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


_A       =					logging.get_logger(__name__)

_A       =					{
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				int 	=						"mpnet"





     def __init__(self :       Union[str, Any]							,       _A :       str=3_0_5_2_7							,       _A :       Dict=7_6_8							,       _A :       Optional[int]=1_2							,       _A :       Optional[Any]=1_2							,       _A :       Optional[int]=3_0_7_2							,       _A :       Optional[int]="gelu"							,       _A :       Optional[int]=0.1							,       _A :       str=0.1							,       _A :       Optional[int]=5_1_2							,       _A :       Optional[Any]=0.02							,       _A :       Dict=1E-12							,       _A :       str=3_2							,       _A :       str=1							,       _A :       str=0							,       _A :       Optional[Any]=2							,       **_A :       int							,       )		->		Dict:
         super().__init__(pad_token_id=_A							,       bos_token_id=_A							,       eos_token_id=_A							,       **_A  )

         snake_case						=   vocab_size
         snake_case						=   hidden_size
         snake_case						=   num_hidden_layers
         snake_case						=   num_attention_heads
         snake_case						=   hidden_act
         snake_case						=   intermediate_size
         snake_case						=   hidden_dropout_prob
         snake_case						=   attention_probs_dropout_prob
         snake_case						=   max_position_embeddings
         snake_case						=   initializer_range
         snake_case						=   layer_norm_eps
         snake_case						=   relative_attention_num_buckets



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

