

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowercase__  :					Any												=	logging.get_logger(__name__)

lowercase__  :					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     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					"rembert"


		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	str=2_5_0_3_0_0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_1_5_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=2_5_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=1_6_6_4  ,  __SCREAMING_SNAKE_CASE				:	str=4_6_0_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]="gelu"  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=0.0  ,  __SCREAMING_SNAKE_CASE				:	int=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Dict=2  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	str=1E-12  ,  __SCREAMING_SNAKE_CASE				:	Dict=True  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=3_1_2  ,  __SCREAMING_SNAKE_CASE				:	List[str]=3_1_3  ,  **__SCREAMING_SNAKE_CASE				:	Any  ,  ):
							super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,  bos_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)

							A    =      vocab_size
							A    =      input_embedding_size
							A    =      output_embedding_size
							A    =      max_position_embeddings
							A    =      hidden_size
							A    =      num_hidden_layers
							A    =      num_attention_heads
							A    =      intermediate_size
							A    =      hidden_act
							A    =      hidden_dropout_prob
							A    =      attention_probs_dropout_prob
							A    =      classifier_dropout_prob
							A    =      initializer_range
							A    =      type_vocab_size
							A    =      layer_norm_eps
							A    =      use_cache
							A    =      False





class     __UpperCamelCase				(     _A		):


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


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							return 1E-4








"""simple docstring"""




from typing import TYPE_CHECKING

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


__A  :					int												=	{
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					Any												=	[
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





