
from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE     :   int							 =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   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__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"rembert"





	def __init__(      self					,							UpperCamelCase__=25_0300					,							UpperCamelCase__=1152					,							UpperCamelCase__=32					,							UpperCamelCase__=18					,							UpperCamelCase__=256					,							UpperCamelCase__=1664					,							UpperCamelCase__=4608					,							UpperCamelCase__="gelu"					,							UpperCamelCase__=0.0					,							UpperCamelCase__=0.0					,							UpperCamelCase__=0.1					,							UpperCamelCase__=512					,							UpperCamelCase__=2					,							UpperCamelCase__=0.0_2					,							UpperCamelCase__=1e-12					,							UpperCamelCase__=True					,							UpperCamelCase__=0					,							UpperCamelCase__=312					,							UpperCamelCase__=313					,							**UpperCamelCase__					,							):
					super().__init__(pad_token_id=UpperCamelCase__					,							bos_token_id=UpperCamelCase__					,							eos_token_id=UpperCamelCase__					,							**UpperCamelCase__		)

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







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





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





	@property
	def 	__snake_case					(      self		):
					return 1e-4

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list[int]							)			->							int:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()