






from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Optional[Any]						,   __A:      str						,   __A:      int      )			-> None:
												_A									=       word
												_A									=       count

							def __eq__(     self:      int						,   __A:      object      )			-> bool:
												if not isinstance(__A						,   __A      ):
																	return NotImplemented
												return self.count == other.count



							def __lt__(     self:      Union[str, Any]						,   __A:      object      )			-> bool:
												if not isinstance(__A						,   __A      ):
																	return NotImplemented
												return self.count < other.count




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       Heap()
					_A									=       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()











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A          = logging.get_logger(__name__)

__A          = {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       initializer_range
												_A									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



