

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__		:






		"""simple docstring"""


		def __init__(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[int]=2     ,       __lowerCamelCase						:		str=32     ,       __lowerCamelCase						:		Dict=16     ,       __lowerCamelCase						:		Optional[Any]=3     ,       __lowerCamelCase						:		Optional[int]=True     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		Optional[Any]=32     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Optional[int]=[0, 1, 2, 3]     ,       __lowerCamelCase						:		Union[str, Any]=4     ,       __lowerCamelCase						:		Optional[int]=37     ,       __lowerCamelCase						:		Optional[Any]="gelu"     ,       __lowerCamelCase						:		Tuple=0.1     ,       __lowerCamelCase						:		Optional[int]=0.1     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Tuple=[1, 384, 24, 24]     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		List[Any]=None     ,       )		->							List[str]:
							SCREAMING_SNAKE_CASE__   =   parent
							SCREAMING_SNAKE_CASE__   =   batch_size
							SCREAMING_SNAKE_CASE__   =   image_size
							SCREAMING_SNAKE_CASE__   =   patch_size
							SCREAMING_SNAKE_CASE__   =   num_channels
							SCREAMING_SNAKE_CASE__   =   is_training
							SCREAMING_SNAKE_CASE__   =   use_labels
							SCREAMING_SNAKE_CASE__   =   hidden_size
							SCREAMING_SNAKE_CASE__   =   num_hidden_layers
							SCREAMING_SNAKE_CASE__   =   backbone_out_indices
							SCREAMING_SNAKE_CASE__   =   num_attention_heads
							SCREAMING_SNAKE_CASE__   =   intermediate_size
							SCREAMING_SNAKE_CASE__   =   hidden_act
							SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
							SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__   =   initializer_range
							SCREAMING_SNAKE_CASE__   =   num_labels
							SCREAMING_SNAKE_CASE__   =   backbone_featmap_shape
							SCREAMING_SNAKE_CASE__   =   scope
							SCREAMING_SNAKE_CASE__   =   is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE__   =   (image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE__   =   num_patches + 1






		def 				lowercase_		(							self						:		List[Any] )		->							int:
							SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

							SCREAMING_SNAKE_CASE__   =   self.get_config()

							return config, pixel_values, labels






		def 				lowercase_		(							self						:		int )		->							List[Any]:
							SCREAMING_SNAKE_CASE__   =   {
							    '''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=UpperCamelCase_     ,       initializer_range=self.initializer_range     ,       is_hybrid=self.is_hybrid     ,       backbone_config=UpperCamelCase_     ,       backbone_featmap_shape=self.backbone_featmap_shape     ,       )






		def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		str )		->							Optional[Any]:
							SCREAMING_SNAKE_CASE__   =   DPTModel(config=UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase_ )
							self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size) )






		def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		Tuple )		->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__   =   self.num_labels
							SCREAMING_SNAKE_CASE__   =   DPTForDepthEstimation(UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase_ )
							self.parent.assertEqual(result.predicted_depth.shape     ,       (self.batch_size, self.image_size, self.image_size) )






		def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		List[str] )		->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__   =   self.num_labels
							SCREAMING_SNAKE_CASE__   =   DPTForSemanticSegmentation(UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase_     ,       labels=UpperCamelCase_ )
							self.parent.assertEqual(
							    result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size, self.image_size) )







		def 				lowercase_		(							self						:		List[str] )		->							int:
							SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   config_and_inputs
							SCREAMING_SNAKE_CASE__   =   {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class 	UpperCAmelCase__		(	__lowerCamelCase						,					__lowerCamelCase						,					unittest.TestCase	):






		"""simple docstring"""


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

		a									= False
		a									= False
		a									= False






		def 				lowercase_		(							self						:		Tuple )		->							Optional[int]:
							SCREAMING_SNAKE_CASE__   =   DPTModelTester(self )
							SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=UpperCamelCase_     ,       has_text_modality=UpperCamelCase_     ,       hidden_size=37 )






		def 				lowercase_		(							self						:		List[Any] )		->							Any:
							self.config_tester.run_common_tests()






		@unittest.skip(reason='''DPT does not use inputs_embeds''' )
		def 				lowercase_		(							self						:		List[Any] )		->							int:
							pass






		def 				lowercase_		(							self						:		Optional[Any] )		->							Optional[Any]:
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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






		def 				lowercase_		(							self						:		Union[str, Any] )		->							Tuple:
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

												SCREAMING_SNAKE_CASE__   =   ['''pixel_values''']
												self.assertListEqual(arg_names[:1]     ,       UpperCamelCase_ )






		def 				lowercase_		(							self						:		Any )		->							List[Any]:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_ )






		def 				lowercase_		(							self						:		Dict )		->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_ )






		def 				lowercase_		(							self						:		int )		->							Dict:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_ )






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

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

												if model_class in get_values(UpperCamelCase_ ):
																	continue

												SCREAMING_SNAKE_CASE__   =   model_class(UpperCamelCase_ )
												model.to(UpperCamelCase_ )
												model.train()
												SCREAMING_SNAKE_CASE__   =   self._prepare_for_class(UpperCamelCase_     ,       UpperCamelCase_     ,       return_labels=UpperCamelCase_ )
												SCREAMING_SNAKE_CASE__   =   model(**UpperCamelCase_ ).loss
												loss.backward()






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

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

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






		def 				lowercase_		(							self						:		Optional[Any] )		->							List[Any]:
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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






		@slow
		def 				lowercase_		(							self						:		str )		->							str:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												SCREAMING_SNAKE_CASE__   =   DPTModel.from_pretrained(UpperCamelCase_ )
												self.assertIsNotNone(UpperCamelCase_ )







		def 				lowercase_		(							self						:		Dict )		->							List[Any]:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE__   =   '''add'''
							with self.assertRaises(UpperCamelCase_ ):
												SCREAMING_SNAKE_CASE__   =   DPTForDepthEstimation(UpperCamelCase_ )



def     UpperCAmelCase_       (						):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
					return image







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






		"""simple docstring"""


		def 				lowercase_		(							self						:		List[str] )		->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__   =   DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
							SCREAMING_SNAKE_CASE__   =   DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(UpperCamelCase_ )

							SCREAMING_SNAKE_CASE__   =   prepare_img()
							SCREAMING_SNAKE_CASE__   =   image_processor(images=UpperCamelCase_     ,       return_tensors='''pt''' ).to(UpperCamelCase_ )

							# forward pass
							with torch.no_grad():
												SCREAMING_SNAKE_CASE__   =   model(**UpperCamelCase_ )
												SCREAMING_SNAKE_CASE__   =   outputs.predicted_depth

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

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

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





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


