"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Any     							=							'convbert'


    def __init__(					self	:  int   ,				__lowerCAmelCase	:  List[str]=3_0522   ,				__lowerCAmelCase	:  List[str]=768   ,				__lowerCAmelCase	:  str=12   ,				__lowerCAmelCase	:  Optional[int]=12   ,				__lowerCAmelCase	:  Dict=3072   ,				__lowerCAmelCase	:  List[Any]="gelu"   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  str=0.1   ,				__lowerCAmelCase	:  int=512   ,				__lowerCAmelCase	:  List[Any]=2   ,				__lowerCAmelCase	:  List[str]=0.02   ,				__lowerCAmelCase	:  List[str]=1e-1_2   ,				__lowerCAmelCase	:  int=1   ,				__lowerCAmelCase	:  Tuple=0   ,				__lowerCAmelCase	:  Optional[int]=2   ,				__lowerCAmelCase	:  List[Any]=768   ,				__lowerCAmelCase	:  List[Any]=2   ,				__lowerCAmelCase	:  Optional[int]=9   ,				__lowerCAmelCase	:  int=1   ,				__lowerCAmelCase	:  Dict=None   ,				**__lowerCAmelCase	:  List[Any]   ,				):
      super().__init__(
          pad_token_id=__lowerCAmelCase   ,				bos_token_id=__lowerCAmelCase   ,				eos_token_id=__lowerCAmelCase   ,				**__lowerCAmelCase   ,				)

      _UpperCAmelCase						 =      vocab_size
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_attention_heads
      _UpperCAmelCase						 =      intermediate_size
      _UpperCAmelCase						 =      hidden_act
      _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						 =      embedding_size
      _UpperCAmelCase						 =      head_ratio
      _UpperCAmelCase						 =      conv_kernel_size
      _UpperCAmelCase						 =      num_groups
      _UpperCAmelCase						 =      classifier_dropout



class        a				(		lowerCAmelCase_   ):


    @property
    def      lowerCAmelCase_  (					self	:  Any					):
      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),
              ("""token_type_ids""", dynamic_axis),
          ]					)

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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