




'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



