






from collections import OrderedDict
from typing import Mapping

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


a	: Union[str, Any]      =				logging.get_logger(__name__)

a	: Any      =				{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 					a					(				SCREAMING_SNAKE_CASE__     ):



      """simple docstring"""


      a						:				Union[str, Any]							 =			'rembert'
      def __init__(   self      :    Tuple	,   __lowercase      :    Tuple=250300	,   __lowercase      :    Optional[Any]=1152	,   __lowercase      :    Union[str, Any]=32	,   __lowercase      :    Union[str, Any]=18	,   __lowercase      :    List[str]=256	,   __lowercase      :    Dict=1664	,   __lowercase      :    Optional[int]=4608	,   __lowercase      :    int="gelu"	,   __lowercase      :    Dict=0.0	,   __lowercase      :    Dict=0.0	,   __lowercase      :    List[Any]=0.1	,   __lowercase      :    List[Any]=512	,   __lowercase      :    Optional[Any]=2	,   __lowercase      :    Any=0.02	,   __lowercase      :    int=1e-1_2	,   __lowercase      :    Tuple=True	,   __lowercase      :    int=0	,   __lowercase      :    Tuple=312	,   __lowercase      :    Optional[Any]=313	,   **__lowercase      :    Optional[int]	,   )							->   Any:
        super().__init__(pad_token_id=__lowercase	,   bos_token_id=__lowercase	,   eos_token_id=__lowercase	,   **__lowercase      )

        __UpperCAmelCase							:			Any     =						vocab_size
        __UpperCAmelCase							:			Union[str, Any]     =						input_embedding_size
        __UpperCAmelCase							:			Dict     =						output_embedding_size
        __UpperCAmelCase							:			Optional[Any]     =						max_position_embeddings
        __UpperCAmelCase							:			Any     =						hidden_size
        __UpperCAmelCase							:			List[Any]     =						num_hidden_layers
        __UpperCAmelCase							:			Optional[int]     =						num_attention_heads
        __UpperCAmelCase							:			str     =						intermediate_size
        __UpperCAmelCase							:			Union[str, Any]     =						hidden_act
        __UpperCAmelCase							:			Union[str, Any]     =						hidden_dropout_prob
        __UpperCAmelCase							:			Tuple     =						attention_probs_dropout_prob
        __UpperCAmelCase							:			Optional[int]     =						classifier_dropout_prob
        __UpperCAmelCase							:			Optional[int]     =						initializer_range
        __UpperCAmelCase							:			Optional[int]     =						type_vocab_size
        __UpperCAmelCase							:			str     =						layer_norm_eps
        __UpperCAmelCase							:			Any     =						use_cache
        __UpperCAmelCase							:			Optional[int]     =						False


class 					a					(				SCREAMING_SNAKE_CASE__     ):



      """simple docstring"""


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



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




      @property
      def 			UpperCAmelCase   (   self      :    str      )							->   float:
        return 1e-4










from typing import TYPE_CHECKING

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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

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


