



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__:       Optional[int]							     =      logging.get_logger(__name__)

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

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	"""rembert"""







	def __init__(						self      :					List[str]		,      __snake_case      :					Tuple=250300		,      __snake_case      :					Union[str, Any]=1152		,      __snake_case      :					Union[str, Any]=32		,      __snake_case      :					List[Any]=18		,      __snake_case      :					List[str]=256		,      __snake_case      :					Union[str, Any]=1664		,      __snake_case      :					Tuple=4608		,      __snake_case      :					int="gelu"		,      __snake_case      :					Union[str, Any]=0.0		,      __snake_case      :					Optional[Any]=0.0		,      __snake_case      :					str=0.1		,      __snake_case      :					Any=512		,      __snake_case      :					Tuple=2		,      __snake_case      :					List[str]=0.02		,      __snake_case      :					Union[str, Any]=1E-12		,      __snake_case      :					Union[str, Any]=True		,      __snake_case      :					Union[str, Any]=0		,      __snake_case      :					List[str]=312		,      __snake_case      :					List[Any]=313		,      **__snake_case      :					Optional[Any]		,      )      ->		int:
								super().__init__(pad_token_id=__snake_case		,      bos_token_id=__snake_case		,      eos_token_id=__snake_case		,      **__snake_case		)

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

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	@property
	def 					A     (						self      :					List[str]		)      ->		Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
															UpperCAmelCase			:	Union[str, Any]										=		{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 					A     (						self      :					Union[str, Any]		)      ->		float:
								return 1E-4










'''simple docstring'''






from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       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__:       Dict							     =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





