



"""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 SCREAMING_SNAKE_CASE__					:



						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=0.02						,     _lowercase=3						,     _lowercase=[1, 384, 24, 24]						,     _lowercase=True						,     _lowercase=None						,     ):





													'''simple docstring'''

													__a		:		Optional[Any]									=       parent
													__a		:		List[Any]									=       batch_size
													__a		:		Tuple									=       image_size
													__a		:		Union[str, Any]									=       patch_size
													__a		:		Tuple									=       num_channels
													__a		:		Dict									=       is_training
													__a		:		str									=       use_labels
													__a		:		List[Any]									=       hidden_size
													__a		:		Dict									=       num_hidden_layers
													__a		:		str									=       backbone_out_indices
													__a		:		List[str]									=       num_attention_heads
													__a		:		Dict									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Any									=       hidden_dropout_prob
													__a		:		Tuple									=       attention_probs_dropout_prob
													__a		:		Any									=       initializer_range
													__a		:		Optional[Any]									=       num_labels
													__a		:		Dict									=       backbone_featmap_shape
													__a		:		List[Any]									=       scope
													__a		:		List[str]									=       is_hybrid
													# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
													__a		:		str									=       (image_size // patch_size) ** 2
													__a		:		List[Any]									=       num_patches + 1



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

													__a		:		Optional[int]									=       self.get_config()

													return config, pixel_values, labels



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       {
													    """global_padding""": """same""",
													    """layer_type""": """bottleneck""",
													    """depths""": [3, 4, 9],
													    """out_features""": ["""stage1""", """stage2""", """stage3"""],
													    """embedding_dynamic_padding""": True,
													    """hidden_sizes""": [96, 192, 384, 768],
													    """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=_lowercase						,     initializer_range=self.initializer_range						,     is_hybrid=self.is_hybrid						,     backbone_config=_lowercase						,     backbone_featmap_shape=self.backbone_featmap_shape						,     )



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       DPTModel(config=_lowercase							)
													model.to(_lowercase							)
													model.eval()
													__a		:		Union[str, Any]									=       model(_lowercase							)
													self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		str									=       self.num_labels
													__a		:		Optional[int]									=       DPTForDepthEstimation(_lowercase							)
													model.to(_lowercase							)
													model.eval()
													__a		:		int									=       model(_lowercase							)
													self.parent.assertEqual(result.predicted_depth.shape						,     (self.batch_size, self.image_size, self.image_size)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Dict									=       self.num_labels
													__a		:		List[Any]									=       DPTForSemanticSegmentation(_lowercase							)
													model.to(_lowercase							)
													model.eval()
													__a		:		Union[str, Any]									=       model(_lowercase						,     labels=_lowercase							)
													self.parent.assertEqual(
													    result.logits.shape						,     (self.batch_size, self.num_labels, self.image_size, self.image_size)							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       self.prepare_config_and_inputs()
													__a							,     __a							,     __a		:		List[str]									=       config_and_inputs
													__a		:		Union[str, Any]									=       {"""pixel_values""": pixel_values}
													return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						_lowerCAmelCase							=					(
						    {
						        "depth-estimation": DPTForDepthEstimation,
						        "feature-extraction": DPTModel,
						        "image-segmentation": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       DPTModelTester(self							)
													__a		:		Optional[int]									=       ConfigTester(self						,     config_class=_lowercase						,     has_text_modality=_lowercase						,     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'''

													__a							,     __a		:		Any									=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__a		:		Dict									=       model_class(_lowercase							)
																				self.assertIsInstance(model.get_input_embeddings()						,     (nn.Module)							)
																				__a		:		Optional[int]									=       model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(_lowercase						,     nn.Linear							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Union[str, Any]									=       self.model_tester.prepare_config_and_inputs_for_common()

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

																				__a		:		List[str]									=       ["""pixel_values"""]
																				self.assertListEqual(arg_names[:1]						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

																				__a							,     __a		:		Dict									=       self.model_tester.prepare_config_and_inputs_for_common()
																				__a		:		Any									=       True

																				if model_class in get_values(_lowercase							):
																											continue

																				__a		:		List[Any]									=       model_class(_lowercase							)
																				model.to(_lowercase							)
																				model.train()
																				__a		:		Optional[Any]									=       self._prepare_for_class(_lowercase						,     _lowercase						,     return_labels=_lowercase							)
																				__a		:		str									=       model(**_lowercase							).loss
																				loss.backward()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

																				__a							,     __a		:		Tuple									=       self.model_tester.prepare_config_and_inputs_for_common()
																				__a		:		List[str]									=       False
																				__a		:		Dict									=       True

																				if model_class in get_values(_lowercase							) or not model_class.supports_gradient_checkpointing:
																											continue
																				__a		:		Optional[Any]									=       model_class(_lowercase							)
																				model.to(_lowercase							)
																				model.gradient_checkpointing_enable()
																				model.train()
																				__a		:		int									=       self._prepare_for_class(_lowercase						,     _lowercase						,     return_labels=_lowercase							)
																				__a		:		Optional[int]									=       model(**_lowercase							).loss
																				loss.backward()



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Dict									=       self.model_tester.prepare_config_and_inputs_for_common()

													__a		:		Optional[int]									=       _config_zero_init(_lowercase							)
													for model_class in self.all_model_classes:
																				__a		:		str									=       model_class(config=_lowercase							)
																				# Skip the check for the backbone
																				__a		:		str									=       []
																				for name, module in model.named_modules():
																											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																		__a		:		List[str]									=       [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:]:
																				__a		:		List[str]									=       DPTModel.from_pretrained(_lowercase							)
																				self.assertIsNotNone(_lowercase							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Optional[int]									=       self.model_tester.prepare_config_and_inputs_for_common()
													__a		:		Dict									=       """add"""
													with self.assertRaises(_lowercase							):
																				__a		:		Union[str, Any]									=       DPTForDepthEstimation(_lowercase							)







def       __magic_name__    (   ):
							__a		:		Optional[Any]									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
							return image







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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""							)
													__a		:		List[str]									=       DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""							).to(_lowercase							)

													__a		:		Dict									=       prepare_img()
													__a		:		List[Any]									=       image_processor(images=_lowercase						,     return_tensors="""pt"""							).to(_lowercase							)

													# forward pass
													with torch.no_grad():
																				__a		:		Dict									=       model(**_lowercase							)
																				__a		:		Tuple									=       outputs.predicted_depth

													# verify the predicted depth
													__a		:		Dict									=       torch.Size((1, 384, 384)							)
													self.assertEqual(predicted_depth.shape						,     _lowercase							)

													__a		:		List[str]									=       torch.tensor(
													    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]							).to(_lowercase							)

													self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						,     _lowercase						,     atol=1e-4							)							)






"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

