



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"ibert"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2			,     a_=1_2			,     a_=3_0_7_2			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=1			,     a_=0			,     a_=2			,     a_="absolute"			,     a_=False			,     a_="none"			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

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




class 			snake_case_						(						a_				):






							@property
							def snake_case_  (   self      ):
									if self.task == "multiple-choice":
											a_     :      List[str] 						=     {0: "batch", 1: "choice", 2: "sequence"}
									else:
											a_     :      Dict 						=     {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									    ]      )




"""simple docstring"""
from typing import TYPE_CHECKING

from ..utils import _LazyModule


SCREAMING_SNAKE_CASE_				= {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

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