import functools
import operator

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


_lowerCAmelCase        =       logging.get_logger(__name__)

_lowerCAmelCase        =       {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   """hubert"""
 def __init__(    self ,       __magic_name__=32 ,       __magic_name__=768 ,       __magic_name__=12 ,       __magic_name__=12 ,       __magic_name__=3072 ,       __magic_name__="gelu" ,       __magic_name__=0.1 ,       __magic_name__=0.1 ,       __magic_name__=0.1 ,       __magic_name__=True ,       __magic_name__=0.0 ,       __magic_name__=0.1 ,       __magic_name__=0.1 ,       __magic_name__=0.02 ,       __magic_name__=1e-5 ,       __magic_name__="group" ,       __magic_name__="gelu" ,       __magic_name__=(512, 512, 512, 512, 512, 512, 512) ,       __magic_name__=(5, 2, 2, 2, 2, 2, 2) ,       __magic_name__=(10, 3, 3, 3, 3, 2, 2) ,       __magic_name__=False ,       __magic_name__=128 ,       __magic_name__=16 ,       __magic_name__=False ,       __magic_name__=True ,       __magic_name__=0.05 ,       __magic_name__=10 ,       __magic_name__=2 ,       __magic_name__=0.0 ,       __magic_name__=10 ,       __magic_name__=0 ,       __magic_name__="sum" ,       __magic_name__=False ,       __magic_name__=False ,       __magic_name__=256 ,       __magic_name__=0 ,       __magic_name__=1 ,       __magic_name__=2 ,       **__magic_name__ ,       ):

        """simple docstring"""

        super().__init__(**lowerCAmelCase_ ,       pad_token_id=lowerCAmelCase_ ,       bos_token_id=lowerCAmelCase_ ,       eos_token_id=lowerCAmelCase_      )
        A_							:       Optional[int]	      =				hidden_size
        A_							:       Optional[Any]	      =				feat_extract_norm
        A_							:       int	      =				feat_extract_activation
        A_							:       Optional[Any]	      =				list(lowerCAmelCase_      )
        A_							:       int	      =				list(lowerCAmelCase_      )
        A_							:       Tuple	      =				list(lowerCAmelCase_      )
        A_							:       str	      =				conv_bias
        A_							:       str	      =				num_conv_pos_embeddings
        A_							:       Union[str, Any]	      =				num_conv_pos_embedding_groups
        A_							:       Optional[int]	      =				len(self.conv_dim      )
        A_							:       Optional[int]	      =				num_hidden_layers
        A_							:       Union[str, Any]	      =				intermediate_size
        A_							:       Tuple	      =				hidden_act
        A_							:       Tuple	      =				num_attention_heads
        A_							:       int	      =				hidden_dropout
        A_							:       Union[str, Any]	      =				attention_dropout
        A_							:       Union[str, Any]	      =				activation_dropout
        A_							:       Dict	      =				feat_proj_layer_norm
        A_							:       List[Any]	      =				feat_proj_dropout
        A_							:       Optional[int]	      =				final_dropout
        A_							:       str	      =				layerdrop
        A_							:       Any	      =				layer_norm_eps
        A_							:       List[Any]	      =				initializer_range
        A_							:       int	      =				vocab_size
        A_							:       int	      =				do_stable_layer_norm
        A_							:       Union[str, Any]	      =				use_weighted_layer_sum
        A_							:       List[Any]	      =				classifier_proj_size

        if (
            (len(self.conv_stride      ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
            or (len(self.conv_dim      ) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                   ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                   f""" {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,"""
                   f""" `len(config.conv_kernel) = {len(self.conv_kernel      )}`."""      )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        A_							:       Tuple	      =				apply_spec_augment
        A_							:       Tuple	      =				mask_time_prob
        A_							:       Any	      =				mask_time_length
        A_							:       Dict	      =				mask_time_min_masks
        A_							:       Any	      =				mask_feature_prob
        A_							:       str	      =				mask_feature_length
        A_							:       List[str]	      =				mask_feature_min_masks

        # ctc loss
        A_							:       int	      =				ctc_loss_reduction
        A_							:       int	      =				ctc_zero_infinity
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return functools.reduce(operator.mul ,       self.conv_stride ,       1      )




'''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	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

def 	snake_case_							(				_a  :				list[str]			,       _a  :				int      ):
      snake_case_    							=				Heap()
      snake_case_    							=				Counter(_a      )
      heap.build_max_heap(
          [WordCount(_a			,       _a      ) for word, count in count_by_word.items()]      )
      return [heap.extract_max().word for _ in range(min(_a			,       len(_a      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

