batch_size, self.image_size, self.image_size)						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Dict,		__A							:					List[Any],		__A							:					Optional[int]						):
     UpperCAmelCase	:							Dict						=			self.num_labels
     UpperCAmelCase	:							Tuple						=			DPTForSemanticSegmentation(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Dict						=			model(__A,		labels=__A						)
     self.parent.assertEqual(
         result.logits.shape,		(self.batch_size, self.num_labels, self.image_size, self.image_size)						)





   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							str						=			self.prepare_config_and_inputs()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			config_and_inputs
     UpperCAmelCase	:							Union[str, Any]						=			{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   UpperCamelCase       					=					(
       {
           """depth-estimation""": DPTForDepthEstimation,
           """feature-extraction""": DPTModel,
           """image-segmentation""": DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							int						=			DPTModelTester(self						)
     UpperCAmelCase	:							List[Any]						=			ConfigTester(self,		config_class=__A,		has_text_modality=__A,		hidden_size=3_7						)



   def 	__magic_name__   (							self							:					Any						):
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''DPT does not use inputs_embeds'''						)
   def 	__magic_name__   (							self							:					int						):
     pass



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       UpperCAmelCase	:							List[Any]						=			model_class(__A						)
       self.assertIsInstance(model.get_input_embeddings(),		(nn.Module)						)
       UpperCAmelCase	:							Union[str, Any]						=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__A,		nn.Linear						)						)



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:							Dict						=			['''pixel_values''']
       self.assertListEqual(arg_names[:1],		__A						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							int						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							List[str]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*__A						)



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

       UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase	:							str						=			True

       if model_class in get_values(__A						):
         continue

       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)
       model.to(__A						)
       model.train()
       UpperCAmelCase	:							str						=			self._prepare_for_class(__A,		__A,		return_labels=__A						)
       UpperCAmelCase	:							Union[str, Any]						=			model(**__A						).loss
       loss.backward()



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

       UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase	:							int						=			False
       UpperCAmelCase	:							int						=			True

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



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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



   @slow
   def 	__magic_name__   (							self							:					Optional[Any]						):
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       UpperCAmelCase	:							Optional[int]						=			DPTModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)





   def 	__magic_name__   (							self							:					int						):
     # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase	:							int						=			'''add'''
     with self.assertRaises(__A						):
       UpperCAmelCase	:							Dict						=			DPTForDepthEstimation(__A						)






def       a__					(		)  ->						Tuple:
  UpperCAmelCase	:							Optional[Any]						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
  return image






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



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Dict						=			DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''						)
     UpperCAmelCase	:							Tuple						=			DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''						).to(__A						)

     UpperCAmelCase	:							List[Any]						=			prepare_img()
     UpperCAmelCase	:							Union[str, Any]						=			image_processor(images=__A,		return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase	:							int						=			model(**__A						)
       UpperCAmelCase	:							int						=			outputs.predicted_depth

     # verify the predicted depth
     UpperCAmelCase	:							Tuple						=			torch.Size((1, 3_8_4, 3_8_4)						)
     self.assertEqual(predicted_depth.shape,		__A						)

     UpperCAmelCase	:							Dict						=			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(__A						)

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0,		__A,		atol=1E-4						)						)





