
from typing import Any


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     data
     UpperCamelCase_									=     None




  def __repr__(     self )			-> str:
     return f"""Node({self.data})"""




class 						_a       :






  """simple docstring"""





  def __init__(     self )			-> Optional[int]:
     UpperCamelCase_									=     None




  def __iter__(     self )			-> Any:
     UpperCamelCase_									=     self.head
     while node:
        yield node.data
        UpperCamelCase_									=     node.next




  def __len__(     self )			-> int:
     return sum(1 for _ in self )




  def __repr__(     self )			-> str:
     return "->".join([str(A__ ) for item in self] )




  def __getitem__(     self		,		_UpperCAmelCase )			-> Any:
     if not 0 <= index < len(self ):
        raise ValueError('list index out of range.' )
     for i, node in enumerate(self ):
        if i == index:
           return node
     return None




  def __setitem__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> None:
     if not 0 <= index < len(self ):
        raise ValueError('list index out of range.' )
     UpperCamelCase_									=     self.head
     for _ in range(A__ ):
        UpperCamelCase_									=     current.next
     UpperCamelCase_									=     data




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> None:
     self.insert_nth(len(self )		,		A__ )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> None:
     self.insert_nth(0		,		A__ )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> None:
     if not 0 <= index <= len(self ):
        raise IndexError('list index out of range' )
     UpperCamelCase_									=     Node(A__ )
     if self.head is None:
        UpperCamelCase_									=     new_node
     elif index == 0:
        UpperCamelCase_									=     self.head  # link new_node to head
        UpperCamelCase_									=     new_node
     else:
        UpperCamelCase_									=     self.head
        for _ in range(index - 1 ):
           UpperCamelCase_									=     temp.next
        UpperCamelCase_									=     temp.next
        UpperCamelCase_									=     new_node




  def    _UpperCAmelCase     (     self )			-> None:  # print every node data
     print(self )




  def    _UpperCAmelCase     (     self )			-> Any:
     return self.delete_nth(0 )




  def    _UpperCAmelCase     (     self )			-> Any:  # delete from tail
     return self.delete_nth(len(self ) - 1 )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase = 0 )			-> Any:
     if not 0 <= index <= len(self ) - 1:  # test if index is valid
        raise IndexError('List index out of range.' )
     UpperCamelCase_									=     self.head  # default first node
     if index == 0:
        UpperCamelCase_									=     self.head.next
     else:
        UpperCamelCase_									=     self.head
        for _ in range(index - 1 ):
           UpperCamelCase_									=     temp.next
        UpperCamelCase_									=     temp.next
        UpperCamelCase_									=     temp.next.next
     return delete_node.data




  def    _UpperCAmelCase     (     self )			-> bool:
     return self.head is None




  def    _UpperCAmelCase     (     self )			-> None:
     UpperCamelCase_									=     None
     UpperCamelCase_									=     self.head

     while current:
        # Store the current node's next node.
        UpperCamelCase_									=     current.next
        # Make the current node's next point backwards
        UpperCamelCase_									=     prev
        # Make the previous node be the current node
        UpperCamelCase_									=     current
        # Make the current node the next node (to progress iteration)
        UpperCamelCase_									=     next_node
     # Return prev in order to put the head at the end
     UpperCamelCase_									=     prev






def     _snake_case    ():
   UpperCamelCase_									=     LinkedList()
   assert linked_list.is_empty() is True
   assert str(__lowercase) == ""

   try:
      linked_list.delete_head()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   try:
      linked_list.delete_tail()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   for i in range(10):
      assert len(__lowercase) == i
      linked_list.insert_nth(__lowercase    ,      i + 1)
   assert str(__lowercase) == "->".join(str(__lowercase) for i in range(1    ,      11))

   linked_list.insert_head(0)
   linked_list.insert_tail(11)
   assert str(__lowercase) == "->".join(str(__lowercase) for i in range(0    ,      12))

   assert linked_list.delete_head() == 0
   assert linked_list.delete_nth(9) == 10
   assert linked_list.delete_tail() == 11
   assert len(__lowercase) == 9
   assert str(__lowercase) == "->".join(str(__lowercase) for i in range(1    ,      10))

   assert all(linked_list[i] == i + 1 for i in range(0    ,      9)) is True

   for i in range(0    ,      9):
      UpperCamelCase_									=     -i
   assert all(linked_list[i] == -i for i in range(0    ,      9)) is True

   linked_list.reverse()
   assert str(__lowercase) == "->".join(str(__lowercase) for i in range(-8    ,      1))






def     _snake_case    ():
   UpperCamelCase_									=     [
       -9,
       100,
       Node(77345112),
       'dlrow olleH',
       7,
       5555,
       0,
       -192.55_555,
       'Hello, world!',
       77.9,
       Node(10),
       None,
       None,
       12.20,
   ]
   UpperCamelCase_									=     LinkedList()

   for i in test_input:
      linked_list.insert_tail(__lowercase)

   # Check if it's empty or not
   assert linked_list.is_empty() is False
   assert (
       str(__lowercase) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
       "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
   )

   # Delete the head
   UpperCamelCase_									=     linked_list.delete_head()
   assert result == -9
   assert (
       str(__lowercase) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
       "Hello, world!->77.9->Node(10)->None->None->12.2"
   )

   # Delete the tail
   UpperCamelCase_									=     linked_list.delete_tail()
   assert result == 12.2
   assert (
       str(__lowercase) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
       "Hello, world!->77.9->Node(10)->None->None"
   )

   # Delete a node in specific location in linked list
   UpperCamelCase_									=     linked_list.delete_nth(10)
   assert result is None
   assert (
       str(__lowercase) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
       "Hello, world!->77.9->Node(10)->None"
   )

   # Add a Node instance to its head
   linked_list.insert_head(Node('Hello again, world!'))
   assert (
       str(__lowercase)
       == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
       "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
   )

   # Add None to its tail
   linked_list.insert_tail(__lowercase)
   assert (
       str(__lowercase)
       == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
       "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
   )

   # Reverse the linked list
   linked_list.reverse()
   assert (
       str(__lowercase)
       == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
       "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
   )






def     _snake_case    ():
   from doctest import testmod

   testmod()

   UpperCamelCase_									=     LinkedList()
   linked_list.insert_head(input('Inserting 1st at head ').strip())
   linked_list.insert_head(input('Inserting 2nd at head ').strip())
   print('\nPrint list:')
   linked_list.print_list()
   linked_list.insert_tail(input('\nInserting 1st at tail ').strip())
   linked_list.insert_tail(input('Inserting 2nd at tail ').strip())
   print('\nPrint list:')
   linked_list.print_list()
   print('\nDelete head')
   linked_list.delete_head()
   print('Delete tail')
   linked_list.delete_tail()
   print('\nPrint list:')
   linked_list.print_list()
   print('\nReverse linked list')
   linked_list.reverse()
   print('\nPrint list:')
   linked_list.print_list()
   print('\nString representation of linked list:')
   print(__lowercase)
   print('\nReading/changing Node data using indexing:')
   print(f"""Element at Position 1: {linked_list[1]}""")
   UpperCamelCase_									=     input('Enter New Value: ').strip()
   print('New list:')
   print(__lowercase)
   print(f"""length of linked_list is : {len(__lowercase)}""")


if __name__ == "__main__":
 main()













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 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=2		,		_UpperCAmelCase=32		,		_UpperCAmelCase=16		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=4		,		_UpperCAmelCase=[0, 1, 2, 3]		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=[1, 384, 24, 24]		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		)			-> Optional[Any]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     backbone_out_indices
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     num_labels
     UpperCamelCase_									=     backbone_featmap_shape
     UpperCamelCase_									=     scope
     UpperCamelCase_									=     is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCamelCase_									=     (image_size // patch_size) ** 2
     UpperCamelCase_									=     num_patches + 1




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     UpperCamelCase_									=     self.get_config()

     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     {
         '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=_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 )			-> Optional[int]:
     UpperCamelCase_									=     DPTModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     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]:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     DPTForDepthEstimation(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.predicted_depth.shape		,		(self.batch_size, self.image_size, self.image_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     DPTForSemanticSegmentation(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     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 )			-> str:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  A_						=   (
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     DPTModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




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




  @unittest.skip(reason='DPT does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     pass




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




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




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




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
           continue

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

        if model_class in get_values(_UpperCAmelCase ):
           continue

        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




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

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

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




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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




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




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     UpperCamelCase_									=     'add'
     with self.assertRaises(_UpperCAmelCase ):
        UpperCamelCase_									=     DPTForDepthEstimation(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


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






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas' )
     UpperCamelCase_									=     DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )
        UpperCamelCase_									=     outputs.predicted_depth

     # verify the predicted depth
     UpperCamelCase_									=     torch.Size((1, 384, 384) )
     self.assertEqual(predicted_depth.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     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] / 100		,		_UpperCAmelCase		,		atol=1e-4 ) )





