


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						"graphormer"
							SCREAMING_SNAKE_CASE   :					str        =						["past_key_values"]





							def __init__(    self				, A_ = 1				, A_ = 512 * 9				, A_ = 512 * 3				, A_ = 512				, A_ = 512				, A_ = 512				, A_ = 128				, A_ = 5				, A_ = 1024				, A_ = "multi_hop"				, A_ = 512				, A_ = False				, A_ = 12				, A_ = 768				, A_ = 768				, A_ = 32				, A_ = 0.1				, A_ = 0.1				, A_ = 0.0				, A_ = False				, A_ = False				, A_ = False				, A_ = "gelu"				, A_ = None				, A_ = False				, A_ = 0				, A_ = False				, A_ = 0.0				, A_ = 8				, A_ = None				, A_ = None				, A_ = True				, A_ = True				, A_=0				, A_=1				, A_=2				, **A_				, )->	str:
											_SCREAMING_SNAKE_CASE      = num_classes
											_SCREAMING_SNAKE_CASE      = num_atoms
											_SCREAMING_SNAKE_CASE      = num_in_degree
											_SCREAMING_SNAKE_CASE      = num_out_degree
											_SCREAMING_SNAKE_CASE      = num_edges
											_SCREAMING_SNAKE_CASE      = num_spatial
											_SCREAMING_SNAKE_CASE      = num_edge_dis
											_SCREAMING_SNAKE_CASE      = edge_type
											_SCREAMING_SNAKE_CASE      = multi_hop_max_dist
											_SCREAMING_SNAKE_CASE      = spatial_pos_max
											_SCREAMING_SNAKE_CASE      = max_nodes
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = embedding_dim
											_SCREAMING_SNAKE_CASE      = embedding_dim
											_SCREAMING_SNAKE_CASE      = ffn_embedding_dim
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = dropout
											_SCREAMING_SNAKE_CASE      = attention_dropout
											_SCREAMING_SNAKE_CASE      = layerdrop
											_SCREAMING_SNAKE_CASE      = encoder_normalize_before
											_SCREAMING_SNAKE_CASE      = pre_layernorm
											_SCREAMING_SNAKE_CASE      = apply_graphormer_init
											_SCREAMING_SNAKE_CASE      = activation_fn
											_SCREAMING_SNAKE_CASE      = embed_scale
											_SCREAMING_SNAKE_CASE      = freeze_embeddings
											_SCREAMING_SNAKE_CASE      = num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE      = share_input_output_embed
											_SCREAMING_SNAKE_CASE      = traceable
											_SCREAMING_SNAKE_CASE      = q_noise
											_SCREAMING_SNAKE_CASE      = qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE      = kdim
											_SCREAMING_SNAKE_CASE      = vdim
											_SCREAMING_SNAKE_CASE      = self_attention
											_SCREAMING_SNAKE_CASE      = bias

											super().__init__(
											    pad_token_id=A_				, bos_token_id=A_				, eos_token_id=A_				, **A_				, )




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    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_				, A_=2				, A_=32				, A_=16				, A_=3				, A_=True				, A_=True				, A_=32				, A_=4				, A_=[0, 1, 2, 3]				, A_=4				, A_=37				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=0.02				, A_=3				, A_=False				, A_=None				, )->	int:
											_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      = 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 		__magic_name__			(    self	)->	Optional[Any]:
											_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 		__magic_name__			(    self	)->	Optional[int]:
											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				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DPTModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = 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	)->	str:
											_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    __lowercase  (       UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE   :					Any        =						(
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE   :					Tuple        =						False
							SCREAMING_SNAKE_CASE   :					str        =						False
							SCREAMING_SNAKE_CASE   :					Dict        =						False





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = DPTModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, has_text_modality=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	str:
											self.config_tester.run_common_tests()





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





							def 		__magic_name__			(    self	)->	int:
											_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(A_	)
															self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)	)
															_SCREAMING_SNAKE_CASE      = model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(A_				, nn.Linear	)	)





							def 		__magic_name__			(    self	)->	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(A_	)
															_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]				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*A_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											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(A_	):
																			continue

															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	Any:
											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(A_	) or not model_class.supports_gradient_checkpointing:
																			continue
															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.gradient_checkpointing_enable()
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE      = _config_zero_init(A_	)
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(config=A_	)
															# 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 		__magic_name__			(    self	)->	List[Any]:
											pass





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = DPTModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)
															_SCREAMING_SNAKE_CASE      = outputs.predicted_depth

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

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

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





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

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

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

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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

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

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

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