



'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ConditionalDetrImageProcessor






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=7  ,__UpperCAmelCase=3  ,__UpperCAmelCase=30  ,__UpperCAmelCase=400  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=[0.5, 0.5, 0.5]  ,__UpperCAmelCase=[0.5, 0.5, 0.5]  ,__UpperCAmelCase=True  ,__UpperCAmelCase=1 / 255  ,__UpperCAmelCase=True  ,)   ->		Optional[int]:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      lowerCAmelCase__			:  Tuple											= size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      lowerCAmelCase__			:  int											= parent
      lowerCAmelCase__			:  Optional[int]											= batch_size
      lowerCAmelCase__			:  str											= num_channels
      lowerCAmelCase__			:  Tuple											= min_resolution
      lowerCAmelCase__			:  Optional[Any]											= max_resolution
      lowerCAmelCase__			:  Tuple											= do_resize
      lowerCAmelCase__			:  Dict											= size
      lowerCAmelCase__			:  Dict											= do_normalize
      lowerCAmelCase__			:  List[str]											= image_mean
      lowerCAmelCase__			:  List[Any]											= image_std
      lowerCAmelCase__			:  Any											= do_rescale
      lowerCAmelCase__			:  Optional[int]											= rescale_factor
      lowerCAmelCase__			:  int											= do_pad




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=False				)   ->		Optional[Any]:
      if not batched:
          lowerCAmelCase__			:  Tuple											= image_inputs[0]
          if isinstance(__UpperCAmelCase  ,Image.Image				):
              lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= image.size
          else:
              lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= image.shape[1], image.shape[2]
          if w < h:
              lowerCAmelCase__			:  str											= int(self.size["""shortest_edge"""] * h / w				)
              lowerCAmelCase__			:  List[str]											= self.size["""shortest_edge"""]
          elif w > h:
              lowerCAmelCase__			:  Union[str, Any]											= self.size["""shortest_edge"""]
              lowerCAmelCase__			:  List[Any]											= int(self.size["""shortest_edge"""] * w / h				)
          else:
              lowerCAmelCase__			:  Optional[Any]											= self.size["""shortest_edge"""]
              lowerCAmelCase__			:  Optional[Any]											= self.size["""shortest_edge"""]

      else:
          lowerCAmelCase__			:  Optional[int]											= []
          for image in image_inputs:
              lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.get_expected_values([image]				)
              expected_values.append((expected_height, expected_width)				)
          lowerCAmelCase__			:  Union[str, Any]											= max(__UpperCAmelCase  ,key=lambda __UpperCAmelCase				: item[0]				)[0]
          lowerCAmelCase__			:  Union[str, Any]											= max(__UpperCAmelCase  ,key=lambda __UpperCAmelCase				: item[1]				)[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				str           =				ConditionalDetrImageProcessor if is_vision_available() else None




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  List[Any]											= ConditionalDetrImageProcessingTester(self				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  int											= self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""image_mean"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""image_std"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_normalize"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""do_resize"""				)				)
      self.assertTrue(hasattr(__UpperCAmelCase  ,"""size"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  List[str]											= self.image_processing_class.from_dict(self.image_processor_dict				)
      self.assertEqual(image_processor.size  ,{"""shortest_edge""": 18, """longest_edge""": 1333}				)
      self.assertEqual(image_processor.do_pad  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= self.image_processing_class.from_dict(
          self.image_processor_dict  ,size=42  ,max_size=84  ,pad_and_return_pixel_mask=__UpperCAmelCase				)
      self.assertEqual(image_processor.size  ,{"""shortest_edge""": 42, """longest_edge""": 84}				)
      self.assertEqual(image_processor.do_pad  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      # Initialize image_processing
      lowerCAmelCase__			:  Optional[Any]											= self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      lowerCAmelCase__			:  int											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,Image.Image				)

      # Test not batched input
      lowerCAmelCase__			:  List[Any]											= image_processing(image_inputs[0]  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape  ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,)

      # Test batched
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= self.image_processor_tester.get_expected_values(__UpperCAmelCase  ,batched=__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= image_processing(__UpperCAmelCase  ,return_tensors="""pt"""				).pixel_values
      self.assertEqual(
          encoded_images.shape  ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,)




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      # Initialize image_processing
      lowerCAmelCase__			:  str											= self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      lowerCAmelCase__			:  Tuple											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase  ,numpify=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,np.ndarray				)

      # Test not batched input
      lowerCAmelCase__			:  List[str]											= image_processing(image_inputs[0]  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape  ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,)

      # Test batched
      lowerCAmelCase__			:  List[str]											= image_processing(__UpperCAmelCase  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= self.image_processor_tester.get_expected_values(__UpperCAmelCase  ,batched=__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape  ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      # Initialize image_processing
      lowerCAmelCase__			:  int											= self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      lowerCAmelCase__			:  str											= prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCAmelCase  ,torchify=__UpperCAmelCase				)
      for image in image_inputs:
          self.assertIsInstance(__UpperCAmelCase  ,torch.Tensor				)

      # Test not batched input
      lowerCAmelCase__			:  List[str]											= image_processing(image_inputs[0]  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape  ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,)

      # Test batched
      lowerCAmelCase__			:  int											= image_processing(__UpperCAmelCase  ,return_tensors="""pt"""				).pixel_values

      lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[int]											= self.image_processor_tester.get_expected_values(__UpperCAmelCase  ,batched=__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape  ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      # prepare image and target
      lowerCAmelCase__			:  Any											= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""  ,"""r"""				) as f:
          lowerCAmelCase__			:  Tuple											= json.loads(f.read()				)

      lowerCAmelCase__			:  Dict											= {"""image_id""": 3_9769, """annotations""": target}

      # encode them
      lowerCAmelCase__			:  List[Any]											= ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
      lowerCAmelCase__			:  Tuple											= image_processing(images=__UpperCAmelCase  ,annotations=__UpperCAmelCase  ,return_tensors="""pt"""				)

      # verify pixel values
      lowerCAmelCase__			:  Dict											= torch.Size([1, 3, 800, 1066]				)
      self.assertEqual(encoding["""pixel_values"""].shape  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,__UpperCAmelCase  ,atol=1E-4				)				)

      # verify area
      lowerCAmelCase__			:  List[str]											= torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,__UpperCAmelCase				)				)
      # verify boxes
      lowerCAmelCase__			:  Any											= torch.Size([6, 4]				)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  str											= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,__UpperCAmelCase  ,atol=1E-3				)				)
      # verify image_id
      lowerCAmelCase__			:  int											= torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,__UpperCAmelCase				)				)
      # verify is_crowd
      lowerCAmelCase__			:  Union[str, Any]											= torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,__UpperCAmelCase				)				)
      # verify class_labels
      lowerCAmelCase__			:  List[Any]											= torch.tensor([75, 75, 63, 65, 17, 17]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,__UpperCAmelCase				)				)
      # verify orig_size
      lowerCAmelCase__			:  Tuple											= torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,__UpperCAmelCase				)				)
      # verify size
      lowerCAmelCase__			:  List[str]											= torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,__UpperCAmelCase				)				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		str:
      # prepare image, target and masks_path
      lowerCAmelCase__			:  str											= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
      with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""  ,"""r"""				) as f:
          lowerCAmelCase__			:  str											= json.loads(f.read()				)

      lowerCAmelCase__			:  Union[str, Any]											= {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      lowerCAmelCase__			:  List[Any]											= pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

      # encode them
      lowerCAmelCase__			:  Optional[int]											= ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
      lowerCAmelCase__			:  Union[str, Any]											= image_processing(images=__UpperCAmelCase  ,annotations=__UpperCAmelCase  ,masks_path=__UpperCAmelCase  ,return_tensors="""pt"""				)

      # verify pixel values
      lowerCAmelCase__			:  Any											= torch.Size([1, 3, 800, 1066]				)
      self.assertEqual(encoding["""pixel_values"""].shape  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,__UpperCAmelCase  ,atol=1E-4				)				)

      # verify area
      lowerCAmelCase__			:  List[str]											= torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,__UpperCAmelCase				)				)
      # verify boxes
      lowerCAmelCase__			:  List[str]											= torch.Size([6, 4]				)
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,__UpperCAmelCase  ,atol=1E-3				)				)
      # verify image_id
      lowerCAmelCase__			:  Optional[Any]											= torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,__UpperCAmelCase				)				)
      # verify is_crowd
      lowerCAmelCase__			:  Tuple											= torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,__UpperCAmelCase				)				)
      # verify class_labels
      lowerCAmelCase__			:  List[Any]											= torch.tensor([17, 17, 63, 75, 75, 93]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,__UpperCAmelCase				)				)
      # verify masks
      lowerCAmelCase__			:  int											= 82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,__UpperCAmelCase				)
      # verify orig_size
      lowerCAmelCase__			:  Dict											= torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,__UpperCAmelCase				)				)
      # verify size
      lowerCAmelCase__			:  str											= torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,__UpperCAmelCase				)				)







'''simple docstring'''





from typing import Any






class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  Optional[Any]											= data
      lowerCAmelCase__			:  str											= None






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







class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self				)   ->		List[Any]:
      lowerCAmelCase__			:  List[str]											= None




  def __iter__(   self				)   ->		Any:
      lowerCAmelCase__			:  int											= self.head
      while node:
          yield node.data
          lowerCAmelCase__			:  Optional[Any]											= node.next




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




  def __repr__(   self				)   ->		str:
      return "->".join([str(__UpperCAmelCase				) 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."""				)
      lowerCAmelCase__			:  List[Any]											= self.head
      for _ in range(__UpperCAmelCase				):
          lowerCAmelCase__			:  List[str]											= current.next
      lowerCAmelCase__			:  Dict											= data




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      self.insert_nth(len(self				)  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      self.insert_nth(0  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		None:
      if not 0 <= index <= len(self				):
          raise IndexError("""list index out of range"""				)
      lowerCAmelCase__			:  str											= Node(__UpperCAmelCase				)
      if self.head is None:
          lowerCAmelCase__			:  List[Any]											= new_node
      elif index == 0:
          lowerCAmelCase__			:  Dict											= self.head  # link new_node to head
          lowerCAmelCase__			:  int											= new_node
      else:
          lowerCAmelCase__			:  Any											= self.head
          for _ in range(index - 1				):
              lowerCAmelCase__			:  Tuple											= temp.next
          lowerCAmelCase__			:  str											= temp.next
          lowerCAmelCase__			:  Optional[int]											= 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."""				)
      lowerCAmelCase__			:  List[Any]											= self.head  # default first node
      if index == 0:
          lowerCAmelCase__			:  str											= self.head.next
      else:
          lowerCAmelCase__			:  int											= self.head
          for _ in range(index - 1				):
              lowerCAmelCase__			:  Union[str, Any]											= temp.next
          lowerCAmelCase__			:  Optional[Any]											= temp.next
          lowerCAmelCase__			:  Any											= temp.next.next
      return delete_node.data




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






  def 							UpperCAmelCase_     (   self				)   ->		None:
      lowerCAmelCase__			:  Optional[int]											= None
      lowerCAmelCase__			:  str											= self.head

      while current:
          # Store the current node's next node.
          lowerCAmelCase__			:  Optional[Any]											= current.next
          # Make the current node's next point backwards
          lowerCAmelCase__			:  Optional[int]											= prev
          # Make the previous node be the current node
          lowerCAmelCase__			:  Optional[Any]											= current
          # Make the current node the next node (to progress iteration)
          lowerCAmelCase__			:  Optional[int]											= next_node
      # Return prev in order to put the head at the end
      lowerCAmelCase__			:  List[str]											= prev







def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= LinkedList()
    assert linked_list.is_empty() is True
    assert str(UpperCamelCase      ) == ""

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

    linked_list.insert_head(0      )
    linked_list.insert_tail(11      )
    assert str(UpperCamelCase      ) == "->".join(str(UpperCamelCase      ) 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(UpperCamelCase      ) == 9
    assert str(UpperCamelCase      ) == "->".join(str(UpperCamelCase      ) 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      ):
        lowerCAmelCase__			:  str											= -i
    assert all(linked_list[i] == -i for i in range(0							,   9      )      ) is True

    linked_list.reverse()
    assert str(UpperCamelCase      ) == "->".join(str(UpperCamelCase      ) for i in range(-8							,   1      )      )
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= [
        -9,
        100,
        Node(77345112      ),
        """dlrow olleH""",
        7,
        5555,
        0,
        -192.5_5555,
        """Hello, world!""",
        77.9,
        Node(10      ),
        None,
        None,
        12.20,
    ]
    lowerCAmelCase__			:  List[Any]											= LinkedList()

    for i in test_input:
        linked_list.insert_tail(UpperCamelCase      )

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

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

    # Delete the tail
    lowerCAmelCase__			:  Union[str, Any]											= linked_list.delete_tail()
    assert result == 12.2
    assert (
        str(UpperCamelCase      ) == "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
    lowerCAmelCase__			:  str											= linked_list.delete_nth(10      )
    assert result is None
    assert (
        str(UpperCamelCase      ) == "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(UpperCamelCase      )
        == "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(UpperCamelCase      )
    assert (
        str(UpperCamelCase      )
        == "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(UpperCamelCase      )
        == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
        "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
    )
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    from doctest import testmod

    testmod()

    lowerCAmelCase__			:  str											= 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(UpperCamelCase      )
    print("""\nReading/changing Node data using indexing:"""      )
    print(f"""Element at Position 1: {linked_list[1]}"""      )
    lowerCAmelCase__			:  Dict											= input("""Enter New Value: """      ).strip()
    print("""New list:"""      )
    print(UpperCamelCase      )
    print(f"""length of linked_list is : {len(UpperCamelCase      )}"""      )


if __name__ == "__main__":
 main()


