

'''simple docstring'''

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]        =       ConvBertConfig.from_json_file(UpperCamelCase_							)
					UpperCAmelCase		:							Tuple        =       ConvBertModel(UpperCamelCase_							)

					UpperCAmelCase		:							Dict        =       load_tf_weights_in_convbert(UpperCamelCase_			,   UpperCamelCase_			,   UpperCamelCase_							)
					model.save_pretrained(UpperCamelCase_							)

					UpperCAmelCase		:							Optional[Any]        =       TFConvBertModel.from_pretrained(UpperCamelCase_			,   from_pt=UpperCamelCase_							)
					tf_model.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
			a       :      int  	=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--convbert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained ConvBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			a       :      Any  	=			parser.parse_args()
			convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)



'''simple docstring'''

import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def 				lowercase (					__magic_name__ = 1			,   __magic_name__ = 1							):



					'''simple docstring'''







					if isinstance(__magic_name__			,   __magic_name__							) or isinstance(__magic_name__			,   __magic_name__							):
										raise TypeError("inputs must be integers."							)
					if (bit_a < 0) or (bit_a < 0):
										raise ValueError("inputs must be positive."							)
					if (math.floor(__magic_name__							) != bit_a) or (math.floor(__magic_name__							) != bit_a):
										raise ValueError("inputs must be exact integers."							)
					if (bit_a > 1) or (bit_a > 1):
										raise ValueError("inputs must be less or equal to 1."							)

					# build registers
					UpperCAmelCase		:							List[Any]        =       QuantumRegister(2			,   "qr"							)
					UpperCAmelCase		:							Optional[int]        =       ClassicalRegister(2			,   "cr"							)

					UpperCAmelCase		:							Optional[int]        =       QuantumCircuit(__magic_name__			,   __magic_name__							)

					# entanglement the qubits
					quantum_circuit.h(1							)
					quantum_circuit.cx(1			,   0							)

					# send the information
					UpperCAmelCase		:							Any        =       str(__magic_name__							) + str(__magic_name__							)

					if c_information == "11":
										quantum_circuit.x(1							)
										quantum_circuit.z(1							)
					elif c_information == "10":
										quantum_circuit.z(1							)
					elif c_information == "01":
										quantum_circuit.x(1							)
					else:
										quantum_circuit.i(1							)

					# unentangled the circuit
					quantum_circuit.cx(1			,   0							)
					quantum_circuit.h(1							)

					# measure the circuit
					quantum_circuit.measure(__magic_name__			,   __magic_name__							)

					UpperCAmelCase		:							Tuple        =       Aer.get_backend("aer_simulator"							)
					UpperCAmelCase		:							Any        =       execute(__magic_name__			,   __magic_name__			,   shots=1000							)

					return job.result().get_counts(__magic_name__							)


if __name__ == "__main__":
				print(F'Counts for classical state send: {superdense_coding(1,1)}')
