



"""simple docstring"""
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=13   ,  _SCREAMING_SNAKE_CASE=7   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=99   ,  _SCREAMING_SNAKE_CASE=64   ,  _SCREAMING_SNAKE_CASE=32   ,  _SCREAMING_SNAKE_CASE=5   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=37   ,  _SCREAMING_SNAKE_CASE="gelu"   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=512   ,  _SCREAMING_SNAKE_CASE=16   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=3   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			parent
    UpperCAmelCase			:      Dict			=			batch_size
    UpperCAmelCase			:      Optional[Any]			=			seq_length
    UpperCAmelCase			:      Any			=			is_training
    UpperCAmelCase			:      Optional[int]			=			use_input_mask
    UpperCAmelCase			:      Optional[int]			=			use_token_type_ids
    UpperCAmelCase			:      Dict			=			use_labels
    UpperCAmelCase			:      Optional[Any]			=			vocab_size
    UpperCAmelCase			:      Dict			=			hidden_size
    UpperCAmelCase			:      List[str]			=			embedding_size
    UpperCAmelCase			:      List[Any]			=			num_hidden_layers
    UpperCAmelCase			:      List[str]			=			num_attention_heads
    UpperCAmelCase			:      Optional[Any]			=			intermediate_size
    UpperCAmelCase			:      str			=			hidden_act
    UpperCAmelCase			:      Optional[Any]			=			hidden_dropout_prob
    UpperCAmelCase			:      int			=			attention_probs_dropout_prob
    UpperCAmelCase			:      str			=			max_position_embeddings
    UpperCAmelCase			:      Optional[int]			=			type_vocab_size
    UpperCAmelCase			:      Any			=			type_sequence_label_size
    UpperCAmelCase			:      int			=			initializer_range
    UpperCAmelCase			:      List[str]			=			num_labels
    UpperCAmelCase			:      Any			=			num_choices
    UpperCAmelCase			:      List[Any]			=			scope

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





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size		)

    UpperCAmelCase			:      Optional[Any]			=			None
    if self.use_input_mask:
     UpperCAmelCase			:      str			=			random_attention_mask([self.batch_size, self.seq_length]		)

    UpperCAmelCase			:      List[Any]			=			None
    if self.use_token_type_ids:
     UpperCAmelCase			:      Union[str, Any]			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.type_vocab_size		)

    UpperCAmelCase			:      List[str]			=			None
    UpperCAmelCase			:      Optional[int]			=			None
    UpperCAmelCase			:      Dict			=			None
    if self.use_labels:
     UpperCAmelCase			:      List[Any]			=			ids_tensor([self.batch_size]   ,  self.type_sequence_label_size		)
     UpperCAmelCase			:      List[Any]			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.num_labels		)
     UpperCAmelCase			:      str			=			ids_tensor([self.batch_size]   ,  self.num_choices		)

    UpperCAmelCase			:      str			=			self.get_config()

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

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





    '''simple docstring'''




    return MobileBertConfig(
        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   ,  embedding_size=self.embedding_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=_SCREAMING_SNAKE_CASE   ,  initializer_range=self.initializer_range   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			MobileBertModel(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			model(_SCREAMING_SNAKE_CASE		)

    self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)		)
    self.parent.assertEqual(result.pooler_output.shape   ,  (self.batch_size, self.hidden_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			MobileBertForMaskedLM(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Dict			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			MobileBertForNextSentencePrediction(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Tuple			=			model(
        _SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE   ,  )
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, 2)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			MobileBertForPreTraining(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Dict			=			model(
        _SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE   ,  next_sentence_label=_SCREAMING_SNAKE_CASE   ,  )
    self.parent.assertEqual(result.prediction_logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)		)
    self.parent.assertEqual(result.seq_relationship_logits.shape   ,  (self.batch_size, 2)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			MobileBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      int			=			model(
        _SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  start_positions=_SCREAMING_SNAKE_CASE   ,  end_positions=_SCREAMING_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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.num_labels
    UpperCAmelCase			:      Dict			=			MobileBertForSequenceClassification(_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      int			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.num_labels
    UpperCAmelCase			:      List[Any]			=			MobileBertForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      str			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.num_labels)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.num_choices
    UpperCAmelCase			:      Optional[Any]			=			MobileBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE		)
    model.to(_SCREAMING_SNAKE_CASE		)
    model.eval()
    UpperCAmelCase			:      Optional[int]			=			input_ids.unsqueeze(1		).expand(-1   ,  self.num_choices   ,  -1		).contiguous()
    UpperCAmelCase			:      str			=			token_type_ids.unsqueeze(1		).expand(-1   ,  self.num_choices   ,  -1		).contiguous()
    UpperCAmelCase			:      Dict			=			input_mask.unsqueeze(1		).expand(-1   ,  self.num_choices   ,  -1		).contiguous()
    UpperCAmelCase			:      Tuple			=			model(
        _SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  token_type_ids=_SCREAMING_SNAKE_CASE   ,  labels=_SCREAMING_SNAKE_CASE   ,  )
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_choices)		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.prepare_config_and_inputs()
    (
        UpperCAmelCase	
    )			:      List[str]			=			config_and_inputs
    UpperCAmelCase			:      Tuple			=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict



@require_torch
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Optional[int]		=		(
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase						:     List[str]		=		(
       {
           'feature-extraction': MobileBertModel,
           'fill-mask': MobileBertForMaskedLM,
           'question-answering': MobileBertForQuestionAnswering,
           'text-classification': MobileBertForSequenceClassification,
           'token-classification': MobileBertForTokenClassification,
           'zero-shot': MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase						:     Dict		=		True

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=False		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			super()._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  return_labels=_SCREAMING_SNAKE_CASE		)

    if return_labels:
     if model_class in get_values(_SCREAMING_SNAKE_CASE		):
      UpperCAmelCase			:      List[Any]			=			torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)   ,  dtype=torch.long   ,  device=_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Dict			=			torch.zeros(
          self.model_tester.batch_size   ,  dtype=torch.long   ,  device=_SCREAMING_SNAKE_CASE		)
    return inputs_dict

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





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			MobileBertModelTester(self		)
    UpperCAmelCase			:      Union[str, Any]			=			ConfigTester(self   ,  config_class=_SCREAMING_SNAKE_CASE   ,  hidden_size=37		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    self.config_tester.run_common_tests()

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





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_model(*_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_masked_lm(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_pretraining(*_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_question_answering(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)




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





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_token_classification(*_SCREAMING_SNAKE_CASE		)

def    _snake_case		(		UpperCamelCase		:	Optional[int]		):
 return torch.tensor(
     UpperCamelCase    ,	dtype=torch.long    ,	device=UpperCamelCase    ,	)


A:     Union[str, Any]										=	1E-3



@require_torch
@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			MobileBertModel.from_pretrained("""google/mobilebert-uncased"""		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			_long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]		)
    with torch.no_grad():
     UpperCAmelCase			:      int			=			model(_SCREAMING_SNAKE_CASE		)[0]
    UpperCAmelCase			:      Any			=			torch.Size((1, 9, 512)		)
    self.assertEqual(output.shape   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			torch.tensor(
        [
            [
                [-2.4736526E07, 8.2691656E04, 1.6521838E05],
                [-5.7541704E-01, 3.9056022E00, 4.4011507E00],
                [2.6047359E00, 1.5677652E00, -1.7324188E-01],
            ]
        ]   ,  device=_SCREAMING_SNAKE_CASE   ,  )

    # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
    # ~1 difference, it's therefore not a good idea to measure using addition.
    # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
    # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
    UpperCAmelCase			:      str			=			torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE		)
    UpperCAmelCase			:      str			=			torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE		)

    self.assertTrue(lower_bound and upper_bound		)





"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A:     List[Any]										=	(3, 9, -1_1, 0, 7, 5, 1, -1)
A:     Union[str, Any]										=	(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     int
   __lowerCAmelCase						:     Node | None



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Node | None			=			None
    for i in sorted(_SCREAMING_SNAKE_CASE   ,  reverse=_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Union[str, Any]			=			Node(_SCREAMING_SNAKE_CASE   ,  self.head		)

   def __iter__(     self		)						->   Iterator[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.head
    while node:
     yield node.data
     UpperCAmelCase			:      List[str]			=			node.next_node

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return sum(1 for _ in self		)




   def __str__(     self		)						->   str:





    '''simple docstring'''




    return " -> ".join([str(_SCREAMING_SNAKE_CASE		) for node in self]		)

def    _snake_case		(		UpperCamelCase		:	SortedLinkedList    ,	UpperCamelCase		:	SortedLinkedList		):
 return SortedLinkedList(list(UpperCamelCase		) + list(UpperCamelCase		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      A:     Dict										=	SortedLinkedList
      print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
