


import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   List[str]:





 """simple docstring"""







 A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)

 A								=				chkpt["""model"""]

 # We have the base model one level deeper than the original XLM repository
 A								=				{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   A								=				v
  else:
   A								=				v

 A								=				chkpt["""params"""]
 A								=				{n: v for n, v in config.items() if not isinstance(UpperCamelCase__							,					(torch.FloatTensor, numpy.ndarray)						)}

 A								=				chkpt["""dico_word2id"""]
 A								=				{s + """</w>""" if s.find("""@@"""						) == -1 and i > 13 else s.replace("""@@"""							,					""""""						): i for s, i in vocab.items()}

 # Save pytorch-model
 A								=				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
 A								=				pytorch_dump_folder_path + """/""" + CONFIG_NAME
 A								=				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

 print(f'Save PyTorch model to {pytorch_weights_dump_path}'						)
 torch.save(UpperCamelCase__							,					UpperCamelCase__						)

 print(f'Save configuration file to {pytorch_config_dump_path}'						)
 with open(UpperCamelCase__							,					"""w"""							,					encoding="""utf-8"""						) as f:
  f.write(json.dumps(UpperCamelCase__							,					indent=2						) + """\n"""						)

 print(f'Save vocab file to {pytorch_config_dump_path}'						)
 with open(UpperCamelCase__							,					"""w"""							,					encoding="""utf-8"""						) as f:
  f.write(json.dumps(UpperCamelCase__							,					indent=2						) + """\n"""						)


if __name__ == "__main__":
    _lowercase       :      Any	=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    _lowercase       :      Optional[int]	=	parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

	# Using Aer's simulator
	A								=				qiskit.Aer.get_backend("""aer_simulator"""						)

	# Creating a Quantum Circuit acting on the q register
	A								=				qiskit.QuantumCircuit(UpperCamelCase__							,					UpperCamelCase__						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1							,					UpperCamelCase__						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1							,					UpperCamelCase__						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCamelCase__						)						)							,					list(range(UpperCamelCase__						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''Total count for various states are: {quantum_entanglement(3)}''')



