


'''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__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase=2       ,			__lowercase=32       ,			__lowercase=16       ,			__lowercase=3       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=32       ,			__lowercase=4       ,			__lowercase=[0, 1, 2, 3]       ,			__lowercase=4       ,			__lowercase=37       ,			__lowercase="gelu"       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.0_2       ,			__lowercase=3       ,			__lowercase=[1, 3_84, 24, 24]       ,			__lowercase=True       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				parent
										__lowerCAmelCase							       =				batch_size
										__lowerCAmelCase							       =				image_size
										__lowerCAmelCase							       =				patch_size
										__lowerCAmelCase							       =				num_channels
										__lowerCAmelCase							       =				is_training
										__lowerCAmelCase							       =				use_labels
										__lowerCAmelCase							       =				hidden_size
										__lowerCAmelCase							       =				num_hidden_layers
										__lowerCAmelCase							       =				backbone_out_indices
										__lowerCAmelCase							       =				num_attention_heads
										__lowerCAmelCase							       =				intermediate_size
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				hidden_dropout_prob
										__lowerCAmelCase							       =				attention_probs_dropout_prob
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				num_labels
										__lowerCAmelCase							       =				backbone_featmap_shape
										__lowerCAmelCase							       =				scope
										__lowerCAmelCase							       =				is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										__lowerCAmelCase							       =				(image_size // patch_size) ** 2
										__lowerCAmelCase							       =				num_patches + 1




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

										__lowerCAmelCase							       =				self.get_config()

										return config, pixel_values, labels




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{
										    "global_padding": "same",
										    "layer_type": "bottleneck",
										    "depths": [3, 4, 9],
										    "out_features": ["stage1", "stage2", "stage3"],
										    "embedding_dynamic_padding": True,
										    "hidden_sizes": [96, 1_92, 3_84, 7_68],
										    "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=A_       ,			initializer_range=self.initializer_range       ,			is_hybrid=self.is_hybrid       ,			backbone_config=A_       ,			backbone_featmap_shape=self.backbone_featmap_shape       ,			)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				DPTModel(config=A_		)
										model.to(A_		)
										model.eval()
										__lowerCAmelCase							       =				model(A_		)
										self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				self.num_labels
										__lowerCAmelCase							       =				DPTForDepthEstimation(A_		)
										model.to(A_		)
										model.eval()
										__lowerCAmelCase							       =				model(A_		)
										self.parent.assertEqual(result.predicted_depth.shape       ,			(self.batch_size, self.image_size, self.image_size)		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				self.num_labels
										__lowerCAmelCase							       =				DPTForSemanticSegmentation(A_		)
										model.to(A_		)
										model.eval()
										__lowerCAmelCase							       =				model(A_       ,			labels=A_		)
										self.parent.assertEqual(
										    result.logits.shape       ,			(self.batch_size, self.num_labels, self.image_size, self.image_size)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.prepare_config_and_inputs()
										__lowerCAmelCase							       =				config_and_inputs
										__lowerCAmelCase							       =				{"pixel_values": pixel_values}
										return config, inputs_dict


@require_torch
class       a__				( __snake_case							,						__snake_case							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Tuple	=	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				__UpperCamelCase    :		List[str]	=	(
				    {
				        'depth-estimation': DPTForDepthEstimation,
				        'feature-extraction': DPTModel,
				        'image-segmentation': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase    :		Optional[Any]	=	False
				__UpperCamelCase    :		str	=	False
				__UpperCamelCase    :		Dict	=	False




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				DPTModelTester(self		)
										__lowerCAmelCase							       =				ConfigTester(self       ,			config_class=A_       ,			has_text_modality=A_       ,			hidden_size=37		)




				def 					_snake_case  (self		):
										self.config_tester.run_common_tests()




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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase							       =				["pixel_values"]
																self.assertListEqual(arg_names[:1]       ,			A_		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*A_		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*A_		)




				def 					_snake_case  (self		):




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

																__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()
																__lowerCAmelCase							       =				True

																if model_class in get_values(A_		):
																						continue

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




				def 					_snake_case  (self		):




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

																__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()
																__lowerCAmelCase							       =				False
																__lowerCAmelCase							       =				True

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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase							       =				_config_zero_init(A_		)
										for model_class in self.all_model_classes:
																__lowerCAmelCase							       =				model_class(config=A_		)
																# Skip the check for the backbone
																__lowerCAmelCase							       =				[]
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												__lowerCAmelCase							       =				[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 					_snake_case  (self		):
										pass




				@slow
				def 					_snake_case  (self		):




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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase							       =				"add"
										with self.assertRaises(A_		):
																__lowerCAmelCase							       =				DPTForDepthEstimation(A_		)

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







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







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''		)
										__lowerCAmelCase							       =				DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''		).to(A_		)

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

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

										# verify the predicted depth
										__lowerCAmelCase							       =				torch.Size((1, 3_84, 3_84)		)
										self.assertEqual(predicted_depth.shape       ,			A_		)

										__lowerCAmelCase							       =				torch.tensor(
										    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]		).to(A_		)

										self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00       ,			A_       ,			atol=1e-4		)		)





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

