
"""simple docstring"""




import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=1_0_0,	SCREAMING_SNAKE_CASE_=" "     ):
					SCREAMING_SNAKE_CASE           =      text.split(SCREAMING_SNAKE_CASE_     )
					return [character.join(text[i : i + n]     ).strip() for i in range(0,	len(SCREAMING_SNAKE_CASE_     ),	SCREAMING_SNAKE_CASE_     )]

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      [], []
					for title, text in zip(documents['title'],	documents['text']     ):
										if text is not None:
															for passage in split_text(SCREAMING_SNAKE_CASE_     ):
																				titles.append(title if title is not None else ''     )
																				texts.append(SCREAMING_SNAKE_CASE_     )
					return {"title": titles, "text": texts}

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      load_dataset(
					    'csv',	data_files=[csv_path],	split='train',	delimiter='\t',	column_names=['title', 'text']     )
					SCREAMING_SNAKE_CASE           =      kb_dataset.map(
					    SCREAMING_SNAKE_CASE_,	batched=SCREAMING_SNAKE_CASE_,	num_proc=1     )  # if you want you can load already splitted csv.
					SCREAMING_SNAKE_CASE           =      [kb_dataset.shard(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	contiguous=SCREAMING_SNAKE_CASE_     ) for i in range(SCREAMING_SNAKE_CASE_     )]
					SCREAMING_SNAKE_CASE           =      kb_list[process_num]

					SCREAMING_SNAKE_CASE           =      'data_' + str(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					SCREAMING_SNAKE_CASE           =      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'     )
					SCREAMING_SNAKE_CASE           =      ctx_encoder.to(device=SCREAMING_SNAKE_CASE_     )

					def embed(
					    SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) -> dict:
										SCREAMING_SNAKE_CASE           =      ctx_tokenizer(
										    documents['title'],	documents['text'],	truncation=SCREAMING_SNAKE_CASE_,	padding='longest',	return_tensors='pt'     )['input_ids']
										SCREAMING_SNAKE_CASE           =      ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE_     ),	return_dict=SCREAMING_SNAKE_CASE_     ).pooler_output
										return {"embeddings": embeddings.detach().cpu().numpy()}

					SCREAMING_SNAKE_CASE           =      Features(
					    {'text': Value('string'     ), 'title': Value('string'     ), 'embeddings': Sequence(Value('float32'     )     )}     )  # optional, save as float32 instead of float64 to save space

					SCREAMING_SNAKE_CASE           =      data_shrad.map(
					    partial(SCREAMING_SNAKE_CASE_,	ctx_encoder=SCREAMING_SNAKE_CASE_,	ctx_tokenizer=SCREAMING_SNAKE_CASE_,	device=SCREAMING_SNAKE_CASE_     ),	batched=SCREAMING_SNAKE_CASE_,	batch_size=1_6,	features=SCREAMING_SNAKE_CASE_,	)
					dataset.save_to_disk(SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      []

					for shard_address in glob(str(SCREAMING_SNAKE_CASE_     ) + '/*/'     ):
										data_shard_list.append(load_from_disk(SCREAMING_SNAKE_CASE_     )     )

					SCREAMING_SNAKE_CASE           =      concatenate_datasets(SCREAMING_SNAKE_CASE_     )
					faiss.omp_set_num_threads(9_6     )

					SCREAMING_SNAKE_CASE           =      faiss.IndexHNSWFlat(7_6_8,	1_2_8,	faiss.METRIC_INNER_PRODUCT     )
					concat.add_faiss_index('embeddings',	custom_index=SCREAMING_SNAKE_CASE_     )
					concat.get_index('embeddings'     ).save(
					    SCREAMING_SNAKE_CASE_     )  # since we load the index in to memory,we can directly update the index in the disk


"""simple docstring"""




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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = np.pi / 2,	SCREAMING_SNAKE_CASE_ = np.pi / 2,	SCREAMING_SNAKE_CASE_ = np.pi / 2     ):
					SCREAMING_SNAKE_CASE           =      QuantumRegister(3,	'qr'     )  # Define the number of quantum bits
					SCREAMING_SNAKE_CASE           =      ClassicalRegister(1,	'cr'     )  # Define the number of classical bits

					SCREAMING_SNAKE_CASE           =      QuantumCircuit(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )  # Define the quantum circuit.

					# Build the circuit
					quantum_circuit.u(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	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
					SCREAMING_SNAKE_CASE           =      Aer.get_backend('aer_simulator'     )
					SCREAMING_SNAKE_CASE           =      execute(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	shots=1_0_0_0     )

					return job.result().get_counts(SCREAMING_SNAKE_CASE_     )


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