






from collections import OrderedDict
from typing import Mapping

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


a	: List[str]      =				logging.get_logger(__name__)

a	: Optional[Any]      =				{
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class 					a					(				A_     ):



      """simple docstring"""


      a						:				Optional[int]							 =			'plbart'
      a						:				Dict							 =			['past_key_values']
      a						:				List[str]							 =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(   self      :    List[Any]	,   __lowercase      :    int=50005	,   __lowercase      :    Tuple=1024	,   __lowercase      :    Optional[int]=6	,   __lowercase      :    Any=3072	,   __lowercase      :    Any=12	,   __lowercase      :    List[str]=6	,   __lowercase      :    Any=3072	,   __lowercase      :    Optional[int]=12	,   __lowercase      :    Any=0.0	,   __lowercase      :    Optional[Any]=0.0	,   __lowercase      :    str=True	,   __lowercase      :    List[Any]=True	,   __lowercase      :    List[str]="gelu"	,   __lowercase      :    List[Any]=768	,   __lowercase      :    Tuple=0.1	,   __lowercase      :    Optional[Any]=0.1	,   __lowercase      :    int=0.0	,   __lowercase      :    Any=0.02	,   __lowercase      :    int=0.0	,   __lowercase      :    Dict=True	,   __lowercase      :    Any=1	,   __lowercase      :    Optional[Any]=0	,   __lowercase      :    List[str]=2	,   __lowercase      :    Optional[Any]=2	,   **__lowercase      :    int	,   )							->   Any:
        __UpperCAmelCase     =						vocab_size
        __UpperCAmelCase     =						max_position_embeddings
        __UpperCAmelCase     =						d_model
        __UpperCAmelCase     =						encoder_ffn_dim
        __UpperCAmelCase     =						encoder_layers
        __UpperCAmelCase     =						encoder_attention_heads
        __UpperCAmelCase     =						decoder_ffn_dim
        __UpperCAmelCase     =						decoder_layers
        __UpperCAmelCase     =						decoder_attention_heads
        __UpperCAmelCase     =						dropout
        __UpperCAmelCase     =						attention_dropout
        __UpperCAmelCase     =						activation_dropout
        __UpperCAmelCase     =						activation_function
        __UpperCAmelCase     =						init_std
        __UpperCAmelCase     =						encoder_layerdrop
        __UpperCAmelCase     =						decoder_layerdrop
        __UpperCAmelCase     =						classifier_dropout
        __UpperCAmelCase     =						use_cache
        __UpperCAmelCase     =						encoder_layers
        __UpperCAmelCase     =						scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__lowercase	,   bos_token_id=__lowercase	,   eos_token_id=__lowercase	,   is_encoder_decoder=__lowercase	,   forced_eos_token_id=__lowercase	,   **__lowercase	,   )


class 					a					(				A_     ):



      """simple docstring"""


      @property
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Any:
        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ]      )




      @property
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Optional[Any]:



        if self.use_past:
          return OrderedDict(
              [
                  ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                  ("""past_keys""", {0: """batch""", 2: """sequence"""}),
                  ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
              ]      )
        else:
          return OrderedDict(
              [
                  ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                  ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
              ]      )










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


