from __future__ import annotations

import os
from dataclasses import dataclass
from typing import Any, Dict, Iterator, List, NewType, Optional, Tuple

import dvc.api
import numpy as np
from numpy.typing import NDArray


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


Key = NewType("Key", str)

Example = Dict[Key, Any]


def load():
    def _gen():
        for split in ["train", "test"]:
            yield load_poker_hand()

    return tuple(_gen())


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


def _download(path: str) -> Any:
    return dvc.api.open(
        f"archive.ics.uci.edu/ml/machine-learning-databases/poker/{path}",
        remote="uci",
    )


def _get_data(split: Optional[str] = None):
    if split is None or split == "train":
        return "poker-hand-training-true.data"
    elif split == "test":
        return "poker-hand-testing.data"

    raise ValueError("Not a valid split")


def _load_poker_hand(path: str) -> Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=",")

        data = dataset[:, :-1]
        targets = dataset[:, -1]

        return Dataset(
            data=data,
            targets=targets,
            target_names=[
                "Nothing",
                "One pair",
                "Two pair",
                "Three of a kind",
                "Straigh",
                "Flush",
                "Full house",
                "Four of a kind",
                "Straight flush",
                "Royal flush",
            ],
            feature_names=[
                "C1",
                "S1",
                "C2",
                "S2",
                "C3",
                "S3",
                "C4",
                "C5",
            ],
        )

96b44c121a9c11dc46ba2dc194c7c57092ea6eeb
<mask token>


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


<mask token>


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


<mask token>


def _get_data(split: Optional[str]=None):
    if split is None or split == 'train':
        return 'poker-hand-training-true.data'
    elif split == 'test':
        return 'poker-hand-testing.data'
    raise ValueError('Not a valid split')


def _load_poker_hand(path: str) ->Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=',')
        data = dataset[:, :-1]
        targets = dataset[:, -1]
        return Dataset(data=data, targets=targets, target_names=['Nothing',
            'One pair', 'Two pair', 'Three of a kind', 'Straigh', 'Flush',
            'Full house', 'Four of a kind', 'Straight flush', 'Royal flush'
            ], feature_names=['C1', 'S1', 'C2', 'S2', 'C3', 'S3', 'C4', 'C5'])

<mask token>


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


<mask token>


def load():

    def _gen():
        for split in ['train', 'test']:
            yield load_poker_hand()
    return tuple(_gen())


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


<mask token>


def _get_data(split: Optional[str]=None):
    if split is None or split == 'train':
        return 'poker-hand-training-true.data'
    elif split == 'test':
        return 'poker-hand-testing.data'
    raise ValueError('Not a valid split')


def _load_poker_hand(path: str) ->Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=',')
        data = dataset[:, :-1]
        targets = dataset[:, -1]
        return Dataset(data=data, targets=targets, target_names=['Nothing',
            'One pair', 'Two pair', 'Three of a kind', 'Straigh', 'Flush',
            'Full house', 'Four of a kind', 'Straight flush', 'Royal flush'
            ], feature_names=['C1', 'S1', 'C2', 'S2', 'C3', 'S3', 'C4', 'C5'])

<mask token>


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


<mask token>


def load():

    def _gen():
        for split in ['train', 'test']:
            yield load_poker_hand()
    return tuple(_gen())


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


def _download(path: str) ->Any:
    return dvc.api.open(
        f'archive.ics.uci.edu/ml/machine-learning-databases/poker/{path}',
        remote='uci')


def _get_data(split: Optional[str]=None):
    if split is None or split == 'train':
        return 'poker-hand-training-true.data'
    elif split == 'test':
        return 'poker-hand-testing.data'
    raise ValueError('Not a valid split')


def _load_poker_hand(path: str) ->Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=',')
        data = dataset[:, :-1]
        targets = dataset[:, -1]
        return Dataset(data=data, targets=targets, target_names=['Nothing',
            'One pair', 'Two pair', 'Three of a kind', 'Straigh', 'Flush',
            'Full house', 'Four of a kind', 'Straight flush', 'Royal flush'
            ], feature_names=['C1', 'S1', 'C2', 'S2', 'C3', 'S3', 'C4', 'C5'])

<mask token>


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


Key = NewType('Key', str)
Example = Dict[Key, Any]


def load():

    def _gen():
        for split in ['train', 'test']:
            yield load_poker_hand()
    return tuple(_gen())


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


def _download(path: str) ->Any:
    return dvc.api.open(
        f'archive.ics.uci.edu/ml/machine-learning-databases/poker/{path}',
        remote='uci')


def _get_data(split: Optional[str]=None):
    if split is None or split == 'train':
        return 'poker-hand-training-true.data'
    elif split == 'test':
        return 'poker-hand-testing.data'
    raise ValueError('Not a valid split')


def _load_poker_hand(path: str) ->Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=',')
        data = dataset[:, :-1]
        targets = dataset[:, -1]
        return Dataset(data=data, targets=targets, target_names=['Nothing',
            'One pair', 'Two pair', 'Three of a kind', 'Straigh', 'Flush',
            'Full house', 'Four of a kind', 'Straight flush', 'Royal flush'
            ], feature_names=['C1', 'S1', 'C2', 'S2', 'C3', 'S3', 'C4', 'C5'])

from __future__ import annotations

import os
from dataclasses import dataclass
from typing import Any, Dict, Iterator, List, NewType, Optional, Tuple

import dvc.api
import numpy as np
from numpy.typing import NDArray


@dataclass
class Dataset(object):
    data: NDArray
    targets: NDArray
    target_names: str
    feature_names: str


Key = NewType("Key", str)

Example = Dict[Key, Any]


def load():
    def _gen():
        for split in ["train", "test"]:
            yield load_poker_hand()

    return tuple(_gen())


def load_poker_hand(split=None):
    data_path = _get_data(split)
    return _load_poker_hand(data_path)


def _download(path: str) -> Any:
    return dvc.api.open(
        f"archive.ics.uci.edu/ml/machine-learning-databases/poker/{path}",
        remote="uci",
    )


def _get_data(split: Optional[str] = None):
    if split is None or split == "train":
        return "poker-hand-training-true.data"
    elif split == "test":
        return "poker-hand-testing.data"

    raise ValueError("Not a valid split")


def _load_poker_hand(path: str) -> Dataset:
    with _download(path) as f:
        dataset = np.genfromtxt(f, delimiter=",")

        data = dataset[:, :-1]
        targets = dataset[:, -1]

        return Dataset(
            data=data,
            targets=targets,
            target_names=[
                "Nothing",
                "One pair",
                "Two pair",
                "Three of a kind",
                "Straigh",
                "Flush",
                "Full house",
                "Four of a kind",
                "Straight flush",
                "Royal flush",
            ],
            feature_names=[
                "C1",
                "S1",
                "C2",
                "S2",
                "C3",
                "S3",
                "C4",
                "C5",
            ],
        )
