'''simple docstring'''




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


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

_a   :	Optional[int] 							=   {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"swinv2"

       snake_case       :Union[str, Any]				    =					{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }






       def __init__(      self				,     UpperCamelCase_=2_24				,     UpperCamelCase_=4				,     UpperCamelCase_=3				,     UpperCamelCase_=96				,     UpperCamelCase_=[2, 2, 6, 2]				,     UpperCamelCase_=[3, 6, 12, 24]				,     UpperCamelCase_=7				,     UpperCamelCase_=4.0				,     UpperCamelCase_=True				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.1				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=False				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-5				,     UpperCamelCase_=32				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )

              __UpperCAmelCase  :				Dict       				=		image_size
              __UpperCAmelCase  :				List[str]       				=		patch_size
              __UpperCAmelCase  :				Dict       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		embed_dim
              __UpperCAmelCase  :				str       				=		depths
              __UpperCAmelCase  :				Dict       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		num_heads
              __UpperCAmelCase  :				str       				=		window_size
              __UpperCAmelCase  :				Any       				=		mlp_ratio
              __UpperCAmelCase  :				List[str]       				=		qkv_bias
              __UpperCAmelCase  :				int       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Tuple       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				Optional[int]       				=		drop_path_rate
              __UpperCAmelCase  :				Tuple       				=		hidden_act
              __UpperCAmelCase  :				int       				=		use_absolute_embeddings
              __UpperCAmelCase  :				Tuple       				=		layer_norm_eps
              __UpperCAmelCase  :				int       				=		initializer_range
              __UpperCAmelCase  :				str       				=		encoder_stride
              # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
              # this indicates the channel dimension after the last stage of the model
              __UpperCAmelCase  :				str       				=		int(embed_dim * 2 ** (len(UpperCamelCase_      ) - 1)      )
              __UpperCAmelCase  :				Tuple       				=		(0, 0, 0, 0)







'''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[str] 							=   logging.get_logger(__name__)

_a   :	Any 							=   {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					"ibert"






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_="absolute"				,     UpperCamelCase_=False				,     UpperCamelCase_="none"				,     **UpperCamelCase_				,     ):
              super().__init__(pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     **UpperCamelCase_      )

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






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              if self.task == "multiple-choice":
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]      )






