






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   lowerCamelCase      :







		'''simple docstring'''



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



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[int]            =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							UpperCAmelCase_    :      Optional[Any]            =						self.get_config()

							return config, pixel_values, labels



		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      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=_UpperCamelCase				,		initializer_range=self.initializer_range				,		is_hybrid=self.is_hybrid				,		backbone_config=_UpperCamelCase				,		backbone_featmap_shape=self.backbone_featmap_shape				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Tuple            =						DPTModel(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Optional[int]            =						model(_UpperCamelCase   )
							self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Optional[Any]            =						self.num_labels
							UpperCAmelCase_    :      Any            =						DPTForDepthEstimation(_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      str            =						model(_UpperCamelCase   )
							self.parent.assertEqual(result.predicted_depth.shape				,		(self.batch_size, self.image_size, self.image_size)   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Union[str, Any]            =						self.num_labels
							UpperCAmelCase_    :      List[Any]            =						DPTForSemanticSegmentation(_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      int            =						model(_UpperCamelCase				,		labels=_UpperCamelCase   )
							self.parent.assertEqual(
							    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size, self.image_size)   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[str]            =						self.prepare_config_and_inputs()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						config_and_inputs
							UpperCAmelCase_    :      Tuple            =						{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class   lowerCamelCase      (_snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



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

		_snake_case    :    Optional[int]       						=  False
		_snake_case    :    List[str]       						=  False
		_snake_case    :    Union[str, Any]       						=  False



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						DPTModelTester(self   )
							UpperCAmelCase_    :      Any            =						ConfigTester(self				,		config_class=_UpperCamelCase				,		has_text_modality=_UpperCamelCase				,		hidden_size=3_7   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							self.config_tester.run_common_tests()



		@unittest.skip(reason='DPT does not use inputs_embeds'   )
		def 					__UpperCAmelCase			(							self   )	->							str:
							pass



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.model_tester.prepare_config_and_inputs_for_common()

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()

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

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Dict            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      List[Any]            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase   )



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

												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase_    :      str            =						True

												if model_class in get_values(_UpperCamelCase   ):
																	continue

												UpperCAmelCase_    :      List[Any]            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.train()
												UpperCAmelCase_    :      str            =						self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase				,		return_labels=_UpperCamelCase   )
												UpperCAmelCase_    :      int            =						model(**_UpperCamelCase   ).loss
												loss.backward()



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

												UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase_    :      List[str]            =						False
												UpperCAmelCase_    :      int            =						True

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



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_    :      int            =						_config_zero_init(_UpperCamelCase   )
							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      Tuple            =						model_class(config=_UpperCamelCase   )
												# Skip the check for the backbone
												UpperCAmelCase_    :      Optional[int]            =						[]
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																						UpperCAmelCase_    :      Optional[int]            =						[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 					__UpperCAmelCase			(							self   )	->							int:
							pass



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												UpperCAmelCase_    :      Tuple            =						DPTModel.from_pretrained(_UpperCamelCase   )
												self.assertIsNotNone(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_    :      Union[str, Any]            =						'add'
							with self.assertRaises(_UpperCamelCase   ):
												UpperCAmelCase_    :      Any            =						DPTForDepthEstimation(_UpperCamelCase   )





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






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







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      int            =						DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
							UpperCAmelCase_    :      int            =						DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						prepare_img()
							UpperCAmelCase_    :      List[Any]            =						image_processor(images=_UpperCamelCase				,		return_tensors='pt'   ).to(_UpperCamelCase   )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_    :      Optional[Any]            =						model(**_UpperCamelCase   )
												UpperCAmelCase_    :      Dict            =						outputs.predicted_depth

							# verify the predicted depth
							UpperCAmelCase_    :      Optional[int]            =						torch.Size((1, 3_8_4, 3_8_4)   )
							self.assertEqual(predicted_depth.shape				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						torch.tensor(
							    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]   ).to(_UpperCamelCase   )

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0				,		_UpperCamelCase				,		atol=1E-4   )   )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






