


'''simple docstring'''






from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


a_					:    List[Any]          =    {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""ernie_m"""
 _lowerCAmelCase 					=	{"dropout": "classifier_dropout", "num_classes": "num_labels"}
 def __init__( self      ,				__magic_name__ = 25_00_02      ,				__magic_name__ = 7_68      ,				__magic_name__ = 12      ,				__magic_name__ = 12      ,				__magic_name__ = 30_72      ,				__magic_name__ = "gelu"      ,				__magic_name__ = 0.1      ,				__magic_name__ = 0.1      ,				__magic_name__ = 5_14      ,				__magic_name__ = 0.0_2      ,				__magic_name__ = 1      ,				__magic_name__ = 1e-05      ,				__magic_name__=None      ,				__magic_name__=False      ,				__magic_name__=0.0      ,				**__magic_name__      ,				)					->							Dict:
      super().__init__(pad_token_id=__magic_name__      ,				**__magic_name__						)
      _a			      =							vocab_size
      _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			      =							max_position_embeddings
      _a			      =							initializer_range
      _a			      =							layer_norm_eps
      _a			      =							classifier_dropout
      _a			      =							is_decoder
      _a			      =							act_dropout





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

