


'''simple docstring'''






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 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[int]		)   ->  Optional[int]:




     '''simple docstring'''



     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 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[int]		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _a			      =							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 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Union[str, Any]		)   ->  Dict:




     '''simple docstring'''



     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     _a			      =							features.copy() if features else default_expected_features
     _a			      =							(
         Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
     )
     _a			      =							ParquetDatasetReader(lowerCAmelCase__				,						features=lowerCAmelCase__				,						cache_dir=lowerCAmelCase__		).read()
     _check_parquet_dataset(lowerCAmelCase__				,						lowerCAmelCase__		)






@pytest.mark.parametrize('split'				,						[None, NamedSplit('train'		), 'train', 'test']		)
def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Any		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     _a			      =							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 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :List[Any]		)   ->  int:




     '''simple docstring'''



     if issubclass(lowerCAmelCase__				,						lowerCAmelCase__		):
          _a			      =							parquet_path
     elif issubclass(lowerCAmelCase__				,						lowerCAmelCase__		):
          _a			      =							[parquet_path]
     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     _a			      =							ParquetDatasetReader(lowerCAmelCase__				,						cache_dir=lowerCAmelCase__		).read()
     _check_parquet_dataset(lowerCAmelCase__				,						lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :List[Any]=("train",)		)   ->  Optional[Any]:




     '''simple docstring'''



     assert isinstance(lowerCAmelCase__				,						lowerCAmelCase__		)
     for split in splits:
          _a			      =							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 	_A				(lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :List[str]		)   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _a			      =							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 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Dict		)   ->  int:




     '''simple docstring'''



     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     _a			      =							features.copy() if features else default_expected_features
     _a			      =							(
         Features({feature: Value(lowerCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
     )
     _a			      =							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 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[Any]		)   ->  Any:




     '''simple docstring'''



     if split:
          _a			      =							{split: parquet_path}
     else:
          _a			      =							'train'
          _a			      =							{'train': parquet_path, 'test': parquet_path}
     _a			      =							tmp_path / 'cache'
     _a			      =							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
     _a			      =							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 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :List[Any]		)   ->  Dict:




     '''simple docstring'''



     _a			      =							ParquetDatasetWriter(lowerCAmelCase__				,						tmp_path / 'foo.parquet'		)
     assert writer.write() > 0
     _a			      =							pq.ParquetFile(tmp_path / 'foo.parquet'		)
     _a			      =							pf.read()
     assert dataset.data.table == output_table






def 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Dict		)   ->  str:




     '''simple docstring'''



     _a			      =							str(shared_datadir / 'test_image_rgb.jpg'		)
     _a			      =							{'image': [image_path]}
     _a			      =							Features({'image': Image()}		)
     _a			      =							Dataset.from_dict(lowerCAmelCase__				,						features=lowerCAmelCase__		)
     _a			      =							ParquetDatasetWriter(lowerCAmelCase__				,						tmp_path / 'foo.parquet'		)
     assert writer.write() > 0

     _a			      =							Dataset.from_parquet(str(tmp_path / 'foo.parquet'		)		)
     assert dataset.features == reloaded_dataset.features

     _a			      =							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 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :List[str]		)   ->  Optional[int]:




     '''simple docstring'''



     assert get_writer_batch_size(lowerCAmelCase__		) == expected





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Any          =    [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 a_					:    List[str]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

