





'''simple docstring'''

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[int]						   =						'fnet'





   def __init__(       self					:      Dict					,lowerCamelCase					:      List[Any]=3_2000					,lowerCamelCase					:      Dict=768					,lowerCamelCase					:      Dict=12					,lowerCamelCase					:      Tuple=3072					,lowerCamelCase					:      Union[str, Any]="gelu_new"					,lowerCamelCase					:      List[str]=0.1					,lowerCamelCase					:      Union[str, Any]=512					,lowerCamelCase					:      List[Any]=4					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      Optional[Any]=1E-1_2					,lowerCamelCase					:      Optional[Any]=False					,lowerCamelCase					:      Dict=512					,lowerCamelCase					:      List[Any]=3					,lowerCamelCase					:      Tuple=1					,lowerCamelCase					:      Dict=2					,**lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      super().__init__(pad_token_id=lowerCamelCase					,bos_token_id=lowerCamelCase					,eos_token_id=lowerCamelCase					,**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							type_vocab_size
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							use_tpu_fourier_optimizations
      __SCREAMING_SNAKE_CASE   				=							tpu_short_seq_length












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						Optional[Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[Any]						       =  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''megatron-bert'''



 def __init__(     self				,       _snake_case=2_90_56				,       _snake_case=10_24				,       _snake_case=24				,       _snake_case=16				,       _snake_case=40_96				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache





