
"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a					:	Optional[int]							=		logging.get_logger(__name__)

a					:	Optional[Any]							=		{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}




class 		a_						( __UpperCAmelCase	):

    '''simple docstring'''





    a      :				Dict     =				'roberta-prelayernorm'







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       str=5_02_65     ,					__UpperCamelCase			:       Optional[Any]=7_68     ,					__UpperCamelCase			:       Tuple=12     ,					__UpperCamelCase			:       Tuple=12     ,					__UpperCamelCase			:       Any=30_72     ,					__UpperCamelCase			:       str="gelu"     ,					__UpperCamelCase			:       Union[str, Any]=0.1     ,					__UpperCamelCase			:       Union[str, Any]=0.1     ,					__UpperCamelCase			:       Tuple=5_12     ,					__UpperCamelCase			:       Any=2     ,					__UpperCamelCase			:       List[str]=0.0_2     ,					__UpperCamelCase			:       Optional[Any]=1e-12     ,					__UpperCamelCase			:       Optional[int]=1     ,					__UpperCamelCase			:       Union[str, Any]=0     ,					__UpperCamelCase			:       Any=2     ,					__UpperCamelCase			:       Any="absolute"     ,					__UpperCamelCase			:       Any=True     ,					__UpperCamelCase			:       Optional[int]=None     ,					**__UpperCamelCase			:       Optional[Any]     ,					)		->List[Any]:






        '''simple docstring'''






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

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







class 		a_						( __UpperCAmelCase	):

    '''simple docstring'''





    @property
    def      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]     )

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PegasusXForConditionalGeneration''',
     '''PegasusXModel''',
     '''PegasusXPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 a					:	Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)