




import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def 		lowercase__ (		__A:						float = np.pi / 2				,__A:						float = np.pi / 2				,__A:						float = np.pi / 2					):





					'''simple docstring'''






					__magic_name__				:						int     				=				QuantumRegister(3				,'''qr'''					)  # Define the number of quantum bits
					__magic_name__				:						Tuple     				=				ClassicalRegister(1				,'''cr'''					)  # Define the number of classical bits

					__magic_name__				:						List[str]     				=				QuantumCircuit(__A				,__A					)  # Define the quantum circuit.

					# Build the circuit
					quantum_circuit.u(__A				,__A				,__A				,0					)  # Quantum State to teleport
					quantum_circuit.h(1					)  # add hadamard gate
					quantum_circuit.cx(
					    1				,2					)  # add control gate with qubit 1 as control and 2 as target.
					quantum_circuit.cx(0				,1					)
					quantum_circuit.h(0					)
					quantum_circuit.cz(0				,2					)  # add control z gate.
					quantum_circuit.cx(1				,2					)

					quantum_circuit.measure([2]				,[0]					)  # measure the qubit.

					# Simulate the circuit using qasm simulator
					__magic_name__				:						Dict     				=				Aer.get_backend('''aer_simulator'''					)
					__magic_name__				:						int     				=				execute(__A				,__A				,shots=1_0_0_0					)

					return job.result().get_counts(__A					)


if __name__ == "__main__":
	print(
	    '''Total count for teleported state is: '''
	    F"""{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"""
	)









import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__lowerCamelCase :    Any      						=      get_logger(__name__)







def 		lowercase__ (		__A:						str				,__A:						str					):





					'''simple docstring'''






					if text.startswith(__A					):
										return text[len(__A					) :]
					return text  # or whatever







def 		lowercase__ (		__A:						List[str]					):





					'''simple docstring'''






					return {remove_prefix(__A				,'''model.'''					): v for k, v in sd.items()}







def 		lowercase__ (		__A:						List[Dict[str, torch.Tensor]]					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				{}
					for k in state_dicts[0].keys():
										__magic_name__				:						str     				=				[sd[k] for sd in state_dicts]
										__magic_name__				:						List[Any]     				=				sum(__A					) / len(__A					)
										assert isinstance(__A				,torch.Tensor					)
										__magic_name__				:						Any     				=				new_t
					return new_sd







def 		lowercase__ (		__A:						str				,__A:						str				,__A:						str					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				AutoModelForSeqaSeqLM.from_pretrained(__A					)
					if os.path.isfile(__A					):
										__magic_name__				:						str     				=				[pl_ckpt_path]
					else:
										assert os.path.isdir(__A					)
										__magic_name__				:						List[str]     				=				list(Path(__A					).glob('''*.ckpt'''					)					)
										assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

					if len(__A					) > 1:
										logger.info(F'''averaging the weights of {ckpt_files}'''					)

					__magic_name__				:						Union[str, Any]     				=				[sanitize(torch.load(__A				,map_location='''cpu'''					)['''state_dict''']					) for x in ckpt_files]
					__magic_name__				:						Dict     				=				average_state_dicts(__A					)

					__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				hf_model.load_state_dict(__A				,strict=__A					)
					assert not missing, F'''missing keys: {missing}'''
					hf_model.save_pretrained(__A					)
					try:
										__magic_name__				:						Optional[Any]     				=				AutoTokenizer.from_pretrained(__A					)
										tok.save_pretrained(__A					)
					except Exception:
										pass
										# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)



