


"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )



class 						snake_case_:






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[Any]=1_3    , UpperCamelCase_     :    str=7    , UpperCamelCase_     :    str=True    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    Tuple=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    str=9_9    , UpperCamelCase_     :    int=3_2    , UpperCamelCase_     :    Dict=2    , UpperCamelCase_     :    Optional[int]=4    , UpperCamelCase_     :    Any=3_7    , UpperCamelCase_     :    List[Any]="gelu"    , UpperCamelCase_     :    Optional[int]=0.1    , UpperCamelCase_     :    List[Any]=0.1    , UpperCamelCase_     :    Optional[Any]=5_1_2    , UpperCamelCase_     :    List[str]=1_6    , UpperCamelCase_     :    Optional[Any]=2    , UpperCamelCase_     :    List[str]=0.02    , UpperCamelCase_     :    int=3    , UpperCamelCase_     :    List[str]=4    , UpperCamelCase_     :    Union[str, Any]=None    , ):
           lowerCAmelCase    :		Dict				=      parent
           lowerCAmelCase    :		Dict				=      1_3
           lowerCAmelCase    :		Tuple				=      7
           lowerCAmelCase    :		int				=      True
           lowerCAmelCase    :		List[Any]				=      True
           lowerCAmelCase    :		Tuple				=      True
           lowerCAmelCase    :		List[str]				=      True
           lowerCAmelCase    :		int				=      9_9
           lowerCAmelCase    :		Dict				=      3_2
           lowerCAmelCase    :		Optional[Any]				=      2
           lowerCAmelCase    :		Union[str, Any]				=      4
           lowerCAmelCase    :		Union[str, Any]				=      3_7
           lowerCAmelCase    :		Optional[Any]				=      '''gelu'''
           lowerCAmelCase    :		Any				=      0.1
           lowerCAmelCase    :		str				=      0.1
           lowerCAmelCase    :		List[str]				=      5_1_2
           lowerCAmelCase    :		Any				=      1_6
           lowerCAmelCase    :		Dict				=      2
           lowerCAmelCase    :		int				=      0.02
           lowerCAmelCase    :		Any				=      3
           lowerCAmelCase    :		Dict				=      4
           lowerCAmelCase    :		List[Any]				=      None






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		List[Any]				=      None
           if self.use_input_mask:
                 lowerCAmelCase    :		Optional[int]				=      random_attention_mask([self.batch_size, self.seq_length] )

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

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

           lowerCAmelCase    :		Dict				=      RoFormerConfig(
               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    , initializer_range=self.initializer_range    , return_dict=_a    , )

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






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    str    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Any ):
           lowerCAmelCase    :		Union[str, Any]				=      TFRoFormerModel(config=_a )
           lowerCAmelCase    :		List[str]				=      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

           lowerCAmelCase    :		List[Any]				=      [input_ids, input_mask]
           lowerCAmelCase    :		List[str]				=      model(_a )

           lowerCAmelCase    :		Dict				=      model(_a )

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






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		Any				=      True
           lowerCAmelCase    :		Tuple				=      TFRoFormerForCausalLM(config=_a )
           lowerCAmelCase    :		Optional[Any]				=      {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           lowerCAmelCase    :		str				=      model(_a )['''logits''']
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape )    , [self.batch_size, self.seq_length, self.vocab_size] )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Tuple ):
           lowerCAmelCase    :		Tuple				=      TFRoFormerForMaskedLM(config=_a )
           lowerCAmelCase    :		str				=      {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           lowerCAmelCase    :		int				=      model(_a )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Union[str, Any] ):
           lowerCAmelCase    :		Tuple				=      self.num_labels
           lowerCAmelCase    :		Optional[int]				=      TFRoFormerForSequenceClassification(config=_a )
           lowerCAmelCase    :		Dict				=      {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           lowerCAmelCase    :		Tuple				=      model(_a )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    str    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		Dict				=      self.num_choices
           lowerCAmelCase    :		Union[str, Any]				=      TFRoFormerForMultipleChoice(config=_a )
           lowerCAmelCase    :		List[str]				=      tf.tile(tf.expand_dims(_a    , 1 )    , (1, self.num_choices, 1) )
           lowerCAmelCase    :		int				=      tf.tile(tf.expand_dims(_a    , 1 )    , (1, self.num_choices, 1) )
           lowerCAmelCase    :		int				=      tf.tile(tf.expand_dims(_a    , 1 )    , (1, self.num_choices, 1) )
           lowerCAmelCase    :		int				=      {
               '''input_ids''': multiple_choice_inputs_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           lowerCAmelCase    :		List[str]				=      model(_a )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_choices) )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Any				=      self.num_labels
           lowerCAmelCase    :		Optional[Any]				=      TFRoFormerForTokenClassification(config=_a )
           lowerCAmelCase    :		Optional[int]				=      {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           lowerCAmelCase    :		Any				=      model(_a )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels) )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Optional[int] ):
           lowerCAmelCase    :		str				=      TFRoFormerForQuestionAnswering(config=_a )
           lowerCAmelCase    :		Tuple				=      {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }

           lowerCAmelCase    :		Optional[int]				=      model(_a )
           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    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Dict				=      self.prepare_config_and_inputs()
           (
               (
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),					(
               lowerCAmelCase						
           ),											
           )    :		List[Any]				=      config_and_inputs
           lowerCAmelCase    :		str				=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict



@require_tf
class 						snake_case_(				A__							,    A__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     __UpperCamelCase     =    (
         {
             '''feature-extraction''': TFRoFormerModel,
             '''fill-mask''': TFRoFormerForMaskedLM,
             '''question-answering''': TFRoFormerForQuestionAnswering,
             '''text-classification''': TFRoFormerForSequenceClassification,
             '''text-generation''': TFRoFormerForCausalLM,
             '''token-classification''': TFRoFormerForTokenClassification,
             '''zero-shot''': TFRoFormerForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Dict ):






           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                 return True

           return False






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Dict				=      TFRoFormerModelTester(self )
           lowerCAmelCase    :		int				=      ConfigTester(self    , config_class=_a    , hidden_size=3_7 )






     def    lowerCamelCase__   (		self     :    List[str] ):
           self.config_tester.run_common_tests()






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a )






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*_a )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Any				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*_a )






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*_a )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_a )






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_a )






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Optional[Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_a )






     @slow
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Any				=      TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
           self.assertIsNotNone(_a )



@require_tf
class 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		str				=      TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
           lowerCAmelCase    :		Optional[int]				=      tf.constant([[0, 1, 2, 3, 4, 5]] )
           lowerCAmelCase    :		int				=      model(_a )[0]

           # TODO Replace vocab size
           lowerCAmelCase    :		Optional[int]				=      5_0_0_0_0

           lowerCAmelCase    :		Dict				=      [1, 6, vocab_size]
           self.assertEqual(output.shape    , _a )

           print(output[:, :3, :3] )

           # TODO Replace values below with what was printed above.
           lowerCAmelCase    :		str				=      tf.constant(
               [
                   [
                       [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                       [-1.5_133_783, 0.197_433, 0.15_190_607],
                       [-5.0_135_403, -3.900_256, -0.84_038_764],
                   ]
               ] )
           tf.debugging.assert_near(output[:, :3, :3]    , _a    , atol=1E-4 )



@require_tf
class 						snake_case_(				unittest.TestCase	):
     __UpperCamelCase     =    1e-4






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Any				=      tf.constant([[4, 1_0]] )
           lowerCAmelCase    :		Any				=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6    , embedding_dim=6 )

           lowerCAmelCase    :		Union[str, Any]				=      emba(input_ids.shape )
           lowerCAmelCase    :		Optional[Any]				=      tf.constant(
               [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )

           tf.debugging.assert_near(_a    , _a    , atol=self.tolerance )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Union[str, Any]				=      tf.constant(
               [
                   [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
                   [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
                   [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
               ] )
           lowerCAmelCase    :		str				=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2    , embedding_dim=5_1_2 )
           emba([2, 1_6, 5_1_2] )
           lowerCAmelCase    :		Optional[Any]				=      emba.weight[:3, :5]

           tf.debugging.assert_near(_a    , _a    , atol=self.tolerance )



@require_tf
class 						snake_case_(				unittest.TestCase	):
     __UpperCamelCase     =    1e-4






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		int				=      tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    , dtype=tf.floataa )    , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0

           lowerCAmelCase    :		List[Any]				=      -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4    , dtype=tf.floataa )    , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0

           lowerCAmelCase    :		Dict				=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2    , embedding_dim=6_4 )
           lowerCAmelCase    :		Union[str, Any]				=      embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]

           lowerCAmelCase,					lowerCAmelCase    :		Any				=      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               _a    , _a    , _a )

           lowerCAmelCase    :		Union[str, Any]				=      tf.constant(
               [
                   [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
                   [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
                   [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
                   [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
                   [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
                   [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
               ] )
           lowerCAmelCase    :		Tuple				=      tf.constant(
               [
                   [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
                   [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
                   [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
                   [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
                   [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
                   [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
               ] )

           tf.debugging.assert_near(query_layer[0, 0, :6, :8]    , _a    , atol=self.tolerance )
           tf.debugging.assert_near(key_layer[0, 0, :6, :8]    , _a    , atol=self.tolerance )





'''simple docstring'''
from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowerCamelCase :      Union[str, Any]					 =     TypeVar("KT")
lowerCamelCase :      Dict					 =     TypeVar("VT")







class 							A__    (       Generic[KT, VT]  ):





  def __init__(		self      :	str							,					_a      :	KT | str = "root"							,					_a      :	VT | None = None )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =key
       _SCREAMING_SNAKE_CASE							    =value
       _SCREAMING_SNAKE_CASE							    =[]





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





       '''simple docstring'''






       return f"Node({self.key}: {self.value})"





  @property
  def 						A  (		self      :	int )	->    int:





       '''simple docstring'''






       return len(self.forward )







class 							A__    (       Generic[KT, VT]  ):





  def __init__(		self      :	Optional[Any]							,					_a      :	float = 0.5							,					_a      :	int = 16 )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =Node[KT, VT]()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =p
       _SCREAMING_SNAKE_CASE							    =max_level





  def __str__(		self      :	Tuple )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =list(self )

       if len(_a ) == 0:
            return f"SkipList(level={self.level})"

       _SCREAMING_SNAKE_CASE							    =max((len(str(_a ) ) for item in items)							,					default=4 )
       _SCREAMING_SNAKE_CASE							    =max(_a							,					4 ) + 4

       _SCREAMING_SNAKE_CASE							    =self.head
       _SCREAMING_SNAKE_CASE							    =[]

       _SCREAMING_SNAKE_CASE							    =node.forward.copy()
       lines.append(f"[{node.key}]".ljust(_a							,					'-' ) + '* ' * len(_a ) )
       lines.append(' ' * label_size + '| ' * len(_a ) )

       while len(node.forward ) != 0:
            _SCREAMING_SNAKE_CASE							    =node.forward[0]

            lines.append(
                f"[{node.key}]".ljust(_a							,					'-' )
                + ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
            lines.append(' ' * label_size + '| ' * len(_a ) )
            _SCREAMING_SNAKE_CASE							    =node.forward

       lines.append('None'.ljust(_a ) + '* ' * len(_a ) )
       return f"SkipList(level={self.level})\n" + "\n".join(_a )





  def __iter__(		self      :	Dict )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.head

       while len(node.forward ) != 0:
            yield node.forward[0].key
            _SCREAMING_SNAKE_CASE							    =node.forward[0]





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =1
       while random() < self.p and level < self.max_level:
            level += 1

       return level





  def 						A  (		self      :	Any							,					_a      :	Any )	->    tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]

       _SCREAMING_SNAKE_CASE							    =self.head

       for i in reversed(range(self.level ) ):
            # i < node.level - When node level is lesser than `i` decrement `i`.
            # node.forward[i].key < key - Jumping to node with key value higher
            #                             or equal to searched key would result
            #                             in skipping searched key.
            while i < node.level and node.forward[i].key < key:
                 _SCREAMING_SNAKE_CASE							    =node.forward[i]
            # Each leftmost node (relative to searched node) will potentially have to
            # be updated.
            update_vector.append(_a )

       update_vector.reverse()  # Note that we were inserting values in reverse order.

       # len(node.forward) != 0 - If current node doesn't contain any further
       #                          references then searched key is not present.
       # node.forward[0].key == key - Next node key should be equal to search key
       #                              if key is present.
       if len(node.forward ) != 0 and node.forward[0].key == key:
            return node.forward[0], update_vector
       else:
            return None, update_vector





  def 						A  (		self      :	Union[str, Any]							,					_a      :	KT )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )

       if node is not None:
            for i, update_node in enumerate(_a ):
                 # Remove or replace all references to removed node.
                 if update_node.level > i and update_node.forward[i].key == key:
                      if node.level > i:
                           _SCREAMING_SNAKE_CASE							    =node.forward[i]
                      else:
                           _SCREAMING_SNAKE_CASE							    =update_node.forward[:i]





  def 						A  (		self      :	Optional[Any]							,					_a      :	KT							,					_a      :	VT )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )
       if node is not None:
            _SCREAMING_SNAKE_CASE							    =value
       else:
            _SCREAMING_SNAKE_CASE							    =self.random_level()

            if level > self.level:
                 # After level increase we have to add additional nodes to head.
                 for _ in range(self.level - 1							,					_a ):
                      update_vector.append(self.head )
                 _SCREAMING_SNAKE_CASE							    =level

            _SCREAMING_SNAKE_CASE							    =Node(_a							,					_a )

            for i, update_node in enumerate(update_vector[:level] ):
                 # Change references to pass through new node.
                 if update_node.level > i:
                      new_node.forward.append(update_node.forward[i] )

                 if update_node.level < i + 1:
                      update_node.forward.append(_a )
                 else:
                      _SCREAMING_SNAKE_CASE							    =new_node





  def 						A  (		self      :	List[str]							,					_a      :	VT )	->    VT | None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =self._locate_node(_a )

       if node is not None:
            return node.value

       return None


def        _lowerCAmelCase					(						)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert('Key1'							, 3				)
     skip_list.insert('Key2'							, 12				)
     skip_list.insert('Key3'							, 41				)
     skip_list.insert('Key4'							, -19				)

     _SCREAMING_SNAKE_CASE							    =skip_list.head
     _SCREAMING_SNAKE_CASE							    ={}
     while node.level != 0:
          _SCREAMING_SNAKE_CASE							    =node.forward[0]
          _SCREAMING_SNAKE_CASE							    =node.value

     assert len(_UpperCamelCase				) == 4
     assert all_values["Key1"] == 3
     assert all_values["Key2"] == 12
     assert all_values["Key3"] == 41
     assert all_values["Key4"] == -19


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert('Key1'							, 10				)
     skip_list.insert('Key1'							, 12				)

     skip_list.insert('Key5'							, 7				)
     skip_list.insert('Key7'							, 10				)
     skip_list.insert('Key10'							, 5				)

     skip_list.insert('Key7'							, 7				)
     skip_list.insert('Key5'							, 5				)
     skip_list.insert('Key10'							, 10				)

     _SCREAMING_SNAKE_CASE							    =skip_list.head
     _SCREAMING_SNAKE_CASE							    ={}
     while node.level != 0:
          _SCREAMING_SNAKE_CASE							    =node.forward[0]
          _SCREAMING_SNAKE_CASE							    =node.value

     if len(_UpperCamelCase				) != 4:
          print()
     assert len(_UpperCamelCase				) == 4
     assert all_values["Key1"] == 12
     assert all_values["Key7"] == 7
     assert all_values["Key5"] == 5
     assert all_values["Key10"] == 10


def        _lowerCAmelCase					(						)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     assert skip_list.find('Some key'				) is None


def        _lowerCAmelCase					(						)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key2'							, 20				)
     assert skip_list.find('Key2'				) == 20

     skip_list.insert('Some Key'							, 10				)
     skip_list.insert('Key2'							, 8				)
     skip_list.insert('V'							, 13				)

     assert skip_list.find('Y'				) is None
     assert skip_list.find('Key2'				) == 8
     assert skip_list.find('Some Key'				) == 10
     assert skip_list.find('V'				) == 13


def        _lowerCAmelCase					(						)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.delete('Some key'				)

     assert len(skip_list.head.forward				) == 0


def        _lowerCAmelCase					(						)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 14				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('V'				)
     skip_list.delete('Key2'				)

     assert skip_list.find('V'				) is None
     assert skip_list.find('Key2'				) is None


def        _lowerCAmelCase					(						)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 14				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('V'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) == 14
     assert skip_list.find('Key1'				) == 12
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('X'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) == 12
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('Key1'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) is None
     assert skip_list.find('Key2'				) == 15

     skip_list.delete('Key2'				)
     assert skip_list.find('V'				) is None
     assert skip_list.find('X'				) is None
     assert skip_list.find('Key1'				) is None
     assert skip_list.find('Key2'				) is None


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()

     skip_list.insert('Key1'							, 12				)
     skip_list.insert('V'							, 13				)
     skip_list.insert('X'							, 1_42				)
     skip_list.insert('Key2'							, 15				)

     skip_list.delete('X'				)

     def traverse_keys(_UpperCamelCase						:     Dict				):
          yield node.key
          for forward_node in node.forward:
               yield from traverse_keys(_UpperCamelCase				)

     assert len(set(traverse_keys(skip_list.head				)				)				) == 4


def        _lowerCAmelCase					(						)      ->			Union[str, Any]:
     """simple docstring"""




     def is_sorted(_UpperCamelCase						:     str				):
          return all(next_item >= item for item, next_item in zip(_UpperCamelCase							, lst[1:]				)				)

     _SCREAMING_SNAKE_CASE							    =SkipList()
     for i in range(10				):
          skip_list.insert(_UpperCamelCase							, _UpperCamelCase				)
     assert is_sorted(list(_UpperCamelCase				)				)
     skip_list.delete(5				)
     skip_list.delete(8				)
     skip_list.delete(2				)
     assert is_sorted(list(_UpperCamelCase				)				)
     skip_list.insert(-12							, -12				)
     skip_list.insert(77							, 77				)
     assert is_sorted(list(_UpperCamelCase				)				)


def        _lowerCAmelCase					(						)      ->			List[str]:
     """simple docstring"""




     for _ in range(1_00				):
          # Repeat test 100 times due to the probabilistic nature of skip list
          # random values == random bugs
          test_insert()
          test_insert_overrides_existing_value()

          test_searching_empty_list_returns_none()
          test_search()

          test_deleting_item_from_empty_list_do_nothing()
          test_deleted_items_are_not_founded_by_find_method()
          test_delete_removes_only_given_key()
          test_delete_doesnt_leave_dead_nodes()

          test_iter_always_yields_sorted_values()


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SkipList()
     skip_list.insert(2							, '2'				)
     skip_list.insert(4							, '4'				)
     skip_list.insert(6							, '4'				)
     skip_list.insert(4							, '5'				)
     skip_list.insert(8							, '4'				)
     skip_list.insert(9							, '4'				)

     skip_list.delete(4				)

     print(_UpperCamelCase				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()
