


"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase__							:List[str]       =  TypeVar("""T""")
UpperCamelCase__							:Any       =  TypeVar("""U""")






class     A(     Generic[T, U]		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  key
					_UpperCamelCase				:Dict				    =  val
					_UpperCamelCase				:int				    =  0
					_UpperCamelCase				:DoubleLinkedListNode[T, U] | None				    =  None
					_UpperCamelCase				:DoubleLinkedListNode[T, U] | None				    =  None





		def __repr__(   self      )			->  str:

					"""simple docstring"""



					return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
					    self.key				,							self.val				,							self.freq				,							self.next is not None				,							self.prev is not None      )






class     A(     Generic[T, U]		):

		"""simple docstring"""





		def __init__(   self      )			->  None:

					"""simple docstring"""



					_UpperCamelCase				:DoubleLinkedListNode[T, U]				    =  DoubleLinkedListNode(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:DoubleLinkedListNode[T, U]				    =  DoubleLinkedListNode(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase    ,			_UpperCamelCase				:Any				    =  self.rear, self.head
		def __repr__(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  ['''DoubleLinkedList''']
					_UpperCamelCase				:Any				    =  self.head
					while node.next is not None:
								rep.append(str(SCREAMING_SNAKE_CASE__      )      )
								_UpperCamelCase				:Any				    =  node.next
					rep.append(str(self.rear      )      )
					return ",\n    ".join(SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  None:

					"""simple docstring"""



					_UpperCamelCase				:Optional[int]				    =  self.rear.prev

					# All nodes other than self.head are guaranteed to have non-None previous
					assert previous is not None

					_UpperCamelCase				:List[str]				    =  node
					_UpperCamelCase				:List[str]				    =  previous
					_UpperCamelCase				:Optional[int]				    =  node
					_UpperCamelCase				:Dict				    =  self.rear
					node.freq += 1
					self._position_node(SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  None:

					"""simple docstring"""



					while node.prev is not None and node.prev.freq > node.freq:
								# swap node with previous node
								_UpperCamelCase				:int				    =  node.prev

								_UpperCamelCase				:Optional[int]				    =  previous_node.prev
								_UpperCamelCase				:Optional[int]				    =  node.prev
								_UpperCamelCase				:Union[str, Any]				    =  previous_node
								_UpperCamelCase				:int				    =  node





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  DoubleLinkedListNode[T, U] | None:

					"""simple docstring"""



					if node.prev is None or node.next is None:
								return None

					_UpperCamelCase				:List[str]				    =  node.next
					_UpperCamelCase				:Union[str, Any]				    =  node.prev
					_UpperCamelCase				:Optional[Any]				    =  None
					_UpperCamelCase				:List[str]				    =  None
					return node






class     A(     Generic[T, U]		):

		"""simple docstring"""





		A         =       {}
		def __init__(   self				,							SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:DoubleLinkedList[T, U]				    =  DoubleLinkedList()
					_UpperCamelCase				:Optional[Any]				    =  capacity
					_UpperCamelCase				:List[Any]				    =  0
					_UpperCamelCase				:Any				    =  0
					_UpperCamelCase				:Union[str, Any]				    =  0
					_UpperCamelCase				:dict[T, DoubleLinkedListNode[T, U]]				    =  {}
		def __repr__(   self      )			->  str:

					"""simple docstring"""



					return (
					    f"CacheInfo(hits={self.hits}, misses={self.miss}, "
					    f"capacity={self.capacity}, current_size={self.num_keys})"
					)
		def __contains__(   self				,							SCREAMING_SNAKE_CASE__      )			->  bool:

					"""simple docstring"""



					return key in self.cache
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  U | None:

					"""simple docstring"""



					if key in self.cache:
								self.hits += 1
								_UpperCamelCase				:DoubleLinkedListNode[T, U]				    =  self.cache[key]
								_UpperCamelCase				:List[str]				    =  self.list.remove(self.cache[key]      )
								assert node == value_node

								# node is guaranteed not None because it is in self.cache
								assert node is not None
								self.list.add(SCREAMING_SNAKE_CASE__      )
								return node.val
					self.miss += 1
					return None
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  None:

					"""simple docstring"""



					if key not in self.cache:
								if self.num_keys >= self.capacity:
											# delete first node when over capacity
											_UpperCamelCase				:int				    =  self.list.head.next

											# guaranteed to have a non-None first node when num_keys > 0
											# explain to type checker via assertions
											assert first_node is not None
											assert first_node.key is not None
											assert self.list.remove(SCREAMING_SNAKE_CASE__      ) is not None
											# first_node guaranteed to be in list

											del self.cache[first_node.key]
											self.num_keys -= 1
								_UpperCamelCase				:Optional[Any]				    =  DoubleLinkedListNode(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
								self.list.add(self.cache[key]      )
								self.num_keys += 1

					else:
								_UpperCamelCase				:Any				    =  self.list.remove(self.cache[key]      )
								assert node is not None  # node guaranteed to be in list
								_UpperCamelCase				:str				    =  value
								self.list.add(SCREAMING_SNAKE_CASE__      )





		@classmethod
		def      _UpperCamelCase(   cls				,							SCREAMING_SNAKE_CASE__ = 1_28      )			->  Callable[[Callable[[T], U]], Callable[..., U]]:

					"""simple docstring"""



					def cache_decorator_inner(SCREAMING_SNAKE_CASE__      ) -> Callable[..., U]:
								def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__      ) -> U:
											if func not in cls.decorator_function_to_instance_map:
														_UpperCamelCase				:List[str]				    =  LFUCache(SCREAMING_SNAKE_CASE__      )

											_UpperCamelCase				:Tuple				    =  cls.decorator_function_to_instance_map[func].get(args[0]      )
											if result is None:
														_UpperCamelCase				:Any				    =  func(*SCREAMING_SNAKE_CASE__      )
														cls.decorator_function_to_instance_map[func].put(args[0]				,							SCREAMING_SNAKE_CASE__      )
											return result

								def cache_info() -> LFUCache[T, U]:
											return cls.decorator_function_to_instance_map[func]

								setattr(SCREAMING_SNAKE_CASE__				,							'''cache_info'''				,							SCREAMING_SNAKE_CASE__      )  # noqa: B010

								return cache_decorator_wrapper

					return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()





"""simple docstring"""






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__							:Optional[int]       =  get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class     A(     lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       ErnieMTokenizer
		A         =       False
		A         =       True
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCamelCase				:int				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )
					tokenizer.save_pretrained(self.tmpdirname      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  '''this is a test'''
					_UpperCamelCase				:Optional[Any]				    =  '''this is a test'''
					return input_text, output_text
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  '''<pad>'''
					_UpperCamelCase				:Optional[Any]				    =  0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  list(self.get_tokenizer().get_vocab().keys()      )

					self.assertEqual(vocab_keys[0]				,							'''<pad>'''      )
					self.assertEqual(vocab_keys[1]				,							'''<unk>'''      )
					self.assertEqual(vocab_keys[-1]				,							'''▁eloquent'''      )
					self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					if not self.test_rust_tokenizer:
								return

					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()
					_UpperCamelCase				:str				    =  self.get_rust_tokenizer()

					_UpperCamelCase				:List[Any]				    =  '''I was born in 92000, and this is falsé.'''

					_UpperCamelCase				:Any				    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[Any]				    =  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[str]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:List[Any]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							do_lower_case=SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:List[str]				    =  tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )				,							[48, 25, 21, 12_89]      )

					_UpperCamelCase				:str				    =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
					_UpperCamelCase				:Optional[int]				    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]      )

					_UpperCamelCase				:Dict				    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:str				    =  tokenizer.encode('''sequence builders'''      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer.encode('''multi-sequence build'''      )

					_UpperCamelCase				:int				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
					    tokenizer.sep_token_id
					] + text_a + [tokenizer.sep_token_id]





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=SCREAMING_SNAKE_CASE__				,							model_name='''susnato/ernie-m-base_pytorch'''				,							sequences=[
					        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
					        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
					        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
					        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
					        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
					        '''conditioning on both left and right context in all layers.''',
					        '''The quick brown fox jumps over the lazy dog.''',
					    ]				,							)

