

import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Union[str, Any] )   ->       Dict:
    assert isinstance(lowerCAmelCase  ,      lowerCAmelCase )
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"  ,      [False, True] )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  str )   ->       Dict:
    _UpperCAmelCase       :		str 		=    tmp_path / "cache"
    _UpperCAmelCase       :		int 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _UpperCAmelCase       :		Tuple 		=    ParquetDatasetReader(lowerCAmelCase  ,      cache_dir=lowerCAmelCase  ,      keep_in_memory=lowerCAmelCase ).read()
    _check_parquet_dataset(lowerCAmelCase  ,      lowerCAmelCase )

@pytest.mark.parametrize(
    "features"  ,      [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,      )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  List[Any] )   ->       Optional[int]:
    _UpperCAmelCase       :		Any 		=    tmp_path / "cache"
    _UpperCAmelCase       :		str 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    _UpperCAmelCase       :		Dict 		=    features.copy() if features else default_expected_features
    _UpperCAmelCase       :		List[Any] 		=    (
        Features({feature: Value(lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
    )
    _UpperCAmelCase       :		Dict 		=    ParquetDatasetReader(lowerCAmelCase  ,      features=lowerCAmelCase  ,      cache_dir=lowerCAmelCase ).read()
    _check_parquet_dataset(lowerCAmelCase  ,      lowerCAmelCase )

@pytest.mark.parametrize("split"  ,      [None, NamedSplit("train" ), "train", "test"] )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Optional[Any] )   ->       Dict:
    _UpperCAmelCase       :		Tuple 		=    tmp_path / "cache"
    _UpperCAmelCase       :		Union[str, Any] 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    _UpperCAmelCase       :		Optional[Any] 		=    ParquetDatasetReader(lowerCAmelCase  ,      cache_dir=lowerCAmelCase  ,      split=lowerCAmelCase ).read()
    _check_parquet_dataset(lowerCAmelCase  ,      lowerCAmelCase )
    assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"  ,      [str, list] )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  List[Any] )   ->       str:
    if issubclass(lowerCAmelCase  ,      lowerCAmelCase ):
        _UpperCAmelCase       :		List[Any] 		=    parquet_path
    elif issubclass(lowerCAmelCase  ,      lowerCAmelCase ):
        _UpperCAmelCase       :		str 		=    [parquet_path]
    _UpperCAmelCase       :		Any 		=    tmp_path / "cache"
    _UpperCAmelCase       :		Union[str, Any] 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    _UpperCAmelCase       :		Optional[Any] 		=    ParquetDatasetReader(lowerCAmelCase  ,      cache_dir=lowerCAmelCase ).read()
    _check_parquet_dataset(lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  int=("train",) )   ->       Optional[Any]:
    assert isinstance(lowerCAmelCase  ,      lowerCAmelCase )
    for split in splits:
        _UpperCAmelCase       :		Optional[Any] 		=    dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"  ,      [False, True] )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  int )   ->       Tuple:
    _UpperCAmelCase       :		Any 		=    tmp_path / "cache"
    _UpperCAmelCase       :		Any 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _UpperCAmelCase       :		Optional[int] 		=    ParquetDatasetReader(
            {"train": parquet_path}  ,      cache_dir=lowerCAmelCase  ,      keep_in_memory=lowerCAmelCase ).read()
    _check_parquet_datasetdict(lowerCAmelCase  ,      lowerCAmelCase )

@pytest.mark.parametrize(
    "features"  ,      [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,      )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  Tuple )   ->       Union[str, Any]:
    _UpperCAmelCase       :		str 		=    tmp_path / "cache"
    _UpperCAmelCase       :		List[Any] 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    _UpperCAmelCase       :		int 		=    features.copy() if features else default_expected_features
    _UpperCAmelCase       :		Optional[Any] 		=    (
        Features({feature: Value(lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
    )
    _UpperCAmelCase       :		str 		=    ParquetDatasetReader({"train": parquet_path}  ,      features=lowerCAmelCase  ,      cache_dir=lowerCAmelCase ).read()
    _check_parquet_datasetdict(lowerCAmelCase  ,      lowerCAmelCase )

@pytest.mark.parametrize("split"  ,      [None, NamedSplit("train" ), "train", "test"] )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Tuple )   ->       List[Any]:
    if split:
        _UpperCAmelCase       :		Any 		=    {split: parquet_path}
    else:
        _UpperCAmelCase       :		List[Any] 		=    "train"
        _UpperCAmelCase       :		List[Any] 		=    {"train": parquet_path, "test": parquet_path}
    _UpperCAmelCase       :		Union[str, Any] 		=    tmp_path / "cache"
    _UpperCAmelCase       :		Optional[int] 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
    _UpperCAmelCase       :		int 		=    ParquetDatasetReader(lowerCAmelCase  ,      cache_dir=lowerCAmelCase ).read()
    _check_parquet_datasetdict(lowerCAmelCase  ,      lowerCAmelCase  ,      splits=list(path.keys() ) )
    assert all(dataset[split].split == split for split in path.keys() )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Optional[Any] )   ->       Optional[Any]:
    _UpperCAmelCase       :		Union[str, Any] 		=    ParquetDatasetWriter(lowerCAmelCase  ,      tmp_path / "foo.parquet" )
    assert writer.write() > 0
    _UpperCAmelCase       :		str 		=    pq.ParquetFile(tmp_path / "foo.parquet" )
    _UpperCAmelCase       :		List[str] 		=    pf.read()
    assert dataset.data.table == output_table

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Union[str, Any] )   ->       int:
    _UpperCAmelCase       :		List[str] 		=    str(shared_datadir / "test_image_rgb.jpg" )
    _UpperCAmelCase       :		str 		=    {"image": [image_path]}
    _UpperCAmelCase       :		List[Any] 		=    Features({"image": Image()} )
    _UpperCAmelCase       :		Optional[Any] 		=    Dataset.from_dict(lowerCAmelCase  ,      features=lowerCAmelCase )
    _UpperCAmelCase       :		List[Any] 		=    ParquetDatasetWriter(lowerCAmelCase  ,      tmp_path / "foo.parquet" )
    assert writer.write() > 0

    _UpperCAmelCase       :		Tuple 		=    Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
    assert dataset.features == reloaded_dataset.features

    _UpperCAmelCase       :		List[str] 		=    ParquetDatasetReader(str(tmp_path / "foo.parquet" )  ,      streaming=lowerCAmelCase ).read()
    assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"  ,      [
        (Features({"foo": Value("int32" )} ), None),
        (Features({"image": Image(), "foo": Value("int32" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]  ,      )
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Any )   ->       Any:
    assert get_writer_batch_size(lowerCAmelCase ) == expected





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


