





'''simple docstring'''

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     :	Optional[Any] ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	Optional[Any]  )					->  None:

												'''simple docstring'''




												_UpperCamelCase       					= word
												_UpperCamelCase       					= count





							def __eq__(							self     :	Optional[Any] ,				lowerCAmelCase__     :	Any  )					->  bool:

												'''simple docstring'''




												if not isinstance(__A ,				__A  ):
																	return NotImplemented
												return self.count == other.count







							def __lt__(							self     :	Union[str, Any] ,				lowerCAmelCase__     :	str  )					->  bool:

												'''simple docstring'''




												if not isinstance(__A ,				__A  ):
																	return NotImplemented
												return self.count < other.count







def       a__       (       lowercase      :  Any,      lowercase      :  Dict   )	->	list[str]:





					"""simple docstring"""

					_UpperCamelCase       					= Heap()
					_UpperCamelCase       					= 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()










'''simple docstring'''

lowercase__		:    Tuple =       '\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowercase__		:    Union[str, Any] =       [{'type': 'code', 'content': INSTALL_CONTENT}]
lowercase__		:    str =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



