


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


__UpperCamelCase       :       Dict						       =						logging.get_logger(__name__)

__UpperCamelCase       :       str						       =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "pegasus"
     A:		int			=      ["past_key_values"]
     A:		Union[str, Any]			=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


     def __init__(      self							:     int							,	lowerCamelCase__							:     Dict=50265							,	lowerCamelCase__							:     Tuple=1024							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Dict=4096							,	lowerCamelCase__							:     Optional[Any]=16							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     str=4096							,	lowerCamelCase__							:     str=16							,	lowerCamelCase__							:     Dict=0.0							,	lowerCamelCase__							:     Any=0.0							,	lowerCamelCase__							:     Dict=True							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Dict="gelu"							,	lowerCamelCase__							:     int=1024							,	lowerCamelCase__							:     Dict=0.1							,	lowerCamelCase__							:     Any=0.0							,	lowerCamelCase__							:     int=0.0							,	lowerCamelCase__							:     Optional[int]=0.02							,	lowerCamelCase__							:     List[str]=0							,	lowerCamelCase__							:     Tuple=False							,	lowerCamelCase__							:     List[Any]=0							,	lowerCamelCase__							:     List[str]=1							,	lowerCamelCase__							:     Tuple=1							,	**lowerCamelCase__							:     List[Any]							,	)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   vocab_size
      UpperCamelCase__					:    int          =   max_position_embeddings
      UpperCamelCase__					:    Optional[int]          =   d_model
      UpperCamelCase__					:    Dict          =   encoder_ffn_dim
      UpperCamelCase__					:    str          =   encoder_layers
      UpperCamelCase__					:    Union[str, Any]          =   encoder_attention_heads
      UpperCamelCase__					:    Any          =   decoder_ffn_dim
      UpperCamelCase__					:    Union[str, Any]          =   decoder_layers
      UpperCamelCase__					:    Optional[int]          =   decoder_attention_heads
      UpperCamelCase__					:    Any          =   dropout
      UpperCamelCase__					:    Any          =   attention_dropout
      UpperCamelCase__					:    Optional[int]          =   activation_dropout
      UpperCamelCase__					:    Union[str, Any]          =   activation_function
      UpperCamelCase__					:    Union[str, Any]          =   init_std
      UpperCamelCase__					:    Any          =   encoder_layerdrop
      UpperCamelCase__					:    int          =   decoder_layerdrop
      UpperCamelCase__					:    Optional[Any]          =   use_cache
      UpperCamelCase__					:    str          =   encoder_layers
      UpperCamelCase__					:    Union[str, Any]          =   scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	is_encoder_decoder=lowerCamelCase__							,	decoder_start_token_id=lowerCamelCase__							,	forced_eos_token_id=lowerCamelCase__							,	**lowerCamelCase__							,	)


     @property
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      int:





      '''simple docstring'''






      return self.encoder_attention_heads


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      int:





      '''simple docstring'''






      return self.d_model






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

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

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






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

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






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


