






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_					:		str           = logging.get_logger(__name__)

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[str]      ,     __magic_name__   :    Optional[Any]      ,     __magic_name__   :    str   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      WavLMForSequenceClassification.from_pretrained(__magic_name__      ,     config=__magic_name__   )
	UpperCamelCase	:Dict		=      downstream_dict["""projector.weight"""]
	UpperCamelCase	:Dict		=      downstream_dict["""projector.bias"""]
	UpperCamelCase	:Union[str, Any]		=      downstream_dict["""model.post_net.linear.weight"""]
	UpperCamelCase	:str		=      downstream_dict["""model.post_net.linear.bias"""]
	return model

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    List[str]      ,     __magic_name__   :    str   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      WavLMForAudioFrameClassification.from_pretrained(__magic_name__      ,     config=__magic_name__   )
	UpperCamelCase	:Optional[Any]		=      downstream_dict["""model.linear.weight"""]
	UpperCamelCase	:str		=      downstream_dict["""model.linear.bias"""]
	return model

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    int      ,     __magic_name__   :    Dict   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Dict		=      WavLMForXVector.from_pretrained(__magic_name__      ,     config=__magic_name__   )
	UpperCamelCase	:List[str]		=      downstream_dict["""connector.weight"""]
	UpperCamelCase	:Dict		=      downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
		UpperCamelCase	:Tuple		=      downstream_dict[
		    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
		]
		UpperCamelCase	:Union[str, Any]		=      downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

	UpperCamelCase	:Optional[Any]		=      downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	UpperCamelCase	:str		=      downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	UpperCamelCase	:str		=      downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	UpperCamelCase	:str		=      downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	UpperCamelCase	:str		=      downstream_dict["""objective.W"""]
	return model

@torch.no_grad()
def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Dict      ,     __magic_name__   :    List[str]      ,     __magic_name__   :    Union[str, Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Any		=      torch.load(__magic_name__      ,     map_location="""cpu"""   )

	UpperCamelCase	:Tuple		=      checkpoint["""Downstream"""]

	UpperCamelCase	:Tuple		=      WavLMConfig.from_pretrained(__magic_name__   )
	UpperCamelCase	:str		=      WavaVecaFeatureExtractor.from_pretrained(
	    __magic_name__      ,     return_attention_mask=__magic_name__      ,     do_normalize=__magic_name__   )

	UpperCamelCase	:Optional[Any]		=      hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""   ):
		UpperCamelCase	:Union[str, Any]		=      convert_classification(__magic_name__      ,     __magic_name__      ,     __magic_name__   )
	elif arch.endswith("""ForAudioFrameClassification"""   ):
		UpperCamelCase	:str		=      convert_diarization(__magic_name__      ,     __magic_name__      ,     __magic_name__   )
	elif arch.endswith("""ForXVector"""   ):
		UpperCamelCase	:Dict		=      convert_xvector(__magic_name__      ,     __magic_name__      ,     __magic_name__   )
	else:
		raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""   )

	if hf_config.use_weighted_layer_sum:
		UpperCamelCase	:Tuple		=      checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(__magic_name__   )
	hf_model.save_pretrained(__magic_name__   )


if __name__ == "__main__":
	UpperCAmelCase_					:		Union[str, Any]           = argparse.ArgumentParser()
	parser.add_argument(
	    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
	)
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
	parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
	UpperCAmelCase_					:		int           = parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2   )						->     qiskit.result.counts.Counts:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

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

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     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
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


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