

'''simple docstring'''



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowercase    =		data_utils.TransfoXLTokenizer
_lowercase    =		data_utils.TransfoXLCorpus
_lowercase    =		data_utils
_lowercase    =		data_utils


def A				(__lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:List[str]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Tuple		):
						if transfo_xl_dataset_file:
												# Convert a pre-processed corpus (see original TensorFlow repo)
												with open(lowerCAmelCase__			,     """rb"""		) as fp:
																		_lowerCAmelCase  						=			pickle.load(lowerCAmelCase__			,     encoding="""latin1"""		)
												# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
												_lowerCAmelCase  						=			pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
												print(f'Save vocabulary to {pytorch_vocab_dump_path}'		)
												_lowerCAmelCase  						=			corpus.vocab.__dict__
												torch.save(lowerCAmelCase__			,     lowerCAmelCase__		)

												_lowerCAmelCase  						=			corpus.__dict__
												corpus_dict_no_vocab.pop("""vocab"""			,     lowerCAmelCase__		)
												_lowerCAmelCase  						=			pytorch_dump_folder_path + '''/''' + CORPUS_NAME
												print(f'Save dataset to {pytorch_dataset_dump_path}'		)
												torch.save(lowerCAmelCase__			,     lowerCAmelCase__		)

						if tf_checkpoint_path:
												# Convert a pre-trained TensorFlow model
												_lowerCAmelCase  						=			os.path.abspath(lowerCAmelCase__		)
												_lowerCAmelCase  						=			os.path.abspath(lowerCAmelCase__		)

												print(f'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'		)
												# Initialise PyTorch model
												if transfo_xl_config_file == "":
																		_lowerCAmelCase  						=			TransfoXLConfig()
												else:
																		_lowerCAmelCase  						=			TransfoXLConfig.from_json_file(lowerCAmelCase__		)
												print(f'Building PyTorch model from configuration: {config}'		)
												_lowerCAmelCase  						=			TransfoXLLMHeadModel(lowerCAmelCase__		)

												_lowerCAmelCase  						=			load_tf_weights_in_transfo_xl(lowerCAmelCase__			,     lowerCAmelCase__			,     lowerCAmelCase__		)
												# Save pytorch-model
												_lowerCAmelCase  						=			os.path.join(lowerCAmelCase__			,     lowerCAmelCase__		)
												_lowerCAmelCase  						=			os.path.join(lowerCAmelCase__			,     lowerCAmelCase__		)
												print(f'Save PyTorch model to {os.path.abspath(lowerCAmelCase__		)}'		)
												torch.save(model.state_dict()			,     lowerCAmelCase__		)
												print(f'Save configuration file to {os.path.abspath(lowerCAmelCase__		)}'		)
												with open(lowerCAmelCase__			,     """w"""			,     encoding="""utf-8"""		) as f:
																		f.write(config.to_json_string()		)


if __name__ == "__main__":
						_lowercase    =		argparse.ArgumentParser()
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
						)
						parser.add_argument(
						    """--tf_checkpoint_path""",
						    default="""""",
						    type=str,
						    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
						)
						parser.add_argument(
						    """--transfo_xl_config_file""",
						    default="""""",
						    type=str,
						    help=(
						        """An optional config json file corresponding to the pre-trained BERT model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--transfo_xl_dataset_file""",
						    default="""""",
						    type=str,
						    help="""An optional dataset file to be converted in a vocabulary.""",
						)
						_lowercase    =		parser.parse_args()
						convert_transfo_xl_checkpoint_to_pytorch(
						    args.tf_checkpoint_path,
						    args.transfo_xl_config_file,
						    args.pytorch_dump_folder_path,
						    args.transfo_xl_dataset_file,
						)


'''simple docstring'''



import numpy as np
import qiskit


def A				(__lowerCamelCase				:int = 8			,     __lowerCamelCase				:int | None = None		):
						_lowerCAmelCase  						=			np.random.default_rng(seed=__lowerCamelCase		)

						# Roughly 25% of the qubits will contribute to the key.
						# So we take more than we need.
						_lowerCAmelCase  						=			6 * key_len
						# Measurement basis for Alice's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# The set of states Alice will prepare.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# Measurement basis for Bob's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)

						# Quantum Circuit to simulate BB84
						_lowerCAmelCase  						=			qiskit.QuantumCircuit(__lowerCamelCase			,     name="""BB84"""		)

						# Alice prepares her qubits according to rules above.
						for index, _ in enumerate(__lowerCamelCase		):
												if alice_state[index] == 1:
																		bbaa_circ.x(__lowerCamelCase		)
												if alice_basis[index] == 1:
																		bbaa_circ.h(__lowerCamelCase		)
						bbaa_circ.barrier()

						# Bob measures the received qubits according to rules above.
						for index, _ in enumerate(__lowerCamelCase		):
												if bob_basis[index] == 1:
																		bbaa_circ.h(__lowerCamelCase		)

						bbaa_circ.barrier()
						bbaa_circ.measure_all()

						# Simulate the quantum circuit.
						_lowerCAmelCase  						=			qiskit.Aer.get_backend("""aer_simulator"""		)
						# We only need to run one shot because the key is unique.
						# Multiple shots will produce the same key.
						_lowerCAmelCase  						=			qiskit.execute(__lowerCamelCase			,     __lowerCamelCase			,     shots=1			,     seed_simulator=__lowerCamelCase		)
						# Returns the result of measurement.
						_lowerCAmelCase  						=			job.result().get_counts(__lowerCamelCase		).most_frequent()

						# Extracting the generated key from the simulation results.
						# Only keep measurement results where Alice and Bob chose the same basis.
						_lowerCAmelCase  						=			"""""".join(
						    [
						        result_bit
						        for alice_basis_bit, bob_basis_bit, result_bit in zip(
						            __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
						        if alice_basis_bit == bob_basis_bit
						    ]		)

						# Get final key. Pad with 0 if too short, otherwise truncate.
						_lowerCAmelCase  						=			gen_key[:key_len] if len(__lowerCamelCase		) >= key_len else gen_key.ljust(__lowerCamelCase			,     """0"""		)
						return key


if __name__ == "__main__":
						print(F"""The generated key is : {bbaa(8, seed=0)}""")
						from doctest import testmod

						testmod()
