

"""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					,						_A					,						_A=2					,						_A=32					,						_A=16					,						_A=3					,						_A=True					,						_A=True					,						_A=32					,						_A=4					,						_A=[0, 1, 2, 3]					,						_A=4					,						_A=37					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=0.02					,						_A=3					,						_A=[1, 3_84, 24, 24]					,						_A=True					,						_A=None					,						)			->			Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase			:							Union[str, Any]	  =    parent
				_UpperCAmelCase			:							Union[str, Any]	  =    batch_size
				_UpperCAmelCase			:							int	  =    image_size
				_UpperCAmelCase			:							Tuple	  =    patch_size
				_UpperCAmelCase			:							int	  =    num_channels
				_UpperCAmelCase			:							Union[str, Any]	  =    is_training
				_UpperCAmelCase			:							int	  =    use_labels
				_UpperCAmelCase			:							List[Any]	  =    hidden_size
				_UpperCAmelCase			:							List[str]	  =    num_hidden_layers
				_UpperCAmelCase			:							List[str]	  =    backbone_out_indices
				_UpperCAmelCase			:							List[Any]	  =    num_attention_heads
				_UpperCAmelCase			:							Union[str, Any]	  =    intermediate_size
				_UpperCAmelCase			:							Optional[int]	  =    hidden_act
				_UpperCAmelCase			:							Tuple	  =    hidden_dropout_prob
				_UpperCAmelCase			:							Optional[Any]	  =    attention_probs_dropout_prob
				_UpperCAmelCase			:							Optional[int]	  =    initializer_range
				_UpperCAmelCase			:							int	  =    num_labels
				_UpperCAmelCase			:							Tuple	  =    backbone_featmap_shape
				_UpperCAmelCase			:							List[str]	  =    scope
				_UpperCAmelCase			:							List[str]	  =    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			:							Optional[Any]	  =    num_patches + 1
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Any	  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

				_UpperCAmelCase			:							Any	  =    self.get_config()

				return config, pixel_values, labels
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[Any]	  =    {
				    "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					,						_A					,						_A					,						_A   )			->			Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[Any]	  =    DPTModel(config=_A   )
				model.to(_A   )
				model.eval()
				_UpperCAmelCase			:							Tuple	  =    model(_A   )
				self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)   )
			def 							__snake_case       (						self					,						_A					,						_A					,						_A   )			->			Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Union[str, Any]	  =    self.num_labels
				_UpperCAmelCase			:							int	  =    DPTForDepthEstimation(_A   )
				model.to(_A   )
				model.eval()
				_UpperCAmelCase			:							List[Any]	  =    model(_A   )
				self.parent.assertEqual(result.predicted_depth.shape					,						(self.batch_size, self.image_size, self.image_size)   )
			def 							__snake_case       (						self					,						_A					,						_A					,						_A   )			->			Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Dict	  =    self.num_labels
				_UpperCAmelCase			:							List[Any]	  =    DPTForSemanticSegmentation(_A   )
				model.to(_A   )
				model.eval()
				_UpperCAmelCase			:							Union[str, Any]	  =    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   )			->			Optional[int]:






				'''simple docstring'''







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






@require_torch
class 			_UpperCAmelCase	(						snake_case__			,   snake_case__			,   unittest.TestCase):
			__a       :				List[Any]     =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			__a       :				Union[str, Any]     =							(
			    {
			        "depth-estimation": DPTForDepthEstimation,
			        "feature-extraction": DPTModel,
			        "image-segmentation": DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__a       :				Optional[int]     =							False
			__a       :				str     =							False
			__a       :				Tuple     =							False
			def 							__snake_case       (						self   )			->			int:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[int]	  =    DPTModelTester(self   )
				_UpperCAmelCase			:							Optional[int]	  =    ConfigTester(self					,						config_class=_A					,						has_text_modality=_A					,						hidden_size=37   )
			def 							__snake_case       (						self   )			->			Optional[int]:






				'''simple docstring'''







				self.config_tester.run_common_tests()
			@unittest.skip(reason="""DPT does not use inputs_embeds"""   )
			def 							__snake_case       (						self   )			->			List[str]:






				'''simple docstring'''







				pass
			def 							__snake_case       (						self   )			->			Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase			:							Union[str, Any]	  =    model_class(_A   )
					self.assertIsInstance(model.get_input_embeddings()					,						(nn.Module)   )
					_UpperCAmelCase			:							List[str]	  =    model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(_A					,						nn.Linear   )   )
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    self.model_tester.prepare_config_and_inputs_for_common()

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

					_UpperCAmelCase			:							int	  =    ["pixel_values"]
					self.assertListEqual(arg_names[:1]					,						_A   )
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[Any]	  =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A   )
			def 							__snake_case       (						self   )			->			Tuple:






				'''simple docstring'''







				_UpperCAmelCase			:							Dict	  =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*_A   )
			def 							__snake_case       (						self   )			->			Dict:






				'''simple docstring'''







				_UpperCAmelCase			:							str	  =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*_A   )
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







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

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

					if model_class in get_values(_A   ):
						continue

					_UpperCAmelCase			:							int	  =    model_class(_A   )
					model.to(_A   )
					model.train()
					_UpperCAmelCase			:							str	  =    self._prepare_for_class(_A					,						_A					,						return_labels=_A   )
					_UpperCAmelCase			:							Dict	  =    model(**_A   ).loss
					loss.backward()
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







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

					_UpperCAmelCase			:							List[Any]	  =    self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCAmelCase			:							Dict	  =    False
					_UpperCAmelCase			:							List[Any]	  =    True

					if model_class in get_values(_A   ) or not model_class.supports_gradient_checkpointing:
						continue
					_UpperCAmelCase			:							Tuple	  =    model_class(_A   )
					model.to(_A   )
					model.gradient_checkpointing_enable()
					model.train()
					_UpperCAmelCase			:							List[Any]	  =    self._prepare_for_class(_A					,						_A					,						return_labels=_A   )
					_UpperCAmelCase			:							Tuple	  =    model(**_A   ).loss
					loss.backward()
			def 							__snake_case       (						self   )			->			str:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[Any]	  =    self.model_tester.prepare_config_and_inputs_for_common()

				_UpperCAmelCase			:							List[str]	  =    _config_zero_init(_A   )
				for model_class in self.all_model_classes:
					_UpperCAmelCase			:							List[Any]	  =    model_class(config=_A   )
					# Skip the check for the backbone
					_UpperCAmelCase			:							Union[str, Any]	  =    []
					for name, module in model.named_modules():
						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
							_UpperCAmelCase			:							Union[str, Any]	  =    [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   )			->			List[str]:






				'''simple docstring'''







				pass
			@slow
			def 							__snake_case       (						self   )			->			Any:






				'''simple docstring'''







				for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
					_UpperCAmelCase			:							Optional[int]	  =    DPTModel.from_pretrained(_A   )
					self.assertIsNotNone(_A   )
			def 							__snake_case       (						self   )			->			int:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    self.model_tester.prepare_config_and_inputs_for_common()
				_UpperCAmelCase			:							str	  =    "add"
				with self.assertRaises(_A   ):
					_UpperCAmelCase			:							List[str]	  =    DPTForDepthEstimation(_A   )
def 					UpperCamelCase    (	) ->	Any:
	_UpperCAmelCase			:							Optional[Any]	  =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image






@require_torch
@require_vision
@slow
class 			_UpperCAmelCase	(						unittest.TestCase):
			def 							__snake_case       (						self   )			->			Dict:






				'''simple docstring'''







				_UpperCAmelCase			:							int	  =    DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""   )
				_UpperCAmelCase			:							List[str]	  =    DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""   ).to(_A   )

				_UpperCAmelCase			:							List[Any]	  =    prepare_img()
				_UpperCAmelCase			:							int	  =    image_processor(images=_A					,						return_tensors="""pt"""   ).to(_A   )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase			:							Union[str, Any]	  =    model(**_A   )
					_UpperCAmelCase			:							List[Any]	  =    outputs.predicted_depth

				# verify the predicted depth
				_UpperCAmelCase			:							List[str]	  =    torch.Size((1, 3_84, 3_84)   )
				self.assertEqual(predicted_depth.shape					,						_A   )

				_UpperCAmelCase			:							Tuple	  =    torch.tensor(
				    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(_A   )

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






'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

