

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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     assert isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    tmp_path / "cache"
     _lowerCAmelCase   :					List[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   :					int        =    ParquetDatasetReader(SCREAMING_SNAKE_CASE		,							cache_dir=SCREAMING_SNAKE_CASE		,							keep_in_memory=SCREAMING_SNAKE_CASE				).read()
     _check_parquet_dataset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)







@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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







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







@pytest.mark.parametrize("split"		,							[None, NamedSplit("train"				), "train", "test"]				)
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tmp_path / "cache"
     _lowerCAmelCase   :					int        =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
     _lowerCAmelCase   :					Tuple        =    ParquetDatasetReader(SCREAMING_SNAKE_CASE		,							cache_dir=SCREAMING_SNAKE_CASE		,							split=SCREAMING_SNAKE_CASE				).read()
     _check_parquet_dataset(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"		,							[str, list]				)
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=("train",)				)       ->      Tuple:

     """simple docstring"""







     assert isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     for split in splits:
          _lowerCAmelCase   :					Optional[int]        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    tmp_path / "cache"
     _lowerCAmelCase   :					Tuple        =    {"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   :					Optional[int]        =    ParquetDatasetReader(
              {"train": parquet_path}		,							cache_dir=SCREAMING_SNAKE_CASE		,							keep_in_memory=SCREAMING_SNAKE_CASE				).read()
     _check_parquet_datasetdict(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)







@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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







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







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

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    ParquetDatasetWriter(SCREAMING_SNAKE_CASE		,							tmp_path / "foo.parquet"				)
     assert writer.write() > 0
     _lowerCAmelCase   :					Any        =    pq.ParquetFile(tmp_path / "foo.parquet"				)
     _lowerCAmelCase   :					Dict        =    pf.read()
     assert dataset.data.table == output_table







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







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

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

     _lowerCAmelCase   :					str        =    ParquetDatasetReader(str(tmp_path / "foo.parquet"				)		,							streaming=SCREAMING_SNAKE_CASE				).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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     assert get_writer_batch_size(SCREAMING_SNAKE_CASE				) == expected









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase    				=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['YolosFeatureExtractor']
  __UpperCAmelCase    				=     ['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

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






