


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "roberta-prelayernorm"




	def __init__(   self						,				__a=5_0265						,				__a=768						,				__a=12						,				__a=12						,				__a=3072						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=1E-1_2						,				__a=1						,				__a=0						,				__a=2						,				__a="absolute"						,				__a=True						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,				bos_token_id=__a						,				eos_token_id=__a						,				**__a							)

						__a			:       Optional[Any]					    =  vocab_size
						__a			:       str					    =  hidden_size
						__a			:       int					    =  num_hidden_layers
						__a			:       Union[str, Any]					    =  num_attention_heads
						__a			:       Any					    =  hidden_act
						__a			:       Union[str, Any]					    =  intermediate_size
						__a			:       int					    =  hidden_dropout_prob
						__a			:       Optional[int]					    =  attention_probs_dropout_prob
						__a			:       Any					    =  max_position_embeddings
						__a			:       str					    =  type_vocab_size
						__a			:       Tuple					    =  initializer_range
						__a			:       Any					    =  layer_norm_eps
						__a			:       List[str]					    =  position_embedding_type
						__a			:       str					    =  use_cache
						__a			:       str					    =  classifier_dropout





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__a			:       List[Any]					    =  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__a			:       Any					    =  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     List[str]													=       len(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[int]													=       []
						for i in range(len(__SCREAMING_SNAKE_CASE							) - pat_len + 1							):
												lowercase_	:     Tuple													=       True
												for j in range(__SCREAMING_SNAKE_CASE							):
																		if s[i + j] != pattern[j]:
																								lowercase_	:     List[str]													=       False
																								break
												if match_found:
																		position.append(__SCREAMING_SNAKE_CASE							)
						return position


if __name__ == "__main__":
							assert naive_pattern_search("ABCDEFG", "DE") == [3]
							print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))



