


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     					=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Optional[Any]       			=  """roberta"""






		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     int=5_02_65   ,  UpperCAmelCase				:     int=7_68   ,  UpperCAmelCase				:     Tuple=12   ,  UpperCAmelCase				:     Optional[int]=12   ,  UpperCAmelCase				:     List[Any]=30_72   ,  UpperCAmelCase				:     Optional[Any]="gelu"   ,  UpperCAmelCase				:     Dict=0.1   ,  UpperCAmelCase				:     List[str]=0.1   ,  UpperCAmelCase				:     List[str]=5_12   ,  UpperCAmelCase				:     Optional[Any]=2   ,  UpperCAmelCase				:     List[Any]=0.02   ,  UpperCAmelCase				:     Union[str, Any]=1E-12   ,  UpperCAmelCase				:     Optional[int]=1   ,  UpperCAmelCase				:     Dict=0   ,  UpperCAmelCase				:     Optional[int]=2   ,  UpperCAmelCase				:     Optional[Any]="absolute"   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     Dict=None   ,  **UpperCAmelCase				:     Tuple   ,  ):
				super().__init__(pad_token_id=UpperCAmelCase   ,  bos_token_id=UpperCAmelCase   ,  eos_token_id=UpperCAmelCase   ,  **UpperCAmelCase)

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




class 						_UpperCAmelCase      (     lowercase							):






		@property
		def _snake_case		(						self				:     Optional[Any]):
				if self.task == "multiple-choice":
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{0: "batch", 1: "choice", 2: "sequence"}
				else:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ])










import qiskit


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						qiskit.Aer.get_backend("aer_simulator" )

		# Create a Quantum Circuit acting on the q register
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						qiskit.QuantumCircuit(a     , a )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0 )
		circuit.x(1 )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]     , [0, 1] )

		# Execute the circuit on the qasm simulator
		SCREAMING_SNAKE_CASE_				:Any				     =						qiskit.execute(a     , a     , shots=1000 )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(a )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			single_qubit_measure(2, 2)
		print(F'''Total count for various states are: {counts}''')






