






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[int],  lowerCamelCase						:   Any,  lowerCamelCase						:   Dict=2,  lowerCamelCase						:   List[Any]=32,  lowerCamelCase						:   Any=16,  lowerCamelCase						:   str=3,  lowerCamelCase						:   Dict=True,  lowerCamelCase						:   Tuple=True,  lowerCamelCase						:   Optional[int]=32,  lowerCamelCase						:   Optional[int]=4,  lowerCamelCase						:   int=[0, 1, 2, 3],  lowerCamelCase						:   int=4,  lowerCamelCase						:   int=37,  lowerCamelCase						:   Any="gelu",  lowerCamelCase						:   List[Any]=0.1,  lowerCamelCase						:   Dict=0.1,  lowerCamelCase						:   Optional[int]=0.02,  lowerCamelCase						:   Tuple=3,  lowerCamelCase						:   int=False,  lowerCamelCase						:   int=None,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      image_size
								lowercase__       			=      patch_size
								lowercase__       			=      num_channels
								lowercase__       			=      is_training
								lowercase__       			=      use_labels
								lowercase__       			=      hidden_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      backbone_out_indices
								lowercase__       			=      num_attention_heads
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_act
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      initializer_range
								lowercase__       			=      num_labels
								lowercase__       			=      scope
								lowercase__       			=      is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								lowercase__       			=      (image_size // patch_size) ** 2
								lowercase__       			=      num_patches + 1




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowercase__       			=      self.get_config()

								return config, pixel_values, labels




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								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,  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      DPTModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   int,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      DPTForDepthEstimation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.predicted_depth.shape,  (self.batch_size, self.image_size, self.image_size)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      DPTForSemanticSegmentation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,  (self.batch_size, self.num_labels, self.image_size, self.image_size)  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								lowercase__			,						lowercase__			,						lowercase__       			=      config_and_inputs
								lowercase__       			=      {'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class 				_UpperCAmelCase (	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     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      DPTModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




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

								'''simple docstring'''
								pass




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

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




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase__       			=      ['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

												if model_class in get_values(lowerCamelCase  ):
																continue

												lowercase__       			=      model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.train()
												lowercase__       			=      self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__       			=      model(**lowerCamelCase  ).loss
												loss.backward()




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

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




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__       			=      _config_zero_init(lowerCamelCase  )
								for model_class in self.all_model_classes:
												lowercase__       			=      model_class(config=lowerCamelCase  )
												# Skip the check for the backbone
												lowercase__       			=      []
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				lowercase__       			=      [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						:   List[Any]  ):

								'''simple docstring'''
								pass




				@slow
				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      DPTModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      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						:   str  ):

								'''simple docstring'''
								lowercase__       			=      DPTImageProcessor.from_pretrained('''Intel/dpt-large'''  )
								lowercase__       			=      DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''  ).to(lowerCamelCase  )

								lowercase__       			=      prepare_img()
								lowercase__       			=      image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__       			=      model(**lowerCamelCase  )
												lowercase__       			=      outputs.predicted_depth

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

								lowercase__       			=      torch.tensor(
								    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3],  lowerCamelCase,  atol=1E-4  )  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''  )
								lowercase__       			=      DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''  ).to(lowerCamelCase  )

								lowercase__       			=      prepare_img()
								lowercase__       			=      image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__       			=      model(**lowerCamelCase  )

								# verify the logits
								lowercase__       			=      torch.Size((1, 150, 480, 480)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )

								lowercase__       			=      torch.tensor(
								    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],  lowerCamelCase,  atol=1E-4  )  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''  )
								lowercase__       			=      DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''  ).to(lowerCamelCase  )

								lowercase__       			=      prepare_img()
								lowercase__       			=      image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__       			=      model(**lowerCamelCase  )

								lowercase__       			=      outputs.logits.detach().cpu()

								lowercase__       			=      image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase,  target_sizes=[(500, 300)]  )
								lowercase__       			=      torch.Size((500, 300)  )
								self.assertEqual(segmentation[0].shape,  lowerCamelCase  )

								lowercase__       			=      image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase  )
								lowercase__       			=      torch.Size((480, 480)  )
								self.assertEqual(segmentation[0].shape,  lowerCamelCase  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




