




"""simple docstring"""



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import (
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
          MraModel,
      )
      from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCAmelCase_  :




    def __init__(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	Dict=2     ,			snake_case_						:	Union[str, Any]=8     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	Any=True     ,			snake_case_						:	Union[str, Any]=True     ,			snake_case_						:	List[str]=True     ,			snake_case_						:	Union[str, Any]=99     ,			snake_case_						:	Dict=16     ,			snake_case_						:	Union[str, Any]=5     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[Any]=36     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Any=0.0     ,			snake_case_						:	Any=0.0     ,			snake_case_						:	int=512     ,			snake_case_						:	Optional[int]=16     ,			snake_case_						:	str=2     ,			snake_case_						:	Dict=0.02     ,			snake_case_						:	int=3     ,			snake_case_						:	Optional[int]=4     ,			snake_case_						:	Union[str, Any]=None     ,			)				->							Any:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    seq_length
     A__	=    is_training
     A__	=    use_input_mask
     A__	=    use_token_type_ids
     A__	=    use_labels
     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    num_labels
     A__	=    num_choices
     A__	=    scope




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size				)

     A__	=    None
     if self.use_input_mask:
      A__	=    random_attention_mask([self.batch_size, self.seq_length]				)

     A__	=    None
     if self.use_token_type_ids:
      A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size				)

     A__	=    None
     A__	=    None
     A__	=    None
     if self.use_labels:
      A__	=    ids_tensor([self.batch_size]     ,			self.type_sequence_label_size				)
      A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels				)
      A__	=    ids_tensor([self.batch_size]     ,			self.num_choices				)

     A__	=    self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




    def 							__magic_name__							(   self						:	int				)				->							Any:




     '''simple docstring'''

     return MraConfig(
         vocab_size=self.vocab_size     ,			hidden_size=self.hidden_size     ,			num_hidden_layers=self.num_hidden_layers     ,			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     ,			max_position_embeddings=self.max_position_embeddings     ,			type_vocab_size=self.type_vocab_size     ,			is_decoder=snake_case_     ,			initializer_range=self.initializer_range     ,			)




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




     '''simple docstring'''

     A__	=    self.get_config()
     A__	=    300
     return config




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




     '''simple docstring'''

     (
         (
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				
     )	=    self.prepare_config_and_inputs()

     A__	=    True
     A__	=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			vocab_size=2				)

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	List[str]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    MraModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any     ,			snake_case_						:	str     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Any     ,			)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    True
     A__	=    MraModel(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			encoder_hidden_states=snake_case_     ,			encoder_attention_mask=snake_case_     ,			)
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			encoder_hidden_states=snake_case_     ,			)
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Any     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	str				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    MraForMaskedLM(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    MraForQuestionAnswering(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			start_positions=snake_case_     ,			end_positions=snake_case_     ,			)
     self.parent.assertEqual(result.start_logits.shape     ,			(self.batch_size, self.seq_length)				)
     self.parent.assertEqual(result.end_logits.shape     ,			(self.batch_size, self.seq_length)				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    MraForSequenceClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[str]     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	str     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    MraForTokenClassification(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.num_choices
     A__	=    MraForMultipleChoice(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    input_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    token_type_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    input_mask.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)				)




    def 							__magic_name__							(   self						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     (
         (
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				
     )	=    config_and_inputs
     A__	=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False

    lowercase__						 =				()




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




     '''simple docstring'''

     A__	=    MraModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			hidden_size=37				)




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




     '''simple docstring'''

     self.config_tester.run_common_tests()




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      A__	=    type
      self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	List[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case_				)




    def 							__magic_name__							(   self						:	List[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							int:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case_				)




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




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case_				)




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							Any:




     '''simple docstring'''

     for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    MraModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)




    @unittest.skip(reason="MRA does not output attentions"				)
    def 							__magic_name__							(   self						:	int				)				->							Any:




     '''simple docstring'''

     return






@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    MraModel.from_pretrained("uw-madison/mra-base-512-4"				)
     A__	=    torch.arange(256				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    torch.Size((1, 256, 768)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]				)

     self.assertTrue(torch.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)




    @slow
    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     A__	=    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"				)
     A__	=    torch.arange(256				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    50_265

     A__	=    torch.Size((1, 256, vocab_size)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]				)

     self.assertTrue(torch.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"				)
     A__	=    torch.arange(4_096				).unsqueeze(0				)

     with torch.no_grad():
      A__	=    model(snake_case_				)[0]

     A__	=    50_265

     A__	=    torch.Size((1, 4_096, vocab_size)				)
     self.assertEqual(output.shape     ,			snake_case_				)

     A__	=    torch.tensor(
         [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]				)

     self.assertTrue(torch.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)











"""simple docstring"""



from typing import Any




class      UpperCAmelCase_  :




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Any				)				->							List[str]:




     '''simple docstring'''

     A__	=    data
     A__	=    None




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




     '''simple docstring'''

     return F"""Node({self.data})"""






class      UpperCAmelCase_  :




    def __init__(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    None




    def __iter__(   self						:	List[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.head
     while node:
      yield node.data
      A__	=    node.next




    def __len__(   self						:	Any				)				->							int:




     '''simple docstring'''

     return sum(1 for _ in self				)




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




     '''simple docstring'''

     return "->".join([str(snake_case_				) for item in self]				)




    def __getitem__(   self						:	str     ,			snake_case_						:	int				)				->							Any:




     '''simple docstring'''

     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						:	Tuple     ,			snake_case_						:	int     ,			snake_case_						:	Any				)				->							None:




     '''simple docstring'''

     if not 0 <= index < len(self				):
      raise ValueError("list index out of range."				)
     A__	=    self.head
     for _ in range(snake_case_				):
      A__	=    current.next
     A__	=    data




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Any				)				->							None:




     '''simple docstring'''

     self.insert_nth(len(self				)     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Any				)				->							None:




     '''simple docstring'''

     self.insert_nth(0     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	int     ,			snake_case_						:	Any				)				->							None:




     '''simple docstring'''

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




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




     '''simple docstring'''

     print(self				)




    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     return self.delete_nth(0				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:  # delete from tail




     '''simple docstring'''

     return self.delete_nth(len(self				) - 1				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	int = 0				)				->							Any:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Dict				)				->							bool:




     '''simple docstring'''

     return self.head is None




    def 							__magic_name__							(   self						:	List[Any]				)				->							None:




     '''simple docstring'''

     A__	=    None
     A__	=    self.head

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







def   _SCREAMING_SNAKE_CASE       (							)						->		None:
 A__	=    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						):
  A__	=    -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   _SCREAMING_SNAKE_CASE       (							)						->		None:
 A__	=    [
     -9,
     1_00,
     Node(77_34_51_12						),
     "dlrow olleH",
     7,
     55_55,
     0,
     -1_9_2.5_5_5_5_5,
     "Hello, world!",
     7_7.9,
     Node(10						),
     None,
     None,
     1_2.2_0,
 ]
 A__	=    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
 A__	=    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
 A__	=    linked_list.delete_tail()
 assert result == 1_2.2
 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
 A__	=    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   _SCREAMING_SNAKE_CASE       (							)						->		Optional[int]:
 from doctest import testmod

 testmod()

 A__	=    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]}"""						)
 A__	=    input("Enter New Value: "						).strip()
 print("New list:"						)
 print(lowercase_						)
 print(f"""length of linked_list is : {len(lowercase_						)}"""						)


if __name__ == "__main__":
      main()





