code: from collections import Counter
  from functools import total_ordering
  
  from data_structures.heap.heap import Heap
  
  @total_ordering
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       str         =  word
         lowerCAmelCase_:       List[str]         =  count
  
  
  
  
      def __eq__(						self					,	lowerCamelCase__   ):
         if not isinstance(lowerCamelCase__					,	lowerCamelCase__   ):
            return NotImplemented
         return self.count == other.count
  
  
  
  
      def __lt__(						self					,	lowerCamelCase__   ):
         if not isinstance(lowerCamelCase__					,	lowerCamelCase__   ):
            return NotImplemented
         return self.count < other.count
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Heap[WordCount]         =  Heap()
     lowerCAmelCase_:       List[str]         =  Counter(lowercase				)
     heap.build_max_heap(
         [WordCount(lowercase       ,							lowercase				) for word, count in count_by_word.items()]				)
     return [heap.extract_max().word for _ in range(min(lowercase       ,							len(lowercase				)				)				)]
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor