
'''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 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=2					,     lowerCAmelCase_=32					,     lowerCAmelCase_=16					,     lowerCAmelCase_=3					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=4					,     lowerCAmelCase_=[0, 1, 2, 3]					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=[1, 3_84, 24, 24]					,     lowerCAmelCase_=True					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

								_snake_case     		=	self.get_config()

								return config, pixel_values, labels


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{
								    '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=lowerCAmelCase_					,     initializer_range=self.initializer_range					,     is_hybrid=self.is_hybrid					,     backbone_config=lowerCAmelCase_					,     backbone_featmap_shape=self.backbone_featmap_shape					,     )


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	DPTModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	DPTForDepthEstimation(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.predicted_depth.shape					,     (self.batch_size, self.image_size, self.image_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	DPTForSemanticSegmentation(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(
								    result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size, self.image_size)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	DPTModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     has_text_modality=lowerCAmelCase_					,     hidden_size=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


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





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

									_snake_case     		=	['pixel_values']
									self.assertListEqual(arg_names[:1]					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

									if model_class in get_values(lowerCAmelCase_						):
										continue

									_snake_case     		=	model_class(lowerCAmelCase_						)
									model.to(lowerCAmelCase_						)
									model.train()
									_snake_case     		=	self._prepare_for_class(lowerCAmelCase_					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)
									_snake_case     		=	model(**lowerCAmelCase_						).loss
									loss.backward()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

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





								"""simple docstring"""






								pass


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	'add'
								with self.assertRaises(lowerCAmelCase_						):
									_snake_case     		=	DPTForDepthEstimation(lowerCAmelCase_						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[Any]:
	_snake_case     		=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image





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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'						)
								_snake_case     		=	DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'						).to(lowerCAmelCase_						)

								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=lowerCAmelCase_					,     return_tensors='pt'						).to(lowerCAmelCase_						)

								# forward pass
								with torch.no_grad():
									_snake_case     		=	model(**lowerCAmelCase_						)
									_snake_case     		=	outputs.predicted_depth

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

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

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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase					:							int   =      {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[int]   =      ["Mask2FormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[Any]   =      [
					    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Mask2FormerForUniversalSegmentation",
					    "Mask2FormerModel",
					    "Mask2FormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					lowercase					:							int   =      _LazyModule(__name__, globals()["__file__"], _import_structure)

