array.py

from __future__ import annotations

import copy
import math
import warnings
from asyncio import gather
from collections.abc import Iterable, Mapping, Sequence
from dataclasses import dataclass, field, replace
from itertools import starmap
from logging import getLogger
from typing import (
    TYPE_CHECKING,
    Any,
    Literal,
    TypedDict,
    cast,
    overload,
)
from warnings import warn

import numpy as np
from typing_extensions import Sentinel, deprecated

import zarr
from zarr.abc.codec import ArrayArrayCodec, ArrayBytesCodec, BytesBytesCodec, Codec
from zarr.abc.numcodec import Numcodec, _is_numcodec
from zarr.codecs._v2 import V2Codec
from zarr.codecs.bytes import BytesCodec
from zarr.codecs.vlen_utf8 import VLenBytesCodec, VLenUTF8Codec
from zarr.codecs.zstd import ZstdCodec
from zarr.core._info import ArrayInfo
from zarr.core._json import buffer_to_json_object
from zarr.core.array_spec import ArrayConfig, ArrayConfigLike, ArraySpec, parse_array_config
from zarr.core.attributes import Attributes
from zarr.core.buffer import (
    BufferPrototype,
    NDArrayLike,
    NDArrayLikeOrScalar,
    NDBuffer,
    default_buffer_prototype,
)
from zarr.core.buffer.cpu import buffer_prototype as cpu_buffer_prototype
from zarr.core.chunk_grids import (
    SHARDED_INNER_CHUNK_MAX_BYTES,
    ChunkGrid,
    _is_auto,
    _is_keep,
    _is_rectilinear_chunks,
    guess_chunks,
    normalize_chunks_nd,
    resolve_outer_and_inner_chunks,
)
from zarr.core.chunk_key_encodings import (
    ChunkKeyEncoding,
    ChunkKeyEncodingLike,
    DefaultChunkKeyEncoding,
    V2ChunkKeyEncoding,
    parse_chunk_key_encoding,
)
from zarr.core.common import (
    JSON,
    ZARR_JSON,
    ZARRAY_JSON,
    ZATTRS_JSON,
    ChunksLike,
    DimensionNamesLike,
    MemoryOrder,
    ShapeLike,
    ZarrFormat,
    _default_zarr_format,
    _warn_order_kwarg,
    ceildiv_int,
    concurrent_map,
    parse_shapelike,
    product,
)
from zarr.core.config import config as zarr_config
from zarr.core.dtype import (
    Structured,
    VariableLengthBytes,
    VariableLengthUTF8,
    ZDType,
    ZDTypeLike,
    parse_dtype,
)
from zarr.core.dtype.common import HasEndianness, HasItemSize, HasObjectCodec
from zarr.core.indexing import (
    AsyncOIndex,
    AsyncVIndex,
    BasicIndexer,
    BasicSelection,
    BlockIndex,
    BlockIndexer,
    CoordinateIndexer,
    CoordinateSelection,
    Fields,
    Indexer,
    MaskIndexer,
    MaskSelection,
    OIndex,
    OrthogonalIndexer,
    OrthogonalSelection,
    Selection,
    VIndex,
    _iter_grid,
    check_fields,
    check_no_multi_fields,
    is_pure_fancy_indexing,
    is_pure_orthogonal_indexing,
    is_scalar,
    pop_fields,
)
from zarr.core.metadata import (
    ArrayMetadata,
    ArrayMetadataDict,
    ArrayMetadataJSON_V3,
    ArrayV2Metadata,
    ArrayV2MetadataDict,
    ArrayV3Metadata,
)
from zarr.core.metadata.io import (
    ARRAY_DOCUMENTS,
    parse_stored_array,
    read_documents,
    save_metadata,
    save_new_metadata,
    upsert_metadata,
)
from zarr.core.metadata.v2 import (
    CompressorLikev2,
    get_object_codec_id,
    parse_compressor,
    parse_filters,
)
from zarr.core.metadata.v3 import (
    ChunkGridMetadata,
    RectilinearChunkGridMetadata,
    RegularChunkGridMetadata,
    create_chunk_grid_metadata,
    parse_node_type_array,
)
from zarr.core.sync import sync
from zarr.errors import (
    ArrayNotFoundError,
    ChunkNotFoundError,
    MetadataValidationError,
    ZarrDeprecationWarning,
    ZarrUserWarning,
)
from zarr.registry import (
    _parse_array_array_codec,
    _parse_array_bytes_codec,
    _parse_bytes_bytes_codec,
    get_pipeline_class,
)
from zarr.storage._common import StorePath, make_store_path
from zarr.storage._utils import _relativize_path

if TYPE_CHECKING:
    from collections.abc import Iterator
    from typing import Self

    import numpy.typing as npt

    from zarr.abc.codec import CodecPipeline
    from zarr.abc.store import Store
    from zarr.codecs.sharding import IndexLocation, ShardingCodec
    from zarr.core.buffer import Buffer
    from zarr.core.dtype.wrapper import TBaseDType, TBaseScalar
    from zarr.storage import StoreLike
    from zarr.types import AnyArray, AnyAsyncArray, ArrayV2, ArrayV3, AsyncArrayV2, AsyncArrayV3


# Array and AsyncArray are defined in the base `zarr` namespace
__all__ = [
    "DEFAULT_FILL_VALUE",
    "create_codec_pipeline",
    "parse_array_metadata",
]

logger = getLogger(__name__)


DEFAULT_FILL_VALUE = Sentinel("DEFAULT_FILL_VALUE")
"""
Sentinel indicating that the default fill value should be used.

This sentinel exists because conventional values used to convey "defaultness" like `None` or
`"auto"` are ambiguous when specifying the fill value parameter of a Zarr array.
The value `None` is ambiguous because it is a valid fill value for Zarr V2
(resulting in `"fill_value": null` in array metadata).
A string like `"auto"` is ambiguous because such a string is a valid fill value for an array
with a string data type.
This sentinel lies outside the space of valid fill values, which means it can
unambiguously express that the default fill value should be used.
"""


def _chunk_sizes_from_shape(
    array_shape: tuple[int, ...], chunk_shape: tuple[int, ...]
) -> tuple[tuple[int, ...], ...]:
    """Compute dask-style chunk sizes from an array shape and uniform chunk shape."""
    result: list[tuple[int, ...]] = []
    for s, c in zip(array_shape, chunk_shape, strict=True):
        nchunks = ceildiv_int(s, c)
        sizes = tuple(min(c, s - i * c) for i in range(nchunks))
        result.append(sizes)
    return tuple(result)


def parse_array_metadata(data: Any, path: str | None = None) -> ArrayMetadata:
    """Array metadata from a metadata object or a metadata document, naming the array at
    `path` in warnings about how an invalid document was read.

    `ArrayV2Metadata` accepts a chunk size of 0, as it always has, though only an
    invalid document holds one: such metadata is read as the documents it would store
    are (see `zarr.core.metadata.repair`), so an array can be built from it. No data
    was read or written under that chunk size, so the reading is silent."""
    if isinstance(data, ArrayV2Metadata) and 0 in data.chunks:
        return parse_stored_array(data.to_buffer_dict(default_buffer_prototype()), 2)
    if isinstance(data, ArrayMetadata):
        return data
    if isinstance(data, dict):
        zarr_format = data.get("zarr_format")
        if zarr_format == 3:
            meta_out = ArrayV3Metadata.from_dict(data, path=path)
            if len(meta_out.storage_transformers) > 0:
                msg = (
                    f"Array metadata contains storage transformers: {meta_out.storage_transformers}."
                    "Arrays with storage transformers are not supported in zarr-python at this time."
                )
                raise ValueError(msg)
            return meta_out
        elif zarr_format == 2:
            return ArrayV2Metadata.from_dict(data, path=path)
        else:
            raise ValueError(f"Invalid zarr_format: {zarr_format}. Expected 2 or 3")
    raise TypeError  # pragma: no cover


def create_codec_pipeline(metadata: ArrayMetadata, *, store: Store | None = None) -> CodecPipeline:
    if store is not None:
        try:
            return get_pipeline_class().from_array_metadata_and_store(
                array_metadata=metadata, store=store
            )
        except NotImplementedError:
            pass

    if isinstance(metadata, ArrayV3Metadata):
        # The pipeline built here is a throwaway: `evolve_from_array_spec` below
        # reconstructs codecs against the evolved spec. `from_codecs` is the
        # chain's first construction, so its advisory warnings (e.g. sharding's
        # "disables partial reads" warning) fire here; `evolve_from_array_spec`
        # re-splits the same already-warned-about chain via
        # `codecs_from_list_unchecked`, so it does not re-emit them.
        pipeline = get_pipeline_class().from_codecs(metadata.codecs)

        # Use the regular chunk shape if available, otherwise use a
        # placeholder. The ChunkTransform is shape-agnostic — the actual
        # chunk shape is passed per-call at decode/encode time.
        if isinstance(metadata.chunk_grid, RegularChunkGridMetadata):
            chunk_shape = metadata.chunk_grid.chunk_shape
        else:
            chunk_shape = (1,) * len(metadata.shape)
        chunk_spec = ArraySpec(
            shape=chunk_shape,
            dtype=metadata.data_type,
            fill_value=metadata.fill_value,
            config=ArrayConfig.from_dict({}),
            prototype=default_buffer_prototype(),
        )
        return pipeline.evolve_from_array_spec(chunk_spec)
    elif isinstance(metadata, ArrayV2Metadata):
        v2_codec = V2Codec(filters=metadata.filters, compressor=metadata.compressor)
        pipeline = get_pipeline_class().from_codecs([v2_codec])
        chunk_spec = ArraySpec(
            shape=metadata.chunks,
            dtype=metadata.dtype,
            fill_value=metadata.fill_value,
            config=ArrayConfig.from_dict({"order": metadata.order}),
            prototype=default_buffer_prototype(),
        )
        return pipeline.evolve_from_array_spec(chunk_spec)
    raise TypeError  # pragma: no cover


async def get_array_metadata(
    store_path: StorePath, zarr_format: ZarrFormat | None = 3
) -> dict[str, JSON]:
    if zarr_format == 2:
        zarray_bytes, zattrs_bytes = await gather(
            (store_path / ZARRAY_JSON).get(prototype=cpu_buffer_prototype),
            (store_path / ZATTRS_JSON).get(prototype=cpu_buffer_prototype),
        )
        if zarray_bytes is None:
            msg = (
                "A Zarr V2 array metadata document was not found in store "
                f"{store_path.store!r} at path {store_path.path!r}."
            )
            raise ArrayNotFoundError(msg)
        return _array_metadata_dict_v2(zarray_bytes, zattrs_bytes)
    elif zarr_format == 3:
        zarr_json_bytes = await (store_path / ZARR_JSON).get(prototype=cpu_buffer_prototype)
        if zarr_json_bytes is None:
            msg = (
                "A Zarr V3 array metadata document was not found in store "
                f"{store_path.store!r} at path {store_path.path!r}."
            )
            raise ArrayNotFoundError(msg)
        return _array_metadata_dict_v3(zarr_json_bytes)
    elif zarr_format is None:
        zarr_json_bytes, zarray_bytes, zattrs_bytes = await gather(
            (store_path / ZARR_JSON).get(prototype=cpu_buffer_prototype),
            (store_path / ZARRAY_JSON).get(prototype=cpu_buffer_prototype),
            (store_path / ZATTRS_JSON).get(prototype=cpu_buffer_prototype),
        )
        if zarr_json_bytes is not None and zarray_bytes is not None:
            # warn and favor v3
            msg = f"Both zarr.json (Zarr format 3) and .zarray (Zarr format 2) metadata objects exist at {store_path}. Zarr v3 will be used."
            warnings.warn(msg, category=ZarrUserWarning, stacklevel=1)
        # favor v3 when both are present
        if zarr_json_bytes is not None:
            return _array_metadata_dict_v3(zarr_json_bytes)
        if zarray_bytes is not None:
            return _array_metadata_dict_v2(zarray_bytes, zattrs_bytes)
        msg = (
            f"Neither Zarr V3 nor Zarr V2 array metadata documents "
            f"were found in store {store_path.store!r} at path {store_path.path!r}."
        )
        raise ArrayNotFoundError(msg)
    else:
        msg = f"Invalid value for 'zarr_format'. Expected 2, 3, or None. Got '{zarr_format}'."  # type: ignore[unreachable]
        raise MetadataValidationError(msg)


def _array_metadata_dict_v2(zarray_bytes: Buffer, zattrs_bytes: Buffer | None) -> dict[str, JSON]:
    """Combine a `.zarray` document and an optional `.zattrs` document into one metadata dict."""
    metadata_dict: dict[str, JSON] = buffer_to_json_object(zarray_bytes)
    metadata_dict["attributes"] = (
        buffer_to_json_object(zattrs_bytes) if zattrs_bytes is not None else {}
    )
    return metadata_dict


def _array_metadata_dict_v3(zarr_json_bytes: Buffer) -> dict[str, JSON]:
    """Parse a `zarr.json` document, checking that it describes an array."""
    metadata_dict: dict[str, JSON] = buffer_to_json_object(zarr_json_bytes)
    parse_node_type_array(metadata_dict.get("node_type"))
    return metadata_dict


@dataclass(frozen=True)
class AsyncArray[T_ArrayMetadata: (ArrayV2Metadata, ArrayV3Metadata)]:
    """
    An asynchronous array class representing a chunked array stored in a Zarr store.

    Parameters
    ----------
    metadata : ArrayMetadata
        The metadata of the array.
    store_path : StorePath
        The path to the Zarr store.
    config : ArrayConfigLike, optional
        The runtime configuration of the array, by default None.

    Attributes
    ----------
    metadata : ArrayMetadata
        The metadata of the array.
    store_path : StorePath
        The path to the Zarr store.
    codec_pipeline : CodecPipeline
        The codec pipeline used for encoding and decoding chunks.
    config : ArrayConfig
        The runtime configuration of the array.
    """

    metadata: T_ArrayMetadata
    store_path: StorePath
    codec_pipeline: CodecPipeline = field(init=False)
    _chunk_grid: ChunkGrid = field(init=False)
    config: ArrayConfig

    @overload
    def __init__(
        self: AsyncArrayV2,
        metadata: ArrayV2Metadata | ArrayV2MetadataDict,
        store_path: StorePath,
        config: ArrayConfigLike | None = None,
    ) -> None: ...

    @overload
    def __init__(
        self: AsyncArrayV3,
        metadata: ArrayV3Metadata | ArrayMetadataJSON_V3,
        store_path: StorePath,
        config: ArrayConfigLike | None = None,
    ) -> None: ...

    def __init__(
        self,
        metadata: ArrayMetadata | ArrayMetadataDict,
        store_path: StorePath,
        config: ArrayConfigLike | None = None,
    ) -> None:
        metadata_parsed = parse_array_metadata(metadata, str(store_path))
        config_parsed = parse_array_config(config)

        object.__setattr__(self, "metadata", metadata_parsed)
        object.__setattr__(self, "store_path", store_path)
        object.__setattr__(self, "config", config_parsed)
        object.__setattr__(self, "_chunk_grid", ChunkGrid.from_metadata(metadata_parsed))
        object.__setattr__(
            self,
            "codec_pipeline",
            create_codec_pipeline(metadata=metadata_parsed, store=store_path.store),
        )

    @classmethod
    async def _create(
        cls,
        store: StoreLike,
        *,
        # v2 and v3
        shape: ShapeLike,
        dtype: ZDTypeLike | ZDType[TBaseDType, TBaseScalar],
        zarr_format: ZarrFormat = 3,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        attributes: dict[str, JSON] | None = None,
        # v3 only
        chunk_shape: ShapeLike | None = None,
        chunk_key_encoding: (
            ChunkKeyEncodingLike
            | tuple[Literal["default"], Literal[".", "/"]]
            | tuple[Literal["v2"], Literal[".", "/"]]
            | None
        ) = None,
        codecs: Iterable[Codec | dict[str, JSON]] | None = None,
        dimension_names: DimensionNamesLike = None,
        # v2 only
        chunks: ShapeLike | None = None,
        dimension_separator: Literal[".", "/"] | None = None,
        order: MemoryOrder | None = None,
        filters: Iterable[dict[str, JSON] | Numcodec] | None = None,
        compressor: CompressorLike = "auto",
        # runtime
        overwrite: bool = False,
        data: npt.ArrayLike | None = None,
        config: ArrayConfigLike | None = None,
    ) -> AnyAsyncArray:
        """Method to create a new asynchronous array instance.
        Deprecated in favor of [`zarr.api.asynchronous.create_array`][].
        """

        dtype_parsed = parse_dtype(dtype, zarr_format=zarr_format)
        store_path = await make_store_path(store)

        shape = parse_shapelike(shape)

        if chunks is not None and chunk_shape is not None:
            raise ValueError("Only one of chunk_shape or chunks can be provided.")

        # Unify the v2 (chunks) and v3 (chunk_shape) parameter names
        _raw_chunks = chunks if chunks is not None else chunk_shape

        config_parsed = parse_array_config(config)

        result: AnyAsyncArray
        if zarr_format == 3:
            if dimension_separator is not None:
                raise ValueError(
                    "dimension_separator cannot be used for arrays with zarr_format 3. Use chunk_key_encoding instead."
                )
            if filters is not None:
                raise ValueError(
                    "filters cannot be used for arrays with zarr_format 3. Use array-to-array codecs instead."
                )
            if compressor != "auto":
                raise ValueError(
                    "compressor cannot be used for arrays with zarr_format 3. Use bytes-to-bytes codecs instead."
                )

            if order is not None:
                _warn_order_kwarg()

            item_size = 1
            if isinstance(dtype_parsed, HasItemSize):
                item_size = dtype_parsed.item_size
            if _raw_chunks is None:
                outer_chunks = guess_chunks(shape, item_size)
            else:
                outer_chunks = normalize_chunks_nd(_raw_chunks, shape)
            chunk_grid = create_chunk_grid_metadata(outer_chunks)
            result = await cls._create_v3(
                store_path,
                shape=shape,
                dtype=dtype_parsed,
                fill_value=fill_value,
                chunk_key_encoding=chunk_key_encoding,
                codecs=codecs,
                dimension_names=dimension_names,
                attributes=attributes,
                overwrite=overwrite,
                config=config_parsed,
                chunk_grid=chunk_grid,
            )
        elif zarr_format == 2:
            if codecs is not None:
                raise ValueError(
                    "codecs cannot be used for arrays with zarr_format 2. Use filters and compressor instead."
                )
            if chunk_key_encoding is not None:
                raise ValueError(
                    "chunk_key_encoding cannot be used for arrays with zarr_format 2. Use dimension_separator instead."
                )
            if dimension_names is not None:
                raise ValueError("dimension_names cannot be used for arrays with zarr_format 2.")
            if _is_rectilinear_chunks(_raw_chunks):
                raise ValueError("Zarr format 2 does not support rectilinear chunk grids.")

            item_size = 1
            if isinstance(dtype_parsed, HasItemSize):
                item_size = dtype_parsed.item_size
            _raw = chunks or chunk_shape
            if _raw is None:
                outer_chunks = guess_chunks(shape, item_size)
            else:
                outer_chunks = normalize_chunks_nd(_raw, shape)
            _chunks = outer_chunks.chunk_shape

            if order is None:
                order_parsed = config_parsed.order
            else:
                order_parsed = order
                config_parsed = replace(config_parsed, order=order)

            result = await cls._create_v2(
                store_path,
                shape=shape,
                dtype=dtype_parsed,
                chunks=_chunks,
                dimension_separator=dimension_separator,
                fill_value=fill_value,
                order=order_parsed,
                config=config_parsed,
                filters=filters,
                compressor=compressor,
                attributes=attributes,
                overwrite=overwrite,
            )
        else:
            raise ValueError(f"zarr_format must be 2 or 3, got {zarr_format}")  # pragma: no cover

        if data is not None:
            # insert user-provided data
            await result.setitem(..., data)

        return result

    @staticmethod
    def _create_metadata_v3(
        shape: ShapeLike,
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunk_grid: ChunkGridMetadata,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        chunk_key_encoding: ChunkKeyEncodingLike | None = None,
        codecs: Iterable[Codec | dict[str, JSON]] | None = None,
        dimension_names: DimensionNamesLike = None,
        attributes: dict[str, JSON] | None = None,
    ) -> ArrayV3Metadata:
        """Create an instance of ArrayV3Metadata."""
        filters: tuple[ArrayArrayCodec, ...]
        compressors: tuple[BytesBytesCodec, ...]

        shape = parse_shapelike(shape)
        if codecs is None:
            # no data types have default filters
            filters = ()
            serializer = default_serializer_v3(dtype)
            compressors = default_compressors_v3(dtype)

            codecs_parsed = (*filters, serializer, *compressors)
        else:
            codecs_parsed = tuple(codecs)

        chunk_key_encoding_parsed: ChunkKeyEncodingLike
        if chunk_key_encoding is None:
            chunk_key_encoding_parsed = {"name": "default", "separator": "/"}
        else:
            chunk_key_encoding_parsed = chunk_key_encoding

        if fill_value is DEFAULT_FILL_VALUE or fill_value is None:
            # Use dtype's default scalar for the DEFAULT_FILL_VALUE sentinel
            # For v3, None is converted to DEFAULT_FILL_VALUE behavior
            fill_value_parsed = dtype.default_scalar()
        else:
            fill_value_parsed = fill_value

        return ArrayV3Metadata(
            shape=shape,
            data_type=dtype,
            chunk_grid=chunk_grid,
            chunk_key_encoding=chunk_key_encoding_parsed,
            fill_value=fill_value_parsed,
            codecs=codecs_parsed,  # type: ignore[arg-type]
            dimension_names=tuple(dimension_names) if dimension_names else None,
            attributes=attributes or {},
        )

    @classmethod
    async def _create_v3(
        cls,
        store_path: StorePath,
        *,
        shape: ShapeLike,
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunk_grid: ChunkGridMetadata,
        config: ArrayConfig,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        chunk_key_encoding: (
            ChunkKeyEncodingLike
            | tuple[Literal["default"], Literal[".", "/"]]
            | tuple[Literal["v2"], Literal[".", "/"]]
            | None
        ) = None,
        codecs: Iterable[Codec | dict[str, JSON]] | None = None,
        dimension_names: DimensionNamesLike = None,
        attributes: dict[str, JSON] | None = None,
        overwrite: bool = False,
    ) -> AsyncArrayV3:
        if isinstance(chunk_key_encoding, tuple):
            chunk_key_encoding = (
                V2ChunkKeyEncoding(separator=chunk_key_encoding[1])
                if chunk_key_encoding[0] == "v2"
                else DefaultChunkKeyEncoding(separator=chunk_key_encoding[1])
            )

        metadata = cls._create_metadata_v3(
            shape=shape,
            dtype=dtype,
            chunk_grid=chunk_grid,
            fill_value=fill_value,
            chunk_key_encoding=chunk_key_encoding,
            codecs=codecs,
            dimension_names=dimension_names,
            attributes=attributes,
        )

        array = cls(metadata=metadata, store_path=store_path, config=config)
        await save_new_metadata(store_path, metadata, overwrite=overwrite, ensure_parents=True)
        return array

    @staticmethod
    def _create_metadata_v2(
        shape: tuple[int, ...],
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunks: tuple[int, ...],
        order: MemoryOrder,
        dimension_separator: Literal[".", "/"] | None = None,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        filters: Iterable[dict[str, JSON] | Numcodec] | None = None,
        compressor: CompressorLikev2 = None,
        attributes: dict[str, JSON] | None = None,
    ) -> ArrayV2Metadata:
        if dimension_separator is None:
            dimension_separator = "."

        # Handle the DEFAULT_FILL_VALUE sentinel
        if fill_value is DEFAULT_FILL_VALUE:
            fill_value_parsed: Any = dtype.default_scalar()
        else:
            # For v2, preserve None as-is (backward compatibility)
            fill_value_parsed = fill_value

        return ArrayV2Metadata(
            shape=shape,
            dtype=dtype,
            chunks=chunks,
            order=order,
            dimension_separator=dimension_separator,
            fill_value=fill_value_parsed,
            compressor=compressor,
            filters=filters,
            attributes=attributes,
        )

    @classmethod
    async def _create_v2(
        cls,
        store_path: StorePath,
        *,
        shape: tuple[int, ...],
        dtype: ZDType[TBaseDType, TBaseScalar],
        chunks: tuple[int, ...],
        order: MemoryOrder,
        config: ArrayConfig,
        dimension_separator: Literal[".", "/"] | None = None,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        filters: Iterable[dict[str, JSON] | Numcodec] | None = None,
        compressor: CompressorLike = "auto",
        attributes: dict[str, JSON] | None = None,
        overwrite: bool = False,
    ) -> AsyncArrayV2:
        compressor_parsed: CompressorLikev2
        if compressor == "auto":
            compressor_parsed = default_compressor_v2(dtype)
        elif isinstance(compressor, BytesBytesCodec):
            raise ValueError(
                "Cannot use a BytesBytesCodec as a compressor for zarr v2 arrays. "
                "Use a numcodecs codec directly instead."
            )
        else:
            compressor_parsed = compressor

        if filters is None:
            filters = default_filters_v2(dtype)

        metadata = cls._create_metadata_v2(
            shape=shape,
            dtype=dtype,
            chunks=chunks,
            order=order,
            dimension_separator=dimension_separator,
            fill_value=fill_value,
            filters=filters,
            compressor=compressor_parsed,
            attributes=attributes,
        )

        array = cls(metadata=metadata, store_path=store_path, config=config)
        await save_new_metadata(store_path, metadata, overwrite=overwrite, ensure_parents=True)
        return array

    @classmethod
    def from_dict(
        cls,
        store_path: StorePath,
        data: dict[str, JSON],
    ) -> AnyAsyncArray:
        """
        Create a Zarr array from a dictionary, with support for both Zarr format 2 and 3 metadata.

        Parameters
        ----------
        store_path : StorePath
            The path within the store where the array should be created.

        data : dict
            A dictionary representing the array data. This dictionary should include necessary metadata
            for the array, such as shape, dtype, and other attributes. The format of the metadata
            will determine whether a Zarr format 2 or 3 array is created.

        Returns
        -------
        AsyncArrayV3 or AsyncArrayV2
            The created Zarr array, either using Zarr format 2 or 3 metadata based on the provided data.

        Raises
        ------
        ValueError
            If the dictionary data is invalid or incompatible with either Zarr format 2 or 3 array creation.
        """
        metadata = parse_array_metadata(data, str(store_path))
        return cls(metadata=metadata, store_path=store_path)

    @classmethod
    async def open(
        cls,
        store: StoreLike,
        zarr_format: ZarrFormat | None = 3,
    ) -> AnyAsyncArray:
        """
        Async method to open an existing Zarr array from a given store.

        Parameters
        ----------
        store : StoreLike
            The store containing the Zarr array. See the
            [storage documentation in the user guide][user-guide-store-like]
            for a description of all valid StoreLike values.
        zarr_format : ZarrFormat | None, optional
            The Zarr format version (default is 3).

        Returns
        -------
        AsyncArray
            The opened Zarr array.

        Examples
        --------
        ```python
        import asyncio
        import zarr
        from zarr.core.array import AsyncArray

        async def example():
            store = zarr.storage.MemoryStore()
            # First create an array to open
            await zarr.api.asynchronous.create_array(
                store=store, shape=(100, 100), dtype="int32"
            )
            # Now open it
            async_arr = await AsyncArray.open(store)
            return async_arr

        async_arr = asyncio.run(example())
        # <AsyncArray memory://... shape=(100, 100) dtype=int32>
        ```
        """
        store_path = await make_store_path(store)
        metadata_dict = await get_array_metadata(store_path, zarr_format=zarr_format)
        # TODO: remove this cast when we have better type hints
        _metadata_dict = cast("ArrayMetadataJSON_V3", metadata_dict)
        return cls(store_path=store_path, metadata=_metadata_dict)

    @property
    def store(self) -> Store:
        return self.store_path.store

    @property
    @deprecated("Use AsyncArray.config instead.", category=ZarrDeprecationWarning)
    def _config(self) -> ArrayConfig:
        return self.config

    @property
    def ndim(self) -> int:
        """Returns the number of dimensions in the Array.

        Returns
        -------
        int
            The number of dimensions in the Array.
        """
        return len(self.metadata.shape)

    @property
    def shape(self) -> tuple[int, ...]:
        """Returns the shape of the Array.

        Returns
        -------
        tuple
            The shape of the Array.
        """
        return self.metadata.shape

    @property
    def chunks(self) -> tuple[int, ...]:
        """Returns the chunk shape of the Array.
        If sharding is used the inner chunk shape is returned, which is defined
        for any chunk grid (inner chunks are always regular).

        Otherwise, only defined for arrays using a regular chunk grid: for a
        non-sharded array with a rectilinear chunk grid, `NotImplementedError`
        is raised. Use `read_chunk_sizes` for general access.

        Returns
        -------
        tuple[int, ...]:
            The chunk shape of the Array.
        """
        # TODO: move sharding awareness out of metadata
        return self.metadata.chunks

    @property
    def read_chunk_sizes(self) -> tuple[tuple[int, ...], ...]:
        """Per-dimension data sizes of chunks used for reading, clipped to the array extent.

        Boundary chunks that extend past the array shape are clipped, so
        the last size along a dimension may be smaller than the declared
        chunk size.  This matches the dask `Array.chunks` convention.

        When sharding is used, returns the inner chunk sizes.
        Otherwise, returns the outer chunk sizes (same as `write_chunk_sizes`).

        Returns
        -------
        tuple[tuple[int, ...], ...]
            One inner tuple per dimension containing the data size of each
            chunk (not the encoded buffer size).

        Examples
        --------
        Without sharding, `read_chunk_sizes` and `write_chunk_sizes` are the same:

        >>> arr = zarr.create_array({}, dtype="i1", shape=(100, 80), chunks=(30, 40))
        >>> arr.read_chunk_sizes
        ((30, 30, 30, 10), (40, 40))

        For a sharded array the two differ: reads are efficient at inner-chunk
        granularity, while writes go to storage one shard at a time:

        >>> sharded = zarr.create_array({}, dtype="i1", shape=(40,), chunks=(10,), shards=(20,))
        >>> sharded.read_chunk_sizes
        ((10, 10, 10, 10),)
        >>> sharded.write_chunk_sizes
        ((20, 20),)
        """
        if (sharding_codec := _sharding_codec(self.metadata)) is not None:
            return _chunk_sizes_from_shape(self.shape, sharding_codec.chunk_shape)
        return self._chunk_grid.chunk_sizes

    @property
    def write_chunk_sizes(self) -> tuple[tuple[int, ...], ...]:
        """Per-dimension data sizes of storage chunks, clipped to the array extent.

        Always returns the outer chunk sizes, regardless of sharding.
        Boundary chunks that extend past the array shape are clipped, so
        the last size along a dimension may be smaller than the declared
        chunk size.  This matches the dask `Array.chunks` convention.

        Returns
        -------
        tuple[tuple[int, ...], ...]
            One inner tuple per dimension containing the data size of each
            chunk (not the encoded buffer size).

        Examples
        --------
        Without sharding, `write_chunk_sizes` and `read_chunk_sizes` are the same:

        >>> arr = zarr.create_array({}, dtype="i1", shape=(100, 80), chunks=(30, 40))
        >>> arr.write_chunk_sizes
        ((30, 30, 30, 10), (40, 40))

        For a sharded array the two differ: writes go to storage one shard at a
        time, while reads are efficient at inner-chunk granularity:

        >>> sharded = zarr.create_array({}, dtype="i1", shape=(40,), chunks=(10,), shards=(20,))
        >>> sharded.write_chunk_sizes
        ((20, 20),)
        >>> sharded.read_chunk_sizes
        ((10, 10, 10, 10),)
        """

        return self._chunk_grid.chunk_sizes

    @property
    def shards(self) -> tuple[int, ...] | None:
        """Returns the shard shape of the Array.
        Returns None if sharding is not used.

        Only defined when the shard grid is regular: for a sharded array with a
        rectilinear chunk grid, `NotImplementedError` is raised. Use
        `write_chunk_sizes` for general access.

        Returns
        -------
        tuple[int, ...]:
            The shard shape of the Array.
        """
        return self.metadata.shards

    @property
    def size(self) -> int:
        """Returns the total number of elements in the array

        Returns
        -------
        int
            Total number of elements in the array
        """
        return math.prod(self.metadata.shape)

    @property
    def filters(self) -> tuple[Numcodec, ...] | tuple[ArrayArrayCodec, ...]:
        """
        Filters that are applied to each chunk of the array, in order, before serializing that
        chunk to bytes.
        """
        if self.metadata.zarr_format == 2:
            filters = self.metadata.filters
            if filters is None:
                return ()
            return filters

        return tuple(
            codec for codec in self.metadata.inner_codecs if isinstance(codec, ArrayArrayCodec)
        )

    @property
    def serializer(self) -> ArrayBytesCodec | None:
        """
        Array-to-bytes codec to use for serializing the chunks into bytes.
        """
        if self.metadata.zarr_format == 2:
            return None

        return next(
            codec for codec in self.metadata.inner_codecs if isinstance(codec, ArrayBytesCodec)
        )

    @property
    @deprecated("Use AsyncArray.compressors instead.", category=ZarrDeprecationWarning)
    def compressor(self) -> Numcodec | None:
        """
        Compressor that is applied to each chunk of the array.

        !!! warning "Deprecated"
            `Array.compressor` is deprecated since v3.0.0 and will be removed in a future release.
            Use [`Array.compressors`][zarr.AsyncArray.compressors] instead.
        """
        if self.metadata.zarr_format == 2:
            return self.metadata.compressor
        raise TypeError("`compressor` is not available for Zarr format 3 arrays.")

    @property
    def compressors(self) -> tuple[Numcodec, ...] | tuple[BytesBytesCodec, ...]:
        """
        Compressors that are applied to each chunk of the array. Compressors are applied in order, and after any
        filters are applied (if any are specified) and the data is serialized into bytes.
        """
        if self.metadata.zarr_format == 2:
            if self.metadata.compressor is not None:
                return (self.metadata.compressor,)
            return ()

        return tuple(
            codec for codec in self.metadata.inner_codecs if isinstance(codec, BytesBytesCodec)
        )

    @property
    def _zdtype(self) -> ZDType[TBaseDType, TBaseScalar]:
        """
        The zarr-specific representation of the array data type
        """
        # `dtype` returns the zarr dtype object for both v2 and v3 metadata
        # (on v3 it is an alias for `data_type`).
        return self.metadata.dtype

    @property
    def dtype(self) -> TBaseDType:
        """Returns the data type of the array.

        Returns
        -------
        np.dtype
            Data type of the array
        """
        return self._zdtype.to_native_dtype()

    @property
    def order(self) -> MemoryOrder:
        """Returns the memory order of the array.

        Returns
        -------
        bool
            Memory order of the array
        """
        if self.metadata.zarr_format == 2:
            return self.metadata.order
        else:
            return self.config.order

    @property
    def attrs(self) -> dict[str, JSON]:
        """Returns the attributes of the array.

        Returns
        -------
        dict
            Attributes of the array
        """
        return self.metadata.attributes

    @property
    def read_only(self) -> bool:
        """Returns True if the array is read-only.

        Returns
        -------
        bool
            True if the array is read-only
        """
        # Backwards compatibility for 2.x
        return self.store_path.read_only

    @property
    def path(self) -> str:
        """Storage path.

        Returns
        -------
        str
            The path to the array in the Zarr store.
        """
        return self.store_path.path

    @property
    def name(self) -> str:
        """Array name following h5py convention.

        Returns
        -------
        str
            The name of the array.
        """
        # follow h5py convention: add leading slash
        name = self.path
        if not name.startswith("/"):
            name = "/" + name
        return name

    @property
    def basename(self) -> str:
        """Final component of name.

        Returns
        -------
        str
            The basename or final component of the array name.
        """
        return self.name.split("/")[-1]

    @property
    def cdata_shape(self) -> tuple[int, ...]:
        """
        The number of chunks along each dimension.

        When sharding is used, this counts inner chunks (not shards) per dimension.

        Returns
        -------
        tuple[int, ...]
            The number of chunks along each dimension.
        """
        return self._chunk_grid_shape

    @property
    def _chunk_grid_shape(self) -> tuple[int, ...]:
        """
        The number of chunks along each dimension.

        When sharding is used, this counts inner chunks (not shards) per dimension.

        Returns
        -------
        tuple[int, ...]
            The number of chunks along each dimension.
        """
        if (sharding_codec := _sharding_codec(self.metadata)) is not None:
            # When sharding, count inner chunks across the whole array
            chunk_shape = sharding_codec.chunk_shape
            return tuple(starmap(ceildiv_int, zip(self.shape, chunk_shape, strict=True)))
        return self._chunk_grid.grid_shape

    @property
    def _shard_grid_shape(self) -> tuple[int, ...]:
        """
        The shape of the shard grid for this array.

        When no shards are present this will automatically fall back to the chunk grid.

        Returns
        -------
        tuple[int, ...]
            The shape of the shard grid for this array.
        """
        # The stored chunk grid is the shard grid when sharding is used, the
        # chunk grid otherwise. Works for regular and rectilinear grids alike.
        return self._chunk_grid.grid_shape

    @property
    def nchunks(self) -> int:
        """
        The number of chunks in this array.

        Note that if a sharding codec is used, then the number of chunks may exceed the number of
        stored objects supporting this array.

        Returns
        -------
        int
            The total number of chunks in the array.
        """
        return product(self._chunk_grid_shape)

    @property
    def _nshards(self) -> int:
        """
        The number of shards in this array.

        If no shards are present this will fall back to giving the number of chunks

        Returns
        -------
        int
            The total number of shards or if absent, chunks in the array.
        """
        return product(self._shard_grid_shape)

    @overload
    def with_config(self: AsyncArrayV2, config: ArrayConfigLike) -> AsyncArrayV2: ...

    @overload
    def with_config(self: AsyncArrayV3, config: ArrayConfigLike) -> AsyncArrayV3: ...

    def with_config(self, config: ArrayConfigLike) -> Self:
        """
        Return a copy of this Array with a new runtime configuration.

        Parameters
        ----------

        config : ArrayConfigLike
            The runtime config for the new Array. Any keys not specified will be inherited
            from the current array's config.

        Returns
        -------
        A new Array
        """
        if isinstance(config, ArrayConfig):
            new_config = config
        else:
            # Merge new config with existing config, so missing keys are inherited
            # from the current array rather than from global defaults
            new_config = ArrayConfig(**{**self.config.to_dict(), **config})  # type: ignore[arg-type]
        return type(self)(metadata=self.metadata, store_path=self.store_path, config=new_config)

    async def nchunks_initialized(self) -> int:
        """
        Calculate the number of chunks that have been initialized in storage.

        This value is calculated as the sum of the number of chunks in every initialized shard
        (shard sizes can vary when the shard grid is rectilinear). Each initialized shard is
        counted at its full declared size, so for an edge shard that extends beyond the array
        extent this includes chunks that lie entirely outside the array, and the result can
        exceed `nchunks`. For arrays that do not use sharding, each stored object holds one
        chunk, so the number of chunks initialized is the same as the number of stored objects
        associated with an array.

        Returns
        -------
        nchunks_initialized : int
            The number of chunks that have been initialized.

        Notes
        -----
        On [`AsyncArray`][zarr.AsyncArray] this is an asynchronous method, unlike the (synchronous)
        property [`Array.nchunks_initialized`][zarr.Array.nchunks_initialized].

        Examples
        --------
        ```python
        import asyncio
        import zarr.api.asynchronous

        async def example():
            arr = await zarr.api.asynchronous.create(shape=(10,), chunks=(1,))
            count = await arr.nchunks_initialized()
            print(f"Initial: {count}")
            #> Initial: 0
            await arr.setitem(slice(5), 1)
            count = await arr.nchunks_initialized()
            print(f"After write: {count}")
            #> After write: 5
            return count

        result = asyncio.run(example())
        ```
        """
        return await _nchunks_initialized(self)

    async def _nshards_initialized(self) -> int:
        """
        Calculate the number of shards that have been initialized in storage.

        This is the number of shards that have been persisted to the storage backend.

        Returns
        -------
        nshards_initialized : int
            The number of shards that have been initialized.

        Notes
        -----
        On [`AsyncArray`][zarr.AsyncArray] this is an asynchronous method, unlike the (synchronous)
        property [`Array._nshards_initialized`][zarr.Array._nshards_initialized].

        Examples
        --------
        ```python
        import asyncio
        import zarr.api.asynchronous

        async def example():
            arr = await zarr.api.asynchronous.create(shape=(10,), chunks=(2,))
            count = await arr._nshards_initialized()
            print(f"Initial: {count}")
            #> Initial: 0
            await arr.setitem(slice(5), 1)
            count = await arr._nshards_initialized()
            print(f"After write: {count}")
            #> After write: 3
            return count

        result = asyncio.run(example())
        ```
        """
        return await _nshards_initialized(self)

    async def nbytes_stored(self) -> int:
        return await _nbytes_stored(self.store_path)

    def _iter_chunk_coords(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[int, ...]]:
        """
        Create an iterator over the coordinates of chunks in chunk grid space.

        If the `origin` keyword is used, iteration will start at the chunk index specified by `origin`.
        The default behavior is to start at the origin of the grid coordinate space.
        If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
        ranging from `[origin, origin selection_shape]`, where the upper bound is exclusive as
        per python indexing conventions.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in chunk grid coordinates.

        Yields
        ------
        chunk_coords: tuple[int, ...]
            The coordinates of each chunk in the selection.
        """
        return _iter_chunk_coords(
            array=self,
            origin=origin,
            selection_shape=selection_shape,
        )

    def _iter_shard_coords(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[int, ...]]:
        """
        Create an iterator over the coordinates of shards in shard grid space.

        This will fall back to chunk grid space in case no shards are present.

        Note that

        If the `origin` keyword is used, iteration will start at the shard index specified by `origin`.
        The default behavior is to start at the origin of the grid coordinate space.
        If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
        ranging from `[origin, origin selection_shape]`, where the upper bound is exclusive as
        per python indexing conventions.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's shard grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in shard grid coordinates.

        Yields
        ------
        chunk_coords: tuple[int, ...]
            The coordinates of each shard in the selection or chunk in case of no shard being present.
        """
        return _iter_shard_coords(
            array=self,
            origin=origin,
            selection_shape=selection_shape,
        )

    def _iter_shard_keys(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[str]:
        """
        Iterate over the keys of the stored objects supporting this array.

        Although only stored objects, e.g. shards should have keys, in case no
        shards are present this automatically falls back to chunks.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in shard grid coordinates.

        Yields
        ------
        key: str
            The storage key of each shard in the selection or in case of no shard
            present of each chunk although the latter case is technically incorrect.
        """
        # Iterate over the coordinates of chunks in chunk grid space.
        return _iter_shard_keys(
            array=self,
            origin=origin,
            selection_shape=selection_shape,
        )

    def _iter_chunk_regions(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[slice, ...]]:
        """
        Iterate over the regions spanned by each chunk.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in chunk grid coordinates.

        Yields
        ------
        region: tuple[slice, ...]
            A tuple of slice objects representing the region spanned by each chunk in the selection.
        """
        return _iter_chunk_regions(
            array=self,
            origin=origin,
            selection_shape=selection_shape,
        )

    def _iter_shard_regions(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[slice, ...]]:
        """
        Iterate over the regions spanned by each shard.

        This will automatically fall back to chunks if no shards are present.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's shard grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in shard grid coordinates.

        Yields
        ------
        region: tuple[slice, ...]
            A tuple of slice objects representing the region spanned by each shard in the selection or chunk in the
            absence of shards.
        """
        return _iter_shard_regions(array=self, origin=origin, selection_shape=selection_shape)

    @property
    def nbytes(self) -> int:
        """
        The total number of bytes that can be stored in the chunks of this array.

        Notes
        -----
        This value is calculated by multiplying the number of elements in the array and the size
        of each element, the latter of which is determined by the dtype of the array.
        For this reason, `nbytes` will likely be inaccurate for arrays with variable-length
        dtypes. It is not possible to determine the size of an array with variable-length elements
        from the shape and dtype alone.
        """
        return self.size * self.dtype.itemsize

    async def _get_selection(
        self,
        indexer: Indexer,
        *,
        prototype: BufferPrototype,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
    ) -> NDArrayLikeOrScalar:
        return await _get_selection(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            indexer,
            prototype=prototype,
            out=out,
            fields=fields,
        )

    async def getitem(
        self,
        selection: BasicSelection,
        *,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        """
        Asynchronous function that retrieves a subset of the array's data based on the provided selection.

        Parameters
        ----------
        selection : BasicSelection
            A selection object specifying the subset of data to retrieve.
        prototype : BufferPrototype, optional
            A buffer prototype to use for the retrieved data (default is None).

        Returns
        -------
        NDArrayLikeOrScalar
            The retrieved subset of the array's data.

        Examples
        --------
        >>> async def example():
        ...     import zarr.api.asynchronous
        ...     import zarr.storage
        ...
        ...     async_arr = await zarr.api.asynchronous.create_array(
        ...          store={},
        ...          shape=(100,100),
        ...          chunks=(10,10),
        ...          dtype="i4",
        ...          fill_value=0,
        ...     )
        ...
        ...     return await async_arr.getitem((0, 1))

        >>> import asyncio
        >>> asyncio.run(example())
        np.int32(0)
        """

        return await _getitem(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            selection,
            prototype=prototype,
        )

    async def get_orthogonal_selection(
        self,
        selection: OrthogonalSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = OrthogonalIndexer(selection, self.metadata.shape, self._chunk_grid)
        return await _get_selection(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            indexer=indexer,
            out=out,
            fields=fields,
            prototype=prototype,
        )

    async def get_mask_selection(
        self,
        mask: MaskSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = MaskIndexer(mask, self.metadata.shape, self._chunk_grid)
        return await _get_selection(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            indexer=indexer,
            out=out,
            fields=fields,
            prototype=prototype,
        )

    async def get_coordinate_selection(
        self,
        selection: CoordinateSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = CoordinateIndexer(selection, self.metadata.shape, self._chunk_grid)
        out_array = await _get_selection(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            indexer=indexer,
            out=out,
            fields=fields,
            prototype=prototype,
        )
        if hasattr(out_array, "shape"):
            # restore shape
            out_array = cast("NDArrayLikeOrScalar", np.array(out_array).reshape(indexer.sel_shape))
        return out_array

    async def _save_metadata(self, metadata: ArrayMetadata, ensure_parents: bool = False) -> None:
        """Store `metadata` as this array's own documents, then clear the
        `_stored_document` mark (see `_stored_document_replaced`)."""
        await save_metadata(self.store_path, metadata, ensure_parents=ensure_parents)
        self._stored_document_replaced()

    def _stored_document_replaced(self) -> None:
        """Record that the store no longer holds a document of this array that needs an
        repair: it holds the repair, a valid document, or none. The metadata this handle
        holds, which a consolidated group handle may share, then stops standing for the
        document it was read from (see `mark_repaired`), so no later write through either
        handle stores that document again."""
        object.__setattr__(self.metadata, "_stored_document", None)

    async def _store_repaired_document(self) -> None:
        """Store the repair of this array's current stored document, if it needs one,
        before chunks are written under this handle's metadata.

        Only for metadata read from a document whose repair moves chunks (see
        `zarr.core.metadata.repair`). The document is read again, because the store may
        hold a newer one than this handle's metadata. If that one lays out chunks
        differently (another writer stored a different chunk size since), this handle
        would write chunks no reader finds, so it raises and stores nothing. If it needs
        no repair (the array was re-saved since, possibly by another implementation), it
        is left as written; if there is none, there is nothing to repair.

        Storing the same repair twice is harmless, so handles that write chunks
        concurrently need no coordination. The read and the store are not one atomic
        step, though: a metadata write by another handle between them (a resize, an
        attribute update) is replaced by the repair of the document read before it, as
        with any two metadata writes that race.
        """
        if self.metadata._stored_document is None:
            return
        zarr_format = self.metadata.zarr_format
        documents = await read_documents(self.store_path, ARRAY_DOCUMENTS[zarr_format])
        try:
            current = parse_stored_array(documents, zarr_format)
        except ArrayNotFoundError:
            pass
        else:
            if _chunk_layout(current) != _chunk_layout(self.metadata):
                raise ValueError(
                    f"The metadata stored for the array at {str(self.store_path)!r} has "
                    "changed since this array was opened: reopen the array to write to it."
                )
            if current._stored_document is not None:
                await upsert_metadata(self.store_path, current, documents)
        self._stored_document_replaced()

    async def _set_selection(
        self,
        indexer: Indexer,
        value: npt.ArrayLike,
        *,
        prototype: BufferPrototype,
        fields: Fields | None = None,
    ) -> None:
        if product(indexer.shape) > 0:
            # Chunks are about to be stored under the repaired metadata, so store it
            # first: every reader of the store then agrees with them.
            await self._store_repaired_document()
        return await _set_selection(
            self.store_path,
            self.metadata,
            self.codec_pipeline,
            self.config,
            self._chunk_grid,
            indexer,
            value,
            prototype=prototype,
            fields=fields,
        )

    async def setitem(
        self,
        selection: BasicSelection,
        value: npt.ArrayLike,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """
        Asynchronously set values in the array using basic indexing.

        Parameters
        ----------
        selection : BasicSelection
            The selection defining the region of the array to set.

        value : numpy.typing.ArrayLike
            The values to be written into the selected region of the array.

        prototype : BufferPrototype or None, optional
            A prototype buffer that defines the structure and properties of the array chunks being modified.
            If None, the default buffer prototype is used. Default is None.

        Returns
        -------
        None
            This method does not return any value.

        Raises
        ------
        IndexError
            If the selection is out of bounds for the array.

        ValueError
            If the values are not compatible with the array's dtype or shape.

        Notes
        -----
        - This method is asynchronous and should be awaited.
        - Supports basic indexing, where the selection is contiguous and does not involve advanced indexing.
        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = BasicIndexer(selection, shape=self.metadata.shape, chunk_grid=self._chunk_grid)
        return await self._set_selection(indexer, value, prototype=prototype)

    @property
    def oindex(self) -> AsyncOIndex[T_ArrayMetadata]:
        """Shortcut for orthogonal (outer) indexing, see [get_orthogonal_selection][zarr.Array.get_orthogonal_selection] and
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection] for documentation and examples."""
        return AsyncOIndex(self)

    @property
    def vindex(self) -> AsyncVIndex[T_ArrayMetadata]:
        """Shortcut for vectorized (inner) indexing, see [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection], [get_mask_selection][zarr.Array.get_mask_selection] and
        [set_mask_selection][zarr.Array.set_mask_selection] for documentation and examples."""
        return AsyncVIndex(self)

    async def resize(self, new_shape: ShapeLike, delete_outside_chunks: bool = True) -> None:
        """
        Asynchronously resize the array to a new shape.

        Parameters
        ----------
        new_shape : tuple[int, ...]
            The desired new shape of the array.

        delete_outside_chunks : bool, optional
            If True (default), chunks that fall outside the new shape will be deleted. If False,
            the data in those chunks will be preserved.

        Returns
        -------
        AsyncArray
            The resized array.

        Raises
        ------
        ValueError
            If the new shape is incompatible with the current array's chunking configuration.

        Notes
        -----
        - This method is asynchronous and should be awaited.
        """
        return await _resize(self, new_shape, delete_outside_chunks)

    async def append(self, data: npt.ArrayLike, axis: int = 0) -> tuple[int, ...]:
        """Append `data` to `axis`.

        Parameters
        ----------
        data : array-like
            Data to be appended.
        axis : int
            Axis along which to append.

        Returns
        -------
        new_shape : tuple

        Notes
        -----
        The size of all dimensions other than `axis` must match between this
        array and `data`.
        """
        return await _append(self, data, axis)

    async def update_attributes(self, new_attributes: dict[str, JSON]) -> Self:
        """
        Asynchronously update the array's attributes.

        Parameters
        ----------
        new_attributes : dict of str to JSON
            A dictionary of new attributes to update or add to the array. The keys represent attribute
            names, and the values must be JSON-compatible.

        Returns
        -------
        AsyncArray
            The array with the updated attributes.

        Raises
        ------
        ValueError
            If the attributes are invalid or incompatible with the array's metadata.

        Notes
        -----
        - This method is asynchronous and should be awaited.
        - The updated attributes will be merged with existing attributes, and any conflicts will be
          overwritten by the new values.
        """
        await _update_attributes(self, new_attributes)
        return self

    def __repr__(self) -> str:
        return f"<AsyncArray {self.store_path} shape={self.shape} dtype={self.dtype}>"

    @property
    def info(self) -> Any:
        """
        Return the statically known information for an array.

        Returns
        -------
        ArrayInfo

        Related
        -------
        [zarr.AsyncArray.info_complete][] - All information about a group, including dynamic information
            like the number of bytes and chunks written.

        Examples
        --------
        >>> import asyncio
        >>> arr = asyncio.run(
        ...     zarr.api.asynchronous.create(
        ...         path="array", shape=(3, 4, 5), chunks=(2, 2, 2)
        ...     )
        ... )
        >>> arr.info
        Type               : Array
        Zarr format        : 3
        Data type          : Float64(endianness=...)
        Fill value         : 0.0
        Shape              : (3, 4, 5)
        Chunk shape        : (2, 2, 2)
        Order              : C
        Read-only          : False
        Store type         : MemoryStore
        Filters            : ()
        Serializer         : BytesCodec(endian='little')
        Compressors        : (ZstdCodec(level=0, checksum=False),)
        No. bytes          : 480
        """
        return self._info()

    async def info_complete(self) -> Any:
        """
        Return all the information for an array, including dynamic information like a storage size.

        In addition to the static information, this provides

        - The count of chunks initialized
        - The sum of the bytes written

        Returns
        -------
        ArrayInfo

        Related
        -------
        [zarr.AsyncArray.info][] - A property giving just the statically known information about an array.
        """
        return await _info_complete(self)

    def _info(
        self, count_chunks_initialized: int | None = None, count_bytes_stored: int | None = None
    ) -> Any:
        rectilinear_grid = _stored_rectilinear_grid_or_none(self.metadata)
        sharded = _sharding_codec(self.metadata) is not None
        # `.chunks` (the inner chunk shape when sharded) is undefined only for a
        # non-sharded rectilinear grid, which ArrayInfo renders as "<variable>";
        # `.shards` is undefined for a rectilinear shard grid, where the
        # "<variable>" sentinel keeps the array rendered as sharded.
        chunk_shape = self.chunks if (rectilinear_grid is None or sharded) else None
        shard_shape: tuple[int, ...] | Literal["<variable>"] | None
        if rectilinear_grid is None:
            shard_shape = self.shards
        elif sharded:
            shard_shape = "<variable>"
        else:
            shard_shape = None
        return ArrayInfo(
            _zarr_format=self.metadata.zarr_format,
            _data_type=self._zdtype,
            _fill_value=self.metadata.fill_value,
            _shape=self.shape,
            _order=self.order,
            _shard_shape=shard_shape,
            _chunk_shape=chunk_shape,
            _read_only=self.read_only,
            _compressors=self.compressors,
            _filters=self.filters,
            _serializer=self.serializer,
            _store_type=type(self.store_path.store).__name__,
            _count_bytes=self.nbytes,
            _count_bytes_stored=count_bytes_stored,
            _count_chunks_initialized=count_chunks_initialized,
        )


# TODO: Array can be a frozen data class again once property setters (e.g. shape) are removed
@dataclass(frozen=False)
class Array[T_ArrayMetadata: (ArrayV2Metadata, ArrayV3Metadata)]:
    """
    A Zarr array.
    """

    _async_array: AsyncArray[T_ArrayMetadata]

    @property
    def async_array(self) -> AsyncArray[T_ArrayMetadata]:
        """An asynchronous version of the current array.  Useful for batching requests.

        Returns
        -------
            An asynchronous array whose metadata + store matches that of this synchronous array.
        """
        return self._async_array

    @property
    def config(self) -> ArrayConfig:
        """
        The runtime configuration for this array. This is a read-only property. To modify the
        runtime configuration, use `Array.with_config` to create a new `Array` with the modified
        configuration.

        Returns
        -------
        An `ArrayConfig` object that defines the runtime configuration for the array.
        """
        return self.async_array.config

    @property
    def _chunk_grid(self) -> ChunkGrid:
        """The chunk grid for this array, bound to the array's shape."""
        return self.async_array._chunk_grid

    @classmethod
    def _create(
        cls,
        store: StoreLike,
        *,
        # v2 and v3
        shape: tuple[int, ...],
        dtype: ZDTypeLike,
        zarr_format: ZarrFormat = 3,
        fill_value: Any | None = DEFAULT_FILL_VALUE,
        attributes: dict[str, JSON] | None = None,
        # v3 only
        chunk_shape: tuple[int, ...] | None = None,
        chunk_key_encoding: (
            ChunkKeyEncoding
            | tuple[Literal["default"], Literal[".", "/"]]
            | tuple[Literal["v2"], Literal[".", "/"]]
            | None
        ) = None,
        codecs: Iterable[Codec | dict[str, JSON]] | None = None,
        dimension_names: DimensionNamesLike = None,
        # v2 only
        chunks: tuple[int, ...] | None = None,
        dimension_separator: Literal[".", "/"] | None = None,
        order: MemoryOrder | None = None,
        filters: list[dict[str, JSON]] | None = None,
        compressor: CompressorLike = "auto",
        # runtime
        overwrite: bool = False,
        config: ArrayConfigLike | None = None,
    ) -> Self:
        """Creates a new Array instance from an initialized store.
        Deprecated in favor of [`zarr.create_array`][].
        """
        async_array = sync(
            AsyncArray._create(
                store=store,
                shape=shape,
                dtype=dtype,
                zarr_format=zarr_format,
                attributes=attributes,
                fill_value=fill_value,
                chunk_shape=chunk_shape,
                chunk_key_encoding=chunk_key_encoding,
                codecs=codecs,
                dimension_names=dimension_names,
                chunks=chunks,
                dimension_separator=dimension_separator,
                order=order,
                filters=filters,
                compressor=compressor,
                overwrite=overwrite,
                config=config,
            ),
        )
        return cls(async_array)

    @classmethod
    def from_dict(
        cls,
        store_path: StorePath,
        data: dict[str, JSON],
    ) -> Self:
        """
        Create a Zarr array from a dictionary.

        Parameters
        ----------
        store_path : StorePath
            The path within the store where the array should be created.

        data : dict
            A dictionary representing the array data. This dictionary should include necessary metadata
            for the array, such as shape, dtype, fill value, and attributes.

        Returns
        -------
        Array
            The created Zarr array.

        Raises
        ------
        ValueError
            If the dictionary data is invalid or missing required fields for array creation.
        """
        async_array = AsyncArray.from_dict(store_path=store_path, data=data)
        return cls(async_array)

    @classmethod
    def open(
        cls,
        store: StoreLike,
    ) -> Self:
        """Opens an existing Array from a store.

        Parameters
        ----------
        store : StoreLike
            Store containing the Array. See the
            [storage documentation in the user guide][user-guide-store-like]
            for a description of all valid StoreLike values.

        Returns
        -------
        Array
            Array opened from the store.
        """
        async_array = sync(AsyncArray.open(store))
        return cls(async_array)

    @property
    def store(self) -> Store:
        return self.async_array.store

    @property
    def ndim(self) -> int:
        """Returns the number of dimensions in the array.

        Returns
        -------
        int
            The number of dimensions in the array.
        """
        return self.async_array.ndim

    @property
    def shape(self) -> tuple[int, ...]:
        """Returns the shape of the array.

        Returns
        -------
        tuple[int, ...]
            The shape of the array.
        """
        return self.async_array.shape

    @shape.setter
    def shape(self, value: tuple[int, ...]) -> None:
        """Sets the shape of the array by calling resize."""
        self.resize(value)

    @property
    def chunks(self) -> tuple[int, ...]:
        """Returns a tuple of integers describing the length of each dimension of a chunk of the array.
        If sharding is used the inner chunk shape is returned, which is defined
        for any chunk grid (inner chunks are always regular).

        Otherwise, only defined for arrays using a regular chunk grid: for a
        non-sharded array with a rectilinear chunk grid, `NotImplementedError`
        is raised. Use `read_chunk_sizes` for general access.

        Returns
        -------
        tuple
            A tuple of integers representing the length of each dimension of a chunk.
        """
        return self.async_array.chunks

    @property
    def read_chunk_sizes(self) -> tuple[tuple[int, ...], ...]:
        """Per-dimension data sizes of chunks used for reading, clipped to the array extent.

        Boundary chunks that extend past the array shape are clipped, so
        the last size along a dimension may be smaller than the declared
        chunk size.  This matches the dask `Array.chunks` convention.

        When sharding is used, returns the inner chunk sizes.
        Otherwise, returns the outer chunk sizes (same as `write_chunk_sizes`).

        Returns
        -------
        tuple[tuple[int, ...], ...]
            One inner tuple per dimension containing the data size of each
            chunk (not the encoded buffer size).

        Examples
        --------
        Without sharding, `read_chunk_sizes` and `write_chunk_sizes` are the same:

        >>> import zarr
        >>> arr = zarr.create_array({}, dtype="i1", shape=(100, 80), chunks=(30, 40))
        >>> arr.read_chunk_sizes
        ((30, 30, 30, 10), (40, 40))

        For a sharded array the two differ: reads are efficient at inner-chunk
        granularity, while writes go to storage one shard at a time:

        >>> sharded = zarr.create_array({}, dtype="i1", shape=(40,), chunks=(10,), shards=(20,))
        >>> sharded.read_chunk_sizes
        ((10, 10, 10, 10),)
        >>> sharded.write_chunk_sizes
        ((20, 20),)
        """
        return self.async_array.read_chunk_sizes

    @property
    def write_chunk_sizes(self) -> tuple[tuple[int, ...], ...]:
        """Per-dimension data sizes of storage chunks, clipped to the array extent.

        Always returns the outer chunk sizes, regardless of sharding.
        Boundary chunks that extend past the array shape are clipped, so
        the last size along a dimension may be smaller than the declared
        chunk size.  This matches the dask `Array.chunks` convention.

        Returns
        -------
        tuple[tuple[int, ...], ...]
            One inner tuple per dimension containing the data size of each
            chunk (not the encoded buffer size).

        Examples
        --------
        Without sharding, `write_chunk_sizes` and `read_chunk_sizes` are the same:

        >>> import zarr
        >>> arr = zarr.create_array({}, dtype="i1", shape=(100, 80), chunks=(30, 40))
        >>> arr.write_chunk_sizes
        ((30, 30, 30, 10), (40, 40))

        For a sharded array the two differ: writes go to storage one shard at a
        time, while reads are efficient at inner-chunk granularity:

        >>> sharded = zarr.create_array({}, dtype="i1", shape=(40,), chunks=(10,), shards=(20,))
        >>> sharded.write_chunk_sizes
        ((20, 20),)
        >>> sharded.read_chunk_sizes
        ((10, 10, 10, 10),)
        """
        return self.async_array.write_chunk_sizes

    @property
    def shards(self) -> tuple[int, ...] | None:
        """Returns a tuple of integers describing the length of each dimension of a shard of the array.
        Returns None if sharding is not used.

        Only defined when the shard grid is regular: for a sharded array with a
        rectilinear chunk grid, `NotImplementedError` is raised. Use
        `write_chunk_sizes` for general access.

        Returns
        -------
        tuple | None
            A tuple of integers representing the length of each dimension of a shard or None if sharding is not used.
        """
        return self.async_array.shards

    @property
    def size(self) -> int:
        """Returns the total number of elements in the array.

        Returns
        -------
        int
            Total number of elements in the array.
        """
        return self.async_array.size

    @property
    def dtype(self) -> np.dtype[Any]:
        """Returns the NumPy data type.

        Returns
        -------
        np.dtype
            The NumPy data type.
        """
        return self.async_array.dtype

    @property
    def attrs(self) -> Attributes:
        """Returns a [MutableMapping][collections.abc.MutableMapping] containing user-defined attributes.

        Returns
        -------
        attrs
            A [MutableMapping][collections.abc.MutableMapping] object containing user-defined attributes.

        Notes
        -----
        Note that attribute values must be JSON serializable.
        """
        return Attributes(self)

    @property
    def path(self) -> str:
        """Storage path."""
        return self.async_array.path

    @property
    def name(self) -> str:
        """Array name following h5py convention."""
        return self.async_array.name

    @property
    def basename(self) -> str:
        """Final component of name."""
        return self.async_array.basename

    @property
    def metadata(self) -> ArrayMetadata:
        return self.async_array.metadata

    @property
    def store_path(self) -> StorePath:
        return self.async_array.store_path

    @property
    def order(self) -> MemoryOrder:
        return self.async_array.order

    @property
    def read_only(self) -> bool:
        return self.async_array.read_only

    @property
    def fill_value(self) -> Any:
        return self.metadata.fill_value

    @property
    def filters(self) -> tuple[Numcodec, ...] | tuple[ArrayArrayCodec, ...]:
        """
        Filters that are applied to each chunk of the array, in order, before serializing that
        chunk to bytes.
        """
        return self.async_array.filters

    @property
    def serializer(self) -> ArrayBytesCodec | None:
        """
        Array-to-bytes codec to use for serializing the chunks into bytes.
        """
        return self.async_array.serializer

    @property
    @deprecated("Use Array.compressors instead.", category=ZarrDeprecationWarning)
    def compressor(self) -> Numcodec | None:
        """
        Compressor that is applied to each chunk of the array.

        !!! warning "Deprecated"
            `array.compressor` is deprecated since v3.0.0 and will be removed in a future release.
            Use [`array.compressors`][zarr.Array.compressors] instead.
        """
        return self.async_array.compressor

    @property
    def compressors(self) -> tuple[Numcodec, ...] | tuple[BytesBytesCodec, ...]:
        """
        Compressors that are applied to each chunk of the array. Compressors are applied in order, and after any
        filters are applied (if any are specified) and the data is serialized into bytes.
        """
        return self.async_array.compressors

    @property
    def cdata_shape(self) -> tuple[int, ...]:
        """
        The number of chunks along each dimension.

        When sharding is used, this counts inner chunks (not shards) per dimension.
        """
        return self._chunk_grid_shape

    @property
    def _chunk_grid_shape(self) -> tuple[int, ...]:
        """
        The number of chunks along each dimension.

        When sharding is used, this counts inner chunks (not shards) per dimension.

        Returns
        -------
        tuple[int, ...]
            The number of chunks along each dimension.
        """
        return self.async_array._chunk_grid_shape

    @property
    def _shard_grid_shape(self) -> tuple[int, ...]:
        """
        The shape of the shard grid for this array.
        """
        return self.async_array._shard_grid_shape

    @property
    def nchunks(self) -> int:
        """
        The number of chunks in this array.

        Note that if a sharding codec is used, then the number of chunks may exceed the number of
        stored objects supporting this array.
        """
        return self.async_array.nchunks

    @property
    def _nshards(self) -> int:
        """
        The number of shards in the stored representation of this array.
        """
        return self.async_array._nshards

    @overload
    def with_config(self: ArrayV2, config: ArrayConfigLike) -> ArrayV2: ...

    @overload
    def with_config(self: ArrayV3, config: ArrayConfigLike) -> ArrayV3: ...

    def with_config(self, config: ArrayConfigLike) -> Self:
        """
        Return a copy of this Array with a new runtime configuration.

        Parameters
        ----------

        config : ArrayConfigLike
            The runtime config for the new Array. Any keys not specified will be inherited
            from the current array's config.

        Returns
        -------
        A new Array
        """
        return type(self)(self._async_array.with_config(config))

    @property
    def nbytes(self) -> int:
        """
        The total number of bytes that can be stored in the chunks of this array.

        Notes
        -----
        This value is calculated by multiplying the number of elements in the array and the size
        of each element, the latter of which is determined by the dtype of the array.
        For this reason, `nbytes` will likely be inaccurate for arrays with variable-length
        dtypes. It is not possible to determine the size of an array with variable-length elements
        from the shape and dtype alone.
        """
        return self.async_array.nbytes

    @property
    def nchunks_initialized(self) -> int:
        """
        Calculate the number of chunks that have been initialized in storage.

        This value is calculated as the sum of the number of chunks in every initialized shard
        (shard sizes can vary when the shard grid is rectilinear). Each initialized shard is
        counted at its full declared size, so for an edge shard that extends beyond the array
        extent this includes chunks that lie entirely outside the array, and the result can
        exceed `nchunks`. For arrays that do not use sharding, each stored object holds one
        chunk, so the number of chunks initialized is the same as the number of stored objects
        associated with an array. For a direct count of the number of initialized stored
        objects, see `nshards_initialized`.

        Returns
        -------
        nchunks_initialized : int
            The number of chunks that have been initialized.

        Examples
        --------
        >>> arr = zarr.create_array(store={}, dtype="i1", shape=(10,), chunks=(1,), shards=(2,))
        >>> arr.nchunks_initialized
        0
        >>> arr[:5] = 1
        >>> arr.nchunks_initialized
        6
        """
        return sync(self.async_array.nchunks_initialized())

    @property
    def _nshards_initialized(self) -> int:
        """
        Calculate the number of shards that have been initialized, i.e. the number of shards that have
        been persisted to the storage backend.

        Returns
        -------
        nshards_initialized : int
            The number of shards that have been initialized.

        Examples
        --------
        >>> arr = zarr.create(shape=(10,), chunks=(2,))
        >>> arr._nshards_initialized
        0
        >>> arr[:5] = 1
        >>> arr._nshards_initialized
        3
        """
        return sync(self.async_array._nshards_initialized())

    def nbytes_stored(self) -> int:
        """
        Determine the size, in bytes, of the array actually written to the store.

        Returns
        -------
        size : int
        """
        return sync(self.async_array.nbytes_stored())

    def _iter_shard_keys(
        self, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[str]:
        """
        Iterate over the storage keys of each shard, relative to an optional origin, and optionally
        limited to a contiguous region in chunk grid coordinates.

        If no shards are present this falls back to chunks, though in this case these are then actually
        not storage keys.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's shard grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in shard grid coordinates.

        Yields
        ------
        str
            The storage key of each shard in the selection or chunk though chunks technically do not have
            storage keys.
        """
        return self.async_array._iter_shard_keys(origin=origin, selection_shape=selection_shape)

    def _iter_chunk_coords(
        self, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[int, ...]]:
        """
        Create an iterator over the coordinates of chunks in chunk grid space.

        If the `origin` keyword is used, iteration will start at the chunk index specified by `origin`.
        The default behavior is to start at the origin of the grid coordinate space.
        If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
        ranging from `[origin, origin + selection_shape]`, where the upper bound is exclusive as
        per python indexing conventions.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in chunk grid coordinates.

        Yields
        ------
        tuple[int, ...]
            The coordinates of each chunk in the selection.
        """
        return self.async_array._iter_chunk_coords(origin=origin, selection_shape=selection_shape)

    def _iter_shard_coords(
        self, *, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[int, ...]]:
        """
        Create an iterator over the coordinates of shards in shard grid space.

        If the `origin` keyword is used, iteration will start at the shard index specified by `origin`.
        The default behavior is to start at the origin of the grid coordinate space.
        If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
        ranging from `[origin, origin selection_shape]`, where the upper bound is exclusive as
        per python indexing conventions.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's shard grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in shard grid coordinates.

        Yields
        ------
        tuple[int, ...]
            The coordinates of each shard in the selection.
        """
        return self.async_array._iter_shard_coords(origin=origin, selection_shape=selection_shape)

    def _iter_chunk_regions(
        self, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[slice, ...]]:
        """
        Iterate over the regions spanned by each chunk.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in chunk grid coordinates.

        Yields
        ------
        tuple[slice, ...]
            A tuple of slice objects representing the region spanned by each chunk in the selection.
        """
        return self.async_array._iter_chunk_regions(origin=origin, selection_shape=selection_shape)

    def _iter_shard_regions(
        self, origin: Sequence[int] | None = None, selection_shape: Sequence[int] | None = None
    ) -> Iterator[tuple[slice, ...]]:
        """
        Iterate over the regions spanned by each shard or chunk if no shard is present.

        Parameters
        ----------
        origin : Sequence[int] | None, default=None
            The origin of the selection relative to the array's chunk grid.
        selection_shape : Sequence[int] | None, default=None
            The shape of the selection in chunk grid coordinates.

        Yields
        ------
        tuple[slice, ...]
            A tuple of slice objects representing the region spanned by each shard or if no shard is present,
            chunk in the selection.
        """
        return self.async_array._iter_shard_regions(origin=origin, selection_shape=selection_shape)

    def __array__(
        self, dtype: npt.DTypeLike | None = None, copy: bool | None = None
    ) -> NDArrayLike:
        """
        This method is used by numpy when converting zarr.Array into a numpy array.
        For more information, see https://numpy.org/devdocs/user/basics.interoperability.html#the-array-method
        """
        if copy is False:
            msg = "`copy=False` is not supported. This method always creates a copy."
            raise ValueError(msg)

        arr = self[...]
        arr_np = np.array(arr, dtype=dtype)

        if dtype is not None:
            arr_np = arr_np.astype(dtype)

        return arr_np

    def __getitem__(self, selection: Selection) -> NDArrayLikeOrScalar:
        """Retrieve data for an item or region of the array.

        Parameters
        ----------
        selection : tuple
            An integer index or slice or tuple of int/slice objects specifying the
            requested item or region for each dimension of the array.

        Returns
        -------
        NDArrayLikeOrScalar
             An array-like or scalar containing the data for the requested region.

        Examples
        --------
        Setup a 1-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(100, dtype="uint16")
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(10,),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve a single item:

            >>> z[5]
            array(5, dtype=uint16)

        Retrieve a region via slicing:

            >>> z[:5]
            array([0, 1, 2, 3, 4], dtype=uint16)
            >>> z[-5:]
            array([95, 96, 97, 98, 99], dtype=uint16)
            >>> z[5:10]
            array([5, 6, 7, 8, 9], dtype=uint16)
            >>> z[5:10:2]
            array([5, 7, 9], dtype=uint16)
            >>> z[::2]
            array([ 0,  2,  4, ..., 94, 96, 98], dtype=uint16)

        Load the entire array into memory:

            >>> z[...]
            array([ 0,  1,  2, ..., 97, 98, 99], dtype=uint16)

        Setup a 2-dimensional array:

            >>> data = np.arange(100, dtype="uint16").reshape(10, 10)
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(10, 10),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve an item:

            >>> z[2, 2]
            array(22, dtype=uint16)

        Retrieve a region via slicing:

            >>> z[1:3, 1:3]
            array([[11, 12],
                   [21, 22]], dtype=uint16)
            >>> z[1:3, :]
            array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                   [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]], dtype=uint16)
            >>> z[:, 1:3]
            array([[ 1,  2],
                   [11, 12],
                   [21, 22],
                   [31, 32],
                   [41, 42],
                   [51, 52],
                   [61, 62],
                   [71, 72],
                   [81, 82],
                   [91, 92]], dtype=uint16)
            >>> z[0:5:2, 0:5:2]
            array([[ 0,  2,  4],
                   [20, 22, 24],
                   [40, 42, 44]], dtype=uint16)
            >>> z[::2, ::2]
            array([[ 0,  2,  4,  6,  8],
                   [20, 22, 24, 26, 28],
                   [40, 42, 44, 46, 48],
                   [60, 62, 64, 66, 68],
                   [80, 82, 84, 86, 88]], dtype=uint16)

        Load the entire array into memory:

            >>> z[...]
            array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
                   [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                   [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
                   [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                   [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
                   [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
                   [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
                   [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
                   [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]], dtype=uint16)

        Notes
        -----
        Slices with step > 1 are supported, but slices with negative step are not.

        For arrays with a structured dtype, see Zarr format 2 for examples of how to use
        fields

        Currently the implementation for __getitem__ is provided by
        [`vindex`][zarr.Array.vindex] if the indexing is pure fancy indexing (ie a
        broadcast-compatible tuple of integer array indices), or by
        [`set_basic_selection`][zarr.Array.set_basic_selection] otherwise.

        Effectively, this means that the following indexing modes are supported:

           - integer indexing
           - slice indexing
           - mixed slice and integer indexing
           - boolean indexing
           - fancy indexing (vectorized list of integers)

        For specific indexing options including outer indexing, see the
        methods listed under Related.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection], [set_basic_selection][zarr.Array.set_basic_selection]
        [get_mask_selection][zarr.Array.get_mask_selection], [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection], [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection], [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection], [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex], [blocks][zarr.Array.blocks], [__setitem__][zarr.Array.__setitem__]
        """

        fields, pure_selection = pop_fields(selection)
        if is_pure_fancy_indexing(pure_selection, self.ndim):
            return self.vindex[cast("CoordinateSelection | MaskSelection", selection)]
        elif is_pure_orthogonal_indexing(pure_selection, self.ndim):
            return self.get_orthogonal_selection(pure_selection, fields=fields)
        else:
            return self.get_basic_selection(cast("BasicSelection", pure_selection), fields=fields)

    def __setitem__(self, selection: Selection, value: npt.ArrayLike) -> None:
        """Modify data for an item or region of the array.

        Parameters
        ----------
        selection : tuple
            An integer index or slice or tuple of int/slice specifying the requested
            region for each dimension of the array.
        value : npt.ArrayLike
            An array-like containing the data to be stored in the selection.

        Examples
        --------
        Setup a 1-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(100,),
            ...     store={},
            ...     chunk_shape=(5,),
            ...     dtype="i4",
            ... )

        Set all array elements to the same scalar value:

            >>> z[...] = 42
            >>> z[...]
            array([42, 42, 42, ..., 42, 42, 42], dtype=int32)

        Set a portion of the array:

            >>> z[:10] = np.arange(10)
            >>> z[-10:] = np.arange(10)[::-1]
            >>> z[...]
            array([ 0, 1, 2, ..., 2, 1, 0], dtype=int32)

        Setup a 2-dimensional array:

            >>> z = zarr.zeros(
            ...     shape=(5, 5),
            ...     store={},
            ...     chunk_shape=(5, 5),
            ...     dtype="i4",
            ... )

        Set all array elements to the same scalar value:

            >>> z[...] = 42

        Set a portion of the array:

            >>> z[0, :] = np.arange(z.shape[1])
            >>> z[:, 0] = np.arange(z.shape[0])
            >>> z[...]
            array([[ 0,  1,  2,  3,  4],
                   [ 1, 42, 42, 42, 42],
                   [ 2, 42, 42, 42, 42],
                   [ 3, 42, 42, 42, 42],
                   [ 4, 42, 42, 42, 42]], dtype=int32)

        Notes
        -----
        Slices with step > 1 are supported, but slices with negative step are not.

        For arrays with a structured dtype, see Zarr format 2 for examples of how to use
        fields

        Currently the implementation for __setitem__ is provided by
        [`vindex`][zarr.Array.vindex] if the indexing is pure fancy indexing (ie a
        broadcast-compatible tuple of integer array indices), or by
        [`set_basic_selection`][zarr.Array.set_basic_selection] otherwise.

        Effectively, this means that the following indexing modes are supported:

            - integer indexing
            - slice indexing
            - mixed slice and integer indexing
            - boolean indexing
            - fancy indexing (vectorized list of integers)

        For specific indexing options including outer indexing, see the
        methods listed under Related.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__]

        """
        # Converting a zarr Array to numpy here avoids a SyncError that occurs when
        # value.__getitem__ is called inside the async codec pipeline (which already
        # runs within a running event loop).  np.asarray triggers Array.__array__,
        # which reads the data synchronously before we enter the async context.
        if isinstance(value, Array):
            value = np.asarray(value)
        fields, pure_selection = pop_fields(selection)
        if is_pure_fancy_indexing(pure_selection, self.ndim):
            self.vindex[cast("CoordinateSelection | MaskSelection", selection)] = value
        elif is_pure_orthogonal_indexing(pure_selection, self.ndim):
            self.set_orthogonal_selection(pure_selection, value, fields=fields)
        else:
            self.set_basic_selection(cast("BasicSelection", pure_selection), value, fields=fields)

    def get_basic_selection(
        self,
        selection: BasicSelection = Ellipsis,
        *,
        out: NDBuffer | None = None,
        prototype: BufferPrototype | None = None,
        fields: Fields | None = None,
    ) -> NDArrayLikeOrScalar:
        """Retrieve data for an item or region of the array.

        Parameters
        ----------
        selection : BasicSelection
            A selection specifying the requested item or region for each dimension of the
            array. May be any combination of int and/or slice or ellipsis for multidimensional arrays.
        out : NDBuffer, optional
            If given, load the selected data directly into this buffer.
        prototype : BufferPrototype, optional
            The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to
            extract data for.

        Returns
        -------
        NDArrayLikeOrScalar
            An array-like or scalar containing the data for the requested region.

        Examples
        --------
        Setup a 1-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(100, dtype="uint16")
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(3,),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve a single item:

            >>> z.get_basic_selection(5)
            np.uint16(5)

        Retrieve a region via slicing:

            >>> z.get_basic_selection(slice(5))
            array([0, 1, 2, 3, 4], dtype=uint16)
            >>> z.get_basic_selection(slice(-5, None))
            array([95, 96, 97, 98, 99], dtype=uint16)
            >>> z.get_basic_selection(slice(5, 10))
            array([5, 6, 7, 8, 9], dtype=uint16)
            >>> z.get_basic_selection(slice(5, 10, 2))
            array([5, 7, 9], dtype=uint16)
            >>> z.get_basic_selection(slice(None, None, 2))
            array([  0,  2,  4, ..., 94, 96, 98], dtype=uint16)

        Setup a 3-dimensional array:

            >>> data = np.arange(1000).reshape(10, 10, 10)
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(5, 5, 5),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve an item:

            >>> z.get_basic_selection((1, 2, 3))
            np.int64(123)

        Retrieve a region via slicing and Ellipsis:

            >>> z.get_basic_selection((slice(1, 3), slice(1, 3), 0))
            array([[110, 120],
                   [210, 220]])
            >>> z.get_basic_selection((0, slice(1, 3), slice(None)))
            array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                   [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])
            >>> z.get_basic_selection((..., 2))
            array([[  2,  12,  22,  32,  42,  52,  62,  72,  82,  92],
                   [102, 112, 122, 132, 142, 152, 162, 172, 182, 192],
                   ...
                   [802, 812, 822, 832, 842, 852, 862, 872, 882, 892],
                   [902, 912, 922, 932, 942, 952, 962, 972, 982, 992]])

        Notes
        -----
        Slices with step > 1 are supported, but slices with negative step are not.

        For arrays with a structured dtype, see Zarr format 2 for examples of how to use
        the `fields` parameter.

        This method provides the implementation for accessing data via the
        square bracket notation (__getitem__). See [`__getitem__`][zarr.Array.__getitem__] for examples
        using the alternative notation.

        Related
        -------
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """

        if prototype is None:
            prototype = default_buffer_prototype()
        return sync(
            self.async_array._get_selection(
                BasicIndexer(selection, self.shape, self._chunk_grid),
                out=out,
                fields=fields,
                prototype=prototype,
            )
        )

    def set_basic_selection(
        self,
        selection: BasicSelection,
        value: npt.ArrayLike,
        *,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """Modify data for an item or region of the array.

        Parameters
        ----------
        selection : tuple
            A tuple specifying the requested item or region for each dimension of the
            array. May be any combination of int and/or slice or ellipsis for multidimensional arrays.
        value : npt.ArrayLike
            An array-like containing values to be stored into the array.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to set
            data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer used for setting the data. If not provided, the
            default buffer prototype is used.

        Examples
        --------
        Setup a 1-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(100,),
            ...     store={},
            ...     chunk_shape=(100,),
            ...     dtype="i4",
            ... )

        Set all array elements to the same scalar value:

            >>> z.set_basic_selection(..., 42)
            >>> z[...]
            array([42, 42, 42, ..., 42, 42, 42], dtype=int32)

        Set a portion of the array:

            >>> z.set_basic_selection(slice(10), np.arange(10))
            >>> z.set_basic_selection(slice(-10, None), np.arange(10)[::-1])
            >>> z[...]
            array([ 0, 1, 2, ..., 2, 1, 0], dtype=int32)

        Setup a 2-dimensional array:

            >>> z = zarr.zeros(
            ...     shape=(5, 5),
            ...     store={},
            ...     chunk_shape=(5, 5),
            ...     dtype="i4",
            ... )

        Set all array elements to the same scalar value:

            >>> z.set_basic_selection(..., 42)

        Set a portion of the array:

            >>> z.set_basic_selection((0, slice(None)), np.arange(z.shape[1]))
            >>> z.set_basic_selection((slice(None), 0), np.arange(z.shape[0]))
            >>> z[...]
            array([[ 0,  1,  2,  3,  4],
                   [ 1, 42, 42, 42, 42],
                   [ 2, 42, 42, 42, 42],
                   [ 3, 42, 42, 42, 42],
                   [ 4, 42, 42, 42, 42]], dtype=int32)

        Notes
        -----
        For arrays with a structured dtype, see Zarr format 2 for examples of how to use
        the `fields` parameter.

        This method provides the underlying implementation for modifying data via square
        bracket notation, see [`__setitem__`][zarr.Array.__setitem__] for equivalent examples using the
        alternative notation.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = BasicIndexer(selection, self.shape, self._chunk_grid)
        sync(self.async_array._set_selection(indexer, value, fields=fields, prototype=prototype))

    def get_orthogonal_selection(
        self,
        selection: OrthogonalSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        """Retrieve data by making a selection for each dimension of the array. For
        example, if an array has 2 dimensions, allows selecting specific rows and/or
        columns. The selection for each dimension can be either an integer (indexing a
        single item), a slice, an array of integers, or a Boolean array where True
        values indicate a selection.

        Parameters
        ----------
        selection : tuple
            A selection for each dimension of the array. May be any combination of int,
            slice, integer array or Boolean array.
        out : NDBuffer, optional
            If given, load the selected data directly into this buffer.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to
            extract data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.

        Returns
        -------
        NDArrayLikeOrScalar
            An array-like or scalar containing the data for the requested selection.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(100).reshape(10, 10)
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=data.shape,
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve rows and columns via any combination of int, slice, integer array and/or
        Boolean array:

            >>> z.get_orthogonal_selection(([1, 4], slice(None)))
            array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])
            >>> z.get_orthogonal_selection((slice(None), [1, 4]))
            array([[ 1,  4],
                   [11, 14],
                   [21, 24],
                   [31, 34],
                   [41, 44],
                   [51, 54],
                   [61, 64],
                   [71, 74],
                   [81, 84],
                   [91, 94]])
            >>> z.get_orthogonal_selection(([1, 4], [1, 4]))
            array([[11, 14],
                   [41, 44]])
            >>> sel = np.zeros(z.shape[0], dtype=bool)
            >>> sel[1] = True
            >>> sel[4] = True
            >>> z.get_orthogonal_selection((sel, sel))
            array([[11, 14],
                   [41, 44]])

        For convenience, the orthogonal selection functionality is also available via the
        `oindex` property, e.g.:

            >>> z.oindex[[1, 4], :]
            array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])
            >>> z.oindex[:, [1, 4]]
            array([[ 1,  4],
                   [11, 14],
                   [21, 24],
                   [31, 34],
                   [41, 44],
                   [51, 54],
                   [61, 64],
                   [71, 74],
                   [81, 84],
                   [91, 94]])
            >>> z.oindex[[1, 4], [1, 4]]
            array([[11, 14],
                   [41, 44]])
            >>> sel = np.zeros(z.shape[0], dtype=bool)
            >>> sel[1] = True
            >>> sel[4] = True
            >>> z.oindex[sel, sel]
            array([[11, 14],
                   [41, 44]])

        Notes
        -----
        Orthogonal indexing is also known as outer indexing.

        Slices with step > 1 are supported, but slices with negative step are not.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = OrthogonalIndexer(selection, self.shape, self._chunk_grid)
        return sync(
            self.async_array._get_selection(
                indexer=indexer, out=out, fields=fields, prototype=prototype
            )
        )

    def set_orthogonal_selection(
        self,
        selection: OrthogonalSelection,
        value: npt.ArrayLike,
        *,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """Modify data via a selection for each dimension of the array.

        Parameters
        ----------
        selection : tuple
            A selection for each dimension of the array. May be any combination of int,
            slice, integer array or Boolean array.
        value : npt.ArrayLike
            An array-like array containing the data to be stored in the array.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to set
            data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer used for setting the data. If not provided, the
            default buffer prototype is used.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(5, 5),
            ...     store={},
            ...     chunk_shape=(5, 5),
            ...     dtype="i4",
            ... )


        Set data for a selection of rows:

            >>> z.set_orthogonal_selection(([1, 4], slice(None)), 1)
            >>> z[...]
            array([[0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1]], dtype=int32)

        Set data for a selection of columns:

            >>> z.set_orthogonal_selection((slice(None), [1, 4]), 2)
            >>> z[...]
            array([[0, 2, 0, 0, 2],
                   [1, 2, 1, 1, 2],
                   [0, 2, 0, 0, 2],
                   [0, 2, 0, 0, 2],
                   [1, 2, 1, 1, 2]], dtype=int32)

        Set data for a selection of rows and columns:

            >>> z.set_orthogonal_selection(([1, 4], [1, 4]), 3)
            >>> z[...]
            array([[0, 2, 0, 0, 2],
                   [1, 3, 1, 1, 3],
                   [0, 2, 0, 0, 2],
                   [0, 2, 0, 0, 2],
                   [1, 3, 1, 1, 3]], dtype=int32)

        Set data from a 2D array:

            >>> values = np.arange(10).reshape(2, 5)
            >>> z.set_orthogonal_selection(([0, 3], ...), values)
            >>> z[...]
            array([[0, 1, 2, 3, 4],
                   [1, 3, 1, 1, 3],
                   [0, 2, 0, 0, 2],
                   [5, 6, 7, 8, 9],
                   [1, 3, 1, 1, 3]], dtype=int32)

        For convenience, this functionality is also available via the `oindex` property:

            >>> z.oindex[[1, 4], [1, 4]] = 4
            >>> z[...]
            array([[0, 1, 2, 3, 4],
                   [1, 4, 1, 1, 4],
                   [0, 2, 0, 0, 2],
                   [5, 6, 7, 8, 9],
                   [1, 4, 1, 1, 4]], dtype=int32)

        Notes
        -----
        Orthogonal indexing is also known as outer indexing.

        Slices with step > 1 are supported, but slices with negative step are not.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]
        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = OrthogonalIndexer(selection, self.shape, self._chunk_grid)
        return sync(
            self.async_array._set_selection(indexer, value, fields=fields, prototype=prototype)
        )

    def get_mask_selection(
        self,
        mask: MaskSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        """Retrieve a selection of individual items, by providing a Boolean array of the
        same shape as the array against which the selection is being made, where True
        values indicate a selected item.

        Parameters
        ----------
        mask : ndarray, bool
            A Boolean array of the same shape as the array against which the selection is
            being made.
        out : NDBuffer, optional
            If given, load the selected data directly into this buffer.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to
            extract data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.

        Returns
        -------
        NDArrayLikeOrScalar
            An array-like or scalar containing the data for the requested selection.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(100).reshape(10, 10)
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=data.shape,
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve items by specifying a mask:

            >>> sel = np.zeros_like(z, dtype=bool)
            >>> sel[1, 1] = True
            >>> sel[4, 4] = True
            >>> z.get_mask_selection(sel)
            array([11, 44])

        For convenience, the mask selection functionality is also available via the
        `vindex` property:

            >>> z.vindex[sel]
            array([11, 44])

        Notes
        -----
        Mask indexing is a form of vectorized or inner indexing, and is equivalent to
        coordinate indexing. Internally the mask array is converted to coordinate
        arrays by calling `np.nonzero`.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]
        """

        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = MaskIndexer(mask, self.shape, self._chunk_grid)
        return sync(
            self.async_array._get_selection(
                indexer=indexer, out=out, fields=fields, prototype=prototype
            )
        )

    def set_mask_selection(
        self,
        mask: MaskSelection,
        value: npt.ArrayLike,
        *,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """Modify a selection of individual items, by providing a Boolean array of the
        same shape as the array against which the selection is being made, where True
        values indicate a selected item.

        Parameters
        ----------
        mask : ndarray, bool
            A Boolean array of the same shape as the array against which the selection is
            being made.
        value : npt.ArrayLike
            An array-like containing values to be stored into the array.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to set
            data for.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(5, 5),
            ...     store={},
            ...     chunk_shape=(5, 5),
            ...     dtype="i4",
            ... )

        Set data for a selection of items:

            >>> sel = np.zeros_like(z, dtype=bool)
            >>> sel[1, 1] = True
            >>> sel[4, 4] = True
            >>> z.set_mask_selection(sel, 1)
            >>> z[...]
            array([[0, 0, 0, 0, 0],
                   [0, 1, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 1]], dtype=int32)

        For convenience, this functionality is also available via the `vindex` property:

            >>> z.vindex[sel] = 2
            >>> z[...]
            array([[0, 0, 0, 0, 0],
                   [0, 2, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 2]], dtype=int32)

        Notes
        -----
        Mask indexing is a form of vectorized or inner indexing, and is equivalent to
        coordinate indexing. Internally the mask array is converted to coordinate
        arrays by calling `np.nonzero`.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = MaskIndexer(mask, self.shape, self._chunk_grid)
        sync(self.async_array._set_selection(indexer, value, fields=fields, prototype=prototype))

    def get_coordinate_selection(
        self,
        selection: CoordinateSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        """Retrieve a selection of individual items, by providing the indices
        (coordinates) for each selected item.

        Parameters
        ----------
        selection : tuple
            An integer (coordinate) array for each dimension of the array.
        out : NDBuffer, optional
            If given, load the selected data directly into this buffer.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to
            extract data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.

        Returns
        -------
        NDArrayLikeOrScalar
            An array-like or scalar containing the data for the requested coordinate selection.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(0, 100, dtype="uint16").reshape((10, 10))
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(3, 3),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve items by specifying their coordinates:

            >>> z.get_coordinate_selection(([1, 4], [1, 4]))
            array([11, 44], dtype=uint16)

        For convenience, the coordinate selection functionality is also available via the
        `vindex` property:

            >>> z.vindex[[1, 4], [1, 4]]
            array([11, 44], dtype=uint16)

        Notes
        -----
        Coordinate indexing is also known as point selection, and is a form of vectorized
        or inner indexing.

        Slices are not supported. Coordinate arrays must be provided for all dimensions
        of the array.

        Coordinate arrays may be multidimensional, in which case the output array will
        also be multidimensional. Coordinate arrays are broadcast against each other
        before being applied. The shape of the output will be the same as the shape of
        each coordinate array after broadcasting.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = CoordinateIndexer(selection, self.shape, self._chunk_grid)
        out_array = sync(
            self.async_array._get_selection(
                indexer=indexer, out=out, fields=fields, prototype=prototype
            )
        )

        if hasattr(out_array, "shape"):
            # restore shape
            out_array = np.array(out_array).reshape(indexer.sel_shape)
        return out_array

    def set_coordinate_selection(
        self,
        selection: CoordinateSelection,
        value: npt.ArrayLike,
        *,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """Modify a selection of individual items, by providing the indices (coordinates)
        for each item to be modified.

        Parameters
        ----------
        selection : tuple
            An integer (coordinate) array for each dimension of the array.
        value : npt.ArrayLike
            An array-like containing values to be stored into the array.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to set
            data for.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(5, 5),
            ...     store={},
            ...     chunk_shape=(5, 5),
            ...     dtype="i4",
            ... )

        Set data for a selection of items:

            >>> z.set_coordinate_selection(([1, 4], [1, 4]), 1)
            >>> z[...]
            array([[0, 0, 0, 0, 0],
                   [0, 1, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 1]], dtype=int32)

        For convenience, this functionality is also available via the `vindex` property:

            >>> z.vindex[[1, 4], [1, 4]] = 2
            >>> z[...]
            array([[0, 0, 0, 0, 0],
                   [0, 2, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 2]], dtype=int32)

        Notes
        -----
        Coordinate indexing is also known as point selection, and is a form of vectorized
        or inner indexing.

        Slices are not supported. Coordinate arrays must be provided for all dimensions
        of the array.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        # setup indexer
        indexer = CoordinateIndexer(selection, self.shape, self._chunk_grid)

        # handle value - need ndarray-like flatten value
        if not is_scalar(value, self.dtype):
            try:
                from numcodecs.compat import ensure_ndarray_like

                value = ensure_ndarray_like(value)  # TODO replace with agnostic
            except TypeError:
                # Handle types like `list` or `tuple`
                value = np.array(value)  # TODO replace with agnostic
        if hasattr(value, "shape") and len(value.shape) > 1:
            value = np.array(value).reshape(-1)

        if not is_scalar(value, self.dtype) and (
            isinstance(value, NDArrayLike) and indexer.shape != value.shape
        ):
            raise ValueError(
                f"Attempting to set a selection of {indexer.sel_shape[0]} "
                f"elements with an array of {value.shape[0]} elements."
            )

        sync(self.async_array._set_selection(indexer, value, fields=fields, prototype=prototype))

    def get_block_selection(
        self,
        selection: BasicSelection,
        *,
        out: NDBuffer | None = None,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> NDArrayLikeOrScalar:
        """Retrieve a selection of individual items, by providing the indices
        (coordinates) for each selected item.

        Parameters
        ----------
        selection : int or slice or tuple of int or slice
            An integer (coordinate) or slice for each dimension of the array.
        out : NDBuffer, optional
            If given, load the selected data directly into this buffer.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to
            extract data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer to use for the output data. If not provided, the default buffer prototype is used.

        Returns
        -------
        NDArrayLikeOrScalar
            An array-like or scalar containing the data for the requested block selection.

        Examples
        --------
        Setup a 2-dimensional array:

            >>> import zarr
            >>> import numpy as np
            >>> data = np.arange(0, 100, dtype="uint16").reshape((10, 10))
            >>> z = zarr.create_array(
            ...     {},
            ...     shape=data.shape,
            ...     chunks=(3, 3),
            ...     dtype=data.dtype,
            ... )
            >>> z[:] = data

        Retrieve items by specifying their block coordinates:

            >>> z.get_block_selection((1, slice(None)))
            array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                   [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]], dtype=uint16)

        Which is equivalent to:

            >>> z[3:6, :]
            array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                   [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]], dtype=uint16)

        For convenience, the block selection functionality is also available via the
        `blocks` property:

            >>> z.blocks[1]
            array([[30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
                   [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
                   [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]], dtype=uint16)

        Notes
        -----
        Block indexing is a convenience indexing method to work on individual chunks
        with chunk index slicing. It has the same concept as Dask's `Array.blocks`
        indexing.

        Slices are supported. However, only with a step size of one.

        Block index arrays may be multidimensional to index multidimensional arrays:

            >>> z.blocks[0, 1:3]
            array([[ 3,  4,  5,  6,  7,  8],
                   [13, 14, 15, 16, 17, 18],
                   [23, 24, 25, 26, 27, 28]], dtype=uint16)

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]
        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = BlockIndexer(selection, self.shape, self._chunk_grid)
        return sync(
            self.async_array._get_selection(
                indexer=indexer, out=out, fields=fields, prototype=prototype
            )
        )

    def set_block_selection(
        self,
        selection: BasicSelection,
        value: npt.ArrayLike,
        *,
        fields: Fields | None = None,
        prototype: BufferPrototype | None = None,
    ) -> None:
        """Modify a selection of individual blocks, by providing the chunk indices
        (coordinates) for each block to be modified.

        Parameters
        ----------
        selection : tuple
            An integer (coordinate) or slice for each dimension of the array.
        value : npt.ArrayLike
            An array-like containing the data to be stored in the block selection.
        fields : str or sequence of str, optional
            For arrays with a structured dtype, one or more fields can be specified to set
            data for.
        prototype : BufferPrototype, optional
            The prototype of the buffer used for setting the data. If not provided, the
            default buffer prototype is used.

        Examples
        --------
        Set up a 2-dimensional array:

            >>> import zarr
            >>> z = zarr.zeros(
            ...     shape=(6, 6),
            ...     store={},
            ...     chunk_shape=(2, 2),
            ...     dtype="i4",
            ... )

        Set data for a selection of items:

            >>> z.set_block_selection((1, 0), 1)
            >>> z[...]
            array([[0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0]], dtype=int32)

        For convenience, this functionality is also available via the `blocks` property:

            >>> z.blocks[2, 1] = 4
            >>> z[...]
            array([[0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0, 0],
                   [0, 0, 4, 4, 0, 0],
                   [0, 0, 4, 4, 0, 0]], dtype=int32)

            >>> z.blocks[:, 2] = 7
            >>> z[...]
            array([[0, 0, 0, 0, 7, 7],
                   [0, 0, 0, 0, 7, 7],
                   [1, 1, 0, 0, 7, 7],
                   [1, 1, 0, 0, 7, 7],
                   [0, 0, 4, 4, 7, 7],
                   [0, 0, 4, 4, 7, 7]], dtype=int32)

        Notes
        -----
        Block indexing is a convenience indexing method to work on individual chunks
        with chunk index slicing. It has the same concept as Dask's `Array.blocks`
        indexing.

        Slices are supported. However, only with a step size of one.

        Related
        -------
        [get_basic_selection][zarr.Array.get_basic_selection],
        [set_basic_selection][zarr.Array.set_basic_selection],
        [get_mask_selection][zarr.Array.get_mask_selection],
        [set_mask_selection][zarr.Array.set_mask_selection],
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection],
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection],
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [get_block_selection][zarr.Array.get_block_selection],
        [set_block_selection][zarr.Array.set_block_selection],
        [vindex][zarr.Array.vindex], [oindex][zarr.Array.oindex],
        [blocks][zarr.Array.blocks], [__getitem__][zarr.Array.__getitem__],
        [__setitem__][zarr.Array.__setitem__]

        """
        if prototype is None:
            prototype = default_buffer_prototype()
        indexer = BlockIndexer(selection, self.shape, self._chunk_grid)
        sync(self.async_array._set_selection(indexer, value, fields=fields, prototype=prototype))

    @property
    def vindex(self) -> VIndex:
        """Shortcut for vectorized (inner) indexing, see
        [get_coordinate_selection][zarr.Array.get_coordinate_selection],
        [set_coordinate_selection][zarr.Array.set_coordinate_selection],
        [get_mask_selection][zarr.Array.get_mask_selection] and
        [set_mask_selection][zarr.Array.set_mask_selection] for documentation and
        examples."""
        return VIndex(self)

    @property
    def oindex(self) -> OIndex:
        """Shortcut for orthogonal (outer) indexing, see
        [get_orthogonal_selection][zarr.Array.get_orthogonal_selection] and
        [set_orthogonal_selection][zarr.Array.set_orthogonal_selection] for
        documentation and examples."""
        return OIndex(self)

    @property
    def blocks(self) -> BlockIndex:
        """Shortcut for blocked chunked indexing, see
        [get_block_selection][zarr.Array.get_block_selection] and
        [set_block_selection][zarr.Array.set_block_selection] for documentation and
        examples."""
        return BlockIndex(self)

    def resize(self, new_shape: ShapeLike) -> None:
        """
        Change the shape of the array by growing or shrinking one or more
        dimensions. This is an in-place operation that modifies the array.

        Parameters
        ----------
        new_shape : tuple
            New shape of the array.

        Notes
        -----
        If one or more dimensions are shrunk, any chunks falling outside the
        new array shape will be deleted from the underlying store.
        However, it is noteworthy that the chunks partially falling inside the new array
        (i.e. boundary chunks) will remain intact, and therefore,
        the data falling outside the new array but inside the boundary chunks
        would be restored by a subsequent resize operation that grows the array size.

        Examples
        --------
        ```python
        import zarr
        z = zarr.zeros(shape=(10000, 10000),
                        chunk_shape=(1000, 1000),
                        dtype="int32",)
        z.shape
        #> (10000, 10000)
        z.resize((20000, 1000))
        z.shape
        #> (20000, 1000)
        z.resize((50, 50))
        z.shape
        #>(50, 50)
        ```
        """
        sync(self.async_array.resize(new_shape))

    def append(self, data: npt.ArrayLike, axis: int = 0) -> tuple[int, ...]:
        """Append `data` to `axis`.

        Parameters
        ----------
        data : array-like
            Data to be appended.
        axis : int
            Axis along which to append.

        Returns
        -------
        new_shape : tuple

        Notes
        -----
        The size of all dimensions other than `axis` must match between this
        array and `data`.

        Examples
        --------
        >>> import numpy as np
        >>> import zarr
        >>> a = np.arange(10000000, dtype="i4").reshape(10000, 1000)
        >>> z = zarr.array(a, chunks=(1000, 100))
        >>> z.shape
        (10000, 1000)
        >>> z.append(a)
        (20000, 1000)
        >>> z.append(np.vstack([a, a]), axis=1)
        (20000, 2000)
        >>> z.shape
        (20000, 2000)
        """
        return sync(self.async_array.append(data, axis=axis))

    def update_attributes(self, new_attributes: dict[str, JSON]) -> Self:
        """
        Update the array's attributes.

        Parameters
        ----------
        new_attributes : dict
            A dictionary of new attributes to update or add to the array. The keys represent attribute
            names, and the values must be JSON-compatible.

        Returns
        -------
        Array
            The array with the updated attributes.

        Raises
        ------
        ValueError
            If the attributes are invalid or incompatible with the array's metadata.

        Notes
        -----
        - The updated attributes will be merged with existing attributes, and any conflicts will be
          overwritten by the new values.
        """
        new_array = sync(self.async_array.update_attributes(new_attributes))
        return type(self)(new_array)

    def __repr__(self) -> str:
        return f"<Array {self.store_path} shape={self.shape} dtype={self.dtype}>"

    @property
    def info(self) -> Any:
        """
        Return the statically known information for an array.

        Returns
        -------
        ArrayInfo

        Related
        -------
        [zarr.Array.info_complete][] - All information about a group,
            including dynamic information like the number of bytes and chunks written.

        Examples
        --------
        >>> arr = zarr.create(shape=(10,), chunks=(2,), dtype="float32")
        >>> arr.info
        Type               : Array
        Zarr format        : 3
        Data type          : Float32(endianness=...)
        Fill value         : 0.0
        Shape              : (10,)
        Chunk shape        : (2,)
        Order              : C
        Read-only          : False
        Store type         : MemoryStore
        Filters            : ()
        Serializer         : BytesCodec(endian='little')
        Compressors        : (ZstdCodec(level=0, checksum=False),)
        No. bytes          : 40
        """
        return self.async_array.info

    def info_complete(self) -> Any:
        """
        Returns all the information about an array, including information from the Store.

        In addition to the statically known information like `name` and `zarr_format`,
        this includes additional information like the size of the array in bytes and
        the number of chunks written.

        Note that this method will need to read metadata from the store.

        Returns
        -------
        ArrayInfo

        Related
        -------
        [zarr.Array.info][] - The statically known subset of metadata about an array.
        """
        return sync(self.async_array.info_complete())


async def _shards_initialized(
    array: AnyAsyncArray,
) -> tuple[str, ...]:
    """
    Return the keys of the shards that have been persisted to the storage backend.

    This will fall back to chunks in case no shards are present.

    Parameters
    ----------
    array : AsyncArray
        The array to inspect.

    Returns
    -------
    chunks_initialized : tuple[str, ...]
        The keys of the shards or if these are not present, chunks that have been initialized.

    Related
    -------
    [nchunks_initialized][zarr.Array.nchunks_initialized]

    """
    store_contents = [
        x async for x in array.store_path.store.list_prefix(prefix=array.store_path.path)
    ]
    store_contents_relative = [
        _relativize_path(path=key, prefix=array.store_path.path)
        for key in store_contents
        # obstore can include a directory marker whose key matches the listed prefix;
        # it is not an initialized shard and must be excluded before relativizing.
        if array.store_path.path == "" or key != array.store_path.path
    ]
    return tuple(
        chunk_key for chunk_key in array._iter_shard_keys() if chunk_key in store_contents_relative
    )


type FiltersLike = (
    Iterable[dict[str, JSON] | ArrayArrayCodec | Numcodec]
    | ArrayArrayCodec
    | Iterable[Numcodec]
    | Numcodec
    | Literal["auto"]
    | None
)
# Union of acceptable types for users to pass in for both v2 and v3 compressors
type CompressorLike = dict[str, JSON] | BytesBytesCodec | Numcodec | Literal["auto"] | None

type CompressorsLike = (
    Iterable[dict[str, JSON] | BytesBytesCodec | Numcodec]
    | Mapping[str, JSON]
    | BytesBytesCodec
    | Numcodec
    | Literal["auto"]
    | None
)
type SerializerLike = dict[str, JSON] | ArrayBytesCodec | Literal["auto"]


class ShardsConfigParam(TypedDict):
    shape: tuple[int, ...]
    index_location: IndexLocation | None


type ShardsLike = (
    tuple[int, ...]
    | Sequence[int | Sequence[int]]
    | ChunkGridMetadata
    | ShardsConfigParam
    | Literal["auto"]
)


async def from_array(
    store: StoreLike,
    *,
    data: AnyArray | npt.ArrayLike,
    write_data: bool = True,
    name: str | None = None,
    chunks: ChunksLike | Literal["auto", "keep"] = "keep",
    shards: ShardsLike | Literal["keep"] | None = "keep",
    filters: FiltersLike | Literal["keep"] = "keep",
    compressors: CompressorsLike | Literal["keep"] = "keep",
    serializer: SerializerLike | Literal["keep"] = "keep",
    fill_value: Any | None = DEFAULT_FILL_VALUE,
    order: MemoryOrder | None = None,
    zarr_format: ZarrFormat | None = None,
    attributes: dict[str, JSON] | None = None,
    chunk_key_encoding: ChunkKeyEncodingLike | None = None,
    dimension_names: DimensionNamesLike = None,
    storage_options: dict[str, Any] | None = None,
    overwrite: bool = False,
    config: ArrayConfigLike | None = None,
) -> AnyAsyncArray:
    """Create an array from an existing array or array-like.

    Parameters
    ----------
    store : StoreLike
        StoreLike object to open. See the
        [storage documentation in the user guide][user-guide-store-like]
        for a description of all valid StoreLike values.
    data : Array | array-like
        The array to copy.
    write_data : bool, default True
        Whether to copy the data from the input array to the new array.
        If `write_data` is `False`, the new array will be created with the same metadata as the
        input array, but without any data.
    name : str or None, optional
        The name of the array within the store. If `name` is `None`, the array will be located
        at the root of the store.
    chunks : tuple[int, ...] or Sequence[Sequence[int]] or "auto" or "keep", optional
        Chunk shape of the array.
        Following values are supported:

        - "auto": Automatically determine the chunk shape based on the array's shape and dtype.
        - "keep": Retain the chunk grid of the data array if it is a zarr Array.
        - tuple[int, ...]: A tuple of integers representing the chunk shape (regular grid).
        - Sequence[Sequence[int]]: Per-dimension chunk edge lists (rectilinear grid).
          Rectilinear chunk grids are experimental and must be explicitly enabled
          with `zarr.config.set({'array.rectilinear_chunks': True})` while the
          feature is stabilizing.

        If not specified, defaults to "keep" if data is a zarr Array, otherwise "auto".
    shards : tuple[int, ...], optional
        Shard shape of the array.
        Following values are supported:

        - "auto": Automatically determine the shard shape based on the array's shape and chunk shape.
        - "keep": Retain the shard shape of the data array if it is a zarr Array.
        - tuple[int, ...]: A tuple of integers representing the shard shape.
        - None: No sharding.

        If not specified, defaults to "keep" if data is a zarr Array, otherwise None.
    filters : Iterable[Codec] | Literal["auto", "keep"], optional
        Iterable of filters to apply to each chunk of the array, in order, before serializing that
        chunk to bytes.

        For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
        and these values must be instances of [`zarr.abc.codec.ArrayArrayCodec`][], or a
        dict representations of [`zarr.abc.codec.ArrayArrayCodec`][].

        For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
        order of your filters is consistent with the behavior of each filter.

        The default value of `"keep"` instructs Zarr to infer `filters` from `data`.
        If that inference is not possible, Zarr will fall back to the behavior specified by `"auto"`,
        which is to choose default filters based on the data type of the array and the Zarr format specified.
        For all data types in Zarr V3, and most data types in Zarr V2, the default filters are the empty tuple `()`.
        The only cases where default filters are not empty is when the Zarr format is 2, and the
        data type is a variable-length data type like [`zarr.dtype.VariableLengthUTF8`][] or
        [`zarr.dtype.VariableLengthUTF8`][]. In these cases, the default filters is a tuple with a
        single element which is a codec specific to that particular data type.

        To create an array with no filters, provide an empty iterable or the value `None`.
    compressors : Iterable[Codec] or "auto" or "keep", optional
        List of compressors to apply to the array. Compressors are applied in order, and after any
        filters are applied (if any are specified) and the data is serialized into bytes.

        For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
        returns another bytestream. Multiple compressors may be provided for Zarr format 3.

        For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
        be provided for Zarr format 2.

        Following values are supported:

        - Iterable[Codec]: List of compressors to apply to the array.
        - "auto": Automatically determine the compressors based on the array's dtype.
        - "keep": Retain the compressors of the input array if it is a zarr Array.

        If no `compressors` are provided, defaults to "keep" if data is a zarr Array, otherwise "auto".
    serializer : dict[str, JSON] | ArrayBytesCodec or "auto" or "keep", optional
        Array-to-bytes codec to use for encoding the array data.
        Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.

        Following values are supported:

        - dict[str, JSON]: A dict representation of an `ArrayBytesCodec`.
        - ArrayBytesCodec: An instance of `ArrayBytesCodec`.
        - "auto": a default serializer will be used. These defaults can be changed by modifying the value of
          `array.v3_default_serializer` in [`zarr.config`][zarr.config].
        - "keep": Retain the serializer of the input array if it is a zarr Array.

    fill_value : Any, optional
        Fill value for the array.
        If not specified, defaults to the fill value of the data array.
        Pass `None` explicitly to use the default scalar of the data type
        (Zarr format 3) or a null fill value (Zarr format 2) instead.
    order : {"C", "F"}, optional
        The memory order of the array (default is "C").
        For Zarr format 2, this parameter sets the memory order of the array.
        For Zarr format 3, this parameter is deprecated, because memory order
        is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
        order for Zarr format 3 arrays is via the `config` parameter, e.g. `{'config': 'C'}`.
        If not specified, defaults to the memory order of the data array.
    zarr_format : {2, 3}, optional
        The zarr format to use when saving.
        If not specified, defaults to the zarr format of the data array.
    attributes : dict, optional
        Attributes for the array.
        If not specified, the source Zarr array's attributes are deep-copied so
        nested containers are independent. Deeply nested attributes can raise
        `RecursionError` during copying even if they can be stored and reopened;
        the threshold depends on Python's recursion limit and the current call stack.
        Pass an empty dict to create the array with no attributes.
    chunk_key_encoding : ChunkKeyEncoding, optional
        A specification of how the chunk keys are represented in storage.
        For Zarr format 3, the default is `{"name": "default", "separator": "/"}`.
        For Zarr format 2, the default is `{"name": "v2", "separator": "."}`.
        If not specified and the data array has the same zarr format as the target array,
        the chunk key encoding of the data array is used.
    dimension_names : Iterable[str | None] | None
        The names of the dimensions (default is None).
        Zarr format 3 only. Zarr format 2 arrays should not use this parameter.
        If not specified, defaults to the dimension names of the data array.
    storage_options : dict, optional
        If using an fsspec URL to create the store, these will be passed to the backend implementation.
        Ignored otherwise.
    overwrite : bool, default False
        Whether to overwrite an array with the same name in the store, if one exists.
    config : ArrayConfig or ArrayConfigLike, optional
        Runtime configuration for the array.

    Returns
    -------
    AsyncArray
        The array.

    Examples
    --------
    Create an array from an existing Array:

        >>> import asyncio
        >>> import zarr
        >>> store = zarr.storage.LocalStore("example.zarr")
        >>> arr = zarr.create_array(
        ...     store={},
        ...     shape=(100,100),
        ...     chunks=(10,10),
        ...     dtype="int32",
        ...     fill_value=0,
        ... )

        >>> arr2 = asyncio.run(from_array(store, data=arr, overwrite=True))
        >>> arr2
        <AsyncArray file://example.zarr shape=(100, 100) dtype=int32>
        >>> asyncio.run(store.clear())  # Remove files generated by test

    Create an array from an existing NumPy array:

        >>> arr3 = asyncio.run(
        ...     from_array({}, data=np.arange(10000, dtype="i4").reshape(100, 100))
        ... )
        >>> arr3
        <AsyncArray memory://... shape=(100, 100) dtype=int32>

    Create an array from any array-like object:

        >>> arr4 = asyncio.run(from_array({}, data=[[1, 2], [3, 4]]))
        >>> arr4
        <AsyncArray memory://... shape=(2, 2) dtype=int64>
        >>> asyncio.run(arr4.getitem(...))
        array([[1, 2],
               [3, 4]])

    Create an array from an existing Array without copying the data:

        >>> arr5 = asyncio.run(from_array({}, data=Array(arr4), write_data=False))
        >>> arr5
        <AsyncArray memory://... shape=(2, 2) dtype=int64>
        >>> asyncio.run(arr5.getitem(...))
        array([[0, 0],
               [0, 0]])
    """
    mode: Literal["a"] = "a"
    config_parsed = parse_array_config(config)
    store_path = await make_store_path(store, path=name, mode=mode, storage_options=storage_options)

    (
        chunks,
        shards,
        filters,
        compressors,
        serializer,
        fill_value,
        order,
        zarr_format,
        chunk_key_encoding,
        dimension_names,
        attributes,
    ) = _parse_keep_array_attr(
        data=data,
        chunks=chunks,
        shards=shards,
        filters=filters,
        compressors=compressors,
        serializer=serializer,
        fill_value=fill_value,
        order=order,
        zarr_format=zarr_format,
        chunk_key_encoding=chunk_key_encoding,
        dimension_names=dimension_names,
        attributes=attributes,
    )
    if not hasattr(data, "dtype") or not hasattr(data, "shape"):
        data = np.array(data)

    result = await init_array(
        store_path=store_path,
        shape=data.shape,
        # A native object dtype cannot identify the source's Zarr data type.
        dtype=data._async_array._zdtype if isinstance(data, Array) else data.dtype,
        chunks=chunks,
        shards=shards,
        filters=filters,
        compressors=compressors,
        serializer=serializer,
        fill_value=fill_value,
        order=order,
        zarr_format=zarr_format,
        attributes=attributes,
        chunk_key_encoding=chunk_key_encoding,
        dimension_names=dimension_names,
        overwrite=overwrite,
        config=config_parsed,
    )

    if write_data:
        if isinstance(data, Array):

            async def _copy_array_region(
                chunk_coords: tuple[int, ...] | slice, _data: AnyArray
            ) -> None:
                arr = await _data.async_array.getitem(chunk_coords)
                await result.setitem(chunk_coords, arr)

            # Stream data from the source array to the new array
            await concurrent_map(
                [(region, data) for region in result._iter_shard_regions()],
                _copy_array_region,
                zarr.core.config.config.get("async.concurrency"),
            )
        else:

            async def _copy_arraylike_region(chunk_coords: slice, _data: NDArrayLike) -> None:
                await result.setitem(chunk_coords, _data[chunk_coords])

            # Stream data from the source array to the new array
            await concurrent_map(
                [(region, data) for region in result._iter_shard_regions()],
                _copy_arraylike_region,
                zarr.core.config.config.get("async.concurrency"),
            )
    return result


async def init_array(
    *,
    store_path: StorePath,
    shape: ShapeLike,
    dtype: ZDTypeLike,
    chunks: ChunksLike | Literal["auto"] = "auto",
    shards: ShardsLike | None = None,
    filters: FiltersLike = "auto",
    compressors: CompressorsLike = "auto",
    serializer: SerializerLike = "auto",
    fill_value: Any | None = DEFAULT_FILL_VALUE,
    order: MemoryOrder | None = None,
    zarr_format: ZarrFormat | None = 3,
    attributes: dict[str, JSON] | None = None,
    chunk_key_encoding: ChunkKeyEncodingLike | None = None,
    dimension_names: DimensionNamesLike = None,
    overwrite: bool = False,
    config: ArrayConfigLike | None = None,
) -> AnyAsyncArray:
    """Create and persist an array metadata document.

    Parameters
    ----------
    store_path : StorePath
        StorePath instance. The path attribute is the name of the array to initialize.
    shape : tuple[int, ...]
        Shape of the array.
    dtype : ZDTypeLike
        Data type of the array.
    chunks : tuple[int, ...], optional
        Chunk shape of the array.
        If not specified, default are guessed based on the shape and dtype.
    shards : tuple[int, ...], optional
        Shard shape of the array. The default value of `None` results in no sharding at all.
    filters : Iterable[Codec] | Literal["auto"], optional
        Iterable of filters to apply to each chunk of the array, in order, before serializing that
        chunk to bytes.

        For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
        and these values must be instances of [`zarr.abc.codec.ArrayArrayCodec`][], or a
        dict representations of [`zarr.abc.codec.ArrayArrayCodec`][].

        For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
        order of your filters is consistent with the behavior of each filter.

        The default value of `"auto"` instructs Zarr to use a default based on the data
        type of the array and the Zarr format specified. For all data types in Zarr V3, and most
        data types in Zarr V2, the default filters are empty. The only cases where default filters
        are not empty is when the Zarr format is 2, and the data type is a variable-length data type like
        [`zarr.dtype.VariableLengthUTF8`][] or [`zarr.dtype.VariableLengthBytes`][]. In these cases,
        the default filters contains a single element which is a codec specific to that particular data type.

        To create an array with no filters, provide an empty iterable or the value `None`.
    compressors : Iterable[Codec] | Literal["auto"], optional
        List of compressors to apply to the array. Compressors are applied in order, and after any
        filters are applied (if any are specified) and the data is serialized into bytes.

        The default value of `"auto"` instructs Zarr to use a default of [`zarr.codecs.ZstdCodec`][].

        To create an array with no compressors, provide an empty iterable or the value `None`.
    serializer : dict[str, JSON] | ArrayBytesCodec | Literal["auto"], optional
        Array-to-bytes codec to use for encoding the array data.
        Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.

        The default value of `"auto"` instructs Zarr to use a default codec based on the data type of the array.
        For most data types this default codec is [`zarr.codecs.BytesCodec`][].
        For [`zarr.dtype.VariableLengthUTF8`][], the default codec is [`zarr.codecs.VlenUTF8Codec`][].
        For [`zarr.dtype.VariableLengthBytes`][], the default codec is [`zarr.codecs.VlenBytesCodec`][].
    fill_value : Any, optional
        Fill value for the array.
    order : {"C", "F"}, optional
        The memory order of the array (default is "C").
        For Zarr format 2, this parameter sets the memory order of the array.
        For Zarr format 3, this parameter is deprecated, because memory order
        is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
        order for Zarr format 3 arrays is via the `config` parameter, e.g. `{'config': 'C'}`.
        If no `order` is provided, a default order will be used.
        This default can be changed by modifying the value of `array.order` in [`zarr.config`][zarr.config].
    zarr_format : {2, 3}, optional
        The zarr format to use when saving.
    attributes : dict, optional
        Attributes for the array.
    chunk_key_encoding : ChunkKeyEncodingLike, optional
        A specification of how the chunk keys are represented in storage.
        For Zarr format 3, the default is `{"name": "default", "separator": "/"}`.
        For Zarr format 2, the default is `{"name": "v2", "separator": "."}`.
    dimension_names : Iterable[str], optional
        The names of the dimensions (default is None).
        Zarr format 3 only. Zarr format 2 arrays should not use this parameter.
    overwrite : bool, default False
        Whether to overwrite an array with the same name in the store, if one exists.
    config : ArrayConfigLike or None, default=None
        Configuration for this array.
        If  `None`, the default array runtime configuration will be used. This default
        is stored in the global configuration object.

    Returns
    -------
    AsyncArray
        The AsyncArray.
    """

    if zarr_format is None:
        zarr_format = _default_zarr_format()

    from zarr.codecs.sharding import ShardingCodec

    zdtype = parse_dtype(dtype, zarr_format=zarr_format)
    shape_parsed = parse_shapelike(shape)
    item_size = zdtype.item_size if isinstance(zdtype, HasItemSize) else 1
    chunk_key_encoding_parsed = _parse_chunk_key_encoding(
        chunk_key_encoding, zarr_format=zarr_format
    )

    # Validate rectilinear chunks constraints
    if _is_rectilinear_chunks(chunks):
        if zarr_format == 2:
            raise ValueError("Zarr format 2 does not support rectilinear chunk grids.")
        if shards is not None:
            raise ValueError(
                "Rectilinear chunks with sharding is not supported. "
                "Use rectilinear shards instead: "
                "chunks=(inner_size, ...), shards=[[shard_sizes], ...]"
            )

    # Normalize the user's chunks into a canonical ChunkGrid

    if _is_auto(chunks):
        max_bytes = None if shards is None else SHARDED_INNER_CHUNK_MAX_BYTES
        chunks_normalized = guess_chunks(shape_parsed, item_size, max_bytes=max_bytes)
    else:
        chunks_normalized = normalize_chunks_nd(chunks, shape_parsed)

    # Resolve chunks + shards into outer_chunks (grid metadata) and
    # inner (sub-chunk structure for ShardingCodec, None if no sharding)
    outer_chunks, inner = resolve_outer_and_inner_chunks(
        array_shape=shape_parsed,
        chunks=chunks_normalized,
        shard_shape=shards,
        item_size=item_size,
    )

    meta: ArrayV2Metadata | ArrayV3Metadata
    if zarr_format == 2:
        if inner is not None:
            msg = (
                "Zarr format 2 arrays can only be created with `shard_shape` set to `None`. "
                f"Got `shard_shape={shards}` instead."
            )

            raise ValueError(msg)
        if serializer != "auto":
            raise ValueError("Zarr format 2 arrays do not support `serializer`.")

        filters_parsed, compressor_parsed = _parse_chunk_encoding_v2(
            compressor=compressors, filters=filters, dtype=zdtype
        )
        if dimension_names is not None:
            raise ValueError("Zarr format 2 arrays do not support dimension names.")
        if order is None:
            order_parsed = zarr_config.get("array.order")
        else:
            order_parsed = order
        chunk_key_encoding_parsed = cast("V2ChunkKeyEncoding", chunk_key_encoding_parsed)

        meta = AsyncArray._create_metadata_v2(
            shape=shape_parsed,
            dtype=zdtype,
            chunks=outer_chunks.chunk_shape,
            dimension_separator=chunk_key_encoding_parsed.separator,
            fill_value=fill_value,
            order=order_parsed,
            filters=filters_parsed,
            compressor=compressor_parsed,
            attributes=attributes,
        )
    else:
        array_array, array_bytes, bytes_bytes = _parse_chunk_encoding_v3(
            compressors=compressors,
            filters=filters,
            serializer=serializer,
            dtype=zdtype,
        )
        sub_codecs = cast("tuple[Codec, ...]", (*array_array, array_bytes, *bytes_bytes))
        # A stored chunk grid passed as chunks= / shards= (e.g. by the "keep"
        # path of from_array) becomes the stored grid verbatim, so the new
        # array preserves the source grid exactly — including trailing edges
        # beyond the extent left behind by a shrinking resize, and bare-int
        # shorthand dimensions.
        if isinstance(shards, ChunkGridMetadata):
            grid = shards
        elif shards is None and isinstance(chunks, ChunkGridMetadata):
            grid = chunks
        else:
            grid = create_chunk_grid_metadata(outer_chunks)
        codecs_out: tuple[Codec, ...]
        if inner is not None:
            inner_chunks_flat = inner.outer_chunks.chunk_shape
            index_location: IndexLocation = "end"
            if isinstance(shards, dict):
                index_location = cast("IndexLocation", shards.get("index_location", "end"))
            sharding_codec = ShardingCodec(
                chunk_shape=inner_chunks_flat, codecs=sub_codecs, index_location=index_location
            )
            sharding_codec.validate(
                shape=inner_chunks_flat,
                dtype=zdtype,
                chunk_grid=grid,
            )
            codecs_out = (sharding_codec,)
        else:
            codecs_out = sub_codecs

        if order is not None:
            _warn_order_kwarg()
        meta = AsyncArray._create_metadata_v3(
            shape=shape_parsed,
            dtype=zdtype,
            fill_value=fill_value,
            chunk_grid=grid,
            chunk_key_encoding=chunk_key_encoding_parsed,
            codecs=codecs_out,
            dimension_names=dimension_names,
            attributes=attributes,
        )

    arr = AsyncArray(metadata=meta, store_path=store_path, config=config)
    await save_new_metadata(store_path, meta, overwrite=overwrite, ensure_parents=True)
    return arr


async def create_array(
    store: StoreLike,
    *,
    name: str | None = None,
    shape: ShapeLike | None = None,
    dtype: ZDTypeLike | None = None,
    data: np.ndarray[Any, np.dtype[Any]] | None = None,
    chunks: ChunksLike | Literal["auto"] = "auto",
    shards: ShardsLike | None = None,
    filters: FiltersLike = "auto",
    compressors: CompressorsLike = "auto",
    serializer: SerializerLike = "auto",
    fill_value: Any | None = DEFAULT_FILL_VALUE,
    order: MemoryOrder | None = None,
    zarr_format: ZarrFormat | None = 3,
    attributes: dict[str, JSON] | None = None,
    chunk_key_encoding: ChunkKeyEncodingLike | None = None,
    dimension_names: DimensionNamesLike = None,
    storage_options: dict[str, Any] | None = None,
    overwrite: bool = False,
    config: ArrayConfigLike | None = None,
    write_data: bool = True,
) -> AnyAsyncArray:
    """Create an array.

    Parameters
    ----------
    store : StoreLike
        StoreLike object to open. See the
        [storage documentation in the user guide][user-guide-store-like]
        for a description of all valid StoreLike values.
    name : str or None, optional
        The name of the array within the store. If `name` is `None`, the array will be located
        at the root of the store.
    shape : ShapeLike, optional
        Shape of the array. Must be `None` if `data` is provided.
    dtype : ZDTypeLike | None
        Data type of the array. Must be `None` if `data` is provided.
    data : np.ndarray, optional
        Array-like data to use for initializing the array. If this parameter is provided, the
        `shape` and `dtype` parameters must be `None`.
    chunks : tuple[int, ...] | Sequence[Sequence[int]] | Literal["auto"], default="auto"
        Chunk shape of the array.
        If chunks is "auto", a chunk shape is guessed based on the shape of the array and the dtype.
        A nested list of per-dimension edge sizes creates a rectilinear grid.
        Rectilinear chunk grids are experimental and must be explicitly enabled
        with `zarr.config.set({'array.rectilinear_chunks': True})` while the
        feature is stabilizing.
    shards : tuple[int, ...], optional
        Shard shape of the array. The default value of `None` results in no sharding at all.
    filters : Iterable[Codec] | Literal["auto"], optional
        Iterable of filters to apply to each chunk of the array, in order, before serializing that
        chunk to bytes.

        For Zarr format 3, a "filter" is a codec that takes an array and returns an array,
        and these values must be instances of [`zarr.abc.codec.ArrayArrayCodec`][], or a
        dict representations of [`zarr.abc.codec.ArrayArrayCodec`][].

        For Zarr format 2, a "filter" can be any numcodecs codec; you should ensure that the
        order of your filters is consistent with the behavior of each filter.

        The default value of `"auto"` instructs Zarr to use a default based on the data
        type of the array and the Zarr format specified. For all data types in Zarr V3, and most
        data types in Zarr V2, the default filters are empty. The only cases where default filters
        are not empty is when the Zarr format is 2, and the data type is a variable-length data type like
        [`zarr.dtype.VariableLengthUTF8`][] or [`zarr.dtype.VariableLengthBytes`][]. In these cases,
        the default filters contains a single element which is a codec specific to that particular data type.

        To create an array with no filters, provide an empty iterable or the value `None`.
    compressors : Iterable[Codec], optional
        List of compressors to apply to the array. Compressors are applied in order, and after any
        filters are applied (if any are specified) and the data is serialized into bytes.

        For Zarr format 3, a "compressor" is a codec that takes a bytestream, and
        returns another bytestream. Multiple compressors may be provided for Zarr format 3.
        If no `compressors` are provided, a default set of compressors will be used.
        These defaults can be changed by modifying the value of `array.v3_default_compressors`
        in [`zarr.config`][zarr.config].
        Use `None` to omit default compressors.

        For Zarr format 2, a "compressor" can be any numcodecs codec. Only a single compressor may
        be provided for Zarr format 2.
        If no `compressor` is provided, a default compressor will be used.
        This default can be changed by modifying the value of `array.v2_default_compressor`
        in [`zarr.config`][zarr.config].
        Use `None` to omit the default compressor.
    serializer : dict[str, JSON] | ArrayBytesCodec, optional
        Array-to-bytes codec to use for encoding the array data.
        Zarr format 3 only. Zarr format 2 arrays use implicit array-to-bytes conversion.
        If no `serializer` is provided, a default serializer will be used.
        These defaults can be changed by modifying the value of `array.v3_default_serializer`
        in [`zarr.config`][zarr.config].
    fill_value : Any, optional
        Fill value for the array.
    order : {"C", "F"}, optional
        The memory order of the array (default is "C").
        For Zarr format 2, this parameter sets the memory order of the array.
        For Zarr format 3, this parameter is deprecated, because memory order
        is a runtime parameter for Zarr format 3 arrays. The recommended way to specify the memory
        order for Zarr format 3 arrays is via the `config` parameter, e.g. `{'config': 'C'}`.
        If no `order` is provided, a default order will be used.
        This default can be changed by modifying the value of `array.order` in [`zarr.config`][zarr.config].
    zarr_format : {2, 3}, optional
        The zarr format to use when saving.
    attributes : dict, optional
        Attributes for the array.
    chunk_key_encoding : ChunkKeyEncodingLike, optional
        A specification of how the chunk keys are represented in storage.
        For Zarr format 3, the default is `{"name": "default", "separator": "/"}`.
        For Zarr format 2, the default is `{"name": "v2", "separator": "."}`.
    dimension_names : Iterable[str], optional
        The names of the dimensions (default is None).
        Zarr format 3 only. Zarr format 2 arrays should not use this parameter.
    storage_options : dict, optional
        If using an fsspec URL to create the store, these will be passed to the backend implementation.
        Ignored otherwise.
    overwrite : bool, default False
        Whether to overwrite an array with the same name in the store, if one exists.
        If `True`, any existing keys under that path are deleted first.
    config : ArrayConfigLike, optional
        Runtime configuration for the array.
    write_data : bool
        If a pre-existing array-like object was provided to this function via the `data` parameter
        then `write_data` determines whether the values in that array-like object should be
        written to the Zarr array created by this function. If `write_data` is `False`, then the
        array will be left empty.

    Returns
    -------
    AsyncArray
        The array.

    Examples
    --------
    >>> import asyncio
    >>> import zarr
    >>> asyncio.run(
    ...     zarr.api.asynchronous.create_array(
    ...         store={},
    ...         shape=(100,100),
    ...         chunks=(10,10),
    ...         dtype="i4",
    ...         fill_value=0
    ...     )
    ... )
    <AsyncArray memory://... shape=(100, 100) dtype=int32>
    """
    data_parsed, shape_parsed, dtype_parsed = _parse_data_params(
        data=data, shape=shape, dtype=dtype
    )
    if data_parsed is not None:
        return await from_array(
            store,
            data=data_parsed,
            write_data=write_data,
            name=name,
            chunks=chunks,
            shards=shards,
            filters=filters,
            compressors=compressors,
            serializer=serializer,
            fill_value=fill_value,
            order=order,
            zarr_format=zarr_format,
            attributes=attributes,
            chunk_key_encoding=chunk_key_encoding,
            dimension_names=dimension_names,
            storage_options=storage_options,
            overwrite=overwrite,
            config=config,
        )
    else:
        mode: Literal["a"] = "a"

        store_path = await make_store_path(
            store, path=name, mode=mode, storage_options=storage_options
        )
        return await init_array(
            store_path=store_path,
            shape=shape_parsed,
            dtype=dtype_parsed,
            chunks=chunks,
            shards=shards,
            filters=filters,
            compressors=compressors,
            serializer=serializer,
            fill_value=fill_value,
            order=order,
            zarr_format=zarr_format,
            attributes=attributes,
            chunk_key_encoding=chunk_key_encoding,
            dimension_names=dimension_names,
            overwrite=overwrite,
            config=config,
        )


def _chunk_layout(
    metadata: ArrayMetadata,
) -> tuple[tuple[int, ...] | ChunkGridMetadata, tuple[int, ...] | None]:
    """How an array's chunks are laid out: its chunk grid and, if it is sharded, the
    inner chunk shape."""
    grid = metadata.chunks if isinstance(metadata, ArrayV2Metadata) else metadata.chunk_grid
    sharding = _sharding_codec(metadata)
    return grid, None if sharding is None else sharding.chunk_shape


def _sharding_codec(metadata: ArrayMetadata) -> ShardingCodec | None:
    """The array's sharding codec, or None if the array is not sharded.

    Zarr format 2 arrays are never sharded.
    """
    if isinstance(metadata, ArrayV3Metadata):
        return metadata.sharding_codec
    return None


def _stored_rectilinear_grid_or_none(
    metadata: ArrayMetadata,
) -> RectilinearChunkGridMetadata | None:
    """The *stored* rectilinear chunk grid, or None if the stored grid is regular
    (in which case `.chunks` and `.shards` are defined).

    Dispatches on the stored metadata, not the runtime ``ChunkGrid``: the
    runtime grid collapses a rectilinear dimension whose edges happen to be
    uniform to a ``FixedDimension`` as an optimization, so it can report regular
    for an array whose stored metadata — and therefore `.chunks` — is
    rectilinear. Zarr format 2 grids are always regular.
    """
    if isinstance(metadata, ArrayV3Metadata) and isinstance(
        metadata.chunk_grid, RectilinearChunkGridMetadata
    ):
        return metadata.chunk_grid
    return None


def _parse_keep_array_attr(
    data: AnyArray | npt.ArrayLike,
    chunks: ChunksLike | Literal["auto", "keep"],
    shards: ShardsLike | Literal["keep"] | None,
    filters: FiltersLike | Literal["keep"],
    compressors: CompressorsLike | Literal["keep"],
    serializer: SerializerLike | Literal["keep"],
    fill_value: Any | None,
    order: MemoryOrder | None,
    zarr_format: ZarrFormat | None,
    chunk_key_encoding: ChunkKeyEncodingLike | None,
    dimension_names: DimensionNamesLike,
    attributes: dict[str, JSON] | None,
) -> tuple[
    ChunksLike | Literal["auto"],
    ShardsLike | None,
    FiltersLike,
    CompressorsLike,
    SerializerLike,
    Any | None,
    MemoryOrder | None,
    ZarrFormat,
    ChunkKeyEncodingLike | None,
    DimensionNamesLike,
    dict[str, JSON] | None,
]:
    # chunks / shards may be numpy arrays, so their sentinel checks go through the
    # isinstance-guarded helpers rather than a bare ``==`` against the string.
    if isinstance(data, Array):
        rectilinear_grid = _stored_rectilinear_grid_or_none(data.metadata)
        sharded = _sharding_codec(data.metadata) is not None
        if _is_keep(chunks):
            if rectilinear_grid is None or sharded:
                # `.chunks` is the inner chunk shape when sharding is used, and
                # inner chunks are regular whatever the shape of the shard grid.
                chunks = data.chunks
            else:
                # Pass the stored grid through as-is: it is O(ndim), not
                # O(nchunks), and init_array stores it verbatim, so the copy
                # preserves the grid exactly — bare-int shorthand dimensions,
                # and trailing edges beyond the extent left behind by a
                # shrinking resize.
                chunks = rectilinear_grid
        if _is_keep(shards):
            if rectilinear_grid is None:
                shards = data.shards
            elif sharded:
                # The stored grid is the shard grid; init_array accepts it as
                # the `shards=` parameter and stores it verbatim.
                shards = rectilinear_grid
            else:
                shards = None
        if zarr_format is None:
            zarr_format = data.metadata.zarr_format
        if filters == "keep":
            if zarr_format == data.metadata.zarr_format:
                filters = data.filters or None
            else:
                filters = "auto"
        if compressors == "keep":
            if zarr_format == data.metadata.zarr_format:
                compressors = data.compressors or None
            else:
                compressors = "auto"
        if serializer == "keep":
            if zarr_format == 3 and data.metadata.zarr_format == 3:
                serializer = cast("SerializerLike", data.serializer)
            else:
                serializer = "auto"
        if fill_value is DEFAULT_FILL_VALUE:
            fill_value = data.fill_value

        if data.metadata.zarr_format == 2 and zarr_format == 3 and data.order == "F":
            # Can't set order="F" for v3 arrays
            warnings.warn(
                "The 'order' attribute of a Zarr format 2 array does not have a direct analogue in Zarr format 3. "
                "The existing order='F' of the source Zarr format 2 array will be ignored.",
                ZarrUserWarning,
                stacklevel=2,
            )
        elif order is None and zarr_format == 2:
            order = data.order

        if chunk_key_encoding is None and zarr_format == data.metadata.zarr_format:
            if isinstance(data.metadata, ArrayV2Metadata):
                chunk_key_encoding = {"name": "v2", "separator": data.metadata.dimension_separator}
            elif isinstance(data.metadata, ArrayV3Metadata):
                chunk_key_encoding = data.metadata.chunk_key_encoding
        if dimension_names is None and data.metadata.zarr_format == 3:
            dimension_names = data.metadata.dimension_names
        if attributes is None:
            # Deep copy so nested containers are not shared between the source
            # array's in-memory metadata and the new array's.
            attributes = copy.deepcopy(dict(data.attrs))
    else:
        if _is_keep(chunks):
            chunks = "auto"
        if _is_keep(shards):
            shards = None
        if zarr_format is None:
            zarr_format = 3
        if filters == "keep":
            filters = "auto"
        if compressors == "keep":
            compressors = "auto"
        if serializer == "keep":
            serializer = "auto"
    # After resolving "keep" above, chunks is never "keep" at this point.
    chunks_out: ChunksLike | Literal["auto"] = chunks
    return (
        chunks_out,
        shards,
        filters,
        compressors,
        serializer,
        fill_value,
        order,
        zarr_format,
        chunk_key_encoding,
        dimension_names,
        attributes,
    )


def _parse_chunk_key_encoding(
    data: ChunkKeyEncodingLike | None, zarr_format: ZarrFormat
) -> ChunkKeyEncoding:
    """
    Take an implicit specification of a chunk key encoding and parse it into a ChunkKeyEncoding object.
    """
    if data is None:
        if zarr_format == 2:
            data = {"name": "v2", "configuration": {"separator": "."}}
        else:
            data = {"name": "default", "configuration": {"separator": "/"}}
    result = parse_chunk_key_encoding(data)

    if zarr_format == 2 and result.name != "v2":
        msg = (
            "Invalid chunk key encoding. For Zarr format 2 arrays, the `name` field of the "
            f"chunk key encoding must be 'v2'. Got `name` = {result.name} instead."
        )
        raise ValueError(msg)
    return result


def default_compressors_v3(dtype: ZDType[Any, Any]) -> tuple[BytesBytesCodec, ...]:
    """
    Given a data type, return the default compressors for that data type.

    This is just a tuple containing `ZstdCodec`
    """
    return (ZstdCodec(),)


def default_serializer_v3(dtype: ZDType[Any, Any]) -> ArrayBytesCodec:
    """
    Given a data type, return the default serializer for that data type.

    The default serializer for most data types is the `BytesCodec`, which may or may not be
    parameterized with an endianness, depending on whether the data type has endianness. Variable
    length strings and variable length bytes have hard-coded serializers -- `VLenUTF8Codec` and
    `VLenBytesCodec`, respectively.

    Structured data types with multi-byte fields use `BytesCodec` with little-endian encoding.
    """
    serializer: ArrayBytesCodec = BytesCodec(endian=None)

    if isinstance(dtype, HasEndianness) or (
        isinstance(dtype, Structured) and dtype.has_multi_byte_fields()
    ):
        serializer = BytesCodec(endian="little")
    elif isinstance(dtype, HasObjectCodec):
        if dtype.object_codec_id == "vlen-bytes":
            serializer = VLenBytesCodec()
        elif dtype.object_codec_id == "vlen-utf8":
            serializer = VLenUTF8Codec()
        else:
            msg = f"Data type {dtype} requires an unknown object codec: {dtype.object_codec_id!r}."
            raise ValueError(msg)
    return serializer


def default_filters_v2(dtype: ZDType[Any, Any]) -> tuple[Numcodec] | None:
    """
    Given a data type, return the default filters for that data type.

    For data types that require an object codec, namely variable length data types,
    this is a tuple containing the object codec. Otherwise it's `None`.
    """
    if isinstance(dtype, HasObjectCodec):
        if dtype.object_codec_id == "vlen-bytes":
            from numcodecs import VLenBytes

            return (VLenBytes(),)
        elif dtype.object_codec_id == "vlen-utf8":
            from numcodecs import VLenUTF8

            return (VLenUTF8(),)
        else:
            msg = f"Data type {dtype} requires an unknown object codec: {dtype.object_codec_id!r}."
            raise ValueError(msg)
    return None


def default_compressor_v2(dtype: ZDType[Any, Any]) -> Numcodec:
    """
    Given a data type, return the default compressors for that data type.

    This is just the numcodecs `Zstd` codec.
    """
    from numcodecs import Zstd

    return Zstd(level=0, checksum=False)  # type: ignore[no-any-return]


def _parse_chunk_encoding_v2(
    *,
    compressor: CompressorsLike,
    filters: FiltersLike,
    dtype: ZDType[TBaseDType, TBaseScalar],
) -> tuple[tuple[Numcodec, ...] | None, Numcodec | None]:
    """
    Generate chunk encoding classes for Zarr format 2 arrays with optional defaults.
    """
    _filters: tuple[Numcodec, ...] | None
    _compressor: Numcodec | None

    if compressor is None or compressor == ():
        _compressor = None
    elif compressor == "auto":
        _compressor = default_compressor_v2(dtype)
    elif isinstance(compressor, tuple | list) and len(compressor) == 1:
        _compressor = parse_compressor(compressor[0])
    else:
        if isinstance(compressor, Iterable) and not isinstance(compressor, dict):
            msg = f"For Zarr format 2 arrays, the `compressor` must be a single codec. Got an iterable with type {type(compressor)} instead."
            raise TypeError(msg)
        _compressor = parse_compressor(compressor)

    if filters is None:
        _filters = None
    elif filters == "auto":
        _filters = default_filters_v2(dtype)
    else:
        if isinstance(filters, Iterable):
            for idx, f in enumerate(filters):
                if not _is_numcodec(f):
                    msg = (
                        "For Zarr format 2 arrays, all elements of `filters` must be numcodecs codecs. "
                        f"Element at index {idx} has type {type(f)}, which is not a numcodecs codec."
                    )
                    raise TypeError(msg)
        _filters = parse_filters(filters)
    if isinstance(dtype, HasObjectCodec):
        # check the filters and the compressor for the object codec required for this data type
        if _filters is None:
            if _compressor is None:
                object_codec_id = None
            else:
                object_codec_id = get_object_codec_id((_compressor.get_config(),))
        else:
            object_codec_id = get_object_codec_id(
                (
                    *[f.get_config() for f in _filters],
                    _compressor.get_config() if _compressor is not None else None,
                )
            )
        if object_codec_id is None:
            if isinstance(dtype, VariableLengthUTF8):  # type: ignore[unreachable]
                codec_name = "the numcodecs.VLenUTF8 codec"  # type: ignore[unreachable]
            elif isinstance(dtype, VariableLengthBytes):  # type: ignore[unreachable]
                codec_name = "the numcodecs.VLenBytes codec"  # type: ignore[unreachable]
            else:
                codec_name = f"an unknown object codec with id {dtype.object_codec_id!r}"
            msg = (
                f"Data type {dtype} requires {codec_name}, "
                "but no such codec was specified in the filters or compressor parameters for "
                "this array. "
            )
            raise ValueError(msg)
    return _filters, _compressor


def _parse_chunk_encoding_v3(
    *,
    compressors: CompressorsLike,
    filters: FiltersLike,
    serializer: SerializerLike,
    dtype: ZDType[TBaseDType, TBaseScalar],
) -> tuple[tuple[ArrayArrayCodec, ...], ArrayBytesCodec, tuple[BytesBytesCodec, ...]]:
    """
    Generate chunk encoding classes for v3 arrays with optional defaults.
    """

    if filters is None:
        out_array_array: tuple[ArrayArrayCodec, ...] = ()
    elif filters == "auto":
        # no data types have default filters
        out_array_array = ()
    else:
        maybe_array_array: Iterable[Codec | dict[str, JSON]]
        if isinstance(filters, dict | Codec):
            maybe_array_array = (filters,)
        else:
            maybe_array_array = cast("Iterable[Codec | dict[str, JSON]]", filters)
        out_array_array = tuple(_parse_array_array_codec(c) for c in maybe_array_array)

    if serializer == "auto":
        out_array_bytes = default_serializer_v3(dtype)
    else:
        # TODO: ensure that the serializer is compatible with the ndarray produced by the
        # array-array codecs. For example, if a sequence of array-array codecs produces an
        # array with a single-byte data type, then the serializer should not specify endiannesss.
        out_array_bytes = _parse_array_bytes_codec(serializer)

    if compressors is None:
        out_bytes_bytes: tuple[BytesBytesCodec, ...] = ()
    elif compressors == "auto":
        out_bytes_bytes = default_compressors_v3(dtype)
    else:
        maybe_bytes_bytes: Iterable[Codec | dict[str, JSON]]
        if isinstance(compressors, dict | Codec):
            maybe_bytes_bytes = (compressors,)
        else:
            maybe_bytes_bytes = cast("Iterable[Codec | dict[str, JSON]]", compressors)

        out_bytes_bytes = tuple(_parse_bytes_bytes_codec(c) for c in maybe_bytes_bytes)

    # TODO: ensure that the serializer is compatible with the ndarray produced by the
    # array-array codecs. For example, if a sequence of array-array codecs produces an
    # array with a single-byte data type, then the serializer should not specify endiannesss.

    # TODO: add checks to ensure that the right serializer is used for vlen data types
    return out_array_array, out_array_bytes, out_bytes_bytes


def _parse_deprecated_compressor(
    compressor: CompressorLike | None, compressors: CompressorsLike, zarr_format: int = 3
) -> CompressorsLike | None:
    if compressor != "auto":
        if compressors != "auto":
            raise ValueError("Cannot specify both `compressor` and `compressors`.")
        if zarr_format == 3:
            warn(
                "The `compressor` argument is deprecated. Use `compressors` instead.",
                category=ZarrUserWarning,
                stacklevel=2,
            )
        if compressor is None:
            # "no compression"
            compressors = ()
        else:
            compressors = (compressor,)
    elif zarr_format == 2 and compressor == compressors == "auto":
        compressors = ({"id": "blosc"},)
    return compressors


def _parse_data_params(
    *,
    data: np.ndarray[Any, np.dtype[Any]] | None,
    shape: ShapeLike | None,
    dtype: ZDTypeLike | None,
) -> tuple[np.ndarray[Any, np.dtype[Any]] | None, ShapeLike, ZDTypeLike]:
    """
    Ensure an array-like `data` parameter is consistent with the `dtype` and `shape`
    parameters.
    """
    if data is None:
        if shape is None:
            msg = (
                "The data parameter was set to None, but shape was not specified. "
                "Either provide a value for data, or specify shape."
            )
            raise ValueError(msg)
        shape_out = shape
        if dtype is None:
            msg = (
                "The data parameter was set to None, but dtype was not specified. "
                "Either provide an array-like value for data, or specify dtype."
            )
            raise ValueError(msg)
        dtype_out = dtype
    else:
        if shape is not None:
            msg = (
                "The data parameter was used, but the shape parameter was also "
                "used. This is an error. Either use the data parameter, or the shape parameter, "
                "but not both."
            )
            raise ValueError(msg)
        shape_out = data.shape
        if dtype is not None:
            msg = (
                "The data parameter was used, but the dtype parameter was also "
                "used. This is an error. Either use the data parameter, or the dtype parameter, "
                "but not both."
            )
            raise ValueError(msg)
        dtype_out = data.dtype
    return data, shape_out, dtype_out


def _iter_chunk_coords(
    array: AnyArray | AnyAsyncArray,
    *,
    origin: Sequence[int] | None = None,
    selection_shape: Sequence[int] | None = None,
) -> Iterator[tuple[int, ...]]:
    """
    Create an iterator over the coordinates of chunks in chunk grid space. If the `origin`
    keyword is used, iteration will start at the chunk index specified by `origin`.
    The default behavior is to start at the origin of the grid coordinate space.
    If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
    ranging from `[origin, origin selection_shape]`, where the upper bound is exclusive as
    per python indexing conventions.

    Parameters
    ----------
    array : Array | AsyncArray
        The array to iterate over.
    origin : Sequence[int] | None, default=None
        The origin of the selection in grid coordinates.
    selection_shape : Sequence[int] | None, default=None
        The shape of the selection in grid coordinates.

    Yields
    ------
    chunk_coords: tuple[int, ...]
        The coordinates of each chunk in the selection.
    """
    return _iter_grid(array._chunk_grid_shape, origin=origin, selection_shape=selection_shape)


def _iter_shard_coords(
    array: AnyArray | AnyAsyncArray,
    *,
    origin: Sequence[int] | None = None,
    selection_shape: Sequence[int] | None = None,
) -> Iterator[tuple[int, ...]]:
    """
    Create an iterator over the coordinates of shards in shard grid space. If the `origin`
    keyword is used, iteration will start at the shard index specified by `origin`.
    The default behavior is to start at the origin of the grid coordinate space.
    If the `selection_shape` keyword is used, iteration will be bounded over a contiguous region
    ranging from `[origin, origin selection_shape]`, where the upper bound is exclusive as
    per python indexing conventions.
    If no shards are present this will iterate over the coordinates of chunks in chunk grid space
    instead.

    Parameters
    ----------
    array : Array | AsyncArray
        The array to iterate over.
    origin : Sequence[int] | None, default=None
        The origin of the selection in grid coordinates.
    selection_shape : Sequence[int] | None, default=None
        The shape of the selection in grid coordinates.

    Yields
    ------
    chunk_coords: tuple[int, ...]
        The coordinates of each shard in the selection or chunks if no shards are present.
    """
    return _iter_grid(array._shard_grid_shape, origin=origin, selection_shape=selection_shape)


def _iter_shard_keys(
    array: AnyArray | AnyAsyncArray,
    *,
    origin: Sequence[int] | None = None,
    selection_shape: Sequence[int] | None = None,
) -> Iterator[str]:
    """
    Iterate over the storage keys of each shard, relative to an optional origin, and optionally
    limited to a contiguous region in shard grid coordinates.

    This automatically falls back to chunks when no shards are present.

    Parameters
    ----------
    array : Array | AsyncArray
        The array to iterate over.
    origin : Sequence[int] | None, default=None
        The origin of the selection in grid coordinates.
    selection_shape : Sequence[int] | None, default=None
        The shape of the selection in grid coordinates.

    Yields
    ------
    key: str
        The storage key of each shard in the selection or chunk when no shards are present.
    """
    # Iterate over the coordinates of chunks in chunk grid space.
    _iter = _iter_grid(array._shard_grid_shape, origin=origin, selection_shape=selection_shape)
    return (array.metadata.encode_chunk_key(k) for k in _iter)


def _iter_shard_regions(
    array: AnyArray | AnyAsyncArray,
    *,
    origin: Sequence[int] | None = None,
    selection_shape: Sequence[int] | None = None,
) -> Iterator[tuple[slice, ...]]:
    """
    Iterate over the regions spanned by each shard.

    These are the smallest regions of the array that are safe to write concurrently. When
    no shards are present this will fall back to chunks.

    Parameters
    ----------
    array : Array | AsyncArray
        The array to iterate over.
    origin : Sequence[int] | None, default=None
        The origin of the selection relative to the array's shard grid.
    selection_shape : Sequence[int] | None, default=None
        The shape of the selection in shard grid coordinates.

    Yields
    ------
    region: tuple[slice, ...]
        A tuple of slice objects representing the region spanned by each shard in the selection or chunk
        when no shards are present.
    """
    # The stored chunk grid always describes the write regions: the shard grid
    # when sharding is used, the chunk grid otherwise. Iterating it directly
    # works for regular and rectilinear grids alike.
    return array._chunk_grid.iter_chunk_regions(origin=origin, selection_shape=selection_shape)


def _iter_chunk_regions(
    array: AnyArray | AnyAsyncArray,
    *,
    origin: Sequence[int] | None = None,
    selection_shape: Sequence[int] | None = None,
) -> Iterator[tuple[slice, ...]]:
    """
    Iterate over the regions spanned by each shard.

    These are the smallest regions of the array that are efficient to read concurrently.

    Parameters
    ----------
    array : Array | AsyncArray
        The array to iterate over.
    origin : Sequence[int] | None, default=None
        The origin of the selection in grid coordinates.
    selection_shape : Sequence[int] | None, default=None
        The shape of the selection in grid coordinates.

    Returns
    -------
    region: tuple[slice, ...]
        A tuple of slice objects representing the region spanned by each shard in the selection.
    """

    return array._chunk_grid.iter_chunk_regions(origin=origin, selection_shape=selection_shape)


async def _nchunks_initialized(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
) -> int:
    """
    Calculate the number of chunks that have been initialized in storage.

    This value is calculated as the sum of the number of chunks in every initialized shard
    (shard sizes can vary when the shard grid is rectilinear). Each initialized shard is
    counted at its full declared size, so for an edge shard that extends beyond the array
    extent this includes chunks that lie entirely outside the array, and the result can
    exceed `nchunks`. For arrays that do not use sharding, each stored object holds one
    chunk, so the number of chunks initialized is the same as the number of stored objects
    associated with an array.

    Parameters
    ----------
    array : AsyncArray
        The array to inspect.

    Returns
    -------
    nchunks_initialized : int
        The number of chunks that have been initialized.
    """
    meta = array.metadata
    if not isinstance(meta, ArrayV3Metadata) or meta.sharding_codec is None:
        return await _nshards_initialized(array)
    # `.chunks` is the inner chunk shape, which is defined for any sharded array.
    inner_chunks = array.chunks
    if isinstance(meta.chunk_grid, RegularChunkGridMetadata):
        # Uniform shard shape: the exact chunk count per shard is a single
        # multiply, O(1) after the storage listing.
        chunks_per_shard = product(
            tuple(a // b for a, b in zip(meta.chunk_grid.chunk_shape, inner_chunks, strict=True))
        )
        return (await _nshards_initialized(array)) * chunks_per_shard
    # Rectilinear shard grid: the chunk count varies per shard, so decode the
    # initialized keys and sum each shard's own count.
    grid = array._chunk_grid
    total = 0
    for key in await _shards_initialized(array):
        spec = grid[meta.chunk_key_encoding.decode_chunk_key(key)]
        if spec is not None:
            total += product(
                tuple(s // c for s, c in zip(spec.codec_shape, inner_chunks, strict=True))
            )
    return total


async def _nshards_initialized(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
) -> int:
    """
    Calculate the number of shards that have been initialized in storage.

    This is the number of shards that have been persisted to the storage backend.

    Parameters
    ----------
    array : AsyncArray
        The array to inspect.

    Returns
    -------
    nshards_initialized : int
        The number of shards that have been initialized.
    """
    return len(await _shards_initialized(array))


async def _nbytes_stored(
    store_path: StorePath,
) -> int:
    """
    Calculate the number of bytes stored for an array.

    Parameters
    ----------
    store_path : StorePath
        The store path of the array.

    Returns
    -------
    nbytes_stored : int
        The number of bytes stored.
    """
    return await store_path.store.getsize_prefix(store_path.path)


def _get_chunk_spec(
    metadata: ArrayMetadata,
    chunk_grid: ChunkGrid,
    chunk_coords: tuple[int, ...],
    array_config: ArrayConfig,
    prototype: BufferPrototype,
) -> ArraySpec:
    """Build an ArraySpec for a single chunk using the ChunkGrid."""
    spec = chunk_grid[chunk_coords]
    if spec is None:
        raise IndexError(f"Chunk coordinates {chunk_coords} are out of bounds.")
    return ArraySpec(
        shape=spec.codec_shape,
        dtype=metadata.dtype,
        fill_value=metadata.fill_value,
        config=array_config,
        prototype=prototype,
    )


async def _get_selection(
    store_path: StorePath,
    metadata: ArrayMetadata,
    codec_pipeline: CodecPipeline,
    config: ArrayConfig,
    chunk_grid: ChunkGrid,
    indexer: Indexer,
    *,
    prototype: BufferPrototype,
    out: NDBuffer | None = None,
    fields: Fields | None = None,
) -> NDArrayLikeOrScalar:
    """
    Get a selection from an array.

    Parameters
    ----------
    store_path : StorePath
        The store path of the array.
    metadata : ArrayMetadata
        The array metadata.
    codec_pipeline : CodecPipeline
        The codec pipeline for encoding/decoding.
    config : ArrayConfig
        The array configuration.
    indexer : Indexer
        The indexer specifying the selection.
    prototype : BufferPrototype
        A buffer prototype to use for the retrieved data.
    out : NDBuffer | None, optional
        An output buffer to write the data to.
    fields : Fields | None, optional
        Fields to select from structured arrays.

    Returns
    -------
    NDArrayLikeOrScalar
        The selected data.
    """
    # `dtype` returns the zarr dtype object for both v2 and v3 metadata.
    zdtype = metadata.dtype
    dtype = zdtype.to_native_dtype()

    # Determine memory order
    if metadata.zarr_format == 2:
        order = metadata.order
    else:
        order = config.order

    # check fields are sensible
    out_dtype = check_fields(fields, dtype)

    # setup output buffer
    if out is not None:
        if isinstance(out, NDBuffer):
            out_buffer = out
        else:
            raise TypeError(f"out argument needs to be an NDBuffer. Got {type(out)!r}")
        if out_buffer.shape != indexer.shape:
            raise ValueError(
                f"shape of out argument doesn't match. Expected {indexer.shape}, got {out.shape}"
            )
    else:
        out_buffer = prototype.nd_buffer.empty(
            shape=indexer.shape,
            dtype=out_dtype,
            order=order,
        )
    if product(indexer.shape) > 0:
        # need to use the order from the metadata for v2
        _config = config
        if metadata.zarr_format == 2:
            _config = replace(_config, order=order)

        # reading chunks and decoding them
        indexed_chunks = list(indexer)
        # For regular grids, all chunks share the same ArraySpec, so build it once
        # and reuse it to avoid per-chunk ChunkGrid lookups and ArraySpec construction.
        regular_grid = chunk_grid.is_regular
        if regular_grid:
            regular_chunk_spec = ArraySpec(
                shape=chunk_grid.chunk_shape,
                dtype=metadata.dtype,
                fill_value=metadata.fill_value,
                config=_config,
                prototype=prototype,
            )
        results = await codec_pipeline.read(
            [
                (
                    store_path / metadata.encode_chunk_key(chunk_coords),
                    regular_chunk_spec
                    if regular_grid
                    else _get_chunk_spec(metadata, chunk_grid, chunk_coords, _config, prototype),
                    chunk_selection,
                    out_selection,
                    is_complete_chunk,
                )
                for chunk_coords, chunk_selection, out_selection, is_complete_chunk in indexed_chunks
            ],
            out_buffer,
            drop_axes=indexer.drop_axes,
        )
        if _config.read_missing_chunks is False:
            missing_info = []
            for i, result in enumerate(results):
                if result["status"] == "missing":
                    coords = indexed_chunks[i][0]
                    key = metadata.encode_chunk_key(coords)
                    missing_info.append(f"  chunk '{key}' (grid position {coords})")
            if missing_info:
                chunks_str = "\n".join(missing_info)
                raise ChunkNotFoundError(
                    f"{len(missing_info)} chunk(s) not found in store '{store_path}'.\n"
                    f"Set the 'array.read_missing_chunks' config to True to fill "
                    f"missing chunks with the fill value.\n"
                    f"Missing chunks:\n{chunks_str}"
                )
    if isinstance(indexer, BasicIndexer) and indexer.shape == ():
        return out_buffer.as_scalar()
    return out_buffer.as_ndarray_like()


async def _getitem(
    store_path: StorePath,
    metadata: ArrayMetadata,
    codec_pipeline: CodecPipeline,
    config: ArrayConfig,
    chunk_grid: ChunkGrid,
    selection: BasicSelection,
    *,
    prototype: BufferPrototype | None = None,
) -> NDArrayLikeOrScalar:
    """
    Retrieve a subset of the array's data based on the provided selection.

    Parameters
    ----------
    store_path : StorePath
        The store path of the array.
    metadata : ArrayMetadata
        The array metadata.
    codec_pipeline : CodecPipeline
        The codec pipeline for encoding/decoding.
    config : ArrayConfig
        The array configuration.
    chunk_grid : ChunkGrid
        The chunk grid.
    selection : BasicSelection
        A selection object specifying the subset of data to retrieve.
    prototype : BufferPrototype, optional
        A buffer prototype to use for the retrieved data (default is None).

    Returns
    -------
    NDArrayLikeOrScalar
        The retrieved subset of the array's data.
    """
    if prototype is None:
        prototype = default_buffer_prototype()
    indexer = BasicIndexer(
        selection,
        shape=metadata.shape,
        chunk_grid=chunk_grid,
    )
    return await _get_selection(
        store_path, metadata, codec_pipeline, config, chunk_grid, indexer, prototype=prototype
    )


async def _set_selection(
    store_path: StorePath,
    metadata: ArrayMetadata,
    codec_pipeline: CodecPipeline,
    config: ArrayConfig,
    chunk_grid: ChunkGrid,
    indexer: Indexer,
    value: npt.ArrayLike,
    *,
    prototype: BufferPrototype,
    fields: Fields | None = None,
) -> None:
    """
    Set a selection in an array.

    Parameters
    ----------
    store_path : StorePath
        The store path of the array.
    metadata : ArrayMetadata
        The array metadata.
    codec_pipeline : CodecPipeline
        The codec pipeline for encoding/decoding.
    config : ArrayConfig
        The array configuration.
    chunk_grid : ChunkGrid
        The chunk grid.
    indexer : Indexer
        The indexer specifying the selection.
    value : npt.ArrayLike
        The values to write.
    prototype : BufferPrototype
        A buffer prototype to use.
    fields : Fields | None, optional
        Fields to select from structured arrays.
    """
    # `dtype` returns the zarr dtype object for both v2 and v3 metadata.
    zdtype = metadata.dtype
    dtype = zdtype.to_native_dtype()

    # check fields are sensible
    check_fields(fields, dtype)
    fields = check_no_multi_fields(fields)

    # check value shape
    if np.isscalar(value):
        array_like = prototype.buffer.create_zero_length().as_array_like()
        if isinstance(array_like, np._typing._SupportsArrayFunc):
            # TODO: need to handle array types that don't support __array_function__
            # like PyTorch and JAX
            array_like_ = cast("np._typing._SupportsArrayFunc", array_like)
        value = np.asanyarray(value, dtype=dtype, like=array_like_)
    else:
        if not hasattr(value, "shape"):
            value = np.asarray(value, dtype)
        # assert (
        #     value.shape == indexer.shape
        # ), f"shape of value doesn't match indexer shape. Expected {indexer.shape}, got {value.shape}"
        if not hasattr(value, "dtype") or value.dtype.name != dtype.name:
            if hasattr(value, "astype"):
                # Handle things that are already NDArrayLike more efficiently
                value = value.astype(dtype=dtype, order="A")
            else:
                value = np.array(value, dtype=dtype, order="A")
    value = cast("NDArrayLike", value)

    # We accept any ndarray like object from the user and convert it
    # to an NDBuffer (or subclass). From this point onwards, we only pass
    # Buffer and NDBuffer between components.
    value_buffer = prototype.nd_buffer.from_ndarray_like(value)

    # Determine memory order
    if metadata.zarr_format == 2:
        order = metadata.order
    else:
        order = config.order

    # need to use the order from the metadata for v2
    _config = config
    if metadata.zarr_format == 2:
        _config = replace(_config, order=order)

    # merging with existing data and encoding chunks
    # For regular grids, all chunks share the same ArraySpec, so build it once
    # and reuse it to avoid per-chunk ChunkGrid lookups and ArraySpec construction.
    regular_grid = chunk_grid.is_regular
    if regular_grid:
        regular_chunk_spec = ArraySpec(
            shape=chunk_grid.chunk_shape,
            dtype=metadata.dtype,
            fill_value=metadata.fill_value,
            config=_config,
            prototype=prototype,
        )
    await codec_pipeline.write(
        [
            (
                store_path / metadata.encode_chunk_key(chunk_coords),
                regular_chunk_spec
                if regular_grid
                else _get_chunk_spec(metadata, chunk_grid, chunk_coords, _config, prototype),
                chunk_selection,
                out_selection,
                is_complete_chunk,
            )
            for chunk_coords, chunk_selection, out_selection, is_complete_chunk in indexer
        ],
        value_buffer,
        drop_axes=indexer.drop_axes,
    )


async def _resize(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
    new_shape: ShapeLike,
    delete_outside_chunks: bool = True,
) -> None:
    """
    Resize an array to a new shape.

    Parameters
    ----------
    array : AsyncArray
        The array to resize.
    new_shape : ShapeLike
        The desired new shape of the array.
    delete_outside_chunks : bool, optional
        If True (default), chunks that fall outside the new shape will be deleted.
        If False, the data in those chunks will be preserved.
    """
    new_shape = parse_shapelike(new_shape)
    if len(new_shape) != len(array.metadata.shape):
        raise ValueError(
            f"The new shape must have the same number of dimensions as the array. "
            f"Got {len(new_shape)} dimension(s), expected {len(array.metadata.shape)}."
        )

    new_metadata = array.metadata.update_shape(new_shape)
    new_chunk_grid = ChunkGrid.from_metadata(new_metadata)

    # ensure deletion is only run if array is shrinking as the delete_outside_chunks path is unbounded in memory
    only_growing = all(new >= old for new, old in zip(new_shape, array.metadata.shape, strict=True))

    if delete_outside_chunks and not only_growing:
        # Remove all chunks outside of the new shape
        old_chunk_coords = set(array._chunk_grid.all_chunk_coords())
        new_chunk_coords = set(new_chunk_grid.all_chunk_coords())

        async def _delete_key(key: str) -> None:
            await (array.store_path / key).delete()

        await concurrent_map(
            [
                (array.metadata.encode_chunk_key(chunk_coords),)
                for chunk_coords in old_chunk_coords.difference(new_chunk_coords)
            ],
            _delete_key,
            zarr_config.get("async.concurrency"),
        )

    # Write new metadata
    await array._save_metadata(new_metadata)

    # Update metadata and chunk_grid (in place)
    object.__setattr__(array, "metadata", new_metadata)
    object.__setattr__(array, "_chunk_grid", new_chunk_grid)


async def _append(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
    data: npt.ArrayLike,
    axis: int = 0,
) -> tuple[int, ...]:
    """
    Append data to an array along the specified axis.

    Parameters
    ----------
    array : AsyncArray
        The array to append to.
    data : npt.ArrayLike
        Data to be appended.
    axis : int
        Axis along which to append.

    Returns
    -------
    new_shape : tuple[int, ...]
        The new shape of the array after appending.

    Notes
    -----
    The size of all dimensions other than `axis` must match between the
    array and `data`.
    """
    # ensure data is array-like
    if not hasattr(data, "shape"):
        data = np.asanyarray(data)

    self_shape_preserved = tuple(s for i, s in enumerate(array.shape) if i != axis)
    data_shape_preserved = tuple(s for i, s in enumerate(data.shape) if i != axis)
    if self_shape_preserved != data_shape_preserved:
        raise ValueError(
            f"shape of data to append is not compatible with the array. "
            f"The shape of the data is ({data_shape_preserved})"
            f"and the shape of the array is ({self_shape_preserved})."
            "All dimensions must match except for the dimension being "
            "appended."
        )
    # remember old shape
    old_shape = array.shape

    # determine new shape
    new_shape = tuple(
        array.shape[i] if i != axis else array.shape[i] + data.shape[i]
        for i in range(len(array.shape))
    )

    # resize
    await _resize(array, new_shape)

    # store data
    append_selection = tuple(
        slice(None) if i != axis else slice(old_shape[i], new_shape[i])
        for i in range(len(array.shape))
    )
    await array.setitem(append_selection, data)

    return new_shape


async def _update_attributes(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
    new_attributes: dict[str, JSON],
) -> AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata]:
    """
    Update the array's attributes.

    Parameters
    ----------
    array : AsyncArray
        The array whose attributes to update.
    new_attributes : dict[str, JSON]
        A dictionary of new attributes to update or add to the array.

    Returns
    -------
    AsyncArray
        The array with the updated attributes.
    """
    array.metadata.attributes.update(new_attributes)

    # Write new metadata
    await array._save_metadata(array.metadata)

    return array


async def _info_complete(
    array: AsyncArray[ArrayV2Metadata] | AsyncArray[ArrayV3Metadata],
) -> Any:
    """
    Return all the information for an array, including dynamic information like storage size.

    Parameters
    ----------
    array : AsyncArray
        The array to get info for.

    Returns
    -------
    ArrayInfo
        Complete information about the array including:
        - The count of chunks initialized
        - The sum of the bytes written
    """
    return array._info(
        await _nshards_initialized(array),
        await array.store_path.store.getsize_prefix(array.store_path.path),
    )