

"""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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							Dict=2  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							Tuple=1_6  ,lowercase_     :							List[Any]=3  ,lowercase_     :							Any=True  ,lowercase_     :							Optional[int]=True  ,lowercase_     :							Tuple=3_2  ,lowercase_     :							Any=4  ,lowercase_     :							List[str]=[0, 1, 2, 3]  ,lowercase_     :							int=4  ,lowercase_     :							Any=3_7  ,lowercase_     :							Optional[Any]="gelu"  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							Tuple=0.02  ,lowercase_     :							List[Any]=3  ,lowercase_     :							int=[1, 3_8_4, 2_4, 2_4]  ,lowercase_     :							List[str]=True  ,lowercase_     :							List[str]=None  ,):
						lowerCAmelCase__      :		Union[str, Any]    							=      parent
						lowerCAmelCase__      :		List[str]    							=      batch_size
						lowerCAmelCase__      :		Tuple    							=      image_size
						lowerCAmelCase__      :		Dict    							=      patch_size
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		Any    							=      is_training
						lowerCAmelCase__      :		Union[str, Any]    							=      use_labels
						lowerCAmelCase__      :		Tuple    							=      hidden_size
						lowerCAmelCase__      :		Tuple    							=      num_hidden_layers
						lowerCAmelCase__      :		Optional[Any]    							=      backbone_out_indices
						lowerCAmelCase__      :		Tuple    							=      num_attention_heads
						lowerCAmelCase__      :		str    							=      intermediate_size
						lowerCAmelCase__      :		Union[str, Any]    							=      hidden_act
						lowerCAmelCase__      :		List[str]    							=      hidden_dropout_prob
						lowerCAmelCase__      :		List[str]    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		Tuple    							=      initializer_range
						lowerCAmelCase__      :		List[str]    							=      num_labels
						lowerCAmelCase__      :		Any    							=      backbone_featmap_shape
						lowerCAmelCase__      :		Optional[Any]    							=      scope
						lowerCAmelCase__      :		Tuple    							=      is_hybrid
						# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
						lowerCAmelCase__      :		Optional[Any]    							=      (image_size // patch_size) ** 2
						lowerCAmelCase__      :		int    							=      num_patches + 1







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		str    							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						lowerCAmelCase__      :		List[Any]    							=      self.get_config()

						return config, pixel_values, labels







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		str    							=      {
						    '''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=lowercase_  ,initializer_range=self.initializer_range  ,is_hybrid=self.is_hybrid  ,backbone_config=lowercase_  ,backbone_featmap_shape=self.backbone_featmap_shape  ,)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							Dict  ,lowercase_     :							List[Any]  ,lowercase_     :							str						):
						lowerCAmelCase__      :		Union[str, Any]    							=      DPTModel(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		List[Any]    							=      model(lowercase_						)
						self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)						)







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Optional[Any]  ,lowercase_     :							int  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      self.num_labels
						lowerCAmelCase__      :		int    							=      DPTForDepthEstimation(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Any    							=      model(lowercase_						)
						self.parent.assertEqual(result.predicted_depth.shape  ,(self.batch_size, self.image_size, self.image_size)						)







					def 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							List[Any]  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							Tuple						):
						lowerCAmelCase__      :		str    							=      self.num_labels
						lowerCAmelCase__      :		Union[str, Any]    							=      DPTForSemanticSegmentation(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Optional[Any]    							=      model(lowercase_  ,labels=lowercase_						)
						self.parent.assertEqual(
						    result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size, self.image_size)						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[int]    							=      self.prepare_config_and_inputs()
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      config_and_inputs
						lowerCAmelCase__      :		int    							=      {'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_torch
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



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

					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      DPTModelTester(self						)
						lowerCAmelCase__      :		Tuple    							=      ConfigTester(self  ,config_class=lowercase_  ,has_text_modality=lowercase_  ,hidden_size=3_7						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						self.config_tester.run_common_tests()







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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      self.model_tester.prepare_config_and_inputs_for_common()

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







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Union[str, Any]    							=      model_class(lowercase_						)
							lowerCAmelCase__      :		List[str]    							=      inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__      :		Tuple    							=      [*signature.parameters.keys()]

							lowerCAmelCase__      :		Optional[int]    							=      ['''pixel_values''']
							self.assertListEqual(arg_names[:1]  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						for model_class in self.all_model_classes:
							if model_class.__name__ == "DPTForDepthEstimation":
								continue

							lowerCAmelCase__							,lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__      :		Dict    							=      True

							if model_class in get_values(lowercase_						):
								continue

							lowerCAmelCase__      :		int    							=      model_class(lowercase_						)
							model.to(lowercase_						)
							model.train()
							lowerCAmelCase__      :		Dict    							=      self._prepare_for_class(lowercase_  ,lowercase_  ,return_labels=lowercase_						)
							lowerCAmelCase__      :		List[str]    							=      model(**lowercase_						).loss
							loss.backward()







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						for model_class in self.all_model_classes:
							if model_class.__name__ == "DPTForDepthEstimation":
								continue

							lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__      :		str    							=      False
							lowerCAmelCase__      :		int    							=      True

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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      self.model_tester.prepare_config_and_inputs_for_common()

						lowerCAmelCase__      :		str    							=      _config_zero_init(lowercase_						)
						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Any    							=      model_class(config=lowercase_						)
							# Skip the check for the backbone
							lowerCAmelCase__      :		Dict    							=      []
							for name, module in model.named_modules():
								if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									lowerCAmelCase__      :		Union[str, 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     :							Tuple						):
						pass







					@slow
					def 			__lowerCAmelCase					(			self     :							str						):
						for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
							lowerCAmelCase__      :		Tuple    							=      DPTModel.from_pretrained(lowercase_						)
							self.assertIsNotNone(lowercase_						)







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

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
	return image







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






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Optional[Any]    							=      DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''						)
						lowerCAmelCase__      :		List[Any]    							=      DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''						).to(lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      prepare_img()
						lowerCAmelCase__      :		Dict    							=      image_processor(images=lowercase_  ,return_tensors='''pt'''						).to(lowercase_						)

						# forward pass
						with torch.no_grad():
							lowerCAmelCase__      :		Union[str, Any]    							=      model(**lowercase_						)
							lowerCAmelCase__      :		int    							=      outputs.predicted_depth

						# verify the predicted depth
						lowerCAmelCase__      :		Any    							=      torch.Size((1, 3_8_4, 3_8_4)						)
						self.assertEqual(predicted_depth.shape  ,lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      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] / 1_0_0  ,lowercase_  ,atol=1E-4						)						)







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




