




from __future__ import annotations

import unittest

from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 						__lowerCAmelCase      :
							UpperCamelCase__   	=    LEDConfig
							UpperCamelCase__   	=    {}
							UpperCamelCase__   	=    '''gelu'''
							def __init__(		self		:str						, __magic_name__		:List[str]						, __magic_name__		:Tuple=13						, __magic_name__		:Union[str, Any]=7						, __magic_name__		:Dict=True						, __magic_name__		:Union[str, Any]=False						, __magic_name__		:Optional[Any]=99						, __magic_name__		:Optional[int]=32						, __magic_name__		:Tuple=2						, __magic_name__		:Optional[Any]=4						, __magic_name__		:int=37						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:Any=0.1						, __magic_name__		:Dict=20						, __magic_name__		:Tuple=2						, __magic_name__		:Optional[int]=1						, __magic_name__		:Optional[int]=0						, __magic_name__		:Union[str, Any]=4						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				seq_length
								a									=				is_training
								a									=				use_labels
								a									=				vocab_size
								a									=				hidden_size
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				intermediate_size

								a									=				hidden_dropout_prob
								a									=				attention_probs_dropout_prob
								a									=				max_position_embeddings
								a									=				eos_token_id
								a									=				pad_token_id
								a									=				bos_token_id
								a									=				attention_window

								# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
								# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
								# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
								# because its local attention only attends to `self.attention_window` and one before and one after
								a									=				self.attention_window + 2

								# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
								# the `test_attention_outputs` and `test_hidden_states_output` tests
								a									=				(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				ids_tensor([self.batch_size, self.seq_length - 1]						, self.vocab_size							)
								a									=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)						, 1							)
								a									=				tf.concat([input_ids, eos_tensor]						, axis=1							)

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

								a									=				self.config_cls(
								    vocab_size=self.vocab_size						, d_model=self.hidden_size						, encoder_layers=self.num_hidden_layers						, decoder_layers=self.num_hidden_layers						, encoder_attention_heads=self.num_attention_heads						, decoder_attention_heads=self.num_attention_heads						, encoder_ffn_dim=self.intermediate_size						, decoder_ffn_dim=self.intermediate_size						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, eos_token_ids=[2]						, bos_token_id=self.bos_token_id						, pad_token_id=self.pad_token_id						, decoder_start_token_id=self.pad_token_id						, attention_window=self.attention_window						, **self.config_updates						, )
								a									=				prepare_led_inputs_dict(__magic_name__						, __magic_name__						, __magic_name__							)
								a									=				tf.concat(
								    [tf.zeros_like(__magic_name__							)[:, :-1], tf.ones_like(__magic_name__							)[:, -1:]]						, axis=-1						, )
								a									=				global_attention_mask
								return config, inputs_dict





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:List[Any]						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								a									=				TFLEDModel(config=__magic_name__							).get_decoder()
								a									=				inputs_dict["""input_ids"""]

								a									=				input_ids[:1, :]
								a									=				inputs_dict["""attention_mask"""][:1, :]
								a									=				1

								# first forward pass
								a									=				model(__magic_name__						, attention_mask=__magic_name__						, use_cache=__magic_name__							)

								a							,					a									=				outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a									=				ids_tensor((self.batch_size, 3)						, config.vocab_size							)
								a									=				tf.cast(ids_tensor((self.batch_size, 3)						, 2							)						, tf.inta							)

								# append to next input_ids and
								a									=				tf.concat([input_ids, next_tokens]						, axis=-1							)
								a									=				tf.concat([attention_mask, next_attn_mask]						, axis=-1							)

								a									=				model(__magic_name__						, attention_mask=__magic_name__							)[0]
								a									=				model(__magic_name__						, attention_mask=__magic_name__						, past_key_values=__magic_name__							)[0]

								self.parent.assertEqual(next_tokens.shape[1]						, output_from_past.shape[1]							)

								# select random slice
								a									=				int(ids_tensor((1,)						, output_from_past.shape[-1]							)							)
								a									=				output_from_no_past[:, -3:, random_slice_idx]
								a									=				output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__						, __magic_name__						, rtol=1E-3							)







def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     __lowerCamelCase=None				,     ) ->			Union[str, Any]:
	if attention_mask is None:
		a									=				tf.cast(tf.math.not_equal(__lowerCamelCase				,     config.pad_token_id       )				,     tf.inta       )
	if decoder_attention_mask is None:
		a									=				tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape				,     dtype=tf.inta       ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,     config.pad_token_id       )				,     tf.inta       ),
		    ]				,     axis=-1				,     )
	if head_mask is None:
		a									=				tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
	if decoder_head_mask is None:
		a									=				tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	    "decoder_input_ids": decoder_input_ids,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	}

@require_tf
class 						__lowerCAmelCase      (      __magic_name__ ,			__magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							UpperCamelCase__   	=    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
							UpperCamelCase__   	=    (
							    {
							        '''conversational''': TFLEDForConditionalGeneration,
							        '''feature-extraction''': TFLEDModel,
							        '''summarization''': TFLEDForConditionalGeneration,
							        '''text2text-generation''': TFLEDForConditionalGeneration,
							        '''translation''': TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase__   	=    True
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    False
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				TFLEDModelTester(self							)
								a									=				ConfigTester(self						, config_class=__magic_name__							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a							,					a									=				self.model_tester.prepare_config_and_inputs_for_common()
								a									=				tf.zeros_like(inputs_dict["""attention_mask"""]							)
								a									=				2
								a									=				tf.where(
								    tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices						, 1						, inputs_dict["""global_attention_mask"""]						, )

								a									=				True
								a									=				self.model_tester.seq_length
								a									=				self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__magic_name__		:List[str]							):
									a									=				outputs.decoder_attentions
									self.assertEqual(len(__magic_name__							)						, self.model_tester.num_hidden_layers							)
									self.assertListEqual(
									    list(decoder_attentions[0].shape[-3:]							)						, [self.model_tester.num_attention_heads, seq_length, seq_length]						, )

								def check_encoder_attentions_output(__magic_name__		:Any							):
									a									=				[t.numpy() for t in outputs.encoder_attentions]
									a									=				[t.numpy() for t in outputs.encoder_global_attentions]
									self.assertEqual(len(__magic_name__							)						, self.model_tester.num_hidden_layers							)
									self.assertEqual(len(__magic_name__							)						, self.model_tester.num_hidden_layers							)
									self.assertListEqual(
									    list(attentions[0].shape[-3:]							)						, [self.model_tester.num_attention_heads, seq_length, seq_length]						, )
									self.assertListEqual(
									    list(global_attentions[0].shape[-3:]							)						, [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						, )

								for model_class in self.all_model_classes:
									a									=				True
									a									=				False
									a									=				False
									a									=				model_class(__magic_name__							)
									a									=				model(self._prepare_for_class(__magic_name__						, __magic_name__							)							)
									a									=				len(__magic_name__							)
									self.assertEqual(config.output_hidden_states						, __magic_name__							)
									check_encoder_attentions_output(__magic_name__							)

									if self.is_encoder_decoder:
										a									=				model_class(__magic_name__							)
										a									=				model(self._prepare_for_class(__magic_name__						, __magic_name__							)							)
										self.assertEqual(config.output_hidden_states						, __magic_name__							)
										check_decoder_attentions_output(__magic_name__							)

									# Check that output attentions can also be changed via the config
									del inputs_dict["output_attentions"]
									a									=				True
									a									=				model_class(__magic_name__							)
									a									=				model(self._prepare_for_class(__magic_name__						, __magic_name__							)							)
									self.assertEqual(config.output_hidden_states						, __magic_name__							)
									check_encoder_attentions_output(__magic_name__							)

									# Check attention is always last and order is fine
									a									=				True
									a									=				True
									a									=				model_class(__magic_name__							)
									a									=				model(self._prepare_for_class(__magic_name__						, __magic_name__							)							)

									self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						, len(__magic_name__							)							)
									self.assertEqual(model.config.output_hidden_states						, __magic_name__							)
									check_encoder_attentions_output(__magic_name__							)
							@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								pass





							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								pass







def 						__A						(      __lowerCamelCase       ) ->			str:
	return tf.constant(__lowerCamelCase				,     dtype=tf.intaa       )


__UpperCamelCase   :			Dict          = 1E-4



@slow
@require_tf
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

								# change to intended input here
								a									=				_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
								a									=				_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
								a									=				prepare_led_inputs_dict(model.config						, __magic_name__						, __magic_name__							)
								a									=				model(**__magic_name__							)[0]
								a									=				(1, 1024, 768)
								self.assertEqual(output.shape						, __magic_name__							)
								# change to expected output here
								a									=				tf.convert_to_tensor(
								    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]						, )
								tf.debugging.assert_near(output[:, :3, :3]						, __magic_name__						, atol=1E-3							)





							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

								# change to intended input here
								a									=				_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
								a									=				_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
								a									=				prepare_led_inputs_dict(model.config						, __magic_name__						, __magic_name__							)
								a									=				model(**__magic_name__							)[0]
								a									=				(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape						, __magic_name__							)
								# change to expected output here
								a									=				tf.convert_to_tensor(
								    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]						, )
								tf.debugging.assert_near(output[:, :3, :3]						, __magic_name__						, atol=1E-3						, rtol=1E-3							)







from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__UpperCamelCase   :			Any          = TypeVar("KT")
__UpperCamelCase   :			List[Any]          = TypeVar("VT")

class 						__lowerCAmelCase      (      Generic[KT, VT]					):
							def __init__(		self		:Union[str, Any]						, __magic_name__		:KT | str = "root"						, __magic_name__		:VT | None = None							):







								'''simple docstring'''







								a									=				key
								a									=				value
								a									=				[]
							def __repr__(		self		:List[Any]							):







								'''simple docstring'''







								return F'Node({self.key}: {self.value})'





							@property
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								return len(self.forward							)







class 						__lowerCAmelCase      (      Generic[KT, VT]					):
							def __init__(		self		:Optional[Any]						, __magic_name__		:float = 0.5						, __magic_name__		:int = 16							):







								'''simple docstring'''







								a									=				Node[KT, VT]()
								a									=				0
								a									=				p
								a									=				max_level
							def __str__(		self		:Tuple							):







								'''simple docstring'''







								a									=				list(self							)

								if len(__magic_name__							) == 0:
									return F'SkipList(level={self.level})'

								a									=				max((len(str(__magic_name__							)							) for item in items)						, default=4							)
								a									=				max(__magic_name__						, 4							) + 4

								a									=				self.head
								a									=				[]

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

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

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

								lines.append("""None""".ljust(__magic_name__							) + """* """ * len(__magic_name__							)							)
								return F'SkipList(level={self.level})\n' + "\n".join(__magic_name__							)
							def __iter__(		self		:int							):







								'''simple docstring'''







								a									=				self.head

								while len(node.forward							) != 0:
									yield node.forward[0].key
									a									=				node.forward[0]
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







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

								return level
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:int							):







								'''simple docstring'''







								a									=				[]

								a									=				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:
										a									=				node.forward[i]
									# Each leftmost node (relative to searched node) will potentially have to
									# be updated.
									update_vector.append(__magic_name__							)

								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  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:KT							):







								'''simple docstring'''







								a							,					a									=				self._locate_node(__magic_name__							)

								if node is not None:
									for i, update_node in enumerate(__magic_name__							):
										# Remove or replace all references to removed node.
										if update_node.level > i and update_node.forward[i].key == key:
											if node.level > i:
												a									=				node.forward[i]
											else:
												a									=				update_node.forward[:i]
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:KT						, __magic_name__		:VT							):







								'''simple docstring'''







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

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

									a									=				Node(__magic_name__						, __magic_name__							)

									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(__magic_name__							)
										else:
											a									=				new_node





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:VT							):







								'''simple docstring'''







								a							,					a									=				self._locate_node(__magic_name__							)

								if node is not None:
									return node.value

								return None







def 						__A						(      ) ->			Tuple:
	a									=				SkipList()
	skip_list.insert("""Key1"""				,     3       )
	skip_list.insert("""Key2"""				,     12       )
	skip_list.insert("""Key3"""				,     41       )
	skip_list.insert("""Key4"""				,     -19       )

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

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





def 						__A						(      ) ->			Tuple:
	a									=				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       )

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

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





def 						__A						(      ) ->			Optional[int]:
	a									=				SkipList()
	assert skip_list.find("""Some key"""       ) is None





def 						__A						(      ) ->			Optional[int]:
	a									=				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 						__A						(      ) ->			str:
	a									=				SkipList()
	skip_list.delete("""Some key"""       )

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





def 						__A						(      ) ->			int:
	a									=				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 						__A						(      ) ->			Union[str, Any]:
	a									=				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 						__A						(      ) ->			Dict:
	a									=				SkipList()

	skip_list.insert("""Key1"""				,     12       )
	skip_list.insert("""V"""				,     13       )
	skip_list.insert("""X"""				,     142       )
	skip_list.insert("""Key2"""				,     15       )

	skip_list.delete("""X"""       )

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

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





def 						__A						(      ) ->			Optional[Any]:
	def is_sorted(__lowerCamelCase       ):
		return all(next_item >= item for item, next_item in zip(__lowerCamelCase				,     lst[1:]       )       )

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





def 						__A						(      ) ->			Optional[Any]:
	for _ in range(100       ):
		# 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 						__A						(      ) ->			Optional[int]:
	a									=				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(__lowerCamelCase       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()

