


'''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       __magic_name__	:


     def __init__(      self							:     Optional[int]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     str=32							,	lowerCamelCase__							:     str=16							,	lowerCamelCase__							:     List[Any]=3							,	lowerCamelCase__							:     Any=True							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     Optional[int]=32							,	lowerCamelCase__							:     Tuple=4							,	lowerCamelCase__							:     Dict=[0, 1, 2, 3]							,	lowerCamelCase__							:     Optional[Any]=4							,	lowerCamelCase__							:     Optional[int]=37							,	lowerCamelCase__							:     Tuple="gelu"							,	lowerCamelCase__							:     List[str]=0.1							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     List[Any]=0.02							,	lowerCamelCase__							:     Any=3							,	lowerCamelCase__							:     List[Any]=[1, 384, 24, 24]							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     int=None							,	)  ->      str:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

      UpperCamelCase__					:    Union[str, Any]          =   self.get_config()

      return config, pixel_values, labels


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   {
          '''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=lowerCamelCase__							,	initializer_range=self.initializer_range							,	is_hybrid=self.is_hybrid							,	backbone_config=lowerCamelCase__							,	backbone_featmap_shape=self.backbone_featmap_shape							,	)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     List[Any]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   DPTModel(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    List[str]          =   model(lowerCamelCase__			)
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)			)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Tuple			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.num_labels
      UpperCamelCase__					:    Any          =   DPTForDepthEstimation(lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    List[str]          =   model(lowerCamelCase__			)
      self.parent.assertEqual(result.predicted_depth.shape							,	(self.batch_size, self.image_size, self.image_size)			)


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     str			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.num_labels
      UpperCamelCase__					:    str          =   DPTForSemanticSegmentation(lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    Tuple          =   model(lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(
          result.logits.shape							,	(self.batch_size, self.num_labels, self.image_size, self.image_size)			)


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   self.prepare_config_and_inputs()
      UpperCamelCase__					:    int          =   config_and_inputs
      UpperCamelCase__					:    Any          =   {'''pixel_values''': pixel_values}
      return config, inputs_dict



@require_torch
class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Tuple			=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     A:		Optional[Any]			=      (
         {
             "depth-estimation": DPTForDepthEstimation,
             "feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A:		List[Any]			=      False
     A:		Optional[int]			=      False
     A:		str			=      False


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   DPTModelTester(self			)
      UpperCamelCase__					:    Union[str, Any]          =   ConfigTester(self							,	config_class=lowerCamelCase__							,	has_text_modality=lowerCamelCase__							,	hidden_size=37			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Optional[Any]:





      '''simple docstring'''






      self.config_tester.run_common_tests()


     @unittest.skip(reason='''DPT does not use inputs_embeds'''			)
     def      UpperCAmelCase__	(      self							:     Any			)  ->      int:





      '''simple docstring'''






      pass


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Any:





      '''simple docstring'''






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

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


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__					:    List[Any]          =   ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[str]:





      '''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__					:    List[str]          =   True

       if model_class in get_values(lowerCamelCase__			):
        continue

       UpperCamelCase__					:    Tuple          =   model_class(lowerCamelCase__			)
       model.to(lowerCamelCase__			)
       model.train()
       UpperCamelCase__					:    List[Any]          =   self._prepare_for_class(lowerCamelCase__							,	lowerCamelCase__							,	return_labels=lowerCamelCase__			)
       UpperCamelCase__					:    str          =   model(**lowerCamelCase__			).loss
       loss.backward()


     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[int]:





      '''simple docstring'''






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

       UpperCamelCase__					:    Optional[int]          =   self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__					:    Optional[Any]          =   False
       UpperCamelCase__					:    Optional[Any]          =   True

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


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.model_tester.prepare_config_and_inputs_for_common()

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





      '''simple docstring'''






      pass


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      Dict:





      '''simple docstring'''






      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       UpperCamelCase__					:    List[str]          =   DPTModel.from_pretrained(lowerCamelCase__			)
       self.assertIsNotNone(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase__					:    List[Any]          =   '''add'''
      with self.assertRaises(lowerCamelCase__			):
       UpperCamelCase__					:    int          =   DPTForDepthEstimation(lowerCamelCase__			)



def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






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


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''			)
      UpperCamelCase__					:    Union[str, Any]          =   DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''			).to(lowerCamelCase__			)

      UpperCamelCase__					:    int          =   prepare_img()
      UpperCamelCase__					:    Tuple          =   image_processor(images=lowerCamelCase__							,	return_tensors='''pt'''			).to(lowerCamelCase__			)

      # forward pass
      with torch.no_grad():
       UpperCamelCase__					:    Dict          =   model(**lowerCamelCase__			)
       UpperCamelCase__					:    str          =   outputs.predicted_depth

      # verify the predicted depth
      UpperCamelCase__					:    Union[str, Any]          =   torch.Size((1, 384, 384)			)
      self.assertEqual(predicted_depth.shape							,	lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   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] / 100							,	lowerCamelCase__							,	atol=1E-4			)			)






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


