






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					,						__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=0.0_2					,						__UpperCamelCase=3					,						__UpperCamelCase=[1, 3_8_4, 2_4, 2_4]					,						__UpperCamelCase=True					,						__UpperCamelCase=None					,						)->	Union[str, Any]:
           __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  __UpperCAmelCase		(	self	)->	Optional[int]:
           __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  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					{
               "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	)->	Any:
           __lowerCAmelCase								=					DPTModel(config=__UpperCamelCase	)
           model.to(__UpperCamelCase	)
           model.eval()
           __lowerCAmelCase								=					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	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.num_labels
           __lowerCAmelCase								=					DPTForDepthEstimation(__UpperCamelCase	)
           model.to(__UpperCamelCase	)
           model.eval()
           __lowerCAmelCase								=					model(__UpperCamelCase	)
           self.parent.assertEqual(result.predicted_depth.shape					,						(self.batch_size, self.image_size, self.image_size)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
           __lowerCAmelCase								=					self.num_labels
           __lowerCAmelCase								=					DPTForSemanticSegmentation(__UpperCamelCase	)
           model.to(__UpperCamelCase	)
           model.eval()
           __lowerCAmelCase								=					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	)->	Dict:
           __lowerCAmelCase								=					self.prepare_config_and_inputs()
           __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					config_and_inputs
           __lowerCAmelCase								=					{"pixel_values": pixel_values}
           return config, inputs_dict



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

      snake_case_  =							False
      snake_case_  =							False
      snake_case_  =							False


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					DPTModelTester(self	)
           __lowerCAmelCase								=					ConfigTester(self					,						config_class=__UpperCamelCase					,						has_text_modality=__UpperCamelCase					,						hidden_size=3_7	)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           self.config_tester.run_common_tests()


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


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase			, __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

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


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

           for model_class in self.all_model_classes:
                __lowerCAmelCase								=					model_class(__UpperCamelCase	)
                __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]					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase	)


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

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

                if model_class in get_values(__UpperCamelCase	):
                     continue

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


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

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

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


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

           __lowerCAmelCase								=					_config_zero_init(__UpperCamelCase	)
           for model_class in self.all_model_classes:
                __lowerCAmelCase								=					model_class(config=__UpperCamelCase	)
                # 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  __UpperCAmelCase		(	self	)->	int:
           pass


      @slow
      def  __UpperCAmelCase		(	self	)->	Tuple:
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                __lowerCAmelCase								=					DPTModel.from_pretrained(__UpperCamelCase	)
                self.assertIsNotNone(__UpperCamelCase	)


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





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



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


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
           __lowerCAmelCase								=					DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(__UpperCamelCase	)

           __lowerCAmelCase								=					prepare_img()
           __lowerCAmelCase								=					image_processor(images=__UpperCamelCase					,						return_tensors="pt"	).to(__UpperCamelCase	)

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

           # verify the predicted depth
           __lowerCAmelCase								=					torch.Size((1, 3_8_4, 3_8_4)	)
           self.assertEqual(predicted_depth.shape					,						__UpperCamelCase	)

           __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(__UpperCamelCase	)

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














import os
import sys
import unittest


lowerCamelCase			:					int         =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:					Any         =	os.path.join(git_repo_path, '''src''', '''diffusers''')



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					find_backend("    if not is_torch_available():"	)
           self.assertEqual(__UpperCamelCase					,						"torch"	)

           # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
           # self.assertEqual(backend_with_underscore, "tensorflow_text")

           __lowerCAmelCase								=					find_backend("    if not (is_torch_available() and is_transformers_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers"	)

           # double_backend_with_underscore = find_backend(
           #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
           # )
           # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

           __lowerCAmelCase								=					find_backend(
               "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers_and_onnx"	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("torch"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("flax_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers_and_onnx"					,						__UpperCamelCase	)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("UNet2DModel"					,						objects["torch"]	)
           self.assertIn("FlaxUNet2DConditionModel"					,						objects["flax"]	)
           self.assertIn("StableDiffusionPipeline"					,						objects["torch_and_transformers"]	)
           self.assertIn("FlaxStableDiffusionPipeline"					,						objects["flax_and_transformers"]	)
           self.assertIn("LMSDiscreteScheduler"					,						objects["torch_and_scipy"]	)
           self.assertIn("OnnxStableDiffusionPipeline"					,						objects["torch_and_transformers_and_onnx"]	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					create_dummy_object("CONSTANT"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						"\nCONSTANT = None\n"	)

           __lowerCAmelCase								=					create_dummy_object("function"					,						"'torch'"	)
           self.assertEqual(
               __UpperCamelCase					,						"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"	)

           __lowerCAmelCase								=					"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
           __lowerCAmelCase								=					create_dummy_object("FakeClass"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
           __lowerCAmelCase								=					create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}	)
           self.assertEqual(dummy_files["torch"]					,						__UpperCamelCase	)






