


from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __lowerCAmelCase     :






  """simple docstring"""





  def __init__(     self       :       List[Any]    ,				_lowerCAmelCase       :       str    ,				_lowerCAmelCase       :       int  )   -> None:




     """simple docstring"""
     snake_case_					    =					word
     snake_case_					    =					count



  def __eq__(     self       :       int    ,				_lowerCAmelCase       :       object  )   -> bool:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase    ,				_lowerCAmelCase  ):
        return NotImplemented
     return self.count == other.count



  def __lt__(     self       :       Tuple    ,				_lowerCAmelCase       :       object  )   -> bool:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase    ,				_lowerCAmelCase  ):
        return NotImplemented
     return self.count < other.count






def 			_lowerCAmelCase						(							lowerCAmelCase_							:list[str]		,						lowerCAmelCase_							:int							)->list[str]:
   '''simple docstring'''






   snake_case_					    =					Heap()
   snake_case_					    =					Counter(lowerCAmelCase_							)
   heap.build_max_heap(
       [WordCount(lowerCAmelCase_		,						lowerCAmelCase_							) for word, count in count_by_word.items()]							)
   return [heap.extract_max().word for _ in range(min(lowerCAmelCase_		,						len(lowerCAmelCase_							)							)							)]


if __name__ == "__main__":
 import doctest

 doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE	:List[Any]  		=     {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:int  		=     [
       '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Speech2Text2ForCausalLM''',
       '''Speech2Text2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
   from .processing_speech_to_text_a import SpeechaTextaProcessor
   from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_to_text_a import (
          SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechaTextaForCausalLM,
          SpeechaTextaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE	:str  		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




