"""Stim (Development Version): a fast quantum stabilizer circuit library."""
# (This a stubs file describing the classes and methods in stim.)
from __future__ import annotations
from typing import overload, TYPE_CHECKING, List, Dict, Tuple, Any, Union, Iterable
if TYPE_CHECKING:
    import io
    import pathlib
    import numpy as np
    import stim
class Circuit:
    """A mutable stabilizer circuit.

    Examples:
        >>> import stim
        >>> c = stim.Circuit()
        >>> c.append("X", 0)
        >>> c.append("M", 0)
        >>> c.compile_sampler().sample(shots=1)
        array([[ True]])

        >>> stim.Circuit('''
        ...    H 0
        ...    CNOT 0 1
        ...    M 0 1
        ...    DETECTOR rec[-1] rec[-2]
        ... ''').compile_detector_sampler().sample(shots=1)
        array([[False]])

    """
    def __add__(
        self,
        second: stim.Circuit,
    ) -> stim.Circuit:
        """Creates a circuit by appending two circuits.

        Examples:
            >>> import stim
            >>> c1 = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> c2 = stim.Circuit('''
            ...    M 0 1 2
            ... ''')
            >>> c1 + c2
            stim.Circuit('''
                X 0
                Y 1 2
                M 0 1 2
            ''')
        """
    def __eq__(
        self,
        arg0: stim.Circuit,
    ) -> bool:
        """Determines if two circuits have identical contents.
        """
    @overload
    def __getitem__(
        self,
        index_or_slice: int,
    ) -> Union[stim.CircuitInstruction, stim.CircuitRepeatBlock]:
        pass
    @overload
    def __getitem__(
        self,
        index_or_slice: slice,
    ) -> stim.Circuit:
        pass
    def __getitem__(
        self,
        index_or_slice: object,
    ) -> object:
        """Returns copies of instructions from the circuit.

        Args:
            index_or_slice: An integer index picking out an instruction to return, or a
                slice picking out a range of instructions to return as a circuit.

        Returns:
            If the index was an integer, then an instruction from the circuit.
            If the index was a slice, then a circuit made up of the instructions in that
            slice.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...    X 0
            ...    X_ERROR(0.5) 2
            ...    REPEAT 100 {
            ...        X 0
            ...        Y 1 2
            ...    }
            ...    TICK
            ...    M 0
            ...    DETECTOR rec[-1]
            ... ''')
            >>> circuit[1]
            stim.CircuitInstruction('X_ERROR', [stim.GateTarget(2)], [0.5])
            >>> circuit[2]
            stim.CircuitRepeatBlock(100, stim.Circuit('''
                X 0
                Y 1 2
            '''))
            >>> circuit[1::2]
            stim.Circuit('''
                X_ERROR(0.5) 2
                TICK
                DETECTOR rec[-1]
            ''')
        """
    def __iadd__(
        self,
        second: stim.Circuit,
    ) -> stim.Circuit:
        """Appends a circuit into the receiving circuit (mutating it).

        Examples:
            >>> import stim
            >>> c1 = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> c2 = stim.Circuit('''
            ...    M 0 1 2
            ... ''')
            >>> c1 += c2
            >>> print(repr(c1))
            stim.Circuit('''
                X 0
                Y 1 2
                M 0 1 2
            ''')
        """
    def __imul__(
        self,
        repetitions: int,
    ) -> stim.Circuit:
        """Mutates the circuit by putting its contents into a REPEAT block.

        Special case: if the repetition count is 0, the circuit is cleared.
        Special case: if the repetition count is 1, nothing happens.

        Args:
            repetitions: The number of times the REPEAT block should repeat.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> c *= 3
            >>> print(repr(c))
            stim.Circuit('''
                REPEAT 3 {
                    X 0
                    Y 1 2
                }
            ''')
        """
    def __init__(
        self,
        stim_program_text: str = '',
    ) -> None:
        """Creates a stim.Circuit.

        Args:
            stim_program_text: Defaults to empty. Describes operations to append into
                the circuit.

        Examples:
            >>> import stim
            >>> empty = stim.Circuit()
            >>> not_empty = stim.Circuit('''
            ...    X 0
            ...    CNOT 0 1
            ...    M 1
            ... ''')
        """
    def __len__(
        self,
    ) -> int:
        """Returns the number of top-level instructions and blocks in the circuit.

        Instructions inside of blocks are not included in this count.

        Examples:
            >>> import stim
            >>> len(stim.Circuit())
            0
            >>> len(stim.Circuit('''
            ...    X 0
            ...    X_ERROR(0.5) 1 2
            ...    TICK
            ...    M 0
            ...    DETECTOR rec[-1]
            ... '''))
            5
            >>> len(stim.Circuit('''
            ...    REPEAT 100 {
            ...        X 0
            ...        Y 1 2
            ...    }
            ... '''))
            1
        """
    def __mul__(
        self,
        repetitions: int,
    ) -> stim.Circuit:
        """Repeats the circuit using a REPEAT block.

        Has special cases for 0 repetitions and 1 repetitions.

        Args:
            repetitions: The number of times the REPEAT block should repeat.

        Returns:
            repetitions=0: An empty circuit.
            repetitions=1: A copy of this circuit.
            repetitions>=2: A circuit with a single REPEAT block, where the contents of
                that repeat block are this circuit.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> c * 3
            stim.Circuit('''
                REPEAT 3 {
                    X 0
                    Y 1 2
                }
            ''')
        """
    def __ne__(
        self,
        arg0: stim.Circuit,
    ) -> bool:
        """Determines if two circuits have non-identical contents.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.Circuit`.
        """
    def __rmul__(
        self,
        repetitions: int,
    ) -> stim.Circuit:
        """Repeats the circuit using a REPEAT block.

        Has special cases for 0 repetitions and 1 repetitions.

        Args:
            repetitions: The number of times the REPEAT block should repeat.

        Returns:
            repetitions=0: An empty circuit.
            repetitions=1: A copy of this circuit.
            repetitions>=2: A circuit with a single REPEAT block, where the contents of
                that repeat block are this circuit.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> 3 * c
            stim.Circuit('''
                REPEAT 3 {
                    X 0
                    Y 1 2
                }
            ''')
        """
    def __str__(
        self,
    ) -> str:
        """Returns stim instructions (that can be saved to a file and parsed by stim) for the current circuit.
        """
    @overload
    def append(
        self,
        name: str,
        targets: Union[int, stim.GateTarget, Iterable[Union[int, stim.GateTarget]]],
        arg: Union[float, Iterable[float]],
    ) -> None:
        pass
    @overload
    def append(
        self,
        name: Union[stim.CircuitOperation, stim.CircuitRepeatBlock],
    ) -> None:
        pass
    def append(
        self,
        name: object,
        targets: object = (),
        arg: object = None,
    ) -> None:
        """Appends an operation into the circuit.

        Note: `stim.Circuit.append_operation` is an alias of `stim.Circuit.append`.

        Examples:
            >>> import stim
            >>> c = stim.Circuit()
            >>> c.append("X", 0)
            >>> c.append("H", [0, 1])
            >>> c.append("M", [0, stim.target_inv(1)])
            >>> c.append("CNOT", [stim.target_rec(-1), 0])
            >>> c.append("X_ERROR", [0], 0.125)
            >>> c.append("CORRELATED_ERROR", [stim.target_x(0), stim.target_y(2)], 0.25)
            >>> print(repr(c))
            stim.Circuit('''
                X 0
                H 0 1
                M 0 !1
                CX rec[-1] 0
                X_ERROR(0.125) 0
                E(0.25) X0 Y2
            ''')

        Args:
            name: The name of the operation's gate (e.g. "H" or "M" or "CNOT").

                This argument can also be set to a `stim.CircuitInstruction` or
                `stim.CircuitInstructionBlock`, which results in the instruction or
                block being appended to the circuit. The other arguments (targets
                and arg) can't be specified when doing so.

                (The argument being called `name` is no longer quite right, but
                is being kept for backwards compatibility.)
            targets: The objects operated on by the gate. This can be either a
                single target or an iterable of multiple targets to broadcast the
                gate over. Each target can be an integer (a qubit), a
                stim.GateTarget, or a special target from one of the `stim.target_*`
                methods (such as a measurement record target like `rec[-1]` from
                `stim.target_rec(-1)`).
            arg: The "parens arguments" for the gate, such as the probability for a
                noise operation. A double or list of doubles parameterizing the
                gate. Different gates take different parens arguments. For example,
                X_ERROR takes a probability, OBSERVABLE_INCLUDE takes an observable
                index, and PAULI_CHANNEL_1 takes three disjoint probabilities.

                Note: Defaults to no parens arguments. Except, for backwards
                compatibility reasons, `cirq.append_operation` (but not
                `cirq.append`) will default to a single 0.0 argument for gates that
                take exactly one argument.
        """
    def append_from_stim_program_text(
        self,
        stim_program_text: str,
    ) -> None:
        """Appends operations described by a STIM format program into the circuit.

        Examples:
            >>> import stim
            >>> c = stim.Circuit()
            >>> c.append_from_stim_program_text('''
            ...    H 0  # comment
            ...    CNOT 0 2
            ...
            ...    M 2
            ...    CNOT rec[-1] 1
            ... ''')
            >>> print(c)
            H 0
            CX 0 2
            M 2
            CX rec[-1] 1

        Args:
            stim_program_text: The STIM program text containing the circuit operations
                to append.
        """
    def append_operation(
        self,
        name: object,
        targets: object = (),
        arg: object = None,
    ) -> None:
        """[DEPRECATED] use stim.Circuit.append instead
        """
    def approx_equals(
        self,
        other: object,
        *,
        atol: float,
    ) -> bool:
        """Checks if a circuit is approximately equal to another circuit.

        Two circuits are approximately equal if they are equal up to slight
        perturbations of instruction arguments such as probabilities. For example,
        `X_ERROR(0.100) 0` is approximately equal to `X_ERROR(0.099)` within an absolute
        tolerance of 0.002. All other details of the circuits (such as the ordering of
        instructions and targets) must be exactly the same.

        Args:
            other: The circuit, or other object, to compare to this one.
            atol: The absolute error tolerance. The maximum amount each probability may
                have been perturbed by.

        Returns:
            True if the given object is a circuit approximately equal up to the
            receiving circuit up to the given tolerance, otherwise False.

        Examples:
            >>> import stim
            >>> base = stim.Circuit('''
            ...    X_ERROR(0.099) 0 1 2
            ...    M 0 1 2
            ... ''')

            >>> base.approx_equals(base, atol=0)
            True

            >>> base.approx_equals(stim.Circuit('''
            ...    X_ERROR(0.101) 0 1 2
            ...    M 0 1 2
            ... '''), atol=0)
            False

            >>> base.approx_equals(stim.Circuit('''
            ...    X_ERROR(0.101) 0 1 2
            ...    M 0 1 2
            ... '''), atol=0.0001)
            False

            >>> base.approx_equals(stim.Circuit('''
            ...    X_ERROR(0.101) 0 1 2
            ...    M 0 1 2
            ... '''), atol=0.01)
            True

            >>> base.approx_equals(stim.Circuit('''
            ...    DEPOLARIZE1(0.099) 0 1 2
            ...    MRX 0 1 2
            ... '''), atol=9999)
            False
        """
    def clear(
        self,
    ) -> None:
        """Clears the contents of the circuit.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0
            ...    Y 1 2
            ... ''')
            >>> c.clear()
            >>> c
            stim.Circuit()
        """
    def compile_detector_sampler(
        self,
        *,
        seed: object = None,
    ) -> stim.CompiledDetectorSampler:
        """Returns an object that can batch sample detection events from the circuit.

        Args:
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how many
                shots are taken. For example, taking 10 shots and then 90 shots will
                give different results from taking 100 shots in one call.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    H 0
            ...    CNOT 0 1
            ...    M 0 1
            ...    DETECTOR rec[-1] rec[-2]
            ... ''')
            >>> s = c.compile_detector_sampler()
            >>> s.sample(shots=1)
            array([[False]])
        """
    def compile_m2d_converter(
        self,
        *,
        skip_reference_sample: bool = False,
    ) -> stim.CompiledMeasurementsToDetectionEventsConverter:
        """Creates a measurement-to-detection-event converter for the given circuit.

        The converter uses a noiseless reference sample, collected from the circuit
        using stim's Tableau simulator during initialization of the converter, as a
        baseline for determining what the expected value of a detector is.

        Note that the expected behavior of gauge detectors (detectors that are not
        actually deterministic under noiseless execution) can vary depending on the
        reference sample. Stim mitigates this by always generating the same reference
        sample for a given circuit.

        Args:
            skip_reference_sample: Defaults to False. When set to True, the reference
                sample used by the converter is initialized to all-zeroes instead of
                being collected from the circuit. This should only be used if it's known
                that the all-zeroes sample is actually a possible result from the
                circuit (under noiseless execution).

        Returns:
            An initialized stim.CompiledMeasurementsToDetectionEventsConverter.

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> converter = stim.Circuit('''
            ...    X 0
            ...    M 0
            ...    DETECTOR rec[-1]
            ... ''').compile_m2d_converter()
            >>> converter.convert(
            ...     measurements=np.array([[0], [1]], dtype=np.bool8),
            ...     append_observables=False,
            ... )
            array([[ True],
                   [False]])
        """
    def compile_sampler(
        self,
        *,
        skip_reference_sample: bool = False,
        seed: object = None,
    ) -> stim.CompiledMeasurementSampler:
        """Returns an object that can quickly batch sample measurements from the circuit.

        Args:
            skip_reference_sample: Defaults to False. When set to True, the reference
                sample used by the sampler is initialized to all-zeroes instead of being
                collected from the circuit. This means that the results returned by the
                sampler are actually whether or not each measurement was *flipped*,
                instead of true measurement results.

                Forcing an all-zero reference sample is useful when you are only
                interested in error propagation and don't want to have to deal with the
                fact that some measurements want to be On when no errors occur. It is
                also useful when you know for sure that the all-zero result is actually
                a possible result from the circuit (under noiseless execution), meaning
                it is a valid reference sample as good as any other. Computing the
                reference sample is the most time consuming and memory intensive part of
                simulating the circuit, so promising that the simulator can safely skip
                that step is an effective optimization.
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how many
                shots are taken. For example, taking 10 shots and then 90 shots will
                give different results from taking 100 shots in one call.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 2
            ...    M 0 1 2
            ... ''')
            >>> s = c.compile_sampler()
            >>> s.sample(shots=1)
            array([[False, False,  True]])
        """
    def copy(
        self,
    ) -> stim.Circuit:
        """Returns a copy of the circuit. An independent circuit with the same contents.

        Examples:
            >>> import stim

            >>> c1 = stim.Circuit("H 0")
            >>> c2 = c1.copy()
            >>> c2 is c1
            False
            >>> c2 == c1
            True
        """
    def detector_error_model(
        self,
        *,
        decompose_errors: bool = False,
        flatten_loops: bool = False,
        allow_gauge_detectors: bool = False,
        approximate_disjoint_errors: float = False,
        ignore_decomposition_failures: bool = False,
        block_decomposition_from_introducing_remnant_edges: bool = False,
    ) -> stim.DetectorErrorModel:
        """Returns a stim.DetectorErrorModel describing the error processes in the circuit.

        Args:
            decompose_errors: Defaults to false. When set to true, the error analysis
                attempts to decompose the components of composite error mechanisms (such
                as depolarization errors) into simpler errors, and suggest this
                decomposition via `stim.target_separator()` between the components. For
                example, in an XZ surface code, single qubit depolarization has a Y
                error term which can be decomposed into simpler X and Z error terms.
                Decomposition fails (causing this method to throw) if it's not possible
                to decompose large errors into simple errors that affect at most two
                detectors.
            flatten_loops: Defaults to false. When set to True, the output will not
                contain any `repeat` blocks. When set to False, the error analysis
                watches for loops in the circuit reaching a periodic steady state with
                respect to the detectors being introduced, the error mechanisms that
                affect them, and the locations of the logical observables. When it
                identifies such a steady state, it outputs a repeat block. This is
                massively more efficient than flattening for circuits that contain
                loops, but creates a more complex output.
            allow_gauge_detectors: Defaults to false. When set to false, the error
                analysis verifies that detectors in the circuit are actually
                deterministic under noiseless execution of the circuit. When set to
                True, these detectors are instead considered to be part of degrees
                freedom that can be removed from the error model. For example, if
                detectors D1 and D3 both anti-commute with a reset, then the error model
                has a gauge `error(0.5) D1 D3`. When gauges are identified, one of the
                involved detectors is removed from the system using Gaussian
                elimination.

                Note that logical observables are still verified to be deterministic,
                even if this option is set.
            approximate_disjoint_errors: Defaults to false. When set to false, composite
                error mechanisms with disjoint components (such as
                `PAULI_CHANNEL_1(0.1, 0.2, 0.0)`) can cause the error analysis to throw
                exceptions (because detector error models can only contain independent
                error mechanisms). When set to true, the probabilities of the disjoint
                cases are instead assumed to be independent probabilities. For example,
                a `PAULI_CHANNEL_1(0.1, 0.2, 0.0)` becomes equivalent to an
                `X_ERROR(0.1)` followed by a `Z_ERROR(0.2)`. This assumption is an
                approximation, but it is a good approximation for small probabilities.

                This argument can also be set to a probability between 0 and 1, setting
                a threshold below which the approximation is acceptable. Any error
                mechanisms that have a component probability above the threshold will
                cause an exception to be thrown.
            ignore_decomposition_failures: Defaults to False.
                When this is set to True, circuit errors that fail to decompose into
                graphlike detector error model errors no longer cause the conversion
                process to abort. Instead, the undecomposed error is inserted into the
                output. Whatever tool the detector error model is then given to is
                responsible for dealing with the undecomposed errors (e.g. a tool may
                choose to simply ignore them).

                Irrelevant unless decompose_errors=True.
            block_decomposition_from_introducing_remnant_edges: Defaults to False.
                Requires that both A B and C D be present elsewhere in the detector
                error model in order to decompose A B C D into A B ^ C D. Normally, only
                one of A B or C D needs to appear to allow this decomposition.

                Remnant edges can be a useful feature for ensuring decomposition
                succeeds, but they can also reduce the effective code distance by giving
                the decoder single edges that actually represent multiple errors in the
                circuit (resulting in the decoder making misinformed choices when
                decoding).

                Irrelevant unless decompose_errors=True.

        Examples:
            >>> import stim

            >>> stim.Circuit('''
            ...     X_ERROR(0.125) 0
            ...     X_ERROR(0.25) 1
            ...     CORRELATED_ERROR(0.375) X0 X1
            ...     M 0 1
            ...     DETECTOR rec[-2]
            ...     DETECTOR rec[-1]
            ... ''').detector_error_model()
            stim.DetectorErrorModel('''
                error(0.125) D0
                error(0.375) D0 D1
                error(0.25) D1
            ''')
        """
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["timeline-text"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["timeline-svg"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["timeline-3d"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["timeline-3d-html"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-svg"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-3d"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-3d-html"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["detector-slice-text"]',
        tick: int,
        filter_coords: Optional[Iterable[Iterable[float]]] = None,
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["detector-slice-svg"]',
        tick: int,
        filter_coords: Optional[Iterable[Iterable[float]]] = None,
    ) -> 'stim._DiagramHelper':
        pass
    def diagram(
        self,
        *,
        type: str,
        tick: Optional[int] = None,
        filter_coords: Optional[Iterable[Iterable[float]]] = None,
    ) -> 'stim._DiagramHelper':
        """Returns a diagram of the circuit, from a variety of options.

        Args:
            type: The type of diagram. Available types are:
                "timeline-text" (default): An ASCII diagram of the
                    operations applied by the circuit over time. Includes
                    annotations showing the measurement record index that
                    each measurement writes to, and the measurements used
                    by detectors.
                "timeline-svg": An SVG image of the operations applied by
                    the circuit over time. Includes annotations showing the
                    measurement record index that each measurement writes
                    to, and the measurements used by detectors.
                "timeline-3d": A 3d model, in GLTF format, of the operations
                    applied by the circuit over time.
                "timeline-3d-html": Same 3d model as 'timeline-3d' but
                    embedded into an HTML web page containing an interactive
                    THREE.js viewer for the 3d model.
                "detector-slice-text": An ASCII diagram of the stabilizers
                    that detectors declared by the circuit correspond to
                    during the TICK instruction identified by the `tick`
                    argument.
                "detector-slice-svg": An SVG image of the stabilizers
                    that detectors declared by the circuit correspond to
                    during the TICK instruction identified by the `tick`
                    argument. For example, a detector slice diagram of a
                    CSS surface code circuit during the TICK between a
                    measurement layer and a reset layer will produce the
                    usual diagram of a surface code.

                    Uses the Pauli color convention XYZ=RGB.
                "match-graph-svg": An SVG image of the match graph extracted
                    from the circuit by stim.Circuit.detector_error_model.
                "match-graph-3d": An 3D model of the match graph extracted
                    from the circuit by stim.Circuit.detector_error_model.
                "match-graph-3d-html": Same 3d model as 'match-graph-3d' but
                    embedded into an HTML web page containing an interactive
                    THREE.js viewer for the 3d model.
            tick: Required for detector slice diagrams. Specifies which TICK
                instruction to slice at. Note that the first TICK in the
                circuit is tick=1. The value tick=0 refers to the very start
                of the circuit.
            filter_coords: A set of acceptable coordinate prefixes. For
                detector slice diagrams, only detectors whose coordinates
                begin with one of these filters will be included.

        Returns:
            An object whose `__str__` method returns the diagram, so that
            writing the diagram to a file works correctly. The returned
            object may also define methods such as `_repr_html_`, so that
            ipython notebooks recognize it can be shown using a specialized
            viewer instead of as raw text.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...     H 0
            ...     CNOT 0 1 1 2
            ... ''')

            >>> print(circuit.diagram(type="timeline-text"))
            q0: -H-@---
                   |
            q1: ---X-@-
                     |
            q2: -----X-

            >>> circuit = stim.Circuit('''
            ...     H 0
            ...     CNOT 0 1
            ...     TICK
            ...     M 0 1
            ...     DETECTOR rec[-1] rec[-2]
            ... ''')

            >>> print(circuit.diagram(type="detector-slice-text", tick=1))
            q0: -Z:D0-
                 |
            q1: -Z:D0-
        """
    def explain_detector_error_model_errors(
        self,
        *,
        dem_filter: object = None,
        reduce_to_one_representative_error: bool = False,
    ) -> List[stim.ExplainedError]:
        """Explains how detector error model errors are produced by circuit errors.

        Args:
            dem_filter: Defaults to None (unused). When used, the output will only
                contain detector error model errors that appear in the given
                `stim.DetectorErrorModel`. Any error mechanisms from the detector error
                model that can't be reproduced using one error from the circuit will
                also be included in the result, but with an empty list of associated
                circuit error mechanisms.
            reduce_to_one_representative_error: Defaults to False. When True, the items
                in the result will contain at most one circuit error mechanism.

        Returns:
            A `List[stim.ExplainedError]` (see `stim.ExplainedError` for more
            information). Each item in the list describes how a detector error model
            error can be produced by individual circuit errors.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...     # Create Bell pair.
            ...     H 0
            ...     CNOT 0 1
            ...
            ...     # Noise.
            ...     DEPOLARIZE1(0.01) 0
            ...
            ...     # Bell basis measurement.
            ...     CNOT 0 1
            ...     H 0
            ...     M 0 1
            ...
            ...     # Both measurements should be False under noiseless execution.
            ...     DETECTOR rec[-1]
            ...     DETECTOR rec[-2]
            ... ''')
            >>> explained_errors = circuit.explain_detector_error_model_errors(
            ...     dem_filter=stim.DetectorErrorModel('error(1) D0 D1'),
            ...     reduce_to_one_representative_error=True,
            ... )
            >>> print(explained_errors[0].circuit_error_locations[0])
            CircuitErrorLocation {
                flipped_pauli_product: Y0
                Circuit location stack trace:
                    (after 0 TICKs)
                    at instruction #3 (DEPOLARIZE1) in the circuit
                    at target #1 of the instruction
                    resolving to DEPOLARIZE1(0.01) 0
            }
        """
    def flattened(
        self,
    ) -> stim.Circuit:
        """Creates an equivalent circuit without REPEAT or SHIFT_COORDS.

        Returns:
            A `stim.Circuit` with the same instructions in the same order,
            but with loops flattened into repeated instructions and with
            all coordinate shifts inlined.

        Examples:
            >>> import stim
            >>> stim.Circuit('''
            ...     REPEAT 5 {
            ...         MR 0 1
            ...         DETECTOR(0, 0) rec[-2]
            ...         DETECTOR(1, 0) rec[-1]
            ...         SHIFT_COORDS(0, 1)
            ...     }
            ... ''').flattened()
            stim.Circuit('''
                MR 0 1
                DETECTOR(0, 0) rec[-2]
                DETECTOR(1, 0) rec[-1]
                MR 0 1
                DETECTOR(0, 1) rec[-2]
                DETECTOR(1, 1) rec[-1]
                MR 0 1
                DETECTOR(0, 2) rec[-2]
                DETECTOR(1, 2) rec[-1]
                MR 0 1
                DETECTOR(0, 3) rec[-2]
                DETECTOR(1, 3) rec[-1]
                MR 0 1
                DETECTOR(0, 4) rec[-2]
                DETECTOR(1, 4) rec[-1]
            ''')
        """
    def flattened_operations(
        self,
    ) -> list:
        """[DEPRECATED]

        Returns a list of tuples encoding the contents of the circuit.
        Instead of this method, use `for instruction in circuit` or, to
        avoid REPEAT blocks, `for instruction in circuit.flattened()`.

        Examples:
            >>> import stim
            >>> stim.Circuit('''
            ...    H 0
            ...    X_ERROR(0.125) 1
            ...    M 0 !1
            ... ''').flattened_operations()
            [('H', [0], 0), ('X_ERROR', [1], 0.125), ('M', [0, ('inv', 1)], 0)]

            >>> stim.Circuit('''
            ...    REPEAT 2 {
            ...        H 6
            ...    }
            ... ''').flattened_operations()
            [('H', [6], 0), ('H', [6], 0)]
        """
    @staticmethod
    def from_file(
        file: Union[io.TextIOBase, str, pathlib.Path],
    ) -> stim.Circuit:
        """Reads a stim circuit from a file.

        The file format is defined at
        https://github.com/quantumlib/Stim/blob/main/doc/file_format_stim_circuit.md

        Args:
            file: A file path or open file object to read from.

        Returns:
            The circuit parsed from the file.

        Examples:
            >>> import stim
            >>> import tempfile

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         print('H 5', file=f)
            ...     circuit = stim.Circuit.from_file(path)
            >>> circuit
            stim.Circuit('''
                H 5
            ''')

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         print('CNOT 4 5', file=f)
            ...     with open(path) as f:
            ...         circuit = stim.Circuit.from_file(path)
            >>> circuit
            stim.Circuit('''
                CX 4 5
            ''')
        """
    @staticmethod
    def generated(
        code_task: str,
        *,
        distance: int,
        rounds: int,
        after_clifford_depolarization: float = 0.0,
        before_round_data_depolarization: float = 0.0,
        before_measure_flip_probability: float = 0.0,
        after_reset_flip_probability: float = 0.0,
    ) -> stim.Circuit:
        """Generates common circuits.

        The generated circuits can include configurable noise.

        The generated circuits include DETECTOR and OBSERVABLE_INCLUDE annotations so
        that their detection events and logical observables can be sampled.

        The generated circuits include TICK annotations to mark the progression of time.
        (E.g. so that converting them using `stimcirq.stim_circuit_to_cirq_circuit` will
        produce a `cirq.Circuit` with the intended moment structure.)

        Args:
            code_task: A string identifying the type of circuit to generate. Available
                code tasks are:
                    - "repetition_code:memory"
                    - "surface_code:rotated_memory_x"
                    - "surface_code:rotated_memory_z"
                    - "surface_code:unrotated_memory_x"
                    - "surface_code:unrotated_memory_z"
                    - "color_code:memory_xyz"
            distance: The desired code distance of the generated circuit. The code
                distance is the minimum number of physical errors needed to cause a
                logical error. This parameter indirectly determines how many qubits the
                generated circuit uses.
            rounds: How many times the measurement qubits in the generated circuit will
                be measured. Indirectly determines the duration of the generated
                circuit.
            after_clifford_depolarization: Defaults to 0. The probability (p) of
                `DEPOLARIZE1(p)` operations to add after every single-qubit Clifford
                operation and `DEPOLARIZE2(p)` operations to add after every two-qubit
                Clifford operation. The after-Clifford depolarizing operations are only
                included if this probability is not 0.
            before_round_data_depolarization: Defaults to 0. The probability (p) of
                `DEPOLARIZE1(p)` operations to apply to every data qubit at the start of
                a round of stabilizer measurements. The start-of-round depolarizing
                operations are only included if this probability is not 0.
            before_measure_flip_probability: Defaults to 0. The probability (p) of
                `X_ERROR(p)` operations applied to qubits before each measurement (X
                basis measurements use `Z_ERROR(p)` instead). The before-measurement
                flips are only included if this probability is not 0.
            after_reset_flip_probability: Defaults to 0. The probability (p) of
                `X_ERROR(p)` operations applied to qubits after each reset (X basis
                resets use `Z_ERROR(p)` instead). The after-reset flips are only
                included if this probability is not 0.

        Returns:
            The generated circuit.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit.generated(
            ...     "repetition_code:memory",
            ...     distance=4,
            ...     rounds=10000,
            ...     after_clifford_depolarization=0.0125)
            >>> print(circuit)
            R 0 1 2 3 4 5 6
            TICK
            CX 0 1 2 3 4 5
            DEPOLARIZE2(0.0125) 0 1 2 3 4 5
            TICK
            CX 2 1 4 3 6 5
            DEPOLARIZE2(0.0125) 2 1 4 3 6 5
            TICK
            MR 1 3 5
            DETECTOR(1, 0) rec[-3]
            DETECTOR(3, 0) rec[-2]
            DETECTOR(5, 0) rec[-1]
            REPEAT 9999 {
                TICK
                CX 0 1 2 3 4 5
                DEPOLARIZE2(0.0125) 0 1 2 3 4 5
                TICK
                CX 2 1 4 3 6 5
                DEPOLARIZE2(0.0125) 2 1 4 3 6 5
                TICK
                MR 1 3 5
                SHIFT_COORDS(0, 1)
                DETECTOR(1, 0) rec[-3] rec[-6]
                DETECTOR(3, 0) rec[-2] rec[-5]
                DETECTOR(5, 0) rec[-1] rec[-4]
            }
            M 0 2 4 6
            DETECTOR(1, 1) rec[-3] rec[-4] rec[-7]
            DETECTOR(3, 1) rec[-2] rec[-3] rec[-6]
            DETECTOR(5, 1) rec[-1] rec[-2] rec[-5]
            OBSERVABLE_INCLUDE(0) rec[-1]
        """
    def get_detector_coordinates(
        self,
        only: object = None,
    ) -> Dict[int, List[float]]:
        """Returns the coordinate metadata of detectors in the circuit.

        Args:
            only: Defaults to None (meaning include all detectors). A list of detector
                indices to include in the result. Detector indices beyond the end of the
                detector error model of the circuit cause an error.

        Returns:
            A dictionary mapping integers (detector indices) to lists of floats
            (coordinates).

            Detectors with no specified coordinate data are mapped to an empty tuple.
            If `only` is specified, then `set(result.keys()) == set(only)`.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...    M 0
            ...    DETECTOR rec[-1]
            ...    DETECTOR(1, 2, 3) rec[-1]
            ...    REPEAT 3 {
            ...        DETECTOR(42) rec[-1]
            ...        SHIFT_COORDS(100)
            ...    }
            ... ''')
            >>> circuit.get_detector_coordinates()
            {0: [], 1: [1.0, 2.0, 3.0], 2: [42.0], 3: [142.0], 4: [242.0]}
            >>> circuit.get_detector_coordinates(only=[1])
            {1: [1.0, 2.0, 3.0]}
        """
    def get_final_qubit_coordinates(
        self,
    ) -> Dict[int, List[float]]:
        """Returns the coordinate metadata of qubits in the circuit.

        If a qubit's coordinates are specified multiple times, only the last specified
        coordinates are returned.

        Returns:
            A dictionary mapping qubit indices (integers) to coordinates (lists of
            floats). Qubits that never had their coordinates specified are not included
            in the result.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...    QUBIT_COORDS(1, 2, 3) 1
            ... ''')
            >>> circuit.get_final_qubit_coordinates()
            {1: [1.0, 2.0, 3.0]}
        """
    @property
    def num_detectors(
        self,
    ) -> int:
        """Counts the number of bits produced when sampling the circuit's detectors.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    M 0
            ...    DETECTOR rec[-1]
            ...    REPEAT 100 {
            ...        M 0 1 2
            ...        DETECTOR rec[-1]
            ...        DETECTOR rec[-2]
            ...    }
            ... ''')
            >>> c.num_detectors
            201
        """
    @property
    def num_measurements(
        self,
    ) -> int:
        """Counts the number of bits produced when sampling the circuit's measurements.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    M 0
            ...    REPEAT 100 {
            ...        M 0 1
            ...    }
            ... ''')
            >>> c.num_measurements
            201
        """
    @property
    def num_observables(
        self,
    ) -> int:
        """Counts the number of logical observables defined by the circuit.

        This is one more than the largest index that appears as an argument to an
        OBSERVABLE_INCLUDE instruction.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    M 0
            ...    OBSERVABLE_INCLUDE(2) rec[-1]
            ...    OBSERVABLE_INCLUDE(5) rec[-1]
            ... ''')
            >>> c.num_observables
            6
        """
    @property
    def num_qubits(
        self,
    ) -> int:
        """Counts the number of qubits used when simulating the circuit.

        This is always one more than the largest qubit index used by the circuit.

        Examples:
            >>> import stim
            >>> stim.Circuit('''
            ...    X 0
            ...    M 0 1
            ... ''').num_qubits
            2
            >>> stim.Circuit('''
            ...    X 0
            ...    M 0 1
            ...    H 100
            ... ''').num_qubits
            101
        """
    @property
    def num_sweep_bits(
        self,
    ) -> int:
        """Returns the number of sweep bits needed to completely configure the circuit.

        This is always one more than the largest sweep bit index used by the circuit.

        Examples:
            >>> import stim
            >>> stim.Circuit('''
            ...    CX sweep[2] 0
            ... ''').num_sweep_bits
            3
            >>> stim.Circuit('''
            ...    CZ sweep[5] 0
            ...    CX sweep[2] 0
            ... ''').num_sweep_bits
            6
        """
    def search_for_undetectable_logical_errors(
        self,
        *,
        dont_explore_detection_event_sets_with_size_above: int,
        dont_explore_edges_with_degree_above: int,
        dont_explore_edges_increasing_symptom_degree: bool,
        canonicalize_circuit_errors: bool = False,
    ) -> List[stim.ExplainedError]:
        """Searches for small sets of errors that form an undetectable logical error.

        THIS IS A HEURISTIC METHOD. It does not guarantee that it will find errors of
        particular sizes, or with particular properties. The errors it finds are a
        tangled combination of the truncation parameters you specify, internal
        optimizations which are correct when not truncating, and minutia of the circuit
        being considered.

        If you want a well behaved method that does provide guarantees of finding errors
        of a particular type, use `stim.Circuit.shortest_graphlike_error`. This method
        is more thorough than that (assuming you don't truncate so hard you omit
        graphlike edges), but exactly how thorough is difficult to describe. It's also
        not guaranteed that the behavior of this method will not be changed in the
        future in a way that permutes which logical errors are found and which are
        missed.

        This search method considers hyper errors, so it has worst case exponential
        runtime. It is important to carefully consider the arguments you are providing,
        which truncate the search space and trade cost for quality.

        The search progresses by starting from each error that crosses a logical
        observable, noting which detection events each error produces, and then
        iteratively adding in errors touching those detection events attempting to
        cancel out the detection event with the lowest index.

        Beware that the choice of logical observable can interact with the truncation
        options. Using different observables can change whether or not the search
        succeeds, even if those observables are equal modulo the stabilizers of the
        code. This is because the edges crossing logical observables are used as
        starting points for the search, and starting from different places along a path
        will result in different numbers of symptoms in intermediate states as the
        search progresses. For example, if the logical observable is next to a boundary,
        then the starting edges are likely boundary edges (degree 1) with 'room to
        grow', whereas if the observable was running through the bulk then the starting
        edges will have degree at least 2.

        Args:
            dont_explore_detection_event_sets_with_size_above: Truncates the search
                space by refusing to cross an edge (i.e. add an error) when doing so
                would produce an intermediate state that has more detection events than
                this limit.
            dont_explore_edges_with_degree_above: Truncates the search space by refusing
                to consider errors that cause a lot of detection events. For example,
                you may only want to consider graphlike errors which have two or fewer
                detection events.
            dont_explore_edges_increasing_symptom_degree: Truncates the search space by
                refusing to cross an edge (i.e. add an error) when doing so would
                produce an intermediate state that has more detection events that the
                previous intermediate state. This massively improves the efficiency of
                the search because instead of, for example, exploring all n^4 possible
                detection event sets with 4 symptoms, the search will attempt to cancel
                out symptoms one by one.
            canonicalize_circuit_errors: Whether or not to use one representative for
                equal-symptom circuit errors.

                False (default): Each DEM error lists every possible circuit error that
                    single handedly produces those symptoms as a potential match. This
                    is verbose but gives complete information.
                True: Each DEM error is matched with one possible circuit error that
                    single handedly produces those symptoms, with a preference towards
                    errors that are simpler (e.g. apply Paulis to fewer qubits). This
                    discards mostly-redundant information about different ways to
                    produce the same symptoms in order to give a succinct result.

        Returns:
            A list of error mechanisms that cause an undetected logical error.

            Each entry in the list is a `stim.ExplainedError` detailing the location
            and effects of a single physical error. The effects of the entire list
            combine to produce a logical frame change without any detection events.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit.generated(
            ...     "surface_code:rotated_memory_x",
            ...     rounds=5,
            ...     distance=5,
            ...     after_clifford_depolarization=0.001)
            >>> print(len(circuit.search_for_undetectable_logical_errors(
            ...     dont_explore_detection_event_sets_with_size_above=4,
            ...     dont_explore_edges_with_degree_above=4,
            ...     dont_explore_edges_increasing_symptom_degree=True,
            ... )))
            5
        """
    def shortest_graphlike_error(
        self,
        *,
        ignore_ungraphlike_errors: bool = True,
        canonicalize_circuit_errors: bool = False,
    ) -> List[stim.ExplainedError]:
        """Finds a minimum set of graphlike errors to produce an undetected logical error.

        A "graphlike error" is an error that creates at most two detection events
        (causes a change in the parity of the measurement sets of at most two DETECTOR
        annotations).

        Note that this method does not pay attention to error probabilities (other than
        ignoring errors with probability 0). It searches for a logical error with the
        minimum *number* of physical errors, not the maximum probability of those
        physical errors all occurring.

        This method works by converting the circuit into a `stim.DetectorErrorModel`
        using `circuit.detector_error_model(...)`, computing the shortest graphlike
        error of the error model, and then converting the physical errors making up that
        logical error back into representative circuit errors.

        Args:
            ignore_ungraphlike_errors:
                False: Attempt to decompose any ungraphlike errors in the circuit into
                    graphlike parts. If this fails, raise an exception instead of
                    continuing.

                    Note: in some cases, graphlike errors only appear as parts of
                    decomposed ungraphlike errors. This can produce a result that lists
                    DEM errors with zero matching circuit errors, because the only way
                    to achieve those errors is by combining a decomposed error with a
                    graphlike error. As a result, when using this option it is NOT
                    guaranteed that the length of the result is an upper bound on the
                    true code distance. That is only the case if every item in the
                    result lists at least one matching circuit error.
                True (default): Ungraphlike errors are simply skipped as if they weren't
                    present, even if they could become graphlike if decomposed. This
                    guarantees the length of the result is an upper bound on the true
                    code distance.
            canonicalize_circuit_errors: Whether or not to use one representative for
                equal-symptom circuit errors.

                False (default): Each DEM error lists every possible circuit error that
                    single handedly produces those symptoms as a potential match. This
                    is verbose but gives complete information.
                True: Each DEM error is matched with one possible circuit error that
                    single handedly produces those symptoms, with a preference towards
                    errors that are simpler (e.g. apply Paulis to fewer qubits). This
                    discards mostly-redundant information about different ways to
                    produce the same symptoms in order to give a succinct result.

        Returns:
            A list of error mechanisms that cause an undetected logical error.

            Each entry in the list is a `stim.ExplainedError` detailing the location
            and effects of a single physical error. The effects of the entire list
            combine to produce a logical frame change without any detection events.

        Examples:
            >>> import stim

            >>> circuit = stim.Circuit.generated(
            ...     "repetition_code:memory",
            ...     rounds=10,
            ...     distance=7,
            ...     before_round_data_depolarization=0.01)
            >>> len(circuit.shortest_graphlike_error())
            7
        """
    def to_file(
        self,
        file: Union[io.TextIOBase, str, pathlib.Path],
    ) -> None:
        """Writes the stim circuit to a file.

        The file format is defined at
        https://github.com/quantumlib/Stim/blob/main/doc/file_format_stim_circuit.md

        Args:
            file: A file path or an open file to write to.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> c = stim.Circuit('H 5\nX 0')

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         c.to_file(f)
            ...     with open(path) as f:
            ...         contents = f.read()
            >>> contents
            'H 5\nX 0\n'

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     c.to_file(path)
            ...     with open(path) as f:
            ...         contents = f.read()
            >>> contents
            'H 5\nX 0\n'
        """
    def without_noise(
        self,
    ) -> stim.Circuit:
        """Returns a copy of the circuit with all noise processes removed.

        Pure noise instructions, such as X_ERROR and DEPOLARIZE2, are not
        included in the result.

        Noisy measurement instructions, like `M(0.001)`, have their noise
        parameter removed.

        Returns:
            A `stim.Circuit` with the same instructions except all noise
            processes have been removed.

        Examples:
            >>> import stim
            >>> stim.Circuit('''
            ...     X_ERROR(0.25) 0
            ...     CNOT 0 1
            ...     M(0.125) 0
            ... ''').without_noise()
            stim.Circuit('''
                CX 0 1
                M 0
            ''')
        """
class CircuitErrorLocation:
    """Describes the location of an error mechanism from a stim circuit.
    """
    def __init__(
        self,
        *,
        tick_offset: int,
        flipped_pauli_product: List[stim.GateTargetWithCoords],
        flipped_measurement: object,
        instruction_targets: stim.CircuitTargetsInsideInstruction,
        stack_frames: List[stim.CircuitErrorLocationStackFrame],
    ) -> None:
        """Creates a stim.CircuitErrorLocation.
        """
    @property
    def flipped_measurement(
        self,
    ) -> Optional[stim.FlippedMeasurement]:
        """The measurement that was flipped by the error mechanism.
        If the error isn't a measurement error, this will be None.
        """
    @property
    def flipped_pauli_product(
        self,
    ) -> List[stim.GateTargetWithCoords]:
        """The Pauli errors that the error mechanism applied to qubits.
        When the error is a measurement error, this will be an empty list.
        """
    @property
    def instruction_targets(
        self,
    ) -> stim.CircuitTargetsInsideInstruction:
        """Within the error instruction, which may have hundreds of
        targets, which specific targets were being executed to
        produce the error.
        """
    @property
    def stack_frames(
        self,
    ) -> List[stim.CircuitErrorLocationStackFrame]:
        """Where in the circuit's execution does the error mechanism occur,
        accounting for things like nested loops that iterate multiple times.
        """
    @property
    def tick_offset(
        self,
    ) -> int:
        """The number of TICKs that executed before the error mechanism being discussed,
        including TICKs that occurred multiple times during loops.
        """
class CircuitErrorLocationStackFrame:
    """Describes the location of an instruction being executed within a
    circuit or loop, distinguishing between separate loop iterations.

    The full location of an instruction is a list of these frames,
    drilling down from the top level circuit to the inner-most loop
    that the instruction is within.
    """
    def __init__(
        self,
        *,
        instruction_offset: int,
        iteration_index: int,
        instruction_repetitions_arg: int,
    ) -> None:
        """Creates a stim.CircuitErrorLocationStackFrame.
        """
    @property
    def instruction_offset(
        self,
    ) -> int:
        """The index of the instruction within the circuit, or within the
        instruction's parent REPEAT block. This is slightly different
        from the line number, because blank lines and commented lines
        don't count and also because the offset of the first instruction
        is 0 instead of 1.
        """
    @property
    def instruction_repetitions_arg(
        self,
    ) -> int:
        """If the instruction being referred to is a REPEAT block,
        this is the repetition count of that REPEAT block. Otherwise
        this field defaults to 0.
        """
    @property
    def iteration_index(
        self,
    ) -> int:
        """Disambiguates which iteration of the loop containing this instruction
        is being referred to. If the instruction isn't in a REPEAT block, this
        field defaults to 0.
        """
class CircuitInstruction:
    """An instruction, like `H 0 1` or `CNOT rec[-1] 5`, from a circuit.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit('''
        ...     H 0
        ...     M 0 1
        ...     X_ERROR(0.125) 5
        ... ''')
        >>> circuit[0]
        stim.CircuitInstruction('H', [stim.GateTarget(0)], [])
        >>> circuit[1]
        stim.CircuitInstruction('M', [stim.GateTarget(0), stim.GateTarget(1)], [])
        >>> circuit[2]
        stim.CircuitInstruction('X_ERROR', [stim.GateTarget(5)], [0.125])
    """
    def __eq__(
        self,
        arg0: stim.CircuitInstruction,
    ) -> bool:
        """Determines if two `stim.CircuitInstruction`s are identical.
        """
    def __init__(
        self,
        name: str,
        targets: List[object],
        gate_args: List[float] = (),
    ) -> None:
        """Initializes a `stim.CircuitInstruction`.

        Args:
            name: The name of the instruction being applied.
            targets: The targets the instruction is being applied to. These can be raw
                values like `0` and `stim.target_rec(-1)`, or instances of
                `stim.GateTarget`.
            gate_args: The sequence of numeric arguments parameterizing a gate. For
                noise gates this is their probabilities. For `OBSERVABLE_INCLUDE`
                instructions it's the index of the logical observable to affect.
        """
    def __ne__(
        self,
        arg0: stim.CircuitInstruction,
    ) -> bool:
        """Determines if two `stim.CircuitInstruction`s are different.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.CircuitInstruction`.
        """
    def __str__(
        self,
    ) -> str:
        """Returns a text description of the instruction as a stim circuit file line.
        """
    def gate_args_copy(
        self,
    ) -> List[float]:
        """Returns the gate's arguments (numbers parameterizing the instruction).

        For noisy gates this typically a list of probabilities.
        For OBSERVABLE_INCLUDE it's a singleton list containing the logical observable
        index.
        """
    @property
    def name(
        self,
    ) -> str:
        """The name of the instruction (e.g. `H` or `X_ERROR` or `DETECTOR`).
        """
    def targets_copy(
        self,
    ) -> List[stim.GateTarget]:
        """Returns a copy of the targets of the instruction.
        """
class CircuitRepeatBlock:
    """A REPEAT block from a circuit.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit('''
        ...     H 0
        ...     REPEAT 5 {
        ...         CX 0 1
        ...         CZ 1 2
        ...     }
        ... ''')
        >>> repeat_block = circuit[1]
        >>> repeat_block.repeat_count
        5
        >>> repeat_block.body_copy()
        stim.Circuit('''
            CX 0 1
            CZ 1 2
        ''')
    """
    def __eq__(
        self,
        arg0: stim.CircuitRepeatBlock,
    ) -> bool:
        """Determines if two `stim.CircuitRepeatBlock`s are identical.
        """
    def __init__(
        self,
        repeat_count: int,
        body: stim.Circuit,
    ) -> None:
        """Initializes a `stim.CircuitRepeatBlock`.

        Args:
            repeat_count: The number of times to repeat the block.
            body: The body of the block, as a circuit.
        """
    def __ne__(
        self,
        arg0: stim.CircuitRepeatBlock,
    ) -> bool:
        """Determines if two `stim.CircuitRepeatBlock`s are different.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equivalent `stim.CircuitRepeatBlock`.
        """
    def body_copy(
        self,
    ) -> stim.Circuit:
        """Returns a copy of the body of the repeat block.

        (Making a copy is enforced to make it clear that editing the result won't change
        the block's body.)

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...     H 0
            ...     REPEAT 5 {
            ...         CX 0 1
            ...         CZ 1 2
            ...     }
            ... ''')
            >>> repeat_block = circuit[1]
            >>> repeat_block.body_copy()
            stim.Circuit('''
                CX 0 1
                CZ 1 2
            ''')
        """
    @property
    def repeat_count(
        self,
    ) -> int:
        """The repetition count of the repeat block.

        Examples:
            >>> import stim
            >>> circuit = stim.Circuit('''
            ...     H 0
            ...     REPEAT 5 {
            ...         CX 0 1
            ...         CZ 1 2
            ...     }
            ... ''')
            >>> repeat_block = circuit[1]
            >>> repeat_block.repeat_count
            5
        """
class CircuitTargetsInsideInstruction:
    """Describes a range of targets within a circuit instruction.
    """
    def __init__(
        self,
        *,
        gate: str,
        args: List[float],
        target_range_start: int,
        target_range_end: int,
        targets_in_range: List[stim.GateTargetWithCoords],
    ) -> None:
        """Creates a stim.CircuitTargetsInsideInstruction.
        """
    @property
    def args(
        self,
    ) -> List[float]:
        """Returns parens arguments of the gate / instruction that was being executed.
        """
    @property
    def gate(
        self,
    ) -> Optional[str]:
        """Returns the name of the gate / instruction that was being executed.
        """
    @property
    def target_range_end(
        self,
    ) -> int:
        """Returns the exclusive end of the range of targets that were executing
        within the gate / instruction.
        """
    @property
    def target_range_start(
        self,
    ) -> int:
        """Returns the inclusive start of the range of targets that were executing
        within the gate / instruction.
        """
    @property
    def targets_in_range(
        self,
    ) -> List[stim.GateTargetWithCoords]:
        """Returns the subset of targets of the gate/instruction that were being executed.

        Includes coordinate data with the targets.
        """
class CompiledDemSampler:
    """A helper class for efficiently sampler from a detector error model.

    Examples:
        >>> import stim
        >>> dem = stim.DetectorErrorModel('''
        ...    error(0) D0
        ...    error(1) D1 D2 L0
        ... ''')
        >>> sampler = dem.compile_sampler()
        >>> det_data, obs_data, err_data = sampler.sample(
        ...     shots=4,
        ...     return_errors=True)
        >>> det_data
        array([[False,  True,  True],
               [False,  True,  True],
               [False,  True,  True],
               [False,  True,  True]])
        >>> obs_data
        array([[ True],
               [ True],
               [ True],
               [ True]])
        >>> err_data
        array([[False,  True],
               [False,  True],
               [False,  True],
               [False,  True]])
    """
    def sample(
        self,
        shots: int,
        *,
        bit_packed: bool = False,
        return_errors: bool = False,
        recorded_errors_to_replay: Optional[np.ndarray] = None,
    ) -> Tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]:
        """Samples the detector error model's error mechanisms to produce sample data.

        Args:
            shots: The number of times to sample from the model.
            bit_packed: Defaults to false.
                False: the returned numpy arrays have dtype=np.bool8.
                True: the returned numpy arrays have dtype=np.uint8 and pack 8 bits into
                    each byte.

                Setting this to True is equivalent to running
                `np.packbits(data, endian='little', axis=1)` on each output value, but
                has the performance benefit of the data never being expanded into an
                unpacked form.
            return_errors: Defaults to False.
                False: the first entry of the returned tuple is None.
                True: the first entry of the returned tuple is a numpy array recording
                which errors were sampled.
            recorded_errors_to_replay: Defaults to None, meaning sample errors randomly.
                If not None, this is expected to be a 2d numpy array specifying which
                errors to apply (e.g. one returned from a previous call to the sample
                method). The array must have dtype=np.bool8 and
                shape=(num_shots, num_errors) or dtype=np.uint8 and
                shape=(num_shots, math.ceil(num_errors / 8)).

        Returns:
            A tuple (detector_data, obs_data, error_data).

            Assuming bit_packed is False and return_errors is True:
                - If error_data[s, k] is True, then the error with index k fired in the
                    shot with index s.
                - If detector_data[s, k] is True, then the detector with index k ended
                    up flipped in the shot with index s.
                - If obs_data[s, k] is True, then the observable with index k ended up
                    flipped in the shot with index s.

            The dtype and shape of the data depends on the arguments:
                if bit_packed:
                    detector_data.shape == (num_shots, num_detectors)
                    detector_data.dtype == np.bool8
                    obs_data.shape == (num_shots, num_observables)
                    obs_data.dtype == np.bool8
                    if return_errors:
                        error_data.shape = (num_shots, num_errors)
                        error_data.dtype = np.bool8
                    else:
                        error_data is None
                else:
                    detector_data.shape == (num_shots, math.ceil(num_detectors / 8))
                    detector_data.dtype == np.uint8
                    obs_data.shape == (num_shots, math.ceil(num_observables / 8))
                    obs_data.dtype == np.uint8
                    if return_errors:
                        error_data.shape = (num_shots, math.ceil(num_errors / 8))
                        error_data.dtype = np.uint8
                    else:
                        error_data is None

            Note that bit packing is done using little endian order on the last axis
            (i.e. like `np.packbits(data, endian='little', axis=1)`).

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> dem = stim.DetectorErrorModel('''
            ...    error(0) D0
            ...    error(1) D1 D2 L0
            ... ''')
            >>> sampler = dem.compile_sampler()

            >>> # Taking samples.
            >>> det_data, obs_data, err_data_not_requested = sampler.sample(shots=4)
            >>> det_data
            array([[False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True]])
            >>> obs_data
            array([[ True],
                   [ True],
                   [ True],
                   [ True]])
            >>> err_data_not_requested is None
            True

            >>> # Recording errors.
            >>> det_data, obs_data, err_data = sampler.sample(
            ...     shots=4,
            ...     return_errors=True)
            >>> det_data
            array([[False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True]])
            >>> obs_data
            array([[ True],
                   [ True],
                   [ True],
                   [ True]])
            >>> err_data
            array([[False,  True],
                   [False,  True],
                   [False,  True],
                   [False,  True]])

            >>> # Bit packing.
            >>> det_data, obs_data, err_data = sampler.sample(
            ...     shots=4,
            ...     return_errors=True,
            ...     bit_packed=True)
            >>> det_data
            array([[6],
                   [6],
                   [6],
                   [6]], dtype=uint8)
            >>> obs_data
            array([[1],
                   [1],
                   [1],
                   [1]], dtype=uint8)
            >>> err_data
            array([[2],
                   [2],
                   [2],
                   [2]], dtype=uint8)

            >>> # Recording and replaying errors.
            >>> noisy_dem = stim.DetectorErrorModel('''
            ...    error(0.125) D0
            ...    error(0.25) D1
            ... ''')
            >>> noisy_sampler = noisy_dem.compile_sampler()
            >>> det_data, obs_data, err_data = noisy_sampler.sample(
            ...     shots=100,
            ...     return_errors=True)
            >>> replay_det_data, replay_obs_data, _ = noisy_sampler.sample(
            ...     shots=100,
            ...     recorded_errors_to_replay=err_data)
            >>> np.array_equal(det_data, replay_det_data)
            True
            >>> np.array_equal(obs_data, replay_obs_data)
            True
        """
    def sample_write(
        self,
        shots: int,
        *,
        det_out_file: Union[None, str, pathlib.Path],
        det_out_format: str = "01",
        obs_out_file: Union[None, str, pathlib.Path],
        obs_out_format: str = "01",
        err_out_file: Union[None, str, pathlib.Path] = None,
        err_out_format: str = "01",
        replay_err_in_file: Union[None, str, pathlib.Path] = None,
        replay_err_in_format: str = "01",
    ) -> None:
        """Samples the detector error model and writes the results to disk.

        Args:
            shots: The number of times to sample from the model.
            det_out_file: Where to write detection event data.
                If None: detection event data is not written.
                If str or pathlib.Path: opens and overwrites the file at the given path.
                NOT IMPLEMENTED: io.IOBase
            det_out_format: The format to write the detection event data in
                (e.g. "01" or "b8").
            obs_out_file: Where to write observable flip data.
                If None: observable flip data is not written.
                If str or pathlib.Path: opens and overwrites the file at the given path.
                NOT IMPLEMENTED: io.IOBase
            obs_out_format: The format to write the observable flip data in
                (e.g. "01" or "b8").
            err_out_file: Where to write errors-that-occurred data.
                If None: errors-that-occurred data is not written.
                If str or pathlib.Path: opens and overwrites the file at the given path.
                NOT IMPLEMENTED: io.IOBase
            err_out_format: The format to write the errors-that-occurred data in
                (e.g. "01" or "b8").
            replay_err_in_file: If this is specified, errors are replayed from data
                instead of generated randomly. The following types are supported:
                - None: errors are generated randomly according to the probabilities
                    in the detector error model.
                - str or pathlib.Path: the file at the given path is opened and
                    errors-to-apply data is read from there.
                - io.IOBase: NOT IMPLEMENTED
            replay_err_in_format: The format to write the errors-that-occurred data in
                (e.g. "01" or "b8").

        Returns:
            Nothing. Results are written to disk.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> import pathlib
            >>> dem = stim.DetectorErrorModel('''
            ...    error(0) D0
            ...    error(0) D1
            ...    error(0) D0
            ...    error(1) D1 D2 L0
            ...    error(0) D0
            ... ''')
            >>> sampler = dem.compile_sampler()
            >>> with tempfile.TemporaryDirectory() as d:
            ...     d = pathlib.Path(d)
            ...     sampler.sample_write(
            ...         shots=1,
            ...         det_out_file=d / 'dets.01',
            ...         det_out_format='01',
            ...         obs_out_file=d / 'obs.01',
            ...         obs_out_format='01',
            ...         err_out_file=d / 'err.hits',
            ...         err_out_format='hits',
            ...     )
            ...     with open(d / 'dets.01') as f:
            ...         assert f.read() == "011\n"
            ...     with open(d / 'obs.01') as f:
            ...         assert f.read() == "1\n"
            ...     with open(d / 'err.hits') as f:
            ...         assert f.read() == "3\n"
        """
class CompiledDetectorSampler:
    """An analyzed stabilizer circuit whose detection events can be sampled quickly.
    """
    def __init__(
        self,
        circuit: stim.Circuit,
        *,
        seed: object = None,
    ) -> None:
        """Creates an object that can sample the detection events from a circuit.

        Args:
            circuit: The circuit to sample from.
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how many
                shots are taken. For example, taking 10 shots and then 90 shots will
                give different results from taking 100 shots in one call.

        Returns:
            An initialized stim.CompiledDetectorSampler.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    H 0
            ...    CNOT 0 1
            ...    X_ERROR(1.0) 0
            ...    M 0 1
            ...    DETECTOR rec[-1] rec[-2]
            ... ''')
            >>> s = c.compile_detector_sampler()
            >>> s.sample(shots=1)
            array([[ True]])
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equivalent `stim.CompiledDetectorSampler`.
        """
    @overload
    def sample(
        self,
        shots: int,
        *,
        prepend_observables: bool = False,
        append_observables: bool = False,
        bit_packed: bool = False,
    ) -> np.ndarray:
        pass
    @overload
    def sample(
        self,
        shots: int,
        *,
        separate_observables: Literal[True],
        bit_packed: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray]:
        pass
    def sample(
        self,
        shots: int,
        *,
        prepend_observables: bool = False,
        append_observables: bool = False,
        separate_observables: bool = False,
        bit_packed: bool = False,
    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """Returns a numpy array containing a batch of detector samples from the circuit.

        The circuit must define the detectors using DETECTOR instructions. Observables
        defined by OBSERVABLE_INCLUDE instructions can also be included in the results
        as honorary detectors.

        Args:
            shots: The number of times to sample every detector in the circuit.
            separate_observables: Defaults to False. When set to True, the return value
                is a (detection_events, observable_flips) tuple instead of a flat
                detection_events array.
            prepend_observables: Defaults to false. When set, observables are included
                with the detectors and are placed at the start of the results.
            append_observables: Defaults to false. When set, observables are included
                with the detectors and are placed at the end of the results.
            bit_packed: Returns a uint8 numpy array with 8 bits per byte, instead of
                a bool8 numpy array with 1 bit per byte. Uses little endian packing.

        Returns:
            A numpy array or tuple of numpy arrays containing the samples.

            if separate_observables=False and bit_packed=False:
                A single numpy array.
                dtype=bool8
                shape=(
                    shots,
                    num_detectors + num_observables * (
                        append_observables + prepend_observables),
                )
                The bit for detection event `m` in shot `s` is at
                    result[s, m]

            if separate_observables=False and bit_packed=True:
                A single numpy array.
                dtype=uint8
                shape=(
                    shots,
                    math.ceil((num_detectors + num_observables * (
                        append_observables + prepend_observables)) / 8),
                )
                The bit for detection event `m` in shot `s` is at
                    (result[s, m // 8] >> (m % 8)) & 1

            if separate_observables=True and bit_packed=False:
                A (dets, obs) tuple.
                dets.dtype=bool8
                dets.shape=(shots, num_detectors)
                obs.dtype=bool8
                obs.shape=(shots, num_observables)
                The bit for detection event `m` in shot `s` is at
                    dets[s, m]
                The bit for observable `m` in shot `s` is at
                    obs[s, m]

            if separate_observables=True and bit_packed=True:
                A (dets, obs) tuple.
                dets.dtype=uint8
                dets.shape=(shots, math.ceil(num_detectors / 8))
                obs.dtype=uint8
                obs.shape=(shots, math.ceil(num_observables / 8))
                The bit for detection event `m` in shot `s` is at
                    (dets[s, m // 8] >> (m % 8)) & 1
                The bit for observable `m` in shot `s` is at
                    (obs[s, m // 8] >> (m % 8)) & 1
        """
    def sample_bit_packed(
        self,
        shots: int,
        *,
        prepend_observables: bool = False,
        append_observables: bool = False,
    ) -> object:
        """[DEPRECATED] Use sampler.sample(..., bit_packed=True) instead.

        Returns a numpy array containing bit packed detector samples from the circuit.

        The circuit must define the detectors using DETECTOR instructions. Observables
        defined by OBSERVABLE_INCLUDE instructions can also be included in the results
        as honorary detectors.

        Args:
            shots: The number of times to sample every detector in the circuit.
            prepend_observables: Defaults to false. When set, observables are included
                with the detectors and are placed at the start of the results.
            append_observables: Defaults to false. When set, observables are included
                with the detectors and are placed at the end of the results.

        Returns:
            A numpy array with `dtype=uint8` and `shape=(shots, n)` where `n` is
            `num_detectors + num_observables*(append_observables+prepend_observables)`.
            The bit for detection event `m` in shot `s` is at
            `result[s, (m // 8)] & 2**(m % 8)`.
        """
    def sample_write(
        self,
        shots: int,
        *,
        filepath: str,
        format: str = '01',
        prepend_observables: bool = False,
        append_observables: bool = False,
        obs_out_filepath: str = None,
        obs_out_format: str = '01',
    ) -> None:
        """Samples detection events from the circuit and writes them to a file.

        Args:
            shots: The number of times to sample every measurement in the circuit.
            filepath: The file to write the results to.
            format: The output format to write the results with.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            obs_out_filepath: Sample observables as part of each shot, and write them to
                this file. This keeps the observable data separate from the detector
                data.
            obs_out_format: If writing the observables to a file, this is the format to
                write them in.

                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            prepend_observables: Sample observables as part of each shot, and put them
                at the start of the detector data.
            append_observables: Sample observables as part of each shot, and put them at
                the end of the detector data.

        Returns:
            None.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> with tempfile.TemporaryDirectory() as d:
            ...     path = f"{d}/tmp.dat"
            ...     c = stim.Circuit('''
            ...         X_ERROR(1) 0
            ...         M 0 1
            ...         DETECTOR rec[-2]
            ...         DETECTOR rec[-1]
            ...     ''')
            ...     c.compile_detector_sampler().sample_write(
            ...         shots=3,
            ...         filepath=path,
            ...         format="dets")
            ...     with open(path) as f:
            ...         print(f.read(), end='')
            shot D0
            shot D0
            shot D0
        """
class CompiledMeasurementSampler:
    """An analyzed stabilizer circuit whose measurements can be sampled quickly.
    """
    def __init__(
        self,
        circuit: stim.Circuit,
        *,
        skip_reference_sample: bool = False,
        seed: object = None,
    ) -> None:
        """Creates a measurement sampler for the given circuit.

        The sampler uses a noiseless reference sample, collected from the circuit using
        stim's Tableau simulator during initialization of the sampler, as a baseline for
        deriving more samples using an error propagation simulator.

        Args:
            circuit: The stim circuit to sample from.
            skip_reference_sample: Defaults to False. When set to True, the reference
                sample used by the sampler is initialized to all-zeroes instead of being
                collected from the circuit. This means that the results returned by the
                sampler are actually whether or not each measurement was *flipped*,
                instead of true measurement results.

                Forcing an all-zero reference sample is useful when you are only
                interested in error propagation and don't want to have to deal with the
                fact that some measurements want to be On when no errors occur. It is
                also useful when you know for sure that the all-zero result is actually
                a possible result from the circuit (under noiseless execution), meaning
                it is a valid reference sample as good as any other. Computing the
                reference sample is the most time consuming and memory intensive part of
                simulating the circuit, so promising that the simulator can safely skip
                that step is an effective optimization.
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how many
                shots are taken. For example, taking 10 shots and then 90 shots will
                give different results from taking 100 shots in one call.

        Returns:
            An initialized stim.CompiledMeasurementSampler.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0   2 3
            ...    M 0 1 2 3
            ... ''')
            >>> s = c.compile_sampler()
            >>> s.sample(shots=1)
            array([[ True, False,  True,  True]])
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.CompiledMeasurementSampler`.
        """
    def sample(
        self,
        shots: int,
        *,
        bit_packed: bool = False,
    ) -> object:
        """Samples a batch of measurement samples from the circuit.

        Args:
            shots: The number of times to sample every measurement in the circuit.
            bit_packed: Returns a uint8 numpy array with 8 bits per byte, instead of
                a bool8 numpy array with 1 bit per byte. Uses little endian packing.

        Returns:
            A numpy array containing the samples.

            If bit_packed=False:
                dtype=bool8
                shape=(shots, circuit.num_measurements)
                The bit for measurement `m` in shot `s` is at
                    result[s, m]
            If bit_packed=True:
                dtype=uint8
                shape=(shots, math.ceil(circuit.num_measurements / 8))
                The bit for measurement `m` in shot `s` is at
                    (result[s, m // 8] >> (m % 8)) & 1

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0   2 3
            ...    M 0 1 2 3
            ... ''')
            >>> s = c.compile_sampler()
            >>> s.sample(shots=1)
            array([[ True, False,  True,  True]])
        """
    def sample_bit_packed(
        self,
        shots: int,
    ) -> object:
        """[DEPRECATED] Use sampler.sample(..., bit_packed=True) instead.

        Samples a bit packed batch of measurement samples from the circuit.

        Args:
            shots: The number of times to sample every measurement in the circuit.

        Returns:
            A numpy array with `dtype=uint8` and
            `shape=(shots, (num_measurements + 7) // 8)`.

            The bit for measurement `m` in shot `s` is at
            `result[s, (m // 8)] & 2**(m % 8)`.

        Examples:
            >>> import stim
            >>> c = stim.Circuit('''
            ...    X 0 1 2 3 4 5 6 7     10
            ...    M 0 1 2 3 4 5 6 7 8 9 10
            ... ''')
            >>> s = c.compile_sampler()
            >>> s.sample_bit_packed(shots=1)
            array([[255,   4]], dtype=uint8)
        """
    def sample_write(
        self,
        shots: int,
        *,
        filepath: str,
        format: str = '01',
    ) -> None:
        """Samples measurements from the circuit and writes them to a file.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> with tempfile.TemporaryDirectory() as d:
            ...     path = f"{d}/tmp.dat"
            ...     c = stim.Circuit('''
            ...         X 0   2 3
            ...         M 0 1 2 3
            ...     ''')
            ...     c.compile_sampler().sample_write(5, filepath=path, format="01")
            ...     with open(path) as f:
            ...         print(f.read(), end='')
            1011
            1011
            1011
            1011
            1011

        Args:
            shots: The number of times to sample every measurement in the circuit.
            filepath: The file to write the results to.
            format: The output format to write the results with.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".

        Returns:
            None.
        """
class CompiledMeasurementsToDetectionEventsConverter:
    """A tool for quickly converting measurements from an analyzed stabilizer circuit into detection events.
    """
    def __init__(
        self,
        circuit: stim.Circuit,
        *,
        skip_reference_sample: bool = False,
    ) -> None:
        """Creates a measurement-to-detection-events converter for the given circuit.

        The converter uses a noiseless reference sample, collected from the circuit
        using stim's Tableau simulator during initialization of the converter, as a
        baseline for determining what the expected value of a detector is.

        Note that the expected behavior of gauge detectors (detectors that are not
        actually deterministic under noiseless execution) can vary depending on the
        reference sample. Stim mitigates this by always generating the same reference
        sample for a given circuit.

        Args:
            circuit: The stim circuit to use for conversions.
            skip_reference_sample: Defaults to False. When set to True, the reference
                sample used by the converter is initialized to all-zeroes instead of
                being collected from the circuit. This should only be used if it's known
                that the all-zeroes sample is actually a possible result from the
                circuit (under noiseless execution).

        Returns:
            An initialized stim.CompiledMeasurementsToDetectionEventsConverter.

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> converter = stim.Circuit('''
            ...    X 0
            ...    M 0
            ...    DETECTOR rec[-1]
            ... ''').compile_m2d_converter()
            >>> converter.convert(
            ...     measurements=np.array([[0], [1]], dtype=np.bool8),
            ...     append_observables=False,
            ... )
            array([[ True],
                   [False]])
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.CompiledMeasurementsToDetectionEventsConverter`.
        """
    @overload
    def convert(
        self,
        *,
        measurements: np.ndarray,
        sweep_bits: Optional[np.ndarray] = None,
        append_observables: bool = False,
        bit_pack_result: bool = False,
    ) -> np.ndarray:
        pass
    @overload
    def convert(
        self,
        *,
        measurements: np.ndarray,
        sweep_bits: Optional[np.ndarray] = None,
        separate_observables: 'Literal[True]',
        append_observables: bool = False,
        bit_pack_result: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray]:
        pass
    def convert(
        self,
        *,
        measurements: np.ndarray,
        sweep_bits: Optional[np.ndarray] = None,
        separate_observables: bool = False,
        append_observables: bool = False,
        bit_pack_result: bool = False,
    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """Converts measurement data into detection event data.

        Args:
            measurements: A numpy array containing measurement data.

                The dtype of the array is used to determine if it is bit packed or not.
                dtype=np.bool8 (unpacked data):
                    shape=(num_shots, circuit.num_measurements)
                dtype=np.uint8 (bit packed data):
                    shape=(num_shots, math.ceil(circuit.num_measurements / 8))
            sweep_bits: Optional. A numpy array containing sweep data for the `sweep[k]`
                controls in the circuit.

                The dtype of the array is used to determine if it is bit packed or not.
                dtype=np.bool8 (unpacked data):
                    shape=(num_shots, circuit.num_sweep_bits)
                dtype=np.uint8 (bit packed data):
                    shape=(num_shots, math.ceil(circuit.num_sweep_bits / 8))
            separate_observables: Defaults to False. When set to True, two numpy arrays
                are returned instead of one, with the second array containing the
                observable flip data.
            append_observables: Defaults to False. When set to True, the observables in
                the circuit are treated as if they were additional detectors. Their
                results are appended to the end of the detection event data.
            bit_pack_result: Defaults to False. When set to True, the returned numpy
                array contains bit packed data (dtype=np.uint8 with 8 bits per item)
                instead of unpacked data (dtype=np.bool8).

        Returns:
            The detection event data and (optionally) observable data. The result is a
            single numpy array if separate_observables is false, otherwise it's a tuple
            of two numpy arrays.

            When returning two numpy arrays, the first array is the detection event data
            and the second is the observable flip data.

            The dtype of the returned arrays is np.bool8 if bit_pack_result is false,
            otherwise they're np.uint8 arrays.

            shape[0] of the array(s) is the number of shots.
            shape[1] of the array(s) is the number of bits per shot (divided by 8 if bit
            packed) (e.g. for just detection event data it would be
            circuit.num_detectors).

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> converter = stim.Circuit('''
            ...    X 0
            ...    M 0 1
            ...    DETECTOR rec[-1]
            ...    DETECTOR rec[-2]
            ...    OBSERVABLE_INCLUDE(0) rec[-2]
            ... ''').compile_m2d_converter()
            >>> dets, obs = converter.convert(
            ...     measurements=np.array([[1, 0],
            ...                            [1, 0],
            ...                            [1, 0],
            ...                            [0, 0],
            ...                            [1, 0]], dtype=np.bool8),
            ...     separate_observables=True,
            ... )
            >>> dets
            array([[False, False],
                   [False, False],
                   [False, False],
                   [False,  True],
                   [False, False]])
            >>> obs
            array([[False],
                   [False],
                   [False],
                   [ True],
                   [False]])
        """
    def convert_file(
        self,
        *,
        measurements_filepath: str,
        measurements_format: str = '01',
        sweep_bits_filepath: str = None,
        sweep_bits_format: str = '01',
        detection_events_filepath: str,
        detection_events_format: str = '01',
        append_observables: bool = False,
        obs_out_filepath: str = None,
        obs_out_format: str = '01',
    ) -> None:
        """Reads measurement data from a file and writes detection events to another file.

        Args:
            measurements_filepath: A file containing measurement data to be converted.
            measurements_format: The format the measurement data is stored in.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            detection_events_filepath: Where to save detection event data to.
            detection_events_format: The format to save the detection event data in.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            sweep_bits_filepath: Defaults to None. A file containing sweep data, or
                None. When specified, sweep data (used for `sweep[k]` controls in the
                circuit, which can vary from shot to shot) will be read from the given
                file. When not specified, all sweep bits default to False and no
                sweep-controlled operations occur.
            sweep_bits_format: The format the sweep data is stored in.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            obs_out_filepath: Sample observables as part of each shot, and write them to
                this file. This keeps the observable data separate from the detector
                data.
            obs_out_format: If writing the observables to a file, this is the format to
                write them in.
                Valid values are "01", "b8", "r8", "hits", "dets", and "ptb64".
                Defaults to "01".
            append_observables: When True, the observables in the circuit are included
                as part of the detection event data. Specifically, they are treated as
                if they were additional detectors at the end of the circuit. When False,
                observable data is not output.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> converter = stim.Circuit('''
            ...    X 0
            ...    M 0
            ...    DETECTOR rec[-1]
            ... ''').compile_m2d_converter()
            >>> with tempfile.TemporaryDirectory() as d:
            ...    with open(f"{d}/measurements.01", "w") as f:
            ...        print("0", file=f)
            ...        print("1", file=f)
            ...    converter.convert_file(
            ...        measurements_filepath=f"{d}/measurements.01",
            ...        detection_events_filepath=f"{d}/detections.01",
            ...        append_observables=False,
            ...    )
            ...    with open(f"{d}/detections.01") as f:
            ...        print(f.read(), end="")
            1
            0
        """
class DemInstruction:
    """An instruction from a detector error model.

    Examples:
        >>> import stim
        >>> model = stim.DetectorErrorModel('''
        ...     error(0.125) D0
        ...     error(0.125) D0 D1 L0
        ...     error(0.125) D1 D2
        ...     error(0.125) D2 D3
        ...     error(0.125) D3
        ... ''')
        >>> instruction = model[0]
        >>> instruction
        stim.DemInstruction('error', [0.125], [stim.target_relative_detector_id(0)])
    """
    def __eq__(
        self,
        arg0: stim.DemInstruction,
    ) -> bool:
        """Determines if two instructions have identical contents.
        """
    def __init__(
        self,
        type: str,
        args: List[float],
        targets: List[object],
    ) -> None:
        """Creates a stim.DemInstruction.

        Args:
            type: The name of the instruction type (e.g. "error" or "shift_detectors").
            args: Numeric values parameterizing the instruction (e.g. the 0.1 in
                "error(0.1)").
            targets: The objects the instruction involves (e.g. the "D0" and "L1" in
                "error(0.1) D0 L1").

        Examples:
            >>> import stim
            >>> instruction = stim.DemInstruction(
            ...     'error',
            ...     [0.125],
            ...     [stim.target_relative_detector_id(5)])
            >>> print(instruction)
            error(0.125) D5
        """
    def __ne__(
        self,
        arg0: stim.DemInstruction,
    ) -> bool:
        """Determines if two instructions have non-identical contents.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.DetectorErrorModel`.
        """
    def __str__(
        self,
    ) -> str:
        """Returns detector error model (.dem) instructions (that can be parsed by stim) for the model.
        """
    def args_copy(
        self,
    ) -> List[float]:
        """Returns a copy of the list of numbers parameterizing the instruction (e.g. the probability of an error).
        """
    def targets_copy(
        self,
    ) -> List[Union[int, stim.DemTarget]]:
        """Returns a copy of the instruction's targets.

        (Making a copy is enforced to make it clear that editing the result won't change
        the instruction's targets.)
        """
    @property
    def type(
        self,
    ) -> str:
        """The name of the instruction type (e.g. "error" or "shift_detectors").
        """
class DemRepeatBlock:
    """A repeat block from a detector error model.

    Examples:
        >>> import stim
        >>> model = stim.DetectorErrorModel('''
        ...     repeat 100 {
        ...         error(0.125) D0 D1
        ...         shift_detectors 1
        ...     }
        ... ''')
        >>> model[0]
        stim.DemRepeatBlock(100, stim.DetectorErrorModel('''
            error(0.125) D0 D1
            shift_detectors 1
        '''))
    """
    def __eq__(
        self,
        arg0: stim.DemRepeatBlock,
    ) -> bool:
        """Determines if two repeat blocks are identical.
        """
    def __init__(
        self,
        repeat_count: int,
        block: stim.DetectorErrorModel,
    ) -> None:
        """Creates a stim.DemRepeatBlock.

        Args:
            repeat_count: The number of times the repeat block's body is supposed to
                execute.
            block: The body of the repeat block as a DetectorErrorModel containing the
                instructions to repeat.

        Examples:
            >>> import stim
            >>> repeat_block = stim.DemRepeatBlock(100, stim.DetectorErrorModel('''
            ...     error(0.125) D0 D1
            ...     shift_detectors 1
            ... '''))
        """
    def __ne__(
        self,
        arg0: stim.DemRepeatBlock,
    ) -> bool:
        """Determines if two repeat blocks are different.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.DemRepeatBlock`.
        """
    def body_copy(
        self,
    ) -> stim.DetectorErrorModel:
        """Returns a copy of the block's body, as a stim.DetectorErrorModel.
        """
    @property
    def repeat_count(
        self,
    ) -> int:
        """The number of times the repeat block's body is supposed to execute.
        """
class DemTarget:
    """An instruction target from a detector error model (.dem) file.
    """
    def __eq__(
        self,
        arg0: stim.DemTarget,
    ) -> bool:
        """Determines if two `stim.DemTarget`s are identical.
        """
    def __ne__(
        self,
        arg0: stim.DemTarget,
    ) -> bool:
        """Determines if two `stim.DemTarget`s are different.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equivalent `stim.DemTarget`.
        """
    def __str__(
        self,
    ) -> str:
        """Returns a text description of the detector error model target.
        """
    def is_logical_observable_id(
        self,
    ) -> bool:
        """Determines if the detector error model target is a logical observable id target.

        In a detector error model file, observable targets are prefixed by `L`. For
        example, in `error(0.25) D0 L1` the `L1` is an observable target.
        """
    def is_relative_detector_id(
        self,
    ) -> bool:
        """Determines if the detector error model target is a relative detector id target.

        In a detector error model file, detectors are prefixed by `D`. For
        example, in `error(0.25) D0 L1` the `D0` is a relative detector target.
        """
    def is_separator(
        self,
    ) -> bool:
        """Determines if the detector error model target is a separator.

        Separates separate the components of a suggested decompositions within an error.
        For example, the `^` in `error(0.25) D1 D2 ^ D3 D4` is the separator.
        """
    @staticmethod
    def logical_observable_id(
        index: int,
    ) -> stim.DemTarget:
        """Returns a logical observable id identifying a frame change.

        Args:
            index: The index of the observable.

        Returns:
            The logical observable target.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel()
            >>> m.append("error", 0.25, [
            ...     stim.DemTarget.logical_observable_id(13)
            ... ])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.25) L13
            ''')
        """
    @staticmethod
    def relative_detector_id(
        index: int,
    ) -> stim.DemTarget:
        """Returns a relative detector id (e.g. "D5" in a .dem file).

        Args:
            index: The index of the detector, relative to the current detector offset.

        Returns:
            The relative detector target.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel()
            >>> m.append("error", 0.25, [
            ...     stim.DemTarget.relative_detector_id(13)
            ... ])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.25) D13
            ''')
        """
    @staticmethod
    def separator(
    ) -> stim.DemTarget:
        """Returns a target separator (e.g. "^" in a .dem file).

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel()
            >>> m.append("error", 0.25, [
            ...     stim.DemTarget.relative_detector_id(1),
            ...     stim.DemTarget.separator(),
            ...     stim.DemTarget.relative_detector_id(2),
            ... ])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.25) D1 ^ D2
            ''')
        """
    @property
    def val(
        self,
    ) -> int:
        """Returns the target's integer value.

        Example:

            >>> import stim
            >>> stim.target_relative_detector_id(5).val
            5
            >>> stim.target_logical_observable_id(6).val
            6
        """
class DemTargetWithCoords:
    """A detector error model instruction target with associated coords.

    It is also guaranteed that, if the type of the DEM target is a
    relative detector id, it is actually absolute (i.e. relative to
    0).

    For example, if the DEM target is a detector from a circuit with
    coordinate arguments given to detectors, the coords field will
    contain the coordinate data for the detector.

    This is helpful information to have available when debugging a
    problem in a circuit, instead of having to constantly manually
    look up the coordinates of a detector index in order to understand
    what is happening.
    """
    def __init__(
        self,
        *,
        dem_target: stim.DemTarget,
        coords: List[float],
    ) -> None:
        """Creates a stim.DemTargetWithCoords.
        """
    @property
    def coords(
        self,
    ) -> List[float]:
        """Returns the associated coordinate information as a list of flaots.

        If there is no coordinate information, returns an empty list.
        """
    @property
    def dem_target(
        self,
    ) -> stim.DemTarget:
        """Returns the actual DEM target as a `stim.DemTarget`.
        """
class DetectorErrorModel:
    """An error model built out of independent error mechanics.

    Error mechanisms are described in terms of the visible detection events and the
    hidden observable frame changes that they causes. Error mechanisms can also
    suggest decompositions of their effects into components, which can be helpful
    for decoders that want to work with a simpler decomposed error model instead of
    the full error model.

    Examples:
        >>> import stim
        >>> model = stim.DetectorErrorModel('''
        ...     error(0.125) D0
        ...     error(0.125) D0 D1 L0
        ...     error(0.125) D1 D2
        ...     error(0.125) D2 D3
        ...     error(0.125) D3
        ... ''')
        >>> len(model)
        5

        >>> stim.Circuit('''
        ...     X_ERROR(0.125) 0
        ...     X_ERROR(0.25) 1
        ...     CORRELATED_ERROR(0.375) X0 X1
        ...     M 0 1
        ...     DETECTOR rec[-2]
        ...     DETECTOR rec[-1]
        ... ''').detector_error_model()
        stim.DetectorErrorModel('''
            error(0.125) D0
            error(0.375) D0 D1
            error(0.25) D1
        ''')
    """
    def __add__(
        self,
        second: stim.DetectorErrorModel,
    ) -> stim.DetectorErrorModel:
        """Creates a detector error model by appending two models.

        Examples:
            >>> import stim
            >>> m1 = stim.DetectorErrorModel('''
            ...    error(0.125) D0
            ... ''')
            >>> m2 = stim.DetectorErrorModel('''
            ...    error(0.25) D1
            ... ''')
            >>> m1 + m2
            stim.DetectorErrorModel('''
                error(0.125) D0
                error(0.25) D1
            ''')
        """
    def __eq__(
        self,
        arg0: stim.DetectorErrorModel,
    ) -> bool:
        """Determines if two detector error models have identical contents.
        """
    @overload
    def __getitem__(
        self,
        index_or_slice: int,
    ) -> Union[stim.DemInstruction, stim.DemRepeatBlock]:
        pass
    @overload
    def __getitem__(
        self,
        index_or_slice: slice,
    ) -> stim.DetectorErrorModel:
        pass
    def __getitem__(
        self,
        index_or_slice: object,
    ) -> object:
        """Returns copies of instructions from the detector error model.

        Args:
            index_or_slice: An integer index picking out an instruction to return, or a
                slice picking out a range of instructions to return as a detector error
                model.

        Examples:
            >>> import stim
            >>> model = stim.DetectorErrorModel('''
            ...    error(0.125) D0
            ...    error(0.125) D1 L1
            ...    repeat 100 {
            ...        error(0.125) D1 D2
            ...        shift_detectors 1
            ...    }
            ...    error(0.125) D2
            ...    logical_observable L0
            ...    detector D5
            ... ''')
            >>> model[0]
            stim.DemInstruction('error', [0.125], [stim.target_relative_detector_id(0)])
            >>> model[2]
            stim.DemRepeatBlock(100, stim.DetectorErrorModel('''
                error(0.125) D1 D2
                shift_detectors 1
            '''))
            >>> model[1::2]
            stim.DetectorErrorModel('''
                error(0.125) D1 L1
                error(0.125) D2
                detector D5
            ''')
        """
    def __iadd__(
        self,
        second: stim.DetectorErrorModel,
    ) -> stim.DetectorErrorModel:
        """Appends a detector error model into the receiving model (mutating it).

        Examples:
            >>> import stim
            >>> m1 = stim.DetectorErrorModel('''
            ...    error(0.125) D0
            ... ''')
            >>> m2 = stim.DetectorErrorModel('''
            ...    error(0.25) D1
            ... ''')
            >>> m1 += m2
            >>> print(repr(m1))
            stim.DetectorErrorModel('''
                error(0.125) D0
                error(0.25) D1
            ''')
        """
    def __imul__(
        self,
        repetitions: int,
    ) -> stim.DetectorErrorModel:
        """Mutates the detector error model by putting its contents into a repeat block.

        Special case: if the repetition count is 0, the model is cleared.
        Special case: if the repetition count is 1, nothing happens.

        Args:
            repetitions: The number of times the repeat block should repeat.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel('''
            ...    error(0.25) D0
            ...    shift_detectors 1
            ... ''')
            >>> m *= 3
            >>> print(m)
            repeat 3 {
                error(0.25) D0
                shift_detectors 1
            }
        """
    def __init__(
        self,
        detector_error_model_text: str = '',
    ) -> None:
        """Creates a stim.DetectorErrorModel.

        Args:
            detector_error_model_text: Defaults to empty. Describes instructions to
                append into the circuit in the detector error model (.dem) format.

        Examples:
            >>> import stim
            >>> empty = stim.DetectorErrorModel()
            >>> not_empty = stim.DetectorErrorModel('''
            ...    error(0.125) D0 L0
            ... ''')
        """
    def __len__(
        self,
    ) -> int:
        """Returns the number of top-level instructions/blocks in the detector error model.

        Instructions inside of blocks are not included in this count.

        Examples:
            >>> import stim
            >>> len(stim.DetectorErrorModel())
            0
            >>> len(stim.DetectorErrorModel('''
            ...    error(0.1) D0 D1
            ...    shift_detectors 100
            ...    logical_observable L5
            ... '''))
            3
            >>> len(stim.DetectorErrorModel('''
            ...    repeat 100 {
            ...        error(0.1) D0 D1
            ...        error(0.1) D1 D2
            ...    }
            ... '''))
            1
        """
    def __mul__(
        self,
        repetitions: int,
    ) -> stim.DetectorErrorModel:
        """Repeats the detector error model using a repeat block.

        Has special cases for 0 repetitions and 1 repetitions.

        Args:
            repetitions: The number of times the repeat block should repeat.

        Returns:
            repetitions=0: An empty detector error model.
            repetitions=1: A copy of this detector error model.
            repetitions>=2: A detector error model with a single repeat block, where the
            contents of that repeat block are this detector error model.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel('''
            ...    error(0.25) D0
            ...    shift_detectors 1
            ... ''')
            >>> m * 3
            stim.DetectorErrorModel('''
                repeat 3 {
                    error(0.25) D0
                    shift_detectors 1
                }
            ''')
        """
    def __ne__(
        self,
        arg0: stim.DetectorErrorModel,
    ) -> bool:
        """Determines if two detector error models have non-identical contents.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equivalent `stim.DetectorErrorModel`.
        """
    def __rmul__(
        self,
        repetitions: int,
    ) -> stim.DetectorErrorModel:
        """Repeats the detector error model using a repeat block.

        Has special cases for 0 repetitions and 1 repetitions.

        Args:
            repetitions: The number of times the repeat block should repeat.

        Returns:
            repetitions=0: An empty detector error model.
            repetitions=1: A copy of this detector error model.
            repetitions>=2: A detector error model with a single repeat block, where the
            contents of that repeat block are this detector error model.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel('''
            ...    error(0.25) D0
            ...    shift_detectors 1
            ... ''')
            >>> 3 * m
            stim.DetectorErrorModel('''
                repeat 3 {
                    error(0.25) D0
                    shift_detectors 1
                }
            ''')
        """
    def __str__(
        self,
    ) -> str:
        """Returns the contents of a detector error model file (.dem) encoding the model.
        """
    def append(
        self,
        instruction: object,
        parens_arguments: object = None,
        targets: List[object] = (),
    ) -> None:
        """Appends an instruction to the detector error model.

        Args:
            instruction: Either the name of an instruction, a stim.DemInstruction, or a
                stim.DemRepeatBlock. The `parens_arguments` and `targets` arguments are
                given if and only if the instruction is a name.
            parens_arguments: Numeric values parameterizing the instruction. The numbers
                inside parentheses in a detector error model file (eg. the `0.25` in
                `error(0.25) D0`). This argument can be given either a list of doubles,
                or a single double (which will be implicitly wrapped into a list).
            targets: The instruction targets, such as the `D0` in `error(0.25) D0`.

        Examples:
            >>> import stim
            >>> m = stim.DetectorErrorModel()
            >>> m.append("error", 0.125, [
            ...     stim.DemTarget.relative_detector_id(1),
            ... ])
            >>> m.append("error", 0.25, [
            ...     stim.DemTarget.relative_detector_id(1),
            ...     stim.DemTarget.separator(),
            ...     stim.DemTarget.relative_detector_id(2),
            ...     stim.DemTarget.logical_observable_id(3),
            ... ])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.125) D1
                error(0.25) D1 ^ D2 L3
            ''')

            >>> m.append("shift_detectors", (1, 2, 3), [5])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.125) D1
                error(0.25) D1 ^ D2 L3
                shift_detectors(1, 2, 3) 5
            ''')

            >>> m += m * 3
            >>> m.append(m[0])
            >>> m.append(m[-2])
            >>> print(repr(m))
            stim.DetectorErrorModel('''
                error(0.125) D1
                error(0.25) D1 ^ D2 L3
                shift_detectors(1, 2, 3) 5
                repeat 3 {
                    error(0.125) D1
                    error(0.25) D1 ^ D2 L3
                    shift_detectors(1, 2, 3) 5
                }
                error(0.125) D1
                repeat 3 {
                    error(0.125) D1
                    error(0.25) D1 ^ D2 L3
                    shift_detectors(1, 2, 3) 5
                }
            ''')
        """
    def approx_equals(
        self,
        other: object,
        *,
        atol: float,
    ) -> bool:
        """Checks if detector error models are approximately equal.

        Two detector error model are approximately equal if they are equal up to slight
        perturbations of instruction arguments such as probabilities. For example
        `error(0.100) D0` is approximately equal to `error(0.099) D0` within an absolute
        tolerance of 0.002. All other details of the models (such as the ordering of
        errors and their targets) must be exactly the same.

        Args:
            other: The detector error model, or other object, to compare to this one.
            atol: The absolute error tolerance. The maximum amount each probability may
                have been perturbed by.

        Returns:
            True if the given object is a detector error model approximately equal up to
            the receiving circuit up to the given tolerance, otherwise False.

        Examples:
            >>> import stim
            >>> base = stim.DetectorErrorModel('''
            ...    error(0.099) D0 D1
            ... ''')

            >>> base.approx_equals(base, atol=0)
            True

            >>> base.approx_equals(stim.DetectorErrorModel('''
            ...    error(0.101) D0 D1
            ... '''), atol=0)
            False

            >>> base.approx_equals(stim.DetectorErrorModel('''
            ...    error(0.101) D0 D1
            ... '''), atol=0.0001)
            False

            >>> base.approx_equals(stim.DetectorErrorModel('''
            ...    error(0.101) D0 D1
            ... '''), atol=0.01)
            True

            >>> base.approx_equals(stim.DetectorErrorModel('''
            ...    error(0.099) D0 D1 L0 L1 L2 L3 L4
            ... '''), atol=9999)
            False
        """
    def clear(
        self,
    ) -> None:
        """Clears the contents of the detector error model.

        Examples:
            >>> import stim
            >>> model = stim.DetectorErrorModel('''
            ...    error(0.1) D0 D1
            ... ''')
            >>> model.clear()
            >>> model
            stim.DetectorErrorModel()
        """
    def compile_sampler(
        self,
        *,
        seed: object = None,
    ) -> stim.CompiledDemSampler:
        """Returns a CompiledDemSampler that can batch sample from detector error models.

        Args:
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how many
                shots are taken. For example, taking 10 shots and then 90 shots will
                give different results from taking 100 shots in one call.

        Returns:
            A seeded stim.CompiledDemSampler for the given detector error model.

        Examples:
            >>> import stim
            >>> dem = stim.DetectorErrorModel('''
            ...    error(0) D0
            ...    error(1) D1 D2 L0
            ... ''')
            >>> sampler = dem.compile_sampler()
            >>> det_data, obs_data, err_data = sampler.sample(
            ...     shots=4,
            ...     return_errors=True)
            >>> det_data
            array([[False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True],
                   [False,  True,  True]])
            >>> obs_data
            array([[ True],
                   [ True],
                   [ True],
                   [ True]])
            >>> err_data
            array([[False,  True],
                   [False,  True],
                   [False,  True],
                   [False,  True]])
        """
    def copy(
        self,
    ) -> stim.DetectorErrorModel:
        """Returns a copy of the detector error model.

        The copy is an independent detector error model with the same contents.

        Examples:
            >>> import stim

            >>> c1 = stim.DetectorErrorModel("error(0.1) D0 D1")
            >>> c2 = c1.copy()
            >>> c2 is c1
            False
            >>> c2 == c1
            True
        """
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-svg"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-3d"]',
    ) -> 'stim._DiagramHelper':
        pass
    @overload
    def diagram(
        self,
        *,
        type: 'Literal["match-graph-3d-html"]',
    ) -> 'stim._DiagramHelper':
        pass
    def diagram(
        self,
        *,
        type: str,
    ) -> Any:
        """Returns a diagram of the circuit, from a variety of options.

        Args:
            type: The type of diagram. Available types are:
                "match-graph-svg": An image of the decoding graph of the
                    detector error model. Red lines are errors crossing a
                    logical observable. Blue lines are undecomposed hyper
                    errors.
                "match-graph-3d": A 3d model of the decoding graph of the
                    detector error model. Red lines are errors crossing a
                    logical observable. Blue lines are undecomposed hyper
                    errors.

                    GLTF files can be opened with a variety of programs, or
                    opened online in viewers such as
                    https://gltf-viewer.donmccurdy.com/ . Red lines are
                    errors crossing a logical observable.
                "match-graph-3d-html": Same 3d model as 'match-graph-3d' but
                    embedded into an HTML web page containing an interactive
                    THREE.js viewer for the 3d model.

        Returns:
            An object whose `__str__` method returns the diagram, so that
            writing the diagram to a file works correctly. The returned
            object also defines a `_repr_html_` method, so that ipython
            notebooks recognize it can be shown using a specialized
            viewer instead of as raw text.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> circuit = stim.Circuit.generated(
            ...     "repetition_code:memory",
            ...     rounds=10,
            ...     distance=7,
            ...     after_clifford_depolarization=0.01)
            >>> dem = circuit.detector_error_model(decompose_errors=True)

            >>> with tempfile.TemporaryDirectory() as d:
            ...     diagram = circuit.diagram(type="match-graph-svg")
            ...     with open(f"{d}/dem_image.svg", "w") as f:
            ...         print(diagram, file=f)

            >>> with tempfile.TemporaryDirectory() as d:
            ...     diagram = circuit.diagram(type="match-graph-3d")
            ...     with open(f"{d}/dem_3d_model.gltf", "w") as f:
            ...         print(diagram, file=f)
        """
    def flattened(
        self,
    ) -> stim.DetectorErrorModel:
        """Returns the detector error model without repeat or detector_shift instructions.

        Returns:
            A `stim.DetectorErrorModel` with the same errors in the same order, but with
            repeat loops flattened into actually repeated instructions and with all
            coordinate/index shifts inlined.

        Examples:
            >>> import stim
            >>> stim.DetectorErrorModel('''
            ...     error(0.125) D0
            ...     REPEAT 5 {
            ...         error(0.25) D0 D1
            ...         shift_detectors 1
            ...     }
            ...     error(0.125) D0 L0
            ... ''').flattened()
            stim.DetectorErrorModel('''
                error(0.125) D0
                error(0.25) D0 D1
                error(0.25) D1 D2
                error(0.25) D2 D3
                error(0.25) D3 D4
                error(0.25) D4 D5
                error(0.125) D5 L0
            ''')
        """
    @staticmethod
    def from_file(
        file: Union[io.TextIOBase, str, pathlib.Path],
    ) -> stim.DetectorErrorModel:
        """Reads a detector error model from a file.

        The file format is defined at
        https://github.com/quantumlib/Stim/blob/main/doc/file_format_dem_detector_error_model.md

        Args:
            file: A file path or open file object to read from.

        Returns:
            The circuit parsed from the file.

        Examples:
            >>> import stim
            >>> import tempfile

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         print('error(0.25) D2 D3', file=f)
            ...     circuit = stim.DetectorErrorModel.from_file(path)
            >>> circuit
            stim.DetectorErrorModel('''
                error(0.25) D2 D3
            ''')

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         print('error(0.25) D2 D3', file=f)
            ...     with open(path) as f:
            ...         circuit = stim.DetectorErrorModel.from_file(path)
            >>> circuit
            stim.DetectorErrorModel('''
                error(0.25) D2 D3
            ''')
        """
    def get_detector_coordinates(
        self,
        only: object = None,
    ) -> Dict[int, List[float]]:
        """Returns the coordinate metadata of detectors in the detector error model.

        Args:
            only: Defaults to None (meaning include all detectors). A list of detector
                indices to include in the result. Detector indices beyond the end of the
                detector error model cause an error.

        Returns:
            A dictionary mapping integers (detector indices) to lists of floats
            (coordinates). Detectors with no specified coordinate data are mapped to an
            empty tuple. If `only` is specified, then `set(result.keys()) == set(only)`.

        Examples:
            >>> import stim
            >>> dem = stim.DetectorErrorModel('''
            ...    error(0.25) D0 D1
            ...    detector(1, 2, 3) D1
            ...    shift_detectors(5) 1
            ...    detector(1, 2) D2
            ... ''')
            >>> dem.get_detector_coordinates()
            {0: [], 1: [1.0, 2.0, 3.0], 2: [], 3: [6.0, 2.0]}
            >>> dem.get_detector_coordinates(only=[1])
            {1: [1.0, 2.0, 3.0]}
        """
    @property
    def num_detectors(
        self,
    ) -> int:
        """Counts the number of detectors (e.g. `D2`) in the error model.

        Detector indices are assumed to be contiguous from 0 up to whatever the maximum
        detector id is. If the largest detector's absolute id is n-1, then the number of
        detectors is n.

        Examples:
            >>> import stim

            >>> stim.Circuit('''
            ...     X_ERROR(0.125) 0
            ...     X_ERROR(0.25) 1
            ...     CORRELATED_ERROR(0.375) X0 X1
            ...     M 0 1
            ...     DETECTOR rec[-2]
            ...     DETECTOR rec[-1]
            ... ''').detector_error_model().num_detectors
            2

            >>> stim.DetectorErrorModel('''
            ...    error(0.1) D0 D199
            ... ''').num_detectors
            200

            >>> stim.DetectorErrorModel('''
            ...    shift_detectors 1000
            ...    error(0.1) D0 D199
            ... ''').num_detectors
            1200
        """
    @property
    def num_errors(
        self,
    ) -> int:
        """Counts the number of errors (e.g. `error(0.1) D0`) in the error model.

        Error instructions inside repeat blocks count once per repetition.
        Redundant errors with the same targets count as separate errors.

        Examples:
            >>> import stim

            >>> stim.DetectorErrorModel('''
            ...     error(0.125) D0
            ...     repeat 100 {
            ...         repeat 5 {
            ...             error(0.25) D1
            ...         }
            ...     }
            ... ''').num_errors
            501
        """
    @property
    def num_observables(
        self,
    ) -> int:
        """Counts the number of frame changes (e.g. `L2`) in the error model.

        Observable indices are assumed to be contiguous from 0 up to whatever the
        maximum observable id is. If the largest observable's id is n-1, then the number
        of observables is n.

        Examples:
            >>> import stim

            >>> stim.Circuit('''
            ...     X_ERROR(0.125) 0
            ...     M 0
            ...     OBSERVABLE_INCLUDE(99) rec[-1]
            ... ''').detector_error_model().num_observables
            100

            >>> stim.DetectorErrorModel('''
            ...    error(0.1) L399
            ... ''').num_observables
            400
        """
    def rounded(
        self,
        arg0: int,
    ) -> stim.DetectorErrorModel:
        """Creates an equivalent detector error model but with rounded error probabilities.

        Args:
            digits: The number of digits to round to.

        Returns:
            A `stim.DetectorErrorModel` with the same instructions in the same order,
            but with the parens arguments of error instructions rounded to the given
            precision.

            Instructions whose error probability was rounded to zero are still
            included in the output.

        Examples:
            >>> import stim
            >>> dem = stim.DetectorErrorModel('''
            ...     error(0.019499) D0
            ...     error(0.000001) D0 D1
            ... ''')

            >>> dem.rounded(2)
            stim.DetectorErrorModel('''
                error(0.02) D0
                error(0) D0 D1
            ''')

            >>> dem.rounded(3)
            stim.DetectorErrorModel('''
                error(0.019) D0
                error(0) D0 D1
            ''')
        """
    def shortest_graphlike_error(
        self,
        ignore_ungraphlike_errors: bool = False,
    ) -> stim.DetectorErrorModel:
        """Finds a minimum set of graphlike errors to produce an undetected logical error.

        Note that this method does not pay attention to error probabilities (other than
        ignoring errors with probability 0). It searches for a logical error with the
        minimum *number* of physical errors, not the maximum probability of those
        physical errors all occurring.

        This method works by looking for errors that have frame changes (eg.
        "error(0.1) D0 D1 L5" flips the frame of observable 5). These errors are
        converted into one or two symptoms and a net frame change. The symptoms can then
        be moved around by following errors touching that symptom. Each symptom is moved
        until it disappears into a boundary or cancels against another remaining
        symptom, while leaving the other symptoms alone (ensuring only one symptom is
        allowed to move significantly reduces waste in the search space). Eventually a
        path or cycle of errors is found that cancels out the symptoms, and if there is
        still a frame change at that point then that path or cycle is a logical error
        (otherwise all that was found was a stabilizer of the system; a dead end). The
        search process advances like a breadth first search, seeded from all the
        frame-change errors and branching them outward in tandem, until one of them wins
        the race to find a solution.

        Args:
            ignore_ungraphlike_errors: Defaults to False. When False, an exception is
                raised if there are any errors in the model that are not graphlike. When
                True, those errors are skipped as if they weren't present.

                A graphlike error is an error with less than two symptoms. For the
                purposes of this method, errors are also considered graphlike if they
                are decomposed into graphlike components:

                graphlike:
                    error(0.1) D0
                    error(0.1) D0 D1
                    error(0.1) D0 D1 L0
                not graphlike but decomposed into graphlike components:
                    error(0.1) D0 D1 ^ D2
                not graphlike, not decomposed into graphlike components:
                    error(0.1) D0 D1 D2
                    error(0.1) D0 D1 D2 ^ D3

        Returns:
            A detector error model containing just the error instructions corresponding
            to an undetectable logical error. There will be no other kinds of
            instructions (no `repeat`s, no `shift_detectors`, etc). The error
            probabilities will all be set to 1.

            The `len` of the returned model is the graphlike code distance of the
            circuit. But beware that in general the true code distance may be smaller.
            For example, in the XZ surface code with twists, the true minimum sized
            logical error is likely to use Y errors. But each Y error decomposes into
            two graphlike components (the X part and the Z part). As a result, the
            graphlike code distance in that context is likely to be nearly twice as
            large as the true code distance.

        Examples:
            >>> import stim

            >>> stim.DetectorErrorModel('''
            ...     error(0.125) D0
            ...     error(0.125) D0 D1
            ...     error(0.125) D1 L55
            ...     error(0.125) D1
            ... ''').shortest_graphlike_error()
            stim.DetectorErrorModel('''
                error(1) D1
                error(1) D1 L55
            ''')

            >>> stim.DetectorErrorModel('''
            ...     error(0.125) D0 D1 D2
            ...     error(0.125) L0
            ... ''').shortest_graphlike_error(ignore_ungraphlike_errors=True)
            stim.DetectorErrorModel('''
                error(1) L0
            ''')

            >>> circuit = stim.Circuit.generated(
            ...     "repetition_code:memory",
            ...     rounds=10,
            ...     distance=7,
            ...     before_round_data_depolarization=0.01)
            >>> model = circuit.detector_error_model(decompose_errors=True)
            >>> len(model.shortest_graphlike_error())
            7
        """
    def to_file(
        self,
        file: Union[io.TextIOBase, str, pathlib.Path],
    ) -> None:
        """Writes the detector error model to a file.

        The file format is defined at
        https://github.com/quantumlib/Stim/blob/main/doc/file_format_dem_detector_error_model.md

        Args:
            file: A file path or an open file to write to.

        Examples:
            >>> import stim
            >>> import tempfile
            >>> c = stim.DetectorErrorModel('error(0.25) D2 D3')

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     with open(path, 'w') as f:
            ...         c.to_file(f)
            ...     with open(path) as f:
            ...         contents = f.read()
            >>> contents
            'error(0.25) D2 D3\n'

            >>> with tempfile.TemporaryDirectory() as tmpdir:
            ...     path = tmpdir + '/tmp.stim'
            ...     c.to_file(path)
            ...     with open(path) as f:
            ...         contents = f.read()
            >>> contents
            'error(0.25) D2 D3\n'
        """
class ExplainedError:
    """Describes the location of an error mechanism from a stim circuit.
    """
    def __init__(
        self,
        *,
        dem_error_terms: List[stim.DemTargetWithCoords],
        circuit_error_locations: List[stim.CircuitErrorLocation],
    ) -> None:
        """Creates a stim.ExplainedError.
        """
    @property
    def circuit_error_locations(
        self,
    ) -> List[stim.CircuitErrorLocation]:
        """The locations of circuit errors that produce the symptoms in dem_error_terms.

        Note: if this list contains a single entry, it may be because a result
        with a single representative error was requested (as opposed to all possible
        errors).

        Note: if this list is empty, it may be because there was a DEM error decomposed
        into parts where one of the parts is impossible to make on its own from a single
        circuit error.
        """
    @property
    def dem_error_terms(
        self,
    ) -> List[stim.DemTargetWithCoords]:
        """The detectors and observables flipped by this error mechanism.
        """
class FlippedMeasurement:
    """Describes a measurement that was flipped.

    Gives the measurement's index in the measurement record, and also
    the observable of the measurement.
    """
    def __init__(
        self,
        *,
        record_index: int,
        observable: object,
    ) -> None:
        """Creates a stim.FlippedMeasurement.
        """
    @property
    def observable(
        self,
    ) -> List[stim.GateTargetWithCoords]:
        """Returns the observable of the flipped measurement.

        For example, an `MX 5` measurement will have the observable X5.
        """
    @property
    def record_index(
        self,
    ) -> int:
        """The measurement record index of the flipped measurement.
        For example, the fifth measurement in a circuit has a measurement
        record index of 4.
        """
class GateTarget:
    """Represents a gate target, like `0` or `rec[-1]`, from a circuit.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit('''
        ...     M 0 !1
        ... ''')
        >>> circuit[0].targets_copy()[0]
        stim.GateTarget(0)
        >>> circuit[0].targets_copy()[1]
        stim.GateTarget(stim.target_inv(1))
    """
    def __eq__(
        self,
        arg0: stim.GateTarget,
    ) -> bool:
        """Determines if two `stim.GateTarget`s are identical.
        """
    def __init__(
        self,
        value: object,
    ) -> None:
        """Initializes a `stim.GateTarget`.

        Args:
            value: A target like `5` or `stim.target_rec(-1)`.
        """
    def __ne__(
        self,
        arg0: stim.GateTarget,
    ) -> bool:
        """Determines if two `stim.GateTarget`s are different.
        """
    def __repr__(
        self,
    ) -> str:
        """Returns text that is a valid python expression evaluating to an equivalent `stim.GateTarget`.
        """
    @property
    def is_combiner(
        self,
    ) -> bool:
        """Returns whether or not this is a `stim.target_combiner()`.
        """
    @property
    def is_inverted_result_target(
        self,
    ) -> bool:
        """Returns whether or not this is an inverted target.

        Inverted targets include inverted qubit targets `stim.target_inv(5)`
        (`!5` in a circuit file) and inverted Pauli targets like
        `stim.target_x(4, invert=True)` (`!X4` in a circuit file).
        """
    @property
    def is_measurement_record_target(
        self,
    ) -> bool:
        """Returns whether or not this is a measurement record target.

        The value returned by `stim.target_rec(-5)`, which would be `rec[-5]` in a
        circuit file, is a measurement record target.
        """
    @property
    def is_qubit_target(
        self,
    ) -> bool:
        """Returns whether or not this is a (possibly inverted) qubit target.

        For example, `5` on its own in a stim circuit is a raw qubit target.
        """
    @property
    def is_sweep_bit_target(
        self,
    ) -> bool:
        """Returns whether or not this is a sweep bit target.

        For example, `sweep[5]` in a circuit file is a sweep target, and the value
        returned by `stim.target_sweep_bit` is a sweep target.
        """
    @property
    def is_x_target(
        self,
    ) -> bool:
        """Returns whether or not this is a (possibly inverted) X pauli target.

        For example, `X5` in a stim circuit is a X Pauli target. The value returned by
        `stim.target_x` is a X pauli target.
        """
    @property
    def is_y_target(
        self,
    ) -> bool:
        """Returns whether or not this is a (possibly inverted) Y pauli target.

        For example, `Y5` in a stim circuit is a Y Pauli target. The value returned by
        `stim.target_y` is a Y pauli target.
        """
    @property
    def is_z_target(
        self,
    ) -> bool:
        """Returns whether or not this is a (possibly inverted) Z pauli target.

        For example, `Z5` in a stim circuit is a Z Pauli target. The value returned by
        `stim.target_z` is a Z pauli target.
        """
    @property
    def value(
        self,
    ) -> int:
        """The numeric part of the target.

        This is non-negative integer for qubit targets, and a negative integer for
        measurement record targets.
        """
class GateTargetWithCoords:
    """A gate target with associated coordinate information.

    For example, if the gate target is a qubit from a circuit with
    QUBIT_COORDS instructions, the coords field will contain the
    coordinate data from the QUBIT_COORDS instruction for the qubit.

    This is helpful information to have available when debugging a
    problem in a circuit, instead of having to constantly manually
    look up the coordinates of a qubit index in order to understand
    what is happening.
    """
    def __init__(
        self,
        *,
        gate_target: object,
        coords: List[float],
    ) -> None:
        """Creates a stim.GateTargetWithCoords.
        """
    @property
    def coords(
        self,
    ) -> List[float]:
        """Returns the associated coordinate information as a list of flaots.

        If there is no coordinate information, returns an empty list.
        """
    @property
    def gate_target(
        self,
    ) -> stim.GateTarget:
        """Returns the actual gate target as a `stim.GateTarget`.
        """
class PauliString:
    """A signed Pauli tensor product (e.g. "+X \u2297 X \u2297 X" or "-Y \u2297 Z".

    Represents a collection of Pauli operations (I, X, Y, Z) applied pairwise to a
    collection of qubits.

    Examples:
        >>> import stim
        >>> stim.PauliString("XX") * stim.PauliString("YY")
        stim.PauliString("-ZZ")
        >>> print(stim.PauliString(5))
        +_____
    """
    def __add__(
        self,
        rhs: stim.PauliString,
    ) -> stim.PauliString:
        """Returns the tensor product of two Pauli strings.

        Concatenates the Pauli strings and multiplies their signs.

        Args:
            rhs: A second stim.PauliString.

        Examples:
            >>> import stim

            >>> stim.PauliString("X") + stim.PauliString("YZ")
            stim.PauliString("+XYZ")

            >>> stim.PauliString("iX") + stim.PauliString("-X")
            stim.PauliString("-iXX")

        Returns:
            The tensor product.
        """
    def __eq__(
        self,
        arg0: stim.PauliString,
    ) -> bool:
        """Determines if two Pauli strings have identical contents.
        """
    @overload
    def __getitem__(
        self,
        index_or_slice: int,
    ) -> int:
        pass
    @overload
    def __getitem__(
        self,
        index_or_slice: slice,
    ) -> stim.PauliString:
        pass
    def __getitem__(
        self,
        index_or_slice: object,
    ) -> object:
        """Returns an individual Pauli or Pauli string slice from the pauli string.

        Individual Paulis are returned as an int using the encoding 0=I, 1=X, 2=Y, 3=Z.
        Slices are returned as a stim.PauliString (always with positive sign).

        Examples:
            >>> import stim
            >>> p = stim.PauliString("_XYZ")
            >>> p[2]
            2
            >>> p[-1]
            3
            >>> p[:2]
            stim.PauliString("+_X")
            >>> p[::-1]
            stim.PauliString("+ZYX_")

        Args:
            index_or_slice: The index of the pauli to return, or the slice of paulis to
                return.

        Returns:
            0: Identity.
            1: Pauli X.
            2: Pauli Y.
            3: Pauli Z.
        """
    def __iadd__(
        self,
        rhs: stim.PauliString,
    ) -> stim.PauliString:
        """Performs an inplace tensor product.

        Concatenates the given Pauli string onto the receiving string and multiplies
        their signs.

        Args:
            rhs: A second stim.PauliString.

        Examples:
            >>> import stim

            >>> p = stim.PauliString("iX")
            >>> alias = p
            >>> p += stim.PauliString("-YY")
            >>> p
            stim.PauliString("-iXYY")
            >>> alias is p
            True

        Returns:
            The mutated pauli string.
        """
    def __imul__(
        self,
        rhs: object,
    ) -> stim.PauliString:
        """Inplace right-multiplies the Pauli string.

        Can multiply by another Pauli string, a complex unit, or a tensor power.

        Args:
            rhs: The right hand side of the multiplication. This can be:
                - A stim.PauliString to right-multiply term-by-term into the paulis of
                    the pauli string.
                - A complex unit (1, -1, 1j, -1j) to multiply into the sign of the pauli
                    string.
                - A non-negative integer indicating the tensor power to raise the pauli
                    string to (how many times to repeat it).

        Examples:
            >>> import stim

            >>> p = stim.PauliString("X")
            >>> p *= 1j
            >>> p
            stim.PauliString("+iX")

            >>> p = stim.PauliString("iXY_")
            >>> p *= 3
            >>> p
            stim.PauliString("-iXY_XY_XY_")

            >>> p = stim.PauliString("X")
            >>> alias = p
            >>> p *= stim.PauliString("Y")
            >>> alias
            stim.PauliString("+iZ")

            >>> p = stim.PauliString("X")
            >>> p *= stim.PauliString("_YY")
            >>> p
            stim.PauliString("+XYY")

        Returns:
            The mutated Pauli string.
        """
    @staticmethod
    def __init__(
        *args,
        **kwargs,
    ):
        """Overloaded function.

        1. __init__(self: stim.PauliString, num_qubits: int) -> None

        Creates an identity Pauli string over the given number of qubits.

        Examples:
            >>> import stim
            >>> p = stim.PauliString(5)
            >>> print(p)
            +_____

        Args:
            num_qubits: The number of qubits the Pauli string acts on.


        2. __init__(self: stim.PauliString, text: str) -> None

        Creates a stim.PauliString from a text string.

        The string can optionally start with a sign ('+', '-', 'i', '+i', or '-i').
        The rest of the string should be characters from '_IXYZ' where
        '_' and 'I' mean identity, 'X' means Pauli X, 'Y' means Pauli Y, and 'Z' means
        Pauli Z.

        Examples:
            >>> import stim
            >>> print(stim.PauliString("YZ"))
            +YZ
            >>> print(stim.PauliString("+IXYZ"))
            +_XYZ
            >>> print(stim.PauliString("-___X_"))
            -___X_
            >>> print(stim.PauliString("iX"))
            +iX

        Args:
            text: A text description of the Pauli string's contents, such as "+XXX" or
                "-_YX" or "-iZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZY".

        Returns:
            The created stim.PauliString.


        3. __init__(self: stim.PauliString, copy: stim.PauliString) -> None

        Creates a copy of a stim.PauliString.

        Examples:
            >>> import stim
            >>> a = stim.PauliString("YZ")
            >>> b = stim.PauliString(a)
            >>> b is a
            False
            >>> b == a
            True

        Args:
            copy: The pauli string to make a copy of.


        4. __init__(self: stim.PauliString, pauli_indices: List[int]) -> None

        Creates a stim.PauliString from a list of integer pauli indices.

        The indexing scheme that is used is:
            0 -> I
            1 -> X
            2 -> Y
            3 -> Z

        Examples:
            >>> import stim
            >>> stim.PauliString([0, 1, 2, 3, 0, 3])
            stim.PauliString("+_XYZ_Z")

        Args:
            pauli_indices: A sequence of integers from 0 to 3 (inclusive) indicating
                paulis.
        """
    def __itruediv__(
        self,
        rhs: complex,
    ) -> stim.PauliString:
        """Inplace divides the Pauli string by a complex unit.

        Args:
            rhs: The divisor. Can be 1, -1, 1j, or -1j.

        Examples:
            >>> import stim

            >>> p = stim.PauliString("X")
            >>> p /= 1j
            >>> p
            stim.PauliString("-iX")

        Returns:
            The mutated Pauli string.

        Raises:
            ValueError: The divisor isn't 1, -1, 1j, or -1j.
        """
    def __len__(
        self,
    ) -> int:
        """Returns the length the pauli string; the number of qubits it operates on.
        """
    def __mul__(
        self,
        rhs: object,
    ) -> stim.PauliString:
        """Right-multiplies the Pauli string.

        Can multiply by another Pauli string, a complex unit, or a tensor power.

        Args:
            rhs: The right hand side of the multiplication. This can be:
                - A stim.PauliString to right-multiply term-by-term with the paulis of
                    the pauli string.
                - A complex unit (1, -1, 1j, -1j) to multiply with the sign of the pauli
                    string.
                - A non-negative integer indicating the tensor power to raise the pauli
                    string to (how many times to repeat it).

        Examples:
            >>> import stim

            >>> stim.PauliString("X") * 1
            stim.PauliString("+X")
            >>> stim.PauliString("X") * -1
            stim.PauliString("-X")
            >>> stim.PauliString("X") * 1j
            stim.PauliString("+iX")

            >>> stim.PauliString("X") * 2
            stim.PauliString("+XX")
            >>> stim.PauliString("-X") * 2
            stim.PauliString("+XX")
            >>> stim.PauliString("iX") * 2
            stim.PauliString("-XX")
            >>> stim.PauliString("X") * 3
            stim.PauliString("+XXX")
            >>> stim.PauliString("iX") * 3
            stim.PauliString("-iXXX")

            >>> stim.PauliString("X") * stim.PauliString("Y")
            stim.PauliString("+iZ")
            >>> stim.PauliString("X") * stim.PauliString("XX_")
            stim.PauliString("+_X_")
            >>> stim.PauliString("XXXX") * stim.PauliString("_XYZ")
            stim.PauliString("+X_ZY")

        Returns:
            The product or tensor power.

        Raises:
            TypeError: The right hand side isn't a stim.PauliString, a non-negative
                integer, or a complex unit (1, -1, 1j, or -1j).
        """
    def __ne__(
        self,
        arg0: stim.PauliString,
    ) -> bool:
        """Determines if two Pauli strings have non-identical contents.
        """
    def __neg__(
        self,
    ) -> stim.PauliString:
        """Returns the negation of the pauli string.

        Examples:
            >>> import stim
            >>> -stim.PauliString("X")
            stim.PauliString("-X")
            >>> -stim.PauliString("-Y")
            stim.PauliString("+Y")
            >>> -stim.PauliString("iZZZ")
            stim.PauliString("-iZZZ")
        """
    def __pos__(
        self,
    ) -> stim.PauliString:
        """Returns a pauli string with the same contents.

        Examples:
            >>> import stim
            >>> +stim.PauliString("+X")
            stim.PauliString("+X")
            >>> +stim.PauliString("-YY")
            stim.PauliString("-YY")
            >>> +stim.PauliString("iZZZ")
            stim.PauliString("+iZZZ")
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equivalent `stim.PauliString`.
        """
    def __rmul__(
        self,
        lhs: object,
    ) -> stim.PauliString:
        """Left-multiplies the Pauli string.

        Can multiply by another Pauli string, a complex unit, or a tensor power.

        Args:
            lhs: The left hand side of the multiplication. This can be:
                - A stim.PauliString to right-multiply term-by-term with the paulis of
                    the pauli string.
                - A complex unit (1, -1, 1j, -1j) to multiply with the sign of the pauli
                    string.
                - A non-negative integer indicating the tensor power to raise the pauli
                    string to (how many times to repeat it).

        Examples:
            >>> import stim

            >>> 1 * stim.PauliString("X")
            stim.PauliString("+X")
            >>> -1 * stim.PauliString("X")
            stim.PauliString("-X")
            >>> 1j * stim.PauliString("X")
            stim.PauliString("+iX")

            >>> 2 * stim.PauliString("X")
            stim.PauliString("+XX")
            >>> 2 * stim.PauliString("-X")
            stim.PauliString("+XX")
            >>> 2 * stim.PauliString("iX")
            stim.PauliString("-XX")
            >>> 3 * stim.PauliString("X")
            stim.PauliString("+XXX")
            >>> 3 * stim.PauliString("iX")
            stim.PauliString("-iXXX")

            >>> stim.PauliString("X") * stim.PauliString("Y")
            stim.PauliString("+iZ")
            >>> stim.PauliString("X") * stim.PauliString("XX_")
            stim.PauliString("+_X_")
            >>> stim.PauliString("XXXX") * stim.PauliString("_XYZ")
            stim.PauliString("+X_ZY")

        Returns:
            The product.

        Raises:
            ValueError: The scalar phase factor isn't 1, -1, 1j, or -1j.
        """
    def __setitem__(
        self,
        index: int,
        new_pauli: object,
    ) -> None:
        """Mutates an entry in the pauli string using the encoding 0=I, 1=X, 2=Y, 3=Z.

        Args:
            index: The index of the pauli to overwrite.
            new_pauli: Either a character from '_IXYZ' or an integer from range(4).

        Examples:
            >>> import stim
            >>> p = stim.PauliString(4)
            >>> p[2] = 1
            >>> print(p)
            +__X_
            >>> p[0] = 3
            >>> p[1] = 2
            >>> p[3] = 0
            >>> print(p)
            +ZYX_
            >>> p[0] = 'I'
            >>> p[1] = 'X'
            >>> p[2] = 'Y'
            >>> p[3] = 'Z'
            >>> print(p)
            +_XYZ
            >>> p[-1] = 'Y'
            >>> print(p)
            +_XYY
        """
    def __str__(
        self,
    ) -> str:
        """Returns a text description.
        """
    def __truediv__(
        self,
        rhs: complex,
    ) -> stim.PauliString:
        """Divides the Pauli string by a complex unit.

        Args:
            rhs: The divisor. Can be 1, -1, 1j, or -1j.

        Examples:
            >>> import stim

            >>> stim.PauliString("X") / 1j
            stim.PauliString("-iX")

        Returns:
            The quotient.

        Raises:
            ValueError: The divisor isn't 1, -1, 1j, or -1j.
        """
    def commutes(
        self,
        other: stim.PauliString,
    ) -> bool:
        """Determines if two Pauli strings commute or not.

        Two Pauli strings commute if they have an even number of matched
        non-equal non-identity Pauli terms. Otherwise they anticommute.

        Args:
            other: The other Pauli string.

        Examples:
            >>> import stim
            >>> xx = stim.PauliString("XX")
            >>> xx.commutes(stim.PauliString("X_"))
            True
            >>> xx.commutes(stim.PauliString("XX"))
            True
            >>> xx.commutes(stim.PauliString("XY"))
            False
            >>> xx.commutes(stim.PauliString("XZ"))
            False
            >>> xx.commutes(stim.PauliString("ZZ"))
            True
            >>> xx.commutes(stim.PauliString("X_Y__"))
            True
            >>> xx.commutes(stim.PauliString(""))
            True

        Returns:
            True if the Pauli strings commute, False if they anti-commute.
        """
    def copy(
        self,
    ) -> stim.PauliString:
        """Returns a copy of the pauli string.

        The copy is an independent pauli string with the same contents.

        Examples:
            >>> import stim
            >>> p1 = stim.PauliString.random(2)
            >>> p2 = p1.copy()
            >>> p2 is p1
            False
            >>> p2 == p1
            True
        """
    def extended_product(
        self,
        other: stim.PauliString,
    ) -> Tuple[complex, stim.PauliString]:
        """[DEPRECATED] Use multiplication (__mul__ or *) instead.
        """
    @staticmethod
    def from_numpy(
        *,
        xs: np.ndarray,
        zs: np.ndarray,
        sign: Union[int, float, complex] = +1,
        num_qubits: Optional[int] = None,
    ) -> stim.PauliString:
        """Creates a pauli string from X bit and Z bit numpy arrays, using the encoding:

            x=0 and z=0 -> P=I
            x=1 and z=0 -> P=X
            x=1 and z=1 -> P=Y
            x=0 and z=1 -> P=Z

        Args:
            xs: The X bits of the pauli string. This array can either be a 1-dimensional
                numpy array with dtype=np.bool8, or a bit packed 1-dimensional numpy
                array with dtype=np.uint8. If the dtype is np.uint8 then the array is
                assumed to be bit packed in little endian order and the "num_qubits"
                argument must be specified. When bit packed, the x bit with offset k is
                stored at (xs[k // 8] >> (k % 8)) & 1.
            zs: The Z bits of the pauli string. This array can either be a 1-dimensional
                numpy array with dtype=np.bool8, or a bit packed 1-dimensional numpy
                array with dtype=np.uint8. If the dtype is np.uint8 then the array is
                assumed to be bit packed in little endian order and the "num_qubits"
                argument must be specified. When bit packed, the x bit with offset k is
                stored at (xs[k // 8] >> (k % 8)) & 1.
            sign: Defaults to +1. Set to +1, -1, 1j, or -1j to control the sign of the
                returned Pauli string.
            num_qubits: Must be specified if xs or zs is a bit packed array. Specifies
                the expected length of the Pauli string.

        Returns:
            The created pauli string.

        Examples:
            >>> import stim
            >>> import numpy as np

            >>> xs = np.array([1, 1, 1, 1, 1, 1, 1, 0, 0], dtype=np.bool8)
            >>> zs = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1], dtype=np.bool8)
            >>> stim.PauliString.from_numpy(xs=xs, zs=zs, sign=-1)
            stim.PauliString("-XXXXYYYZZ")

            >>> xs = np.array([127, 0], dtype=np.uint8)
            >>> zs = np.array([240, 1], dtype=np.uint8)
            >>> stim.PauliString.from_numpy(xs=xs, zs=zs, num_qubits=9)
            stim.PauliString("+XXXXYYYZZ")
        """
    @staticmethod
    def from_unitary_matrix(
        matrix: Iterable[Iterable[float]],
        *,
        endian: str = 'little',
    ) -> stim.PauliString:
        """Creates a stim.PauliString from the unitary matrix of a Pauli group member.

        Args:
            matrix: A unitary matrix specified as an iterable of rows, with each row is
                an iterable of amplitudes. The unitary matrix must correspond to a
                Pauli string, including global phase.
            endian:
                "little": matrix entries are in little endian order, where higher index
                    qubits correspond to larger changes in row/col indices.
                "big": matrix entries are in big endian order, where higher index
                    qubits correspond to smaller changes in row/col indices.

        Returns:
            The pauli string equal to the given unitary matrix.

        Raises:
            ValueError: The given matrix isn't the unitary matrix of a Pauli string.

        Examples:
            >>> import stim
            >>> stim.PauliString.from_unitary_matrix([
            ...     [1j, 0],
            ...     [0, -1j],
            ... ], endian='little')
            stim.PauliString("+iZ")

            >>> stim.PauliString.from_unitary_matrix([
            ...     [0, 1, 0, 0],
            ...     [1, 0, 0, 0],
            ...     [0, 0, 0, -1],
            ...     [0, 0, -1, 0],
            ... ], endian='little')
            stim.PauliString("+XZ")
        """
    @staticmethod
    def random(
        num_qubits: int,
        *,
        allow_imaginary: bool = False,
    ) -> stim.PauliString:
        """Samples a uniformly random Hermitian Pauli string.

        Args:
            num_qubits: The number of qubits the Pauli string should act on.
            allow_imaginary: Defaults to False. If True, the sign of the result may be
                1j or -1j in addition to +1 or -1. In other words, setting this to True
                allows the result to be non-Hermitian.

        Examples:
            >>> import stim
            >>> p = stim.PauliString.random(5)
            >>> len(p)
            5
            >>> p.sign in [-1, +1]
            True

            >>> p2 = stim.PauliString.random(3, allow_imaginary=True)
            >>> len(p2)
            3
            >>> p2.sign in [-1, +1, 1j, -1j]
            True

        Returns:
            The sampled Pauli string.
        """
    @property
    def sign(
        self,
    ) -> complex:
        """The sign of the Pauli string. Can be +1, -1, 1j, or -1j.

        Examples:
            >>> import stim
            >>> stim.PauliString("X").sign
            (1+0j)
            >>> stim.PauliString("-X").sign
            (-1+0j)
            >>> stim.PauliString("iX").sign
            1j
            >>> stim.PauliString("-iX").sign
            (-0-1j)
        """
    @sign.setter
    def sign(self, value: complex):
        pass
    def to_numpy(
        self,
        *,
        bit_packed: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Decomposes the contents of the pauli string into X bit and Z bit numpy arrays.

        Args:
            bit_packed: Defaults to False. Determines whether the output numpy arrays
                use dtype=bool8 or dtype=uint8 with 8 bools packed into each byte.

        Returns:
            An (xs, zs) tuple encoding the paulis from the string. The k'th Pauli from
            the string is encoded into k'th bit of xs and the k'th bit of zs using
            the "xz" encoding:

                P=I -> x=0 and z=0
                P=X -> x=1 and z=0
                P=Y -> x=1 and z=1
                P=Z -> x=0 and z=1

            The dtype and shape of the result depends on the bit_packed argument.

            If bit_packed=False:
                Each bit gets its own byte.
                xs.dtype = zs.dtype = np.bool8
                xs.shape = zs.shape = len(self)
                xs_k = xs[k]
                zs_k = zs[k]

            If bit_packed=True:
                Equivalent to applying np.packbits(bitorder='little') to the result.
                xs.dtype = zs.dtype = np.uint8
                xs.shape = zs.shape = math.ceil(len(self) / 8)
                xs_k = (xs[k // 8] >> (k % 8)) & 1
                zs_k = (zs[k // 8] >> (k % 8)) & 1

        Examples:
            >>> import stim

            >>> xs, zs = stim.PauliString("XXXXYYYZZ").to_numpy()
            >>> xs
            array([ True,  True,  True,  True,  True,  True,  True, False, False])
            >>> zs
            array([False, False, False, False,  True,  True,  True,  True,  True])

            >>> xs, zs = stim.PauliString("XXXXYYYZZ").to_numpy(bit_packed=True)
            >>> xs
            array([127,   0], dtype=uint8)
            >>> zs
            array([240,   1], dtype=uint8)
        """
    def to_tableau(
        self,
    ) -> stim.Tableau:
        """Creates a Tableau equivalent to this Pauli string.

        The tableau represents a Clifford operation that multiplies qubits
        by the corresponding Pauli operations from this Pauli string.
        The global phase of the pauli operation is lost in the conversion.

        Returns:
            The created tableau.

        Examples:
            >>> import stim
            >>> p = stim.PauliString("ZZ")
            >>> p.to_tableau()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("-X_"),
                    stim.PauliString("-_X"),
                ],
                zs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_Z"),
                ],
            )
            >>> q = stim.PauliString("YX_Z")
            >>> q.to_tableau()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("-X___"),
                    stim.PauliString("+_X__"),
                    stim.PauliString("+__X_"),
                    stim.PauliString("-___X"),
                ],
                zs=[
                    stim.PauliString("-Z___"),
                    stim.PauliString("-_Z__"),
                    stim.PauliString("+__Z_"),
                    stim.PauliString("+___Z"),
                ],
            )
        """
    def to_unitary_matrix(
        self,
        *,
        endian: str,
    ) -> np.ndarray[np.complex64]:
        """Converts the pauli string into a unitary matrix.

        Args:
            endian:
                "little": The first qubit is the least significant (corresponds
                    to an offset of 1 in the matrix).
                "big": The first qubit is the most significant (corresponds
                    to an offset of 2**(n - 1) in the matrix).

        Returns:
            A numpy array with dtype=np.complex64 and
            shape=(1 << len(pauli_string), 1 << len(pauli_string)).

        Example:
            >>> import stim
            >>> stim.PauliString("-YZ").to_unitary_matrix(endian="little")
            array([[0.+0.j, 0.+1.j, 0.+0.j, 0.+0.j],
                   [0.-1.j, 0.+0.j, 0.+0.j, 0.+0.j],
                   [0.+0.j, 0.+0.j, 0.+0.j, 0.-1.j],
                   [0.+0.j, 0.+0.j, 0.+1.j, 0.+0.j]], dtype=complex64)
        """
class Tableau:
    """A stabilizer tableau.

    Represents a Clifford operation by explicitly storing how that operation
    conjugates a list of Pauli group generators into composite Pauli products.

    Examples:
        >>> import stim
        >>> stim.Tableau.from_named_gate("H")
        stim.Tableau.from_conjugated_generators(
            xs=[
                stim.PauliString("+Z"),
            ],
            zs=[
                stim.PauliString("+X"),
            ],
        )

        >>> t = stim.Tableau.random(5)
        >>> t_inv = t**-1
        >>> print(t * t_inv)
        +-xz-xz-xz-xz-xz-
        | ++ ++ ++ ++ ++
        | XZ __ __ __ __
        | __ XZ __ __ __
        | __ __ XZ __ __
        | __ __ __ XZ __
        | __ __ __ __ XZ

        >>> x2z3 = t.x_output(2) * t.z_output(3)
        >>> t_inv(x2z3)
        stim.PauliString("+__XZ_")
    """
    def __add__(
        self,
        rhs: stim.Tableau,
    ) -> stim.Tableau:
        """Returns the direct sum (diagonal concatenation) of two Tableaus.

        Args:
            rhs: A second stim.Tableau.

        Examples:
            >>> import stim

            >>> s = stim.Tableau.from_named_gate("S")
            >>> cz = stim.Tableau.from_named_gate("CZ")
            >>> print(s + cz)
            +-xz-xz-xz-
            | ++ ++ ++
            | YZ __ __
            | __ XZ Z_
            | __ Z_ XZ

        Returns:
            The direct sum.
        """
    def __call__(
        self,
        pauli_string: stim.PauliString,
    ) -> stim.PauliString:
        """Returns the conjugation of a PauliString by the Tableau's Clifford operation.

        The conjugation of P by C is equal to C**-1 * P * C. If P is a Pauli product
        before C, then P2 = C**-1 * P * C is an equivalent Pauli product after C.

        Args:
            pauli_string: The pauli string to conjugate.

        Returns:
            The new conjugated pauli string.

        Examples:
            >>> import stim
            >>> t = stim.Tableau.from_named_gate("CNOT")
            >>> p = stim.PauliString("XX")
            >>> result = t(p)
            >>> print(result)
            +X_
        """
    def __eq__(
        self,
        arg0: stim.Tableau,
    ) -> bool:
        """Determines if two tableaus have identical contents.
        """
    def __iadd__(
        self,
        rhs: stim.Tableau,
    ) -> stim.Tableau:
        """Performs an inplace direct sum (diagonal concatenation).

        Args:
            rhs: A second stim.Tableau.

        Examples:
            >>> import stim

            >>> s = stim.Tableau.from_named_gate("S")
            >>> cz = stim.Tableau.from_named_gate("CZ")
            >>> alias = s
            >>> s += cz
            >>> alias is s
            True
            >>> print(s)
            +-xz-xz-xz-
            | ++ ++ ++
            | YZ __ __
            | __ XZ Z_
            | __ Z_ XZ

        Returns:
            The mutated tableau.
        """
    def __init__(
        self,
        num_qubits: int,
    ) -> None:
        """Creates an identity tableau over the given number of qubits.

        Examples:
            >>> import stim
            >>> t = stim.Tableau(3)
            >>> print(t)
            +-xz-xz-xz-
            | ++ ++ ++
            | XZ __ __
            | __ XZ __
            | __ __ XZ

        Args:
            num_qubits: The number of qubits the tableau's operation acts on.
        """
    def __len__(
        self,
    ) -> int:
        """Returns the number of qubits operated on by the tableau.
        """
    def __mul__(
        self,
        rhs: stim.Tableau,
    ) -> stim.Tableau:
        """Returns the product of two tableaus.

        If the tableau T1 represents the Clifford operation with unitary C1,
        and the tableau T2 represents the Clifford operation with unitary C2,
        then the tableau T1*T2 represents the Clifford operation with unitary C1*C2.

        Args:
            rhs: The tableau  on the right hand side of the multiplication.

        Examples:
            >>> import stim
            >>> t1 = stim.Tableau.random(4)
            >>> t2 = stim.Tableau.random(4)
            >>> t3 = t2 * t1
            >>> p = stim.PauliString.random(4)
            >>> t3(p) == t2(t1(p))
            True
        """
    def __ne__(
        self,
        arg0: stim.Tableau,
    ) -> bool:
        """Determines if two tableaus have non-identical contents.
        """
    def __pow__(
        self,
        exponent: int,
    ) -> stim.Tableau:
        """Raises the tableau to an integer power.

        Large powers are reached efficiently using repeated squaring.
        Negative powers are reached by inverting the tableau.

        Args:
            exponent: The power to raise to. Can be negative, zero, or positive.

        Examples:
            >>> import stim
            >>> s = stim.Tableau.from_named_gate("S")
            >>> s**0 == stim.Tableau(1)
            True
            >>> s**1 == s
            True
            >>> s**2 == stim.Tableau.from_named_gate("Z")
            True
            >>> s**-1 == s**3 == stim.Tableau.from_named_gate("S_DAG")
            True
            >>> s**5 == s
            True
            >>> s**(400000000 + 1) == s
            True
            >>> s**(-400000000 + 1) == s
            True
        """
    def __repr__(
        self,
    ) -> str:
        """Returns valid python code evaluating to an equal `stim.Tableau`.
        """
    def __str__(
        self,
    ) -> str:
        """Returns a text description.
        """
    def append(
        self,
        gate: stim.Tableau,
        targets: List[int],
    ) -> None:
        """Appends an operation's effect into this tableau, mutating this tableau.

        Time cost is O(n*m*m) where n=len(self) and m=len(gate).

        Args:
            gate: The tableau of the operation being appended into this tableau.
            targets: The qubits being targeted by the gate.

        Examples:
            >>> import stim
            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> t = stim.Tableau(2)
            >>> t.append(cnot, [0, 1])
            >>> t.append(cnot, [1, 0])
            >>> t.append(cnot, [0, 1])
            >>> t == stim.Tableau.from_named_gate("SWAP")
            True
        """
    def copy(
        self,
    ) -> stim.Tableau:
        """Returns a copy of the tableau. An independent tableau with the same contents.

        Examples:
            >>> import stim
            >>> t1 = stim.Tableau.random(2)
            >>> t2 = t1.copy()
            >>> t2 is t1
            False
            >>> t2 == t1
            True
        """
    @staticmethod
    def from_circuit(
        circuit: stim.Circuit,
        *,
        ignore_noise: bool = False,
        ignore_measurement: bool = False,
        ignore_reset: bool = False,
    ) -> stim.Tableau:
        """Converts a circuit into an equivalent stabilizer tableau.

        Args:
            circuit: The circuit to compile into a tableau.
            ignore_noise: Defaults to False. When False, any noise operations in the
                circuit will cause the conversion to fail with an exception. When True,
                noise operations are skipped over as if they weren't even present in the
                circuit.
            ignore_measurement: Defaults to False. When False, any measurement
                operations in the circuit will cause the conversion to fail with an
                exception. When True, measurement operations are skipped over as if they
                weren't even present in the circuit.
            ignore_reset: Defaults to False. When False, any reset operations in the
                circuit will cause the conversion to fail with an exception. When True,
                reset operations are skipped over as if they weren't even present in the
                circuit.

        Returns:
            The tableau equivalent to the given circuit (up to global phase).

        Raises:
            ValueError:
                The circuit contains noise operations but ignore_noise=False.
                OR
                The circuit contains measurement operations but
                ignore_measurement=False.
                OR
                The circuit contains reset operations but ignore_reset=False.

        Examples:
            >>> import stim
            >>> stim.Tableau.from_circuit(stim.Circuit('''
            ...     H 0
            ...     CNOT 0 1
            ... '''))
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+ZZ"),
                ],
            )
        """
    @staticmethod
    def from_conjugated_generators(
        *,
        xs: List[stim.PauliString],
        zs: List[stim.PauliString],
    ) -> stim.Tableau:
        """Creates a tableau from the given outputs for each generator.

        Verifies that the tableau is well formed.

        Args:
            xs: A List[stim.PauliString] with the results of conjugating X0, X1, etc.
            zs: A List[stim.PauliString] with the results of conjugating Z0, Z1, etc.

        Returns:
            The created tableau.

        Raises:
            ValueError: The given outputs are malformed. Their lengths are inconsistent,
                or they don't satisfy the required commutation relationships.

        Examples:
            >>> import stim
            >>> identity3 = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("X__"),
            ...         stim.PauliString("_X_"),
            ...         stim.PauliString("__X"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("Z__"),
            ...         stim.PauliString("_Z_"),
            ...         stim.PauliString("__Z"),
            ...     ],
            ... )
            >>> identity3 == stim.Tableau(3)
            True
        """
    @staticmethod
    def from_named_gate(
        name: str,
    ) -> stim.Tableau:
        """Returns the tableau of a named Clifford gate.

        Args:
            name: The name of the Clifford gate.

        Returns:
            The gate's tableau.

        Examples:
            >>> import stim
            >>> print(stim.Tableau.from_named_gate("H"))
            +-xz-
            | ++
            | ZX
            >>> print(stim.Tableau.from_named_gate("CNOT"))
            +-xz-xz-
            | ++ ++
            | XZ _Z
            | X_ XZ
            >>> print(stim.Tableau.from_named_gate("S"))
            +-xz-
            | ++
            | YZ
        """
    def from_numpy(
        self,
        *,
        bit_packed: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """Creates a tableau from numpy arrays x2x, x2z, z2x, z2z, x_signs, and z_signs.

        The x2x, x2z, z2x, z2z arrays are the four quadrants of the table defined in
        Aaronson and Gottesman's "Improved Simulation of Stabilizer Circuits"
        ( https://arxiv.org/abs/quant-ph/0406196 ).

        Args:
            x2x: A 2d numpy array containing the x-to-x coupling bits. The bits can be
                bit packed (dtype=uint8) or not (dtype=bool8). When not bit packed, the
                result will satisfy result.x_output_pauli(i, j) in [1, 2] == x2x[i, j].
                Bit packing must be in little endian order and only applies to the
                second axis.
            x2z: A 2d numpy array containing the x-to-z coupling bits. The bits can be
                bit packed (dtype=uint8) or not (dtype=bool8). When not bit packed, the
                result will satisfy result.x_output_pauli(i, j) in [2, 3] == x2z[i, j].
                Bit packing must be in little endian order and only applies to the
                second axis.
            z2x: A 2d numpy array containing the z-to-x coupling bits. The bits can be
                bit packed (dtype=uint8) or not (dtype=bool8). When not bit packed, the
                result will satisfy result.z_output_pauli(i, j) in [1, 2] == z2x[i, j].
                Bit packing must be in little endian order and only applies to the
                second axis.
            z2z: A 2d numpy array containing the z-to-z coupling bits. The bits can be
                bit packed (dtype=uint8) or not (dtype=bool8). When not bit packed, the
                result will satisfy result.z_output_pauli(i, j) in [2, 3] == z2z[i, j].
                Bit packing must be in little endian order and only applies to the
                second axis.
            x_signs: Defaults to all-positive if not specified. A 1d numpy array
                containing the sign bits for the X generator outputs. False means
                positive and True means negative. The bits can be bit packed
                (dtype=uint8) or not (dtype=bool8). Bit packing must be in little endian
                order.
            z_signs: Defaults to all-positive if not specified. A 1d numpy array
                containing the sign bits for the Z generator outputs. False means
                positive and True means negative. The bits can be bit packed
                (dtype=uint8) or not (dtype=bool8). Bit packing must be in little endian
                order.

        Returns:
            The tableau created from the numpy data.

        Examples:
            >>> import stim
            >>> import numpy as np

            >>> tableau = stim.Tableau.from_numpy(
            ...     x2x=np.array([[1, 1], [0, 1]], dtype=np.bool8),
            ...     z2x=np.array([[0, 0], [0, 0]], dtype=np.bool8),
            ...     x2z=np.array([[0, 0], [0, 0]], dtype=np.bool8),
            ...     z2z=np.array([[1, 0], [1, 1]], dtype=np.bool8),
            ... )
            >>> print(repr(tableau))
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+ZZ"),
                ],
            )
            >>> tableau == stim.Tableau.from_named_gate("CNOT")
            True

            >>> tableau = stim.Tableau.from_numpy(
            ...     x2x=np.array([[9], [5], [7], [6]], dtype=np.uint8),
            ...     x2z=np.array([[13], [13], [0], [3]], dtype=np.uint8),
            ...     z2x=np.array([[8], [5], [9], [15]], dtype=np.uint8),
            ...     z2z=np.array([[6], [11], [2], [3]], dtype=np.uint8),
            ...     x_signs=np.array([7], dtype=np.uint8),
            ...     z_signs=np.array([9], dtype=np.uint8),
            ... )
            >>> print(repr(tableau))
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("-Y_ZY"),
                    stim.PauliString("-Y_YZ"),
                    stim.PauliString("-XXX_"),
                    stim.PauliString("+ZYX_"),
                ],
                zs=[
                    stim.PauliString("-_ZZX"),
                    stim.PauliString("+YZXZ"),
                    stim.PauliString("+XZ_X"),
                    stim.PauliString("-YYXX"),
                ],
            )
        """
    @staticmethod
    def from_stabilizers(
        stabilizers: Iterable[stim.PauliString],
        *,
        allow_redundant: bool = False,
        allow_underconstrained: bool = False,
    ) -> stim.Tableau:
        """Creates a tableau representing a state with the given stabilizers.

        Args:
            stabilizers: A list of `stim.PauliString`s specifying the stabilizers that
                the state must have. It is permitted for stabilizers to have different
                lengths. All stabilizers are padded up to the length of the longest
                stabilizer by appending identity terms.
            allow_redundant: Defaults to False. If set to False, then the given
                stabilizers must all be independent. If any one of them is a product of
                the others (including the empty product), an exception will be raised.
                If set to True, then redundant stabilizers are simply ignored.
            allow_underconstrained: Defaults to False. If set to False, then the given
                stabilizers must form a complete set of generators. They must exactly
                specify the desired stabilizer state, with no degrees of freedom left
                over. For an n-qubit state there must be n independent stabilizers. If
                set to True, then there can be leftover degrees of freedom which can be
                set arbitrarily.

        Returns:
            A tableau which, when applied to the all-zeroes state, produces a state
            with the given stabilizers.

            Guarantees that result.z_output(k) will be equal to the k'th independent
            stabilizer from the `stabilizers` argument.

        Raises:
            ValueError:
                A stabilizer is redundant but allow_redundant=True wasn't set.
                OR
                The given stabilizers are contradictory (e.g. "+Z" and "-Z" both
                specified).
                OR
                The given stabilizers anticommute (e.g. "+Z" and "+X" both specified).
                OR
                The stabilizers left behind a degree of freedom but
                allow_underconstrained=True wasn't set.
                OR
                A stabilizer has an imaginary sign (i or -i).

        Examples:

            >>> import stim
            >>> stim.Tableau.from_stabilizers([
            ...     stim.PauliString("XX"),
            ...     stim.PauliString("ZZ"),
            ... ])
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+ZZ"),
                ],
            )

            >>> stim.Tableau.from_stabilizers([
            ...     stim.PauliString("XX_"),
            ...     stim.PauliString("ZZ_"),
            ...     stim.PauliString("-YY_"),
            ...     stim.PauliString(""),
            ... ], allow_underconstrained=True, allow_redundant=True)
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z__"),
                    stim.PauliString("+_X_"),
                    stim.PauliString("+__X"),
                ],
                zs=[
                    stim.PauliString("+XX_"),
                    stim.PauliString("+ZZ_"),
                    stim.PauliString("+__Z"),
                ],
            )
        """
    def from_state_vector(
        self,
        state_vector: Iterable[float],
        *,
        endian: str,
    ) -> stim.Tableau:
        """Creates a tableau representing the stabilizer state of the given state vector.

        Args:
            state_vector: A list of complex amplitudes specifying a superposition. The
                vector must correspond to a state that is reachable using Clifford
                operations, and must be normalized (i.e. it must be a unit vector).
            endian:
                "little": state vector is in little endian order, where higher index
                    qubits correspond to larger changes in the state index.
                "big": state vector is in big endian order, where higher index qubits
                    correspond to smaller changes in the state index.

        Returns:
            A tableau which, when applied to the all-zeroes state, produces a state
            with the given state vector.

        Raises:
            ValueError:
                The given state vector isn't a list of complex values specifying a
                stabilizer state.
                OR
                The given endian value isn't 'little' or 'big'.

        Examples:

            >>> import stim
            >>> stim.Tableau.from_state_vector([
            ...     0.5**0.5,
            ...     0.5**0.5 * 1j,
            ... ], endian='little')
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z"),
                ],
                zs=[
                    stim.PauliString("+Y"),
                ],
            )
            >>> stim.Tableau.from_state_vector([
            ...     0.5**0.5,
            ...     0,
            ...     0,
            ...     0.5**0.5,
            ... ], endian='little')
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+ZZ"),
                ],
            )
        """
    @staticmethod
    def from_unitary_matrix(
        matrix: Iterable[Iterable[float]],
        *,
        endian: str = 'little',
    ) -> stim.Tableau:
        """Creates a tableau from the unitary matrix of a Clifford operation.

        Args:
            matrix: A unitary matrix specified as an iterable of rows, with each row is
                an iterable of amplitudes. The unitary matrix must correspond to a
                Clifford operation.
            endian:
                "little": matrix entries are in little endian order, where higher index
                    qubits correspond to larger changes in row/col indices.
                "big": matrix entries are in big endian order, where higher index
                    qubits correspond to smaller changes in row/col indices.
        Returns:
            The tableau equivalent to the given unitary matrix (up to global phase).

        Raises:
            ValueError: The given matrix isn't the unitary matrix of a Clifford
                operation.

        Examples:
            >>> import stim
            >>> stim.Tableau.from_unitary_matrix([
            ...     [1, 0],
            ...     [0, 1j],
            ... ], endian='little')
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Y"),
                ],
                zs=[
                    stim.PauliString("+Z"),
                ],
            )

            >>> stim.Tableau.from_unitary_matrix([
            ...     [1, 0, 0, 0],
            ...     [0, 1, 0, 0],
            ...     [0, 0, 0, -1j],
            ...     [0, 0, 1j, 0],
            ... ], endian='little')
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+XZ"),
                    stim.PauliString("+YX"),
                ],
                zs=[
                    stim.PauliString("+ZZ"),
                    stim.PauliString("+_Z"),
                ],
            )
        """
    def inverse(
        self,
        *,
        unsigned: bool = False,
    ) -> stim.Tableau:
        """Computes the inverse of the tableau.

        The inverse T^-1 of a tableau T is the unique tableau with the property that
        T * T^-1 = T^-1 * T = I where I is the identity tableau.

        Args:
            unsigned: Defaults to false. When set to true, skips computing the signs of
                the output observables and instead just set them all to be positive.
                This is beneficial because computing the signs takes O(n^3) time and the
                rest of the inverse computation is O(n^2) where n is the number of
                qubits in the tableau. So, if you only need the Pauli terms (not the
                signs), it is significantly cheaper.

        Returns:
            The inverse tableau.

        Examples:
            >>> import stim

            >>> # Check that the inverse agrees with hard-coded tableaus.
            >>> s = stim.Tableau.from_named_gate("S")
            >>> s_dag = stim.Tableau.from_named_gate("S_DAG")
            >>> s.inverse() == s_dag
            True
            >>> z = stim.Tableau.from_named_gate("Z")
            >>> z.inverse() == z
            True

            >>> # Check that multiplying by the inverse produces the identity.
            >>> t = stim.Tableau.random(10)
            >>> t_inv = t.inverse()
            >>> identity = stim.Tableau(10)
            >>> t * t_inv == t_inv * t == identity
            True

            >>> # Check a manual case.
            >>> t = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("-__Z"),
            ...         stim.PauliString("+XZ_"),
            ...         stim.PauliString("+_ZZ"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("-YYY"),
            ...         stim.PauliString("+Z_Z"),
            ...         stim.PauliString("-ZYZ")
            ...     ],
            ... )
            >>> print(t.inverse())
            +-xz-xz-xz-
            | -- +- --
            | XX XX YX
            | XZ Z_ X_
            | X_ YX Y_
            >>> print(t.inverse(unsigned=True))
            +-xz-xz-xz-
            | ++ ++ ++
            | XX XX YX
            | XZ Z_ X_
            | X_ YX Y_
        """
    def inverse_x_output(
        self,
        input_index: int,
        *,
        unsigned: bool = False,
    ) -> stim.PauliString:
        """Conjugates a single-qubit X Pauli generator by the inverse of the tableau.

        A faster version of `tableau.inverse(unsigned).x_output(input_index)`.

        Args:
            input_index: Identifies the column (the qubit of the X generator) to return
                from the inverse tableau.
            unsigned: Defaults to false. When set to true, skips computing the result's
                sign and instead just sets it to positive. This is beneficial because
                computing the sign takes O(n^2) time whereas all other parts of the
                computation take O(n) time where n is the number of qubits in the
                tableau.

        Returns:
            The result of conjugating an X generator by the inverse of the tableau.

        Examples:
            >>> import stim

            # Check equivalence with the inverse's x_output.
            >>> t = stim.Tableau.random(4)
            >>> expected = t.inverse().x_output(0)
            >>> t.inverse_x_output(0) == expected
            True
            >>> expected.sign = +1
            >>> t.inverse_x_output(0, unsigned=True) == expected
            True
        """
    def inverse_x_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.inverse().x_output(input_index)[output_index]`

        Args:
            input_index: Identifies the column (the qubit of the input X generator) in
                the inverse tableau.
            output_index: Identifies the row (the output qubit) in the inverse tableau.

        Returns:
            An integer identifying Pauli at the given location in the inverse tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t_inv = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... ).inverse()
            >>> t_inv.inverse_x_output_pauli(0, 0)
            2
            >>> t_inv.inverse_x_output_pauli(0, 1)
            0
            >>> t_inv.inverse_x_output_pauli(1, 0)
            2
            >>> t_inv.inverse_x_output_pauli(1, 1)
            3
        """
    def inverse_y_output(
        self,
        input_index: int,
        *,
        unsigned: bool = False,
    ) -> stim.PauliString:
        """Conjugates a single-qubit Y Pauli generator by the inverse of the tableau.

        A faster version of `tableau.inverse(unsigned).y_output(input_index)`.

        Args:
            input_index: Identifies the column (the qubit of the Y generator) to return
                from the inverse tableau.
            unsigned: Defaults to false. When set to true, skips computing the result's
                sign and instead just sets it to positive. This is beneficial because
                computing the sign takes O(n^2) time whereas all other parts of the
                computation take O(n) time where n is the number of qubits in the
                tableau.

        Returns:
            The result of conjugating a Y generator by the inverse of the tableau.

        Examples:
            >>> import stim

            # Check equivalence with the inverse's y_output.
            >>> t = stim.Tableau.random(4)
            >>> expected = t.inverse().y_output(0)
            >>> t.inverse_y_output(0) == expected
            True
            >>> expected.sign = +1
            >>> t.inverse_y_output(0, unsigned=True) == expected
            True
        """
    def inverse_y_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.inverse().y_output(input_index)[output_index]`

        Args:
            input_index: Identifies the column (the qubit of the input Y generator) in
                the inverse tableau.
            output_index: Identifies the row (the output qubit) in the inverse tableau.

        Returns:
            An integer identifying Pauli at the given location in the inverse tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t_inv = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... ).inverse()
            >>> t_inv.inverse_y_output_pauli(0, 0)
            1
            >>> t_inv.inverse_y_output_pauli(0, 1)
            2
            >>> t_inv.inverse_y_output_pauli(1, 0)
            0
            >>> t_inv.inverse_y_output_pauli(1, 1)
            2
        """
    def inverse_z_output(
        self,
        input_index: int,
        *,
        unsigned: bool = False,
    ) -> stim.PauliString:
        """Conjugates a single-qubit Z Pauli generator by the inverse of the tableau.

        A faster version of `tableau.inverse(unsigned).z_output(input_index)`.

        Args:
            input_index: Identifies the column (the qubit of the Z generator) to return
                from the inverse tableau.
            unsigned: Defaults to false. When set to true, skips computing the result's
                sign and instead just sets it to positive. This is beneficial because
                computing the sign takes O(n^2) time whereas all other parts of the
                computation take O(n) time where n is the number of qubits in the
                tableau.

        Returns:
            The result of conjugating a Z generator by the inverse of the tableau.

        Examples:
            >>> import stim

            >>> import stim

            # Check equivalence with the inverse's z_output.
            >>> t = stim.Tableau.random(4)
            >>> expected = t.inverse().z_output(0)
            >>> t.inverse_z_output(0) == expected
            True
            >>> expected.sign = +1
            >>> t.inverse_z_output(0, unsigned=True) == expected
            True
        """
    def inverse_z_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.inverse().z_output(input_index)[output_index]`

        Args:
            input_index: Identifies the column (the qubit of the input Z generator) in
                the inverse tableau.
            output_index: Identifies the row (the output qubit) in the inverse tableau.

        Returns:
            An integer identifying Pauli at the given location in the inverse tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t_inv = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... ).inverse()
            >>> t_inv.inverse_z_output_pauli(0, 0)
            3
            >>> t_inv.inverse_z_output_pauli(0, 1)
            2
            >>> t_inv.inverse_z_output_pauli(1, 0)
            2
            >>> t_inv.inverse_z_output_pauli(1, 1)
            1
        """
    @staticmethod
    def iter_all(
        num_qubits: int,
        *,
        unsigned: bool = False,
    ) -> stim.TableauIterator:
        """Returns an iterator that iterates over all Tableaus of a given size.

        Args:
            num_qubits: The size of tableau to iterate over.
            unsigned: Defaults to False. If set to True, only tableaus where
                all columns have positive sign are yielded by the iterator.
                This substantially reduces the total number of tableaus to
                iterate over.

        Returns:
            An Iterable[stim.Tableau] that yields the requested tableaus.

        Examples:
            >>> import stim
            >>> single_qubit_gate_reprs = set()
            >>> for t in stim.Tableau.iter_all(1):
            ...     single_qubit_gate_reprs.add(repr(t))
            >>> len(single_qubit_gate_reprs)
            24

            >>> num_2q_gates_mod_paulis = 0
            >>> for _ in stim.Tableau.iter_all(2, unsigned=True):
            ...     num_2q_gates_mod_paulis += 1
            >>> num_2q_gates_mod_paulis
            720
        """
    def prepend(
        self,
        gate: stim.Tableau,
        targets: List[int],
    ) -> None:
        """Prepends an operation's effect into this tableau, mutating this tableau.

        Time cost is O(n*m*m) where n=len(self) and m=len(gate).

        Args:
            gate: The tableau of the operation being prepended into this tableau.
            targets: The qubits being targeted by the gate.

        Examples:
            >>> import stim
            >>> h = stim.Tableau.from_named_gate("H")
            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> t = stim.Tableau.from_named_gate("H")
            >>> t.prepend(stim.Tableau.from_named_gate("X"), [0])
            >>> t == stim.Tableau.from_named_gate("SQRT_Y_DAG")
            True
        """
    @staticmethod
    def random(
        num_qubits: int,
    ) -> stim.Tableau:
        """Samples a uniformly random Clifford operation and returns its tableau.

        Args:
            num_qubits: The number of qubits the tableau should act on.

        Returns:
            The sampled tableau.

        Examples:
            >>> import stim
            >>> t = stim.Tableau.random(42)

        References:
            "Hadamard-free circuits expose the structure of the Clifford group"
            Sergey Bravyi, Dmitri Maslov
            https://arxiv.org/abs/2003.09412
        """
    def then(
        self,
        second: stim.Tableau,
    ) -> stim.Tableau:
        """Returns the result of composing two tableaus.

        If the tableau T1 represents the Clifford operation with unitary C1,
        and the tableau T2 represents the Clifford operation with unitary C2,
        then the tableau T1.then(T2) represents the Clifford operation with unitary
        C2*C1.

        Args:
            second: The result is equivalent to applying the second tableau after
                the receiving tableau.

        Examples:
            >>> import stim
            >>> t1 = stim.Tableau.random(4)
            >>> t2 = stim.Tableau.random(4)
            >>> t3 = t1.then(t2)
            >>> p = stim.PauliString.random(4)
            >>> t3(p) == t2(t1(p))
            True
        """
    def to_circuit(
        self,
        *,
        method: str,
    ) -> stim.Circuit:
        """Synthesizes a circuit that implements the tableau's Clifford operation.

        The circuits returned by this method are not guaranteed to be stable
        from version to version, and may be produced using randomization.

        Args:
            method: The method to use when synthesizing the circuit. Available values:
                "elimination": Cancels off-diagonal terms using Gaussian elimination.
                    Gate set: H, S, CX
                    Circuit qubit count: n
                    Circuit operation count: O(n^2)
                    Circuit depth: O(n^2)

        Returns:
            The synthesized circuit.

        Example:
            >>> import stim
            >>> tableau = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("-_YZ"),
            ...         stim.PauliString("-YY_"),
            ...         stim.PauliString("-XZX"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("+Y_Y"),
            ...         stim.PauliString("-_XY"),
            ...         stim.PauliString("-Y__"),
            ...     ],
            ... )
            >>> tableau.to_circuit(method="elimination")
            stim.Circuit('''
                CX 2 0 0 2 2 0
                S 0
                H 0
                S 0
                H 1
                CX 0 1 0 2
                H 1 2
                CX 1 0 2 0 2 1 1 2 2 1
                H 1
                S 1 2
                H 2
                CX 2 1
                S 2
                H 0 1 2
                S 0 0 1 1 2 2
                H 0 1 2
                S 1 1 2 2
            ''')
        """
    def to_numpy(
        self,
        *,
        bit_packed: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """Decomposes the contents of the tableau into six numpy arrays.

        The first four numpy arrays correspond to the four quadrants of the table
        defined in Aaronson and Gottesman's "Improved Simulation of Stabilizer Circuits"
        ( https://arxiv.org/abs/quant-ph/0406196 ).

        The last two numpy arrays are the X and Z sign bit vectors of the tableau.

        Args:
            bit_packed: Defaults to False. Determines whether the output numpy arrays
                use dtype=bool8 or dtype=uint8 with 8 bools packed into each byte.

        Returns:
            An (x2x, x2z, z2x, z2z, x_signs, z_signs) tuple encoding the tableau.

            x2x: A 2d table of whether tableau(X_i)_j is X or Y (instead of I or Z).
            x2z: A 2d table of whether tableau(X_i)_j is Z or Y (instead of I or X).
            z2x: A 2d table of whether tableau(Z_i)_j is X or Y (instead of I or Z).
            z2z: A 2d table of whether tableau(Z_i)_j is Z or Y (instead of I or X).
            x_signs: A vector of whether tableau(X_i) is negative.
            z_signs: A vector of whether tableau(Z_i) is negative.

            If bit_packed=False then:
                *.dtype = = np.bool8
                *2*.shape = (len(tableau), len(tableau))
                *_signs.shape = len(tableau)
                x2x[i, j] = tableau.x_output_pauli(i, j) in [1, 2]
                x2z[i, j] = tableau.x_output_pauli(i, j) in [2, 3]
                z2x[i, j] = tableau.z_output_pauli(i, j) in [1, 2]
                z2z[i, j] = tableau.z_output_pauli(i, j) in [2, 3]

            If bit_packed=True then:
                *.dtype = = np.uint8
                *2*.shape = (len(tableau), math.ceil(len(tableau) / 8))
                *_signs.shape = math.ceil(len(tableau) / 8)
                (x2x[i, j // 8] >> (j % 8)) & 1 = tableau.x_output_pauli(i, j) in [1, 2]
                (x2z[i, j // 8] >> (j % 8)) & 1 = tableau.x_output_pauli(i, j) in [2, 3]
                (z2x[i, j // 8] >> (j % 8)) & 1 = tableau.z_output_pauli(i, j) in [1, 2]
                (z2z[i, j // 8] >> (j % 8)) & 1 = tableau.z_output_pauli(i, j) in [2, 3]

        Examples:
            >>> import stim
            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> print(repr(cnot))
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+ZZ"),
                ],
            )
            >>> x2x, x2z, z2x, z2z, x_signs, z_signs = cnot.to_numpy()
            >>> x2x
            array([[ True,  True],
                   [False,  True]])
            >>> x2z
            array([[False, False],
                   [False, False]])
            >>> z2x
            array([[False, False],
                   [False, False]])
            >>> z2z
            array([[ True, False],
                   [ True,  True]])
            >>> x_signs
            array([False, False])
            >>> z_signs
            array([False, False])

            >>> t = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("-Y_ZY"),
            ...         stim.PauliString("-Y_YZ"),
            ...         stim.PauliString("-XXX_"),
            ...         stim.PauliString("+ZYX_"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("-_ZZX"),
            ...         stim.PauliString("+YZXZ"),
            ...         stim.PauliString("+XZ_X"),
            ...         stim.PauliString("-YYXX"),
            ...     ],
            ... )

            >>> x2x, x2z, z2x, z2z, x_signs, z_signs = t.to_numpy()
            >>> x2x
            array([[ True, False, False,  True],
                   [ True, False,  True, False],
                   [ True,  True,  True, False],
                   [False,  True,  True, False]])
            >>> x2z
            array([[ True, False,  True,  True],
                   [ True, False,  True,  True],
                   [False, False, False, False],
                   [ True,  True, False, False]])
            >>> z2x
            array([[False, False, False,  True],
                   [ True, False,  True, False],
                   [ True, False, False,  True],
                   [ True,  True,  True,  True]])
            >>> z2z
            array([[False,  True,  True, False],
                   [ True,  True, False,  True],
                   [False,  True, False, False],
                   [ True,  True, False, False]])
            >>> x_signs
            array([ True,  True,  True, False])
            >>> z_signs
            array([ True, False, False,  True])

            >>> x2x, x2z, z2x, z2z, x_signs, z_signs = t.to_numpy(bit_packed=True)
            >>> x2x
            array([[9],
                   [5],
                   [7],
                   [6]], dtype=uint8)
            >>> x2z
            array([[13],
                   [13],
                   [ 0],
                   [ 3]], dtype=uint8)
            >>> z2x
            array([[ 8],
                   [ 5],
                   [ 9],
                   [15]], dtype=uint8)
            >>> z2z
            array([[ 6],
                   [11],
                   [ 2],
                   [ 3]], dtype=uint8)
            >>> x_signs
            array([7], dtype=uint8)
            >>> z_signs
            array([9], dtype=uint8)
        """
    def to_pauli_string(
        self,
    ) -> stim.PauliString:
        """Return a Pauli string equivalent to the tableau.

        If the tableau is equivalent to a pauli product, creates
        an equivalent pauli string. If not, then an error is raised.

        Returns:
            The created pauli string

        Raises:
            ValueError: The Tableau isn't equivalent to a Pauli product.

        Example:
            >>> import stim
            >>> t = (stim.Tableau.from_named_gate("Z") +
            ...      stim.Tableau.from_named_gate("Y") +
            ...      stim.Tableau.from_named_gate("I") +
            ...      stim.Tableau.from_named_gate("X"))
            >>> print(t)
            +-xz-xz-xz-xz-
            | -+ -- ++ +-
            | XZ __ __ __
            | __ XZ __ __
            | __ __ XZ __
            | __ __ __ XZ
            >>> print(t.to_pauli_string())
            +ZY_X
        """
    def to_state_vector(
        self,
        *,
        endian: str = 'little',
    ) -> np.ndarray[np.complex64]:
        """Returns the state vector produced by applying the tableau to the |0..0> state.

        This function takes O(n * 2**n) time and O(2**n) space, where n is the number of
        qubits. The computation is done by initialization a random state vector and
        iteratively projecting it into the +1 eigenspace of each stabilizer of the
        state. The state is then canonicalized so that zero values are actually exactly
        0, and so that the first non-zero entry is positive.

        Args:
            endian:
                "little" (default): state vector is in little endian order, where higher
                    index qubits correspond to larger changes in the state index.
                "big": state vector is in big endian order, where higher index qubits
                    correspond to smaller changes in the state index.

        Returns:
            A `numpy.ndarray[numpy.complex64]` of computational basis amplitudes.

            If the result is in little endian order then the amplitude at offset
            b_0 + b_1*2 + b_2*4 + ... + b_{n-1}*2^{n-1} is the amplitude for the
            computational basis state where the qubit with index 0 is storing the bit
            b_0, the qubit with index 1 is storing the bit b_1, etc.

            If the result is in big endian order then the amplitude at offset
            b_0 + b_1*2 + b_2*4 + ... + b_{n-1}*2^{n-1} is the amplitude for the
            computational basis state where the qubit with index 0 is storing the bit
            b_{n-1}, the qubit with index 1 is storing the bit b_{n-2}, etc.

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> i2 = stim.Tableau.from_named_gate('I')
            >>> x = stim.Tableau.from_named_gate('X')
            >>> h = stim.Tableau.from_named_gate('H')

            >>> (x + i2).to_state_vector(endian='little')
            array([0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], dtype=complex64)

            >>> (i2 + x).to_state_vector(endian='little')
            array([0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j], dtype=complex64)

            >>> (i2 + x).to_state_vector(endian='big')
            array([0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], dtype=complex64)

            >>> (h + h).to_state_vector(endian='little')
            array([0.5+0.j, 0.5+0.j, 0.5+0.j, 0.5+0.j], dtype=complex64)
        """
    def to_unitary_matrix(
        self,
        *,
        endian: str,
    ) -> np.ndarray[np.complex64]:
        """Converts the tableau into a unitary matrix.

        Args:
            endian:
                "little": The first qubit is the least significant (corresponds
                    to an offset of 1 in the state vector).
                "big": The first qubit is the most significant (corresponds
                    to an offset of 2**(n - 1) in the state vector).

        Returns:
            A numpy array with dtype=np.complex64 and
            shape=(1 << len(tableau), 1 << len(tableau)).

        Example:
            >>> import stim
            >>> cnot = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("XX"),
            ...         stim.PauliString("_X"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("Z_"),
            ...         stim.PauliString("ZZ"),
            ...     ],
            ... )
            >>> cnot.to_unitary_matrix(endian='big')
            array([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
                   [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],
                   [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],
                   [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j]], dtype=complex64)
        """
    def x_output(
        self,
        target: int,
    ) -> stim.PauliString:
        """Returns the result of conjugating a Pauli X by the tableau's Clifford operation.

        Args:
            target: The qubit targeted by the Pauli X operation.

        Examples:
            >>> import stim
            >>> h = stim.Tableau.from_named_gate("H")
            >>> h.x_output(0)
            stim.PauliString("+Z")

            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> cnot.x_output(0)
            stim.PauliString("+XX")
            >>> cnot.x_output(1)
            stim.PauliString("+_X")
        """
    def x_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.x_output(input_index)[output_index]`

        Args:
            input_index: Identifies the tableau column (the qubit of the input X
                generator).
            output_index: Identifies the tableau row (the output qubit).

        Returns:
            An integer identifying Pauli at the given location in the tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... )
            >>> t.x_output_pauli(0, 0)
            2
            >>> t.x_output_pauli(0, 1)
            0
            >>> t.x_output_pauli(1, 0)
            2
            >>> t.x_output_pauli(1, 1)
            3
        """
    def y_output(
        self,
        target: int,
    ) -> stim.PauliString:
        """Returns the result of conjugating a Pauli Y by the tableau's Clifford operation.

        Args:
            target: The qubit targeted by the Pauli Y operation.

        Examples:
            >>> import stim
            >>> h = stim.Tableau.from_named_gate("H")
            >>> h.y_output(0)
            stim.PauliString("-Y")

            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> cnot.y_output(0)
            stim.PauliString("+YX")
            >>> cnot.y_output(1)
            stim.PauliString("+ZY")
        """
    def y_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.y_output(input_index)[output_index]`

        Args:
            input_index: Identifies the tableau column (the qubit of the input Y
                generator).
            output_index: Identifies the tableau row (the output qubit).

        Returns:
            An integer identifying Pauli at the given location in the tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... )
            >>> t.y_output_pauli(0, 0)
            1
            >>> t.y_output_pauli(0, 1)
            2
            >>> t.y_output_pauli(1, 0)
            0
            >>> t.y_output_pauli(1, 1)
            2
        """
    def z_output(
        self,
        target: int,
    ) -> stim.PauliString:
        """Returns the result of conjugating a Pauli Z by the tableau's Clifford operation.

        Args:
            target: The qubit targeted by the Pauli Z operation.

        Examples:
            >>> import stim
            >>> h = stim.Tableau.from_named_gate("H")
            >>> h.z_output(0)
            stim.PauliString("+X")

            >>> cnot = stim.Tableau.from_named_gate("CNOT")
            >>> cnot.z_output(0)
            stim.PauliString("+Z_")
            >>> cnot.z_output(1)
            stim.PauliString("+ZZ")
        """
    def z_output_pauli(
        self,
        input_index: int,
        output_index: int,
    ) -> int:
        """Constant-time version of `tableau.z_output(input_index)[output_index]`

        Args:
            input_index: Identifies the tableau column (the qubit of the input Z
                generator).
            output_index: Identifies the tableau row (the output qubit).

        Returns:
            An integer identifying Pauli at the given location in the tableau:

                0: I
                1: X
                2: Y
                3: Z

        Examples:
            >>> import stim

            >>> t = stim.Tableau.from_conjugated_generators(
            ...     xs=[stim.PauliString("-Y_"), stim.PauliString("+YZ")],
            ...     zs=[stim.PauliString("-ZY"), stim.PauliString("+YX")],
            ... )
            >>> t.z_output_pauli(0, 0)
            3
            >>> t.z_output_pauli(0, 1)
            2
            >>> t.z_output_pauli(1, 0)
            2
            >>> t.z_output_pauli(1, 1)
            1
        """
class TableauIterator:
    """Iterates over all stabilizer tableaus of a specified size.

    Examples:
        >>> import stim
        >>> tableau_iterator = stim.Tableau.iter_all(1)
        >>> n = 0
        >>> for single_qubit_clifford in tableau_iterator:
        ...     n += 1
        >>> n
        24
    """
    def __iter__(
        self,
    ) -> stim.TableauIterator:
        """Returns an independent copy of the tableau iterator.

        Since for-loops and loop-comprehensions call `iter` on things they
        iterate, this effectively allows the iterator to be iterated
        multiple times.
        """
    def __next__(
        self,
    ) -> stim.Tableau:
        """Returns the next iterated tableau.
        """
class TableauSimulator:
    """A stabilizer circuit simulator that tracks an inverse stabilizer tableau.

    Supports interactive usage, where gates and measurements are applied on demand.

    Examples:
        >>> import stim
        >>> s = stim.TableauSimulator()
        >>> s.h(0)
        >>> if s.measure(0):
        ...     s.h(1)
        ...     s.cnot(1, 2)
        >>> s.measure(1) == s.measure(2)
        True

        >>> s = stim.TableauSimulator()
        >>> s.h(0)
        >>> s.cnot(0, 1)
        >>> s.current_inverse_tableau()
        stim.Tableau.from_conjugated_generators(
            xs=[
                stim.PauliString("+ZX"),
                stim.PauliString("+_X"),
            ],
            zs=[
                stim.PauliString("+X_"),
                stim.PauliString("+XZ"),
            ],
        )
    """
    def __init__(
        self,
        *,
        seed: Optional[int] = None,
    ) -> None:
        """Initializes a stim.TableauSimulator.

        Args:
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng is seeded from system entropy.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how the
                circuit is executed. For example, reordering whether a reset on one
                qubit happens before or after a reset on another qubit can result in
                different measurement results being observed starting from the same
                seed.

        Returns:
            An initialized stim.TableauSimulator.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator(seed=0)
            >>> s2 = stim.TableauSimulator(seed=0)
            >>> s.h(0)
            >>> s2.h(0)
            >>> s.measure(0) == s2.measure(0)
            True
        """
    def c_xyz(
        self,
        *targets,
    ) -> None:
        """Applies a C_XYZ gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def c_zyx(
        self,
        *targets,
    ) -> None:
        """Applies a C_ZYX gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def canonical_stabilizers(
        self,
    ) -> List[stim.PauliString]:
        """Returns a standardized list of the simulator's current stabilizer generators.

        Two simulators have the same canonical stabilizers if and only if their current
        quantum state is equal (and tracking the same number of qubits).

        The canonical form is computed as follows:

            1. Get a list of stabilizers using the `z_output`s of
                `simulator.current_inverse_tableau()**-1`.
            2. Perform Gaussian elimination on each generator g.
                2a) The generators are considered in order X0, Z0, X1, Z1, X2, Z2, etc.
                2b) Pick any stabilizer that uses the generator g. If there are none,
                    go to the next g.
                2c) Multiply that stabilizer into all other stabilizers that use the
                    generator g.
                2d) Swap that stabilizer with the stabilizer at position `next_output`
                    then increment `next_output`.

        Returns:
            A List[stim.PauliString] of the simulator's state's stabilizers.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.h(0)
            >>> s.cnot(0, 1)
            >>> s.x(2)
            >>> for e in s.canonical_stabilizers():
            ...     print(repr(e))
            stim.PauliString("+XX_")
            stim.PauliString("+ZZ_")
            stim.PauliString("-__Z")

            >>> # Scramble the stabilizers then check the canonical form is unchanged.
            >>> s.set_inverse_tableau(s.current_inverse_tableau()**-1)
            >>> s.cnot(0, 1)
            >>> s.cz(0, 2)
            >>> s.s(0, 2)
            >>> s.cy(2, 1)
            >>> s.set_inverse_tableau(s.current_inverse_tableau()**-1)
            >>> for e in s.canonical_stabilizers():
            ...     print(repr(e))
            stim.PauliString("+XX_")
            stim.PauliString("+ZZ_")
            stim.PauliString("-__Z")
        """
    def cnot(
        self,
        *targets,
    ) -> None:
        """Applies a controlled X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def copy(
        self,
        *,
        copy_rng: bool = False,
        seed: Optional[int] = None,
    ) -> stim.TableauSimulator:
        """Returns a simulator with the same internal state, except perhaps its prng.

        Args:
            copy_rng: By default, new simulator's prng is reinitialized with a random
                seed. However, one can set this argument to True in order to have the
                prng state copied together with the rest of the original simulator's
                state. Consequently, in this case the two simulators will produce the
                same measurement outcomes for the same quantum circuits.  If both seed
                and copy_rng are set, an exception is raised. Defaults to False.
            seed: PARTIALLY determines simulation results by deterministically seeding
                the random number generator.

                Must be None or an integer in range(2**64).

                Defaults to None. When None, the prng state is either copied from the
                original simulator or reseeded from system entropy, depending on the
                copy_rng argument.

                When set to an integer, making the exact same series calls on the exact
                same machine with the exact same version of Stim will produce the exact
                same simulation results.

                CAUTION: simulation results *WILL NOT* be consistent between versions of
                Stim. This restriction is present to make it possible to have future
                optimizations to the random sampling, and is enforced by introducing
                intentional differences in the seeding strategy from version to version.

                CAUTION: simulation results *MAY NOT* be consistent across machines that
                differ in the width of supported SIMD instructions. For example, using
                the same seed on a machine that supports AVX instructions and one that
                only supports SSE instructions may produce different simulation results.

                CAUTION: simulation results *MAY NOT* be consistent if you vary how the
                circuit is executed. For example, reordering whether a reset on one
                qubit happens before or after a reset on another qubit can result in
                different measurement results being observed starting from the same
                seed.

        Examples:
            >>> import stim

            >>> s1 = stim.TableauSimulator()
            >>> s1.set_inverse_tableau(stim.Tableau.random(1))
            >>> s2 = s1.copy()
            >>> s2 is s1
            False
            >>> s2.current_inverse_tableau() == s1.current_inverse_tableau()
            True

            >>> s1 = stim.TableauSimulator()
            >>> s2 = s1.copy(copy_rng=True)
            >>> s1.h(0)
            >>> s2.h(0)
            >>> assert s1.measure(0) == s2.measure(0)

            >>> s = stim.TableauSimulator()
            >>> def brute_force_post_select(qubit, desired_result):
            ...     global s
            ...     while True:
            ...         s2 = s.copy()
            ...         if s2.measure(qubit) == desired_result:
            ...             s = s2
            ...             break
            >>> s.h(0)
            >>> brute_force_post_select(qubit=0, desired_result=True)
            >>> s.measure(0)
            True
        """
    def current_inverse_tableau(
        self,
    ) -> stim.Tableau:
        """Returns a copy of the internal state of the simulator as a stim.Tableau.

        Returns:
            A stim.Tableau copy of the simulator's state.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.h(0)
            >>> s.current_inverse_tableau()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z"),
                ],
                zs=[
                    stim.PauliString("+X"),
                ],
            )
            >>> s.cnot(0, 1)
            >>> s.current_inverse_tableau()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+ZX"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+X_"),
                    stim.PauliString("+XZ"),
                ],
            )
        """
    def current_measurement_record(
        self,
    ) -> List[bool]:
        """Returns a copy of the record of all measurements performed by the simulator.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.current_measurement_record()
            []
            >>> s.measure(0)
            False
            >>> s.x(0)
            >>> s.measure(0)
            True
            >>> s.current_measurement_record()
            [False, True]
            >>> s.do(stim.Circuit("M 0"))
            >>> s.current_measurement_record()
            [False, True, True]

        Returns:
            A list of booleans containing the result of every measurement performed by
            the simulator so far.
        """
    def cx(
        self,
        *targets,
    ) -> None:
        """Applies a controlled X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def cy(
        self,
        *targets,
    ) -> None:
        """Applies a controlled Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def cz(
        self,
        *targets,
    ) -> None:
        """Applies a controlled Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def do(
        self,
        circuit_or_pauli_string: Union[stim.Circuit, stim.PauliString, stim.CircuitInstruction, stim.CircuitRepeatBlock],
    ) -> None:
        """Applies a circuit or pauli string to the simulator's state.

        Args:
            circuit_or_pauli_string: A stim.Circuit, stim.PauliString,
                stim.CircuitInstruction, or stim.CircuitRepeatBlock
                with operations to apply to the simulator's state.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.do(stim.Circuit('''
            ...     X 0
            ...     M 0
            ... '''))
            >>> s.current_measurement_record()
            [True]

            >>> s = stim.TableauSimulator()
            >>> s.do(stim.PauliString("IXYZ"))
            >>> s.measure_many(0, 1, 2, 3)
            [False, True, True, False]
        """
    def do_circuit(
        self,
        circuit: stim.Circuit,
    ) -> None:
        """Applies a circuit to the simulator's state.

        Args:
            circuit: A stim.Circuit containing operations to apply.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.do_circuit(stim.Circuit('''
            ...     X 0
            ...     M 0
            ... '''))
            >>> s.current_measurement_record()
            [True]
        """
    def do_pauli_string(
        self,
        pauli_string: stim.PauliString,
    ) -> None:
        """Applies the paulis from a pauli string to the simulator's state.

        Args:
            pauli_string: A stim.PauliString containing Paulis to apply.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.do_pauli_string(stim.PauliString("IXYZ"))
            >>> s.measure_many(0, 1, 2, 3)
            [False, True, True, False]
        """
    def do_tableau(
        self,
        tableau: stim.Tableau,
        targets: List[int],
    ) -> None:
        """Applies a custom tableau operation to qubits in the simulator.

        Note that this method has to compute the inverse of the tableau, because the
        simulator's internal state is an inverse tableau.

        Args:
            tableau: A stim.Tableau representing the Clifford operation to apply.
            targets: The indices of the qubits to operate on.

        Examples:
            >>> import stim
            >>> sim = stim.TableauSimulator()
            >>> sim.h(1)
            >>> sim.h_yz(2)
            >>> [str(sim.peek_bloch(k)) for k in range(4)]
            ['+Z', '+X', '+Y', '+Z']
            >>> rot3 = stim.Tableau.from_conjugated_generators(
            ...     xs=[
            ...         stim.PauliString("_X_"),
            ...         stim.PauliString("__X"),
            ...         stim.PauliString("X__"),
            ...     ],
            ...     zs=[
            ...         stim.PauliString("_Z_"),
            ...         stim.PauliString("__Z"),
            ...         stim.PauliString("Z__"),
            ...     ],
            ... )

            >>> sim.do_tableau(rot3, [1, 2, 3])
            >>> [str(sim.peek_bloch(k)) for k in range(4)]
            ['+Z', '+Z', '+X', '+Y']

            >>> sim.do_tableau(rot3, [1, 2, 3])
            >>> [str(sim.peek_bloch(k)) for k in range(4)]
            ['+Z', '+Y', '+Z', '+X']
        """
    def h(
        self,
        *targets,
    ) -> None:
        """Applies a Hadamard gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def h_xy(
        self,
        *targets,
    ) -> None:
        """Applies an operation that swaps the X and Y axes to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def h_xz(
        self,
        *targets,
    ) -> None:
        """Applies a Hadamard gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def h_yz(
        self,
        *targets,
    ) -> None:
        """Applies an operation that swaps the Y and Z axes to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def iswap(
        self,
        *targets,
    ) -> None:
        """Applies an ISWAP gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def iswap_dag(
        self,
        *targets,
    ) -> None:
        """Applies an ISWAP_DAG gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def measure(
        self,
        target: int,
    ) -> bool:
        """Measures a single qubit.

        Unlike the other methods on TableauSimulator, this one does not broadcast
        over multiple targets. This is to avoid returning a list, which would
        create a pitfall where typing `if sim.measure(qubit)` would be a bug.

        To measure multiple qubits, use `TableauSimulator.measure_many`.

        Args:
            target: The index of the qubit to measure.

        Returns:
            The measurement result as a bool.
        """
    def measure_kickback(
        self,
        target: int,
    ) -> tuple:
        """Measures a qubit and returns the result as well as its Pauli kickback (if any).

        The "Pauli kickback" of a stabilizer circuit measurement is a set of Pauli
        operations that flip the post-measurement system state between the two possible
        post-measurement states. For example, consider measuring one of the qubits in
        the state |00>+|11> in the Z basis. If the measurement result is False, then the
        system projects into the state |00>. If the measurement result is True, then the
        system projects into the state |11>. Applying a Pauli X operation to both qubits
        flips between |00> and |11>. Therefore the Pauli kickback of the measurement is
        `stim.PauliString("XX")`. Note that there are often many possible equivalent
        Pauli kickbacks. For example, if in the previous example there was a third qubit
        in the |0> state, then both `stim.PauliString("XX_")` and
        `stim.PauliString("XXZ")` are valid kickbacks.

        Measurements with deterministic results don't have a Pauli kickback.

        Args:
            target: The index of the qubit to measure.

        Returns:
            A (result, kickback) tuple.
            The result is a bool containing the measurement's output.
            The kickback is either None (meaning the measurement was deterministic) or a
            stim.PauliString (meaning the measurement was random, and the operations in
            the Pauli string flip between the two possible post-measurement states).

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()

            >>> s.measure_kickback(0)
            (False, None)

            >>> s.h(0)
            >>> s.measure_kickback(0)[1]
            stim.PauliString("+X")

            >>> def pseudo_post_select(qubit, desired_result):
            ...     m, kick = s.measure_kickback(qubit)
            ...     if m != desired_result:
            ...         if kick is None:
            ...             raise ValueError("Post-selected the impossible!")
            ...         s.do(kick)
            >>> s = stim.TableauSimulator()
            >>> s.h(0)
            >>> s.cnot(0, 1)
            >>> s.cnot(0, 2)
            >>> pseudo_post_select(qubit=2, desired_result=True)
            >>> s.measure_many(0, 1, 2)
            [True, True, True]
        """
    def measure_many(
        self,
        *targets,
    ) -> List[bool]:
        """Measures multiple qubits.

        Args:
            *targets: The indices of the qubits to measure.

        Returns:
            The measurement results as a list of bools.
        """
    @property
    def num_qubits(
        self,
    ) -> int:
        """Returns the number of qubits currently being tracked by the simulator.

        Note that the number of qubits being tracked will implicitly increase if qubits
        beyond the current limit are touched. Untracked qubits are always assumed to be
        in the |0> state.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.num_qubits
            0
            >>> s.h(2)
            >>> s.num_qubits
            3
        """
    def peek_bloch(
        self,
        target: int,
    ) -> stim.PauliString:
        """Returns the state of the qubit as a single-qubit stim.PauliString stabilizer.

        This is a non-physical operation. It reports information about the qubit without
        disturbing it.

        Args:
            target: The qubit to peek at.

        Returns:
            stim.PauliString("I"):
                The qubit is entangled. Its bloch vector is x=y=z=0.
            stim.PauliString("+Z"):
                The qubit is in the |0> state. Its bloch vector is z=+1, x=y=0.
            stim.PauliString("-Z"):
                The qubit is in the |1> state. Its bloch vector is z=-1, x=y=0.
            stim.PauliString("+Y"):
                The qubit is in the |i> state. Its bloch vector is y=+1, x=z=0.
            stim.PauliString("-Y"):
                The qubit is in the |-i> state. Its bloch vector is y=-1, x=z=0.
            stim.PauliString("+X"):
                The qubit is in the |+> state. Its bloch vector is x=+1, y=z=0.
            stim.PauliString("-X"):
                The qubit is in the |-> state. Its bloch vector is x=-1, y=z=0.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.peek_bloch(0)
            stim.PauliString("+Z")
            >>> s.x(0)
            >>> s.peek_bloch(0)
            stim.PauliString("-Z")
            >>> s.h(0)
            >>> s.peek_bloch(0)
            stim.PauliString("-X")
            >>> s.sqrt_x(1)
            >>> s.peek_bloch(1)
            stim.PauliString("-Y")
            >>> s.cz(0, 1)
            >>> s.peek_bloch(0)
            stim.PauliString("+_")
        """
    def peek_observable_expectation(
        self,
        observable: stim.PauliString,
    ) -> int:
        """Determines the expected value of an observable.

        Because the simulator's state is always a stabilizer state, the expectation will
        always be exactly -1, 0, or +1.

        This is a non-physical operation.
        It reports information about the quantum state without disturbing it.

        Args:
            observable: The observable to determine the expected value of.
                This observable must have a real sign, not an imaginary sign.

        Returns:
            +1: Observable will be deterministically false when measured.
            -1: Observable will be deterministically true when measured.
            0: Observable will be random when measured.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.peek_observable_expectation(stim.PauliString("+Z"))
            1
            >>> s.peek_observable_expectation(stim.PauliString("+X"))
            0
            >>> s.peek_observable_expectation(stim.PauliString("-Z"))
            -1

            >>> s.do(stim.Circuit('''
            ...     H 0
            ...     CNOT 0 1
            ... '''))
            >>> queries = ['XX', 'YY', 'ZZ', '-ZZ', 'ZI', 'II', 'IIZ']
            >>> for q in queries:
            ...     print(q, s.peek_observable_expectation(stim.PauliString(q)))
            XX 1
            YY -1
            ZZ 1
            -ZZ -1
            ZI 0
            II 1
            IIZ 1
        """
    def peek_x(
        self,
        target: int,
    ) -> int:
        """Returns the expected value of a qubit's X observable.

        Because the simulator's state is always a stabilizer state, the expectation will
        always be exactly -1, 0, or +1.

        This is a non-physical operation.
        It reports information about the quantum state without disturbing it.

        Args:
            target: The qubit to analyze.

        Returns:
            +1: Qubit is in the |+> state.
            -1: Qubit is in the |-> state.
            0: Qubit is in some other state.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.reset_z(0)
            >>> s.peek_x(0)
            0
            >>> s.reset_x(0)
            >>> s.peek_x(0)
            1
            >>> s.z(0)
            >>> s.peek_x(0)
            -1
        """
    def peek_y(
        self,
        target: int,
    ) -> int:
        """Returns the expected value of a qubit's Y observable.

        Because the simulator's state is always a stabilizer state, the expectation will
        always be exactly -1, 0, or +1.

        This is a non-physical operation.
        It reports information about the quantum state without disturbing it.

        Args:
            target: The qubit to analyze.

        Returns:
            +1: Qubit is in the |i> state.
            -1: Qubit is in the |-i> state.
            0: Qubit is in some other state.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.reset_z(0)
            >>> s.peek_y(0)
            0
            >>> s.reset_y(0)
            >>> s.peek_y(0)
            1
            >>> s.z(0)
            >>> s.peek_y(0)
            -1
        """
    def peek_z(
        self,
        target: int,
    ) -> int:
        """Returns the expected value of a qubit's Z observable.

        Because the simulator's state is always a stabilizer state, the expectation will
        always be exactly -1, 0, or +1.

        This is a non-physical operation.
        It reports information about the quantum state without disturbing it.

        Args:
            target: The qubit to analyze.

        Returns:
            +1: Qubit is in the |0> state.
            -1: Qubit is in the |1> state.
            0: Qubit is in some other state.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.reset_x(0)
            >>> s.peek_z(0)
            0
            >>> s.reset_z(0)
            >>> s.peek_z(0)
            1
            >>> s.x(0)
            >>> s.peek_z(0)
            -1
        """
    def postselect_x(
        self,
        targets: Union[int, Iterable[int]],
        *,
        desired_value: bool,
    ) -> None:
        """Postselects qubits in the X basis, or raises an exception.

        Postselecting a qubit forces it to collapse to a specific state, as
        if it was measured and that state was the result of the measurement.

        Args:
            targets: The qubit index or indices to postselect.
            desired_value:
                False: postselect targets into the |+> state.
                True: postselect targets into the |-> state.

        Raises:
            ValueError:
                The postselection failed. One of the qubits was in a state
                orthogonal to the desired state, so it was literally
                impossible for a measurement of the qubit to return the
                desired result.
        """
    def postselect_y(
        self,
        targets: Union[int, Iterable[int]],
        *,
        desired_value: bool,
    ) -> None:
        """Postselects qubits in the Y basis, or raises an exception.

        Postselecting a qubit forces it to collapse to a specific state, as
        if it was measured and that state was the result of the measurement.

        Args:
            targets: The qubit index or indices to postselect.
            desired_value:
                False: postselect targets into the |i> state.
                True: postselect targets into the |-i> state.

        Raises:
            ValueError:
                The postselection failed. One of the qubits was in a state
                orthogonal to the desired state, so it was literally
                impossible for a measurement of the qubit to return the
                desired result.
        """
    def postselect_z(
        self,
        targets: Union[int, Iterable[int]],
        *,
        desired_value: bool,
    ) -> None:
        """Postselects qubits in the Z basis, or raises an exception.

        Postselecting a qubit forces it to collapse to a specific state, as if it was
        measured and that state was the result of the measurement.

        Args:
            targets: The qubit index or indices to postselect.
            desired_value:
                False: postselect targets into the |0> state.
                True: postselect targets into the |1> state.

        Raises:
            ValueError:
                The postselection failed. One of the qubits was in a state
                orthogonal to the desired state, so it was literally
                impossible for a measurement of the qubit to return the
                desired result.
        """
    def reset(
        self,
        *targets,
    ) -> None:
        """Resets qubits to the |0> state.

        Args:
            *targets: The indices of the qubits to reset.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.x(0)
            >>> s.reset(0)
            >>> s.peek_bloch(0)
            stim.PauliString("+Z")
        """
    def reset_x(
        self,
        *targets,
    ) -> None:
        """Resets qubits to the |+> state.

        Args:
            *targets: The indices of the qubits to reset.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.reset_x(0)
            >>> s.peek_bloch(0)
            stim.PauliString("+X")
        """
    def reset_y(
        self,
        *targets,
    ) -> None:
        """Resets qubits to the |i> state.

        Args:
            *targets: The indices of the qubits to reset.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.reset_y(0)
            >>> s.peek_bloch(0)
            stim.PauliString("+Y")
        """
    def reset_z(
        self,
        *targets,
    ) -> None:
        """Resets qubits to the |0> state.

        Args:
            *targets: The indices of the qubits to reset.

        Example:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> s.h(0)
            >>> s.reset_z(0)
            >>> s.peek_bloch(0)
            stim.PauliString("+Z")
        """
    def s(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def s_dag(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_Z_DAG gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def set_inverse_tableau(
        self,
        new_inverse_tableau: stim.Tableau,
    ) -> None:
        """Overwrites the simulator's internal state with the given inverse tableau.

        The inverse tableau specifies how Pauli product observables of qubits at the
        current time transform into equivalent Pauli product observables at the
        beginning of time, when all qubits were in the |0> state. For example, if the Z
        observable on qubit 5 maps to a product of Z observables at the start of time
        then a Z basis measurement on qubit 5 will be deterministic and equal to the
        sign of the product. Whereas if it mapped to a product of observables including
        an X or a Y then the Z basis measurement would be random.

        Any qubits not within the length of the tableau are implicitly in the |0> state.

        Args:
            new_inverse_tableau: The tableau to overwrite the internal state with.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> t = stim.Tableau.random(4)
            >>> s.set_inverse_tableau(t)
            >>> s.current_inverse_tableau() == t
            True
        """
    def set_num_qubits(
        self,
        new_num_qubits: int,
    ) -> None:
        """Resizes the simulator's internal state.

        This forces the simulator's internal state to track exactly the qubits whose
        indices are in `range(new_num_qubits)`.

        Note that untracked qubits are always assumed to be in the |0> state. Therefore,
        calling this method will effectively force any qubit whose index is outside
        `range(new_num_qubits)` to be reset to |0>.

        Note that this method does not prevent future operations from implicitly
        expanding the size of the tracked state (e.g. setting the number of qubits to 5
        will not prevent a Hadamard from then being applied to qubit 100, increasing the
        number of qubits back to 101).

        Args:
            new_num_qubits: The length of the range of qubits the internal simulator
                should be tracking.

        Examples:
            >>> import stim
            >>> s = stim.TableauSimulator()
            >>> len(s.current_inverse_tableau())
            0

            >>> s.set_num_qubits(5)
            >>> len(s.current_inverse_tableau())
            5

            >>> s.x(0, 1, 2, 3)
            >>> s.set_num_qubits(2)
            >>> s.measure_many(0, 1, 2, 3)
            [True, True, False, False]
        """
    def set_state_from_stabilizers(
        self,
        stabilizers: Iterable[stim.PauliString],
        *,
        allow_redundant: bool = False,
        allow_underconstrained: bool = False,
    ) -> None:
        """Sets the tableau simulator's state to a state satisfying the given stabilizers.

        The old quantum state is completely overwritten, even if the new state is
        underconstrained by the given stabilizers. The number of qubits is changed to
        exactly match the number of qubits in the longest given stabilizer.

        Args:
            stabilizers: A list of `stim.PauliString`s specifying the stabilizers that
                the new state must have. It is permitted for stabilizers to have
                different lengths. All stabilizers are padded up to the length of the
                longest stabilizer by appending identity terms.
            allow_redundant: Defaults to False. If set to False, then the given
                stabilizers must all be independent. If any one of them is a product of
                the others (including the empty product), an exception will be raised.
                If set to True, then redundant stabilizers are simply ignored.
            allow_underconstrained: Defaults to False. If set to False, then the given
                stabilizers must form a complete set of generators. They must exactly
                specify the desired stabilizer state, with no degrees of freedom left
                over. For an n-qubit state there must be n independent stabilizers. If
                set to True, then there can be leftover degrees of freedom which can be
                set arbitrarily.

        Returns:
            Nothing. Mutates the states of the simulator to match the desired
            stabilizers.

            Guarantees that self.current_inverse_tableau().inverse_z_output(k) will be
            equal to the k'th independent stabilizer from the `stabilizers` argument.

        Raises:
            ValueError:
                A stabilizer is redundant but allow_redundant=True wasn't set.
                OR
                The given stabilizers are contradictory (e.g. "+Z" and "-Z" both
                specified).
                OR
                The given stabilizers anticommute (e.g. "+Z" and "+X" both specified).
                OR
                The stabilizers left behind a degree of freedom but
                allow_underconstrained=True wasn't set.
                OR
                A stabilizer has an imaginary sign (i or -i).

        Examples:

            >>> import stim
            >>> tab_sim = stim.TableauSimulator()
            >>> tab_sim.set_state_from_stabilizers([
            ...     stim.PauliString("XX"),
            ...     stim.PauliString("ZZ"),
            ... ])
            >>> tab_sim.current_inverse_tableau().inverse()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+ZZ"),
                ],
            )

            >>> tab_sim.set_state_from_stabilizers([
            ...     stim.PauliString("XX_"),
            ...     stim.PauliString("ZZ_"),
            ...     stim.PauliString("-YY_"),
            ...     stim.PauliString(""),
            ... ], allow_underconstrained=True, allow_redundant=True)
            >>> tab_sim.current_inverse_tableau().inverse()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z__"),
                    stim.PauliString("+_X_"),
                    stim.PauliString("+__X"),
                ],
                zs=[
                    stim.PauliString("+XX_"),
                    stim.PauliString("+ZZ_"),
                    stim.PauliString("+__Z"),
                ],
            )
        """
    def set_state_from_state_vector(
        self,
        state_vector: Iterable[float],
        *,
        endian: str,
    ) -> None:
        """Sets the simulator's state to a superposition specified by an amplitude vector.

        Args:
            state_vector: A list of complex amplitudes specifying a superposition. The
                vector must correspond to a state that is reachable using Clifford
                operations, and must be normalized (i.e. it must be a unit vector).
            endian:
                "little": state vector is in little endian order, where higher index
                    qubits correspond to larger changes in the state index.
                "big": state vector is in big endian order, where higher index qubits
                    correspond to smaller changes in the state index.

        Returns:
            Nothing. Mutates the states of the simulator to match the desired state.

        Raises:
            ValueError:
                The given state vector isn't a list of complex values specifying a
                stabilizer state.
                OR
                The given endian value isn't 'little' or 'big'.

        Examples:

            >>> import stim
            >>> tab_sim = stim.TableauSimulator()
            >>> tab_sim.set_state_from_state_vector([
            ...     0.5**0.5,
            ...     0.5**0.5 * 1j,
            ... ], endian='little')
            >>> tab_sim.current_inverse_tableau().inverse()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z"),
                ],
                zs=[
                    stim.PauliString("+Y"),
                ],
            )
            >>> tab_sim.set_state_from_state_vector([
            ...     0.5**0.5,
            ...     0,
            ...     0,
            ...     0.5**0.5,
            ... ], endian='little')
            >>> tab_sim.current_inverse_tableau().inverse()
            stim.Tableau.from_conjugated_generators(
                xs=[
                    stim.PauliString("+Z_"),
                    stim.PauliString("+_X"),
                ],
                zs=[
                    stim.PauliString("+XX"),
                    stim.PauliString("+ZZ"),
                ],
            )
        """
    def sqrt_x(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def sqrt_x_dag(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_X_DAG gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def sqrt_y(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def sqrt_y_dag(
        self,
        *targets,
    ) -> None:
        """Applies a SQRT_Y_DAG gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def state_vector(
        self,
        *,
        endian: str = 'little',
    ) -> np.ndarray[np.complex64]:
        """Returns a wavefunction for the simulator's current state.

        This function takes O(n * 2**n) time and O(2**n) space, where n is the number of
        qubits. The computation is done by initialization a random state vector and
        iteratively projecting it into the +1 eigenspace of each stabilizer of the
        state. The state is then canonicalized so that zero values are actually exactly
        0, and so that the first non-zero entry is positive.

        Args:
            endian:
                "little" (default): state vector is in little endian order, where higher
                    index qubits correspond to larger changes in the state index.
                "big": state vector is in big endian order, where higher index qubits
                    correspond to smaller changes in the state index.

        Returns:
            A `numpy.ndarray[numpy.complex64]` of computational basis amplitudes.

            If the result is in little endian order then the amplitude at offset
            b_0 + b_1*2 + b_2*4 + ... + b_{n-1}*2^{n-1} is the amplitude for the
            computational basis state where the qubit with index 0 is storing the bit
            b_0, the qubit with index 1 is storing the bit b_1, etc.

            If the result is in big endian order then the amplitude at offset
            b_0 + b_1*2 + b_2*4 + ... + b_{n-1}*2^{n-1} is the amplitude for the
            computational basis state where the qubit with index 0 is storing the bit
            b_{n-1}, the qubit with index 1 is storing the bit b_{n-2}, etc.

        Examples:
            >>> import stim
            >>> import numpy as np
            >>> s = stim.TableauSimulator()
            >>> s.x(2)
            >>> list(s.state_vector(endian='little'))
            [0j, 0j, 0j, 0j, (1+0j), 0j, 0j, 0j]

            >>> list(s.state_vector(endian='big'))
            [0j, (1+0j), 0j, 0j, 0j, 0j, 0j, 0j]

            >>> s.sqrt_x(1, 2)
            >>> list(s.state_vector())
            [(0.5+0j), 0j, -0.5j, 0j, 0.5j, 0j, (0.5+0j), 0j]
        """
    def swap(
        self,
        *targets,
    ) -> None:
        """Applies a swap gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def x(
        self,
        *targets,
    ) -> None:
        """Applies a Pauli X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def xcx(
        self,
        *targets,
    ) -> None:
        """Applies an X-controlled X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def xcy(
        self,
        *targets,
    ) -> None:
        """Applies an X-controlled Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def xcz(
        self,
        *targets,
    ) -> None:
        """Applies an X-controlled Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def y(
        self,
        *targets,
    ) -> None:
        """Applies a Pauli Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def ycx(
        self,
        *targets,
    ) -> None:
        """Applies a Y-controlled X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def ycy(
        self,
        *targets,
    ) -> None:
        """Applies a Y-controlled Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def ycz(
        self,
        *targets,
    ) -> None:
        """Applies a Y-controlled Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def z(
        self,
        *targets,
    ) -> None:
        """Applies a Pauli Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
        """
    def zcx(
        self,
        *targets,
    ) -> None:
        """Applies a controlled X gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def zcy(
        self,
        *targets,
    ) -> None:
        """Applies a controlled Y gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
    def zcz(
        self,
        *targets,
    ) -> None:
        """Applies a controlled Z gate to the simulator's state.

        Args:
            *targets: The indices of the qubits to target with the gate.
                Applies the gate to the first two targets, then the next two targets,
                and so forth. There must be an even number of targets.
        """
def main(
    *,
    command_line_args: List[str],
) -> int:
    """Runs the command line tool version of stim on the given arguments.

    Note that by default any input will be read from stdin, any output
    will print to stdout (as opposed to being intercepted). For most
    commands, you can use arguments like `--out` to write to a file
    instead of stdout and `--in` to read from a file instead of stdin.

    Returns:
        An exit code (0 means success, not zero means failure).

    Raises:
        A large variety of errors, depending on what you are doing and
        how it failed! Beware that many errors are caught by the main
        method itself and printed to stderr, with the only indication
        that something went wrong being the return code.

    Example:
        >>> import stim
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as d:
        ...     path = f'{d}/tmp.out'
        ...     return_code = stim.main(command_line_args=[
        ...         "gen",
        ...         "--code=repetition_code",
        ...         "--task=memory",
        ...         "--rounds=1000",
        ...         "--distance=2",
        ...         "--out",
        ...         path,
        ...     ])
        ...     assert return_code == 0
        ...     with open(path) as f:
        ...         print(f.read(), end='')
        # Generated repetition_code circuit.
        # task: memory
        # rounds: 1000
        # distance: 2
        # before_round_data_depolarization: 0
        # before_measure_flip_probability: 0
        # after_reset_flip_probability: 0
        # after_clifford_depolarization: 0
        # layout:
        # L0 Z1 d2
        # Legend:
        #     d# = data qubit
        #     L# = data qubit with logical observable crossing
        #     Z# = measurement qubit
        R 0 1 2
        TICK
        CX 0 1
        TICK
        CX 2 1
        TICK
        MR 1
        DETECTOR(1, 0) rec[-1]
        REPEAT 999 {
            TICK
            CX 0 1
            TICK
            CX 2 1
            TICK
            MR 1
            SHIFT_COORDS(0, 1)
            DETECTOR(1, 0) rec[-1] rec[-2]
        }
        M 0 2
        DETECTOR(1, 1) rec[-1] rec[-2] rec[-3]
        OBSERVABLE_INCLUDE(0) rec[-1]
    """
def read_shot_data_file(
    *,
    path: Union[str, pathlib.Path],
    format: Union[str, 'Literal["01", "b8", "r8", "ptb64", "hits", "dets"]'],
    bit_packed: bool = False,
    num_measurements: int = 0,
    num_detectors: int = 0,
    num_observables: int = 0,
) -> np.ndarray:
    """Reads shot data, such as measurement samples, from a file.

    Args:
        path: The path to the file to read the data from.
        format: The format that the data is stored in, such as 'b8'.
            See https://github.com/quantumlib/Stim/blob/main/doc/result_formats.md
        bit_packed: Defaults to false. Determines whether the result is a bool8
            numpy array with one bit per byte, or a uint8 numpy array with 8 bits
            per byte.
        num_measurements: How many measurements there are per shot.
        num_detectors: How many detectors there are per shot.
        num_observables: How many observables there are per shot.
            Note that this only refers to observables *stored in the file*, not to
            observables from the original circuit that was sampled.

    Returns:
        A numpy array containing the loaded data.

        If bit_packed=False:
            dtype = np.bool8
            shape = (num_shots, num_measurements + num_detectors + num_observables)
            bit b from shot s is at result[s, b]
        If bit_packed=True:
            dtype = np.uint8
            shape = (num_shots, math.ceil(
                (num_measurements + num_detectors + num_observables) / 8))
            bit b from shot s is at result[s, b // 8] & (1 << (b % 8))

    Examples:
        >>> import stim
        >>> import pathlib
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as d:
        ...     path = pathlib.Path(d) / 'shots'
        ...     with open(path, 'w') as f:
        ...         print("0000", file=f)
        ...         print("0101", file=f)
        ...
        ...     read = stim.read_shot_data_file(
        ...         path=str(path),
        ...         format='01',
        ...         num_measurements=4)
        >>> read
        array([[False, False, False, False],
               [False,  True, False,  True]])
    """
def target_combiner(
) -> stim.GateTarget:
    """Returns a target combiner that can be used to build Pauli products.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("MPP", [
        ...     stim.target_x(2),
        ...     stim.target_combiner(),
        ...     stim.target_y(3),
        ...     stim.target_combiner(),
        ...     stim.target_z(5),
        ... ])
        >>> circuit
        stim.Circuit('''
            MPP X2*Y3*Z5
        ''')
    """
def target_inv(
    qubit_index: int,
) -> stim.GateTarget:
    """Returns a target flagged as inverted.

    Inverted targets are used to indicate measurement results should be flipped.

    Args:
        qubit_index: The underlying qubit index of the inverted target.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("M", [2, stim.target_inv(3)])
        >>> circuit
        stim.Circuit('''
            M 2 !3
        ''')

    For example, the '!1' in 'M 0 !1 2' is qubit 1 flagged as inverted,
    meaning the measurement result from qubit 1 should be inverted when reported.
    """
def target_logical_observable_id(
    index: int,
) -> stim.DemTarget:
    """Returns a logical observable id identifying a frame change.

    Args:
        index: The index of the observable.

    Returns:
        The logical observable target.

    Examples:
        >>> import stim
        >>> m = stim.DetectorErrorModel()
        >>> m.append("error", 0.25, [
        ...     stim.target_logical_observable_id(13)
        ... ])
        >>> print(repr(m))
        stim.DetectorErrorModel('''
            error(0.25) L13
        ''')
    """
def target_rec(
    lookback_index: int,
) -> stim.GateTarget:
    """Returns a measurement record target with the given lookback.

    Measurement record targets are used to refer back to the measurement record;
    the list of measurements that have been performed so far. Measurement record
    targets always specify an index relative to the *end* of the measurement record.
    The latest measurement is `stim.target_rec(-1)`, the next most recent
    measurement is `stim.target_rec(-2)`, and so forth. Indexing is done this way
    in order to make it possible to write loops.

    Args:
        lookback_index: A negative integer indicating how far to look back, relative
            to the end of the measurement record.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("M", [5, 7, 11])
        >>> circuit.append("CX", [stim.target_rec(-2), 3])
        >>> circuit
        stim.Circuit('''
            M 5 7 11
            CX rec[-2] 3
        ''')
    """
def target_relative_detector_id(
    index: int,
) -> stim.DemTarget:
    """Returns a relative detector id (e.g. "D5" in a .dem file).

    Args:
        index: The index of the detector, relative to the current detector offset.

    Returns:
        The relative detector target.

    Examples:
        >>> import stim
        >>> m = stim.DetectorErrorModel()
        >>> m.append("error", 0.25, [
        ...     stim.target_relative_detector_id(13)
        ... ])
        >>> print(repr(m))
        stim.DetectorErrorModel('''
            error(0.25) D13
        ''')
    """
def target_separator(
) -> stim.DemTarget:
    """Returns a target separator (e.g. "^" in a .dem file).

    Examples:
        >>> import stim
        >>> m = stim.DetectorErrorModel()
        >>> m.append("error", 0.25, [
        ...     stim.target_relative_detector_id(1),
        ...     stim.target_separator(),
        ...     stim.target_relative_detector_id(2),
        ... ])
        >>> print(repr(m))
        stim.DetectorErrorModel('''
            error(0.25) D1 ^ D2
        ''')
    """
def target_sweep_bit(
    sweep_bit_index: int,
) -> stim.GateTarget:
    """Returns a sweep bit target that can be passed into `stim.Circuit.append`.

    Args:
        sweep_bit_index: The index of the sweep bit to target.

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("CX", [stim.target_sweep_bit(2), 5])
        >>> circuit
        stim.Circuit('''
            CX sweep[2] 5
        ''')
    """
def target_x(
    qubit_index: int,
    invert: bool = False,
) -> stim.GateTarget:
    """Returns a Pauli X target that can be passed into `stim.Circuit.append`.

    Args:
        qubit_index: The qubit that the Pauli applies to.
        invert: Defaults to False. If True, the target is inverted (indicating
            that, for example, measurement results should be inverted).

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("MPP", [
        ...     stim.target_x(2),
        ...     stim.target_combiner(),
        ...     stim.target_y(3, invert=True),
        ...     stim.target_combiner(),
        ...     stim.target_z(5),
        ... ])
        >>> circuit
        stim.Circuit('''
            MPP X2*!Y3*Z5
        ''')
    """
def target_y(
    qubit_index: int,
    invert: bool = False,
) -> stim.GateTarget:
    """Returns a Pauli Y target that can be passed into `stim.Circuit.append`.

    Args:
        qubit_index: The qubit that the Pauli applies to.
        invert: Defaults to False. If True, the target is inverted (indicating
            that, for example, measurement results should be inverted).

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("MPP", [
        ...     stim.target_x(2),
        ...     stim.target_combiner(),
        ...     stim.target_y(3, invert=True),
        ...     stim.target_combiner(),
        ...     stim.target_z(5),
        ... ])
        >>> circuit
        stim.Circuit('''
            MPP X2*!Y3*Z5
        ''')
    """
def target_z(
    qubit_index: int,
    invert: bool = False,
) -> stim.GateTarget:
    """Returns a Pauli Z target that can be passed into `stim.Circuit.append`.

    Args:
        qubit_index: The qubit that the Pauli applies to.
        invert: Defaults to False. If True, the target is inverted (indicating
            that, for example, measurement results should be inverted).

    Examples:
        >>> import stim
        >>> circuit = stim.Circuit()
        >>> circuit.append("MPP", [
        ...     stim.target_x(2),
        ...     stim.target_combiner(),
        ...     stim.target_y(3, invert=True),
        ...     stim.target_combiner(),
        ...     stim.target_z(5),
        ... ])
        >>> circuit
        stim.Circuit('''
            MPP X2*!Y3*Z5
        ''')
    """
def write_shot_data_file(
    *,
    data: np.ndarray,
    path: Union[str, pathlib.Path],
    format: str,
    num_measurements: int = 0,
    num_detectors: int = 0,
    num_observables: int = 0,
) -> None:
    """Writes shot data, such as measurement samples, to a file.

    Args:
        data: The data to write to the file. This must be a numpy array. The dtype
            of the array determines whether or not the data is bit packed, and the
            shape must match the bits per shot.

            dtype=np.bool8: Not bit packed. Shape must be
                (num_shots, num_measurements + num_detectors + num_observables).
            dtype=np.uint8: Yes bit packed. Shape must be
                (num_shots, math.ceil(
                    (num_measurements + num_detectors + num_observables) / 8)).
        path: The path to the file to write the data to.
        format: The format that the data is stored in, such as 'b8'.
            See https://github.com/quantumlib/Stim/blob/main/doc/result_formats.md
        num_measurements: How many measurements there are per shot.
        num_detectors: How many detectors there are per shot.
        num_observables: How many observables there are per shot.
            Note that this only refers to observables *in the given shot data*, not
            to observables from the original circuit that was sampled.

    Examples:
        >>> import stim
        >>> import pathlib
        >>> import tempfile
        >>> import numpy as np
        >>> with tempfile.TemporaryDirectory() as d:
        ...     path = pathlib.Path(d) / 'shots'
        ...     shot_data = np.array([
        ...         [0, 1, 0],
        ...         [0, 1, 1],
        ...     ], dtype=np.bool8)
        ...
        ...     stim.write_shot_data_file(
        ...         path=str(path),
        ...         data=shot_data,
        ...         format='01',
        ...         num_measurements=3)
        ...
        ...     with open(path) as f:
        ...         written = f.read()
        >>> written
        '010\n011\n'
    """
