






from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__a       :	Union[str, Any]             = TypeVar('KT')
__a       :	int             = TypeVar('VT')







class        _SCREAMING_SNAKE_CASE						(  Generic[KT, VT]							):





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]				,		__A:     KT | str = "root"				,		__A:     VT | None = None    ):

												'''simple docstring'''

												a__          =  key
												a__          =  value
												a__          =  []




							def __repr__(   self:     str    ):

												'''simple docstring'''

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





							@property
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												return len(self.forward    )







class        _SCREAMING_SNAKE_CASE						(  Generic[KT, VT]							):





							"""simple docstring"""





							def __init__(   self:     int				,		__A:     float = 0.5				,		__A:     int = 16    ):

												'''simple docstring'''

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




							def __str__(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  list(self    )

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

												a__          =  max((len(str(A_    )    ) for item in items)				,		default=4    )
												a__          =  max(A_				,		4    ) + 4

												a__          =  self.head
												a__          =  []

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

												while len(node.forward    ) != 0:
																	a__          =  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_    )    )
																	a__          =  node.forward

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




							def __iter__(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.head

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




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

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

												return level




							def  lowercase		(   self:     Dict				,		__A:     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(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  lowercase		(   self:     List[Any]				,		__A:     KT    ):

												'''simple docstring'''

												a__       ,a__          =  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:
																																a__          =  node.forward[i]
																											else:
																																a__          =  update_node.forward[:i]




							def  lowercase		(   self:     Tuple				,		__A:     KT				,		__A:     VT    ):

												'''simple docstring'''

												a__       ,a__          =  self._locate_node(A_    )
												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				,		A_    ):
																											update_vector.append(self.head    )
																						a__          =  level

																	a__          =  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:
																											a__          =  new_node





							def  lowercase		(   self:     str				,		__A:     VT    ):

												'''simple docstring'''

												a__       ,a__          =  self._locate_node(A_    )

												if node is not None:
																	return node.value

												return None





def        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					a__          =  SkipList()
					assert skip_list.find('''Some key''') is None





def        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					a__          =  SkipList()
					skip_list.delete('''Some key''')

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





def        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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        SCREAMING_SNAKE_CASE       (      ):
					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()










"""simple docstring"""







import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import BertGenerationDecoder, BertGenerationEncoder







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     Optional[int]=13				,		__A:     Tuple=7				,		__A:     Optional[Any]=True				,		__A:     Tuple=True				,		__A:     str=99				,		__A:     int=32				,		__A:     Union[str, Any]=5				,		__A:     Optional[Any]=4				,		__A:     Any=37				,		__A:     Union[str, Any]="gelu"				,		__A:     int=0.1				,		__A:     Optional[Any]=0.1				,		__A:     Optional[int]=50				,		__A:     str=0.0_2				,		__A:     Dict=True				,		__A:     Dict=None				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_input_mask
												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__          =  initializer_range
												a__          =  use_labels
												a__          =  scope




							def  lowercase		(   self:     List[Any]    ):

												'''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]    )

												if self.use_labels:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  self.get_config()

												return config, input_ids, input_mask, token_labels




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												return BertGenerationConfig(
												    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				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												(
												    (
												    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,
												    input_mask,
												    token_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)




							def  lowercase		(   self:     str				,		__A:     List[str]				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     Optional[int]				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     Any				,		__A:     int				,		__A:     Optional[int]				,		__A:     int				,		__A:     List[Any]				,		__A:     Any				,		**__A:     List[str]				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		)
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		)
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Dict				,		__A:     Tuple				,		__A:     str				,		__A:     str				,		__A:     Any				,		__A:     int				,		__A:     Any				,		**__A:     Dict				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  True
												a__          =  BertGenerationDecoder(config=__A    ).to(__A    ).eval()

												# first forward pass
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		use_cache=__A				,		)
												a__          =  outputs.past_key_values

												# create hypothetical multiple next token and extent to next_input_ids
												a__          =  ids_tensor((self.batch_size, 3)				,		config.vocab_size    )
												a__          =  ids_tensor((self.batch_size, 3)				,		vocab_size=2    )

												# append to next input_ids and
												a__          =  torch.cat([input_ids, next_tokens]				,		dim=-1    )
												a__          =  torch.cat([input_mask, next_mask]				,		dim=-1    )

												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		output_hidden_states=__A				,		)['''hidden_states'''][0]
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		past_key_values=__A				,		output_hidden_states=__A				,		)['''hidden_states'''][0]

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

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(__A				,		__A				,		atol=1e-3    )    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     List[Any]				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		*__A:     Optional[int]				,		):

												'''simple docstring'''

												a__          =  BertGenerationDecoder(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A				,		labels=__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)    )





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__       ,a__       ,a__       ,a__          =  self.prepare_config_and_inputs()
												a__          =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(BertGenerationDecoder,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoderTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__       ,a__       ,a__          =  self.model_tester.prepare_config_and_inputs()
												a__          =  '''bert'''
												self.model_tester.create_and_check_model(__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs_for_decoder()

												a__          =  None

												self.model_tester.create_and_check_model_as_decoder(
												    __A				,		__A				,		__A				,		__A				,		__A				,		__A				,		)




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_for_causal_lm(*__A    )





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												self.assertIsNotNone(__A    )







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 1024]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  torch.tensor(
												    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]    )
												self.assertTrue(torch.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 50358]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  torch.tensor(
												    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]    )
												self.assertTrue(torch.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


