code: from collections import Counter
  from functools import total_ordering
  
  from data_structures.heap.heap import Heap
  
  
  
  @total_ordering
  class        lowerCAmelCase_	:
         def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	None:
            UpperCamelCase					:						Union[str, Any]					=			word
            UpperCamelCase					:						Optional[Any]					=			count
         def __eq__(    self,    SCREAMING_SNAKE_CASE_ )  ->	bool:
  
  
  
  
  
  
  
            if not isinstance(UpperCamelCase__,    UpperCamelCase__ ):
               return NotImplemented
            return self.count == other.count
  
  
  
  
  
  
         def __lt__(    self,    SCREAMING_SNAKE_CASE_ )  ->	bool:
  
  
  
  
  
  
  
            if not isinstance(UpperCamelCase__,    UpperCamelCase__ ):
               return NotImplemented
            return self.count < other.count
  
  
  
  
  
  
  def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					Tuple			)			->  list[str]:
     UpperCamelCase					:						Heap[WordCount]					=			Heap()
     UpperCamelCase					:						int					=			Counter(SCREAMING_SNAKE_CASE__			)
     heap.build_max_heap(
         [WordCount(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__			) for word, count in count_by_word.items()]			)
     return [heap.extract_max().word for _ in range(min(SCREAMING_SNAKE_CASE__ ,  len(SCREAMING_SNAKE_CASE__			)			)			)]
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
  
  
style_context: 
  
  
  
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __UpperCAmelCase      = {
      '''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'''},
  }
  
  __UpperCAmelCase      = {
      '''openai-gpt''': 512,
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						VOCAB_FILES_NAMES
         UpperCAmelCase__       :      int       =						PRETRAINED_VOCAB_FILES_MAP
         UpperCAmelCase__       :      Optional[int]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         UpperCAmelCase__       :      Optional[int]       =						["input_ids", "attention_mask"]
         UpperCAmelCase__       :      Tuple       =						OpenAIGPTTokenizer
         def __init__(    self,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_="<unk>",    **SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return True
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	Tuple[str]:
            UpperCamelCase					:						List[Any]					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_ )
            return tuple(SCREAMING_SNAKE_CASE_ )
  
  
  
  