






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Optional[int]			     = {
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       int			     = [
           "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "AutoformerForPrediction",
           "AutoformerModel",
           "AutoformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_autoformer import (
           AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           AutoformerConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_autoformer import (
                  AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AutoformerForPrediction,
                  AutoformerModel,
                  AutoformerPreTrainedModel,
              )

else:
       import sys

       a		:       Any			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      Optional[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():
    lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Tuple			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Union[str, Any]			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Dict			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    split=__a		).read()
  _check_parquet_dataset(__a	,    __a		)
  assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"	,    [str, list]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Dict:







  """simple docstring"""





  if issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =parquet_path
  elif issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =[parquet_path]
  lowerCamelCase__:      Optional[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a=("train",)		)     ->		Union[str, Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  for split in splits:
    lowerCamelCase__:      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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      str			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      List[str]			  =ParquetDatasetReader(
        {"train": parquet_path}	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      int			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      Union[str, Any]			  =ParquetDatasetReader({"train": parquet_path}	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if split:
    lowerCamelCase__:      Union[str, Any]			  ={split: parquet_path}
  else:
    lowerCamelCase__:      int			  ="train"
    lowerCamelCase__:      Union[str, Any]			  ={"train": parquet_path, "test": parquet_path}
  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Union[str, Any]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[Any]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a	,    splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0
  lowerCamelCase__:      Tuple			  =pq.ParquetFile(tmp_path / "foo.parquet"		)
  lowerCamelCase__:      Optional[int]			  =pf.read()
  assert dataset.data.table == output_table





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





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

  lowerCamelCase__:      Optional[Any]			  =Dataset.from_parquet(str(tmp_path / "foo.parquet"		)		)
  assert dataset.features == reloaded_dataset.features

  lowerCamelCase__:      List[str]			  =ParquetDatasetReader(str(tmp_path / "foo.parquet"		)	,    streaming=__a		).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      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  assert get_writer_batch_size(__a		) == expected

