



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


__a           =							logging.get_logger(__name__)

__a           =							{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Tuple		=	"""rembert"""






					def __init__(			self:				Tuple		,	snake_case:				int=250_300		,	snake_case:				Optional[Any]=1_152		,	snake_case:				Any=32		,	snake_case:				Tuple=18		,	snake_case:				List[Any]=256		,	snake_case:				Tuple=1_664		,	snake_case:				Any=4_608		,	snake_case:				Optional[Any]="gelu"		,	snake_case:				Any=0.0		,	snake_case:				Dict=0.0		,	snake_case:				Dict=0.1		,	snake_case:				Optional[int]=512		,	snake_case:				Tuple=2		,	snake_case:				Tuple=0.0_2		,	snake_case:				Optional[Any]=1E-12		,	snake_case:				List[str]=True		,	snake_case:				Tuple=0		,	snake_case:				Optional[int]=312		,	snake_case:				str=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		)

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

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





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






					@property
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      float:
											return 1E-4





"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Union[str, Any]		=     quantum_engine.allocate_qubit()
						H | qubit
						Measure | qubit
						return int(_lowercase      )


if __name__ == "__main__":
						doctest.testmod()

						# initialises a new quantum backend
						__a           =							projectq.MainEngine()

						# Generate a list of 10 random numbers
						__a           =							[get_random_number(quantum_engine) for _ in range(10)]

						# Flushes the quantum engine from memory
						quantum_engine.flush()

						print("Random numbers", random_numbers_list)
