





from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase						:   List[Any]			  =     {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Tuple					    =	'''roc_bert'''



				def __init__(			self     ,					_A=30522     ,					_A=768     ,					_A=12     ,					_A=12     ,					_A=3072     ,					_A="gelu"     ,					_A=0.1     ,					_A=0.1     ,					_A=512     ,					_A=2     ,					_A=0.0_2     ,					_A=1e-1_2     ,					_A=True     ,					_A=0     ,					_A="absolute"     ,					_A=None     ,					_A=True     ,					_A=True     ,					_A=768     ,					_A=910     ,					_A=512     ,					_A=24858     ,					_A=True     ,					**_A     ,					):
									__A	:						Optional[Any]       =				vocab_size
									__A	:						Optional[int]       =				max_position_embeddings
									__A	:						Optional[int]       =				hidden_size
									__A	:						Tuple       =				num_hidden_layers
									__A	:						Optional[Any]       =				num_attention_heads
									__A	:						int       =				intermediate_size
									__A	:						Optional[Any]       =				hidden_act
									__A	:						List[str]       =				hidden_dropout_prob
									__A	:						Optional[Any]       =				attention_probs_dropout_prob
									__A	:						List[Any]       =				initializer_range
									__A	:						List[Any]       =				type_vocab_size
									__A	:						List[Any]       =				layer_norm_eps
									__A	:						Tuple       =				use_cache
									__A	:						Optional[int]       =				enable_pronunciation
									__A	:						Union[str, Any]       =				enable_shape
									__A	:						Optional[int]       =				pronunciation_embed_dim
									__A	:						Union[str, Any]       =				pronunciation_vocab_size
									__A	:						List[Any]       =				shape_embed_dim
									__A	:						str       =				shape_vocab_size
									__A	:						List[str]       =				concat_input
									__A	:						int       =				position_embedding_type
									__A	:						List[Any]       =				classifier_dropout
									super().__init__(pad_token_id=_A     ,					**_A     )











import math







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[str]       =				[]
					__A	:						Any       =				2
					__A	:						Union[str, Any]       =				int(math.sqrt(a  )  )  # Size of every segment
					__A	:						Any       =				[True] * (end + 1)
					__A	:						List[Any]       =				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(a  )
															for i in range(start * start					,						end + 1					,						a  ):
																				__A	:						Optional[int]       =				False
										start += 1
					prime += in_prime

					__A	:						Any       =				end + 1
					__A	:						Any       =				min(2 * end					,						a  )

					while low <= n:
										__A	:						List[Any]       =				[True] * (high - low + 1)
										for each in in_prime:
															__A	:						List[str]       =				math.floor(low / each  ) * each
															if t < low:
																				t += each

															for j in range(a					,						high + 1					,						a  ):
																				__A	:						Optional[int]       =				False

										for j in range(len(a  )  ):
															if temp[j] is True:
																				prime.append(j + low  )

										__A	:						Optional[int]       =				high + 1
										__A	:						Tuple       =				min(high + end					,						a  )

					return prime




print(sieve(10**6))




