




'''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						:	str   ,				lowercase						:	List[Any]   ,				lowercase						:	Dict=2   ,				lowercase						:	str=32   ,				lowercase						:	Optional[Any]=16   ,				lowercase						:	Optional[Any]=3   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Optional[int]=32   ,				lowercase						:	Any=4   ,				lowercase						:	str=[0, 1, 2, 3]   ,				lowercase						:	List[Any]=4   ,				lowercase						:	str=37   ,				lowercase						:	Optional[Any]="gelu"   ,				lowercase						:	Tuple=0.1   ,				lowercase						:	Tuple=0.1   ,				lowercase						:	Union[str, Any]=0.02   ,				lowercase						:	int=3   ,				lowercase						:	int=[1, 384, 24, 24]   ,				lowercase						:	str=True   ,				lowercase						:	List[Any]=None   ,				):



           '''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     A      (	self						:	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     A      (	self						:	str				):



           '''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, 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=lowercase   ,				initializer_range=self.initializer_range   ,				is_hybrid=self.is_hybrid   ,				backbone_config=lowercase   ,				backbone_featmap_shape=self.backbone_featmap_shape   ,				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTModel(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)
       def     A      (	self						:	List[str]   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[int]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		DPTForDepthEstimation(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(result.predicted_depth.shape   ,				(self.batch_size, self.image_size, self.image_size)				)
       def     A      (	self						:	int   ,				lowercase						:	Dict   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	int				):



           '''simple docstring'''

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







       def     A      (	self						:	Tuple				):



           '''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  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Dict   				=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       __a				:     Optional[int]   				=					(
           {
               """depth-estimation""": DPTForDepthEstimation,
               """feature-extraction""": DPTModel,
               """image-segmentation""": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __a				:     Any   				=					False
       __a				:     List[Any]   				=					False
       __a				:     Dict   				=					False
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTModelTester(self				)
           UpperCAmelCase			       =		ConfigTester(self   ,				config_class=lowercase   ,				has_text_modality=lowercase   ,				hidden_size=37				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       @unittest.skip(reason='''DPT does not use inputs_embeds'''				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)
               self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)				)
               UpperCAmelCase			       =		model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowercase   ,				nn.Linear				)				)
       def     A      (	self						:	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(lowercase				)
               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]   ,				lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

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



           '''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(lowercase				):
                   continue

               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       def     A      (	self						:	str				):



           '''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(lowercase				) or not model_class.supports_gradient_checkpointing:
                   continue
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.gradient_checkpointing_enable()
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           UpperCAmelCase			       =		_config_zero_init(lowercase				)
           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(config=lowercase				)
               # 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     A      (	self						:	int				):



           '''simple docstring'''

           pass
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

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







       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		'''add'''
           with self.assertRaises(lowercase				):
               UpperCAmelCase			       =		DPTForDepthEstimation(lowercase				)






def 						snake_case_							():
    UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image







@require_torch
@require_vision
@slow
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''				)
           UpperCAmelCase			       =		DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''				).to(lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				).to(lowercase				)

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

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

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

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








'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


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



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp