
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


_A     :			List[Any]  						=			logging.get_logger(__name__)

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

class 						a__		( a_		):
   __lowerCAmelCase				=							"""roberta-prelayernorm"""
   def __init__(     self      ,       _a=50_265      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=512      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=1      ,       _a=0      ,       _a=2      ,       _a="absolute"      ,       _a=True      ,       _a=None      ,       **_a      ,       ):
          super().__init__(pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       **_a							)

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


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Tuple									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Tuple									=				{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, is_vision_available


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


