



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


A_				:					Optional[Any]							=      logging.get_logger(__name__)

A_				:					List[str]							=      {
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """rembert"""







 def __init__(				self    ,a_=250_300    ,a_=1_152    ,a_=32    ,a_=18    ,a_=256    ,a_=1_664    ,a_=4_608    ,a_="gelu"    ,a_=0.0    ,a_=0.0    ,a_=0.1    ,a_=512    ,a_=2    ,a_=0.02    ,a_=1E-1_2    ,a_=True    ,a_=0    ,a_=312    ,a_=313    ,**a_    ,)						->  List[Any]:
        super().__init__(pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_    ,**a_				)

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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







 @property
 def _snake_case					(				self				)						->  float:
        return 1E-4










from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

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


