# Copyright (C) Unitary Fund
#
# This source code is licensed under the GPL license (v3) found in the
# LICENSE file in the root directory of this source tree.

"""Unit tests for readout confusion inversion."""
from typing import List
from functools import partial
import numpy as np
import pytest

import cirq
from cirq.experiments.single_qubit_readout_calibration_test import (
    NoisySingleQubitReadoutSampler,
)

from mitiq import MeasurementResult, Executor, Observable, PauliString
from mitiq.rem.inverse_confusion_matrix import (
    generate_inverse_confusion_matrix,
)
from mitiq.rem import execute_with_rem, mitigate_executor, rem_decorator
from mitiq.raw import execute as raw_execute
from mitiq.interface.mitiq_cirq import sample_bitstrings

# Default qubit register and circuit for unit tests
qreg = [cirq.LineQubit(i) for i in range(2)]
circ_with_measurements = cirq.Circuit(
    cirq.ops.X.on_each(*qreg), cirq.measure_each(*qreg)
)
circ = cirq.Circuit(cirq.ops.X.on_each(*qreg))
observable = Observable(PauliString("ZI"), PauliString("IZ"))


def noisy_readout_executor(
    circuit, p0: float = 0.01, p1: float = 0.01, shots: int = 8192
) -> MeasurementResult:
    simulator = NoisySingleQubitReadoutSampler(p0, p1)
    result = simulator.run(circuit, repetitions=shots)

    return MeasurementResult(
        result=np.column_stack(list(result.measurements.values())),
        qubit_indices=tuple(
            # q[2:-1] is necessary to convert "q(number)" into "number"
            int(q[2:-1])
            for k in result.measurements.keys()
            for q in k.split(",")
        ),
    )


npX = np.array([[0, 1], [1, 0]])
"""Defines the sigma_x Pauli matrix in SU(2) algebra as a (2,2) `np.array`."""

npZ = np.array([[1, 0], [0, -1]])
"""Defines the sigma_z Pauli matrix in SU(2) algebra as a (2,2) `np.array`."""


# An invalid executor for unit tests since it returns an expectation
def invalid_executor(circuit) -> float:
    wavefunction = circuit.final_state_vector(
        ignore_terminal_measurements=True
    )
    return np.real(wavefunction.conj().T @ np.kron(npX, npZ) @ wavefunction)


def test_rem_invalid_executor():
    identity = np.identity(4)
    with pytest.raises(TypeError, match="not of type MeasurementResult"):
        execute_with_rem(
            circ,
            invalid_executor,
            observable=None,
            inverse_confusion_matrix=identity,
        )


def test_rem_identity():
    executor = partial(sample_bitstrings, noise_level=(0,))
    identity = np.identity(4)
    result = execute_with_rem(
        circ, executor, observable, inverse_confusion_matrix=identity
    )
    assert np.isclose(result, -2.0)


def test_rem_with_matrix():
    # test with an executor that completely flips results
    p0 = 1
    p1 = 1
    noisy_executor = partial(noisy_readout_executor, p0=p0, p1=p1)
    unmitigated = raw_execute(circ, noisy_executor, observable)
    assert np.isclose(unmitigated, 2.0)

    inverse_confusion_matrix = np.array(
        [
            [0, 0, 0, 1],
            [0, 0, 1, 0],
            [0, 1, 0, 0],
            [1, 0, 0, 0],
        ]
    )

    mitigated = execute_with_rem(
        circ,
        Executor(noisy_executor),
        observable,
        inverse_confusion_matrix=inverse_confusion_matrix,
    )
    assert np.isclose(mitigated, -2.0)


def test_rem_with_invalid_matrix():
    executor = partial(sample_bitstrings, noise_level=(0,))
    identity = np.identity(2)
    with pytest.raises(ValueError):
        execute_with_rem(
            circ, executor, observable, inverse_confusion_matrix=identity
        )


def test_doc_is_preserved():
    """Tests that the doc of the original executor is preserved."""

    def first_executor(circuit):
        """Doc of the original executor."""
        return MeasurementResult()

    identity = np.identity(4)

    mit_executor = mitigate_executor(
        first_executor, inverse_confusion_matrix=identity
    )
    assert mit_executor.__doc__ == first_executor.__doc__

    @rem_decorator(inverse_confusion_matrix=identity)
    def second_executor(circuit):
        """Doc of the original executor."""
        return 0

    assert second_executor.__doc__ == first_executor.__doc__


@pytest.mark.parametrize(
    "p0, p1, atol",
    [
        (0, 0, 0),
        (1, 1, 0),
        (
            1,
            0,
            2.1,
        ),  # catastrophic failure from all 0s state, which is why atol=2.1
        (
            0,
            1,
            0,
        ),  # undetectable: results in all 1s state, which is the desired state
        (0.3, 0.1, 0),
        (0.1, 0.3, 0),
        (0.001, 0.001, 1e-3),
        (0.02, 0.04, 0),
        (0.3, 0.7, 0),
    ],
)
def test_mitigate_executor(p0, p1, atol):
    true_rem_value = -2.0

    noisy_executor = partial(noisy_readout_executor, p0=p0, p1=p1)

    num_qubits = len(qreg)
    inverse_confusion_matrix = generate_inverse_confusion_matrix(
        num_qubits, p0=p0, p1=p1
    )

    base = raw_execute(circ, noisy_executor, observable)

    mitigated_executor = mitigate_executor(
        noisy_executor,
        inverse_confusion_matrix=inverse_confusion_matrix,
    )
    rem_value = raw_execute(circ, mitigated_executor, observable)
    assert abs(true_rem_value - rem_value) <= abs(
        true_rem_value - base
    ) or np.isclose(
        abs(true_rem_value - rem_value),
        abs(true_rem_value - base),
        atol=atol,
    )


def test_rem_decorator():
    true_rem_value = -2.0

    num_qubits = len(qreg)

    # test with an executor that completely flips results
    p0 = 1
    p1 = 1
    inverse_confusion_matrix = generate_inverse_confusion_matrix(
        num_qubits, p0=p0, p1=p1
    )

    @rem_decorator(inverse_confusion_matrix=inverse_confusion_matrix)
    def noisy_readout_decorated_executor(qp) -> MeasurementResult:
        return noisy_readout_executor(qp, p0=p0, p1=p1)

    noisy_executor = partial(noisy_readout_executor, p0=p0, p1=p1)

    base = raw_execute(circ, noisy_executor, observable)

    rem_value = raw_execute(circ, noisy_readout_decorated_executor, observable)
    assert abs(true_rem_value - rem_value) < abs(true_rem_value - base)


def test_rem_decorator_batched():
    circuits = [circ, 3 * circ]
    true_values = [-2.0, -2.0]
    num_qubits = len(qreg)
    p0 = 0.2
    p1 = 0.1
    inverse_confusion_matrix = generate_inverse_confusion_matrix(
        num_qubits, p0=p0, p1=p1
    )

    @rem_decorator(inverse_confusion_matrix=inverse_confusion_matrix)
    def noisy_readout_batched(circuits) -> List[MeasurementResult]:
        return [noisy_readout_executor(c, p0=p0, p1=p1) for c in circuits]

    noisy_executor = partial(noisy_readout_executor, p0=p0, p1=p1)
    base_values = [
        raw_execute(c, noisy_executor, observable) for c in circuits
    ]
    rem_values = Executor(noisy_readout_batched).evaluate(circuits, observable)
    for true_val, base, rem_val in zip(true_values, base_values, rem_values):
        assert abs(true_val - rem_val) < abs(true_val - base)
        assert np.isclose(true_val, rem_val, atol=0.05)
