


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		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     assert isinstance(_lowercase					,  _lowercase )
     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		(		_lowercase					,  _lowercase					,  _lowercase )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			tmp_path / """cache"""
     __UpperCamelCase =			{"""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 =			ParquetDatasetReader(_lowercase					,  cache_dir=_lowercase					,  keep_in_memory=_lowercase ).read()
     _check_parquet_dataset(_lowercase					,  _lowercase )






@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		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


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






@pytest.mark.parametrize('split'					,  [None, NamedSplit('train' ), 'train', 'test'] )
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> int:


     """simple docstring"""


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






@pytest.mark.parametrize('path_type'					,  [str, list] )
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=("train",) )					-> Optional[Any]:


     """simple docstring"""


     assert isinstance(_lowercase					,  _lowercase )
     for split in splits:
          __UpperCamelCase =			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		(		_lowercase					,  _lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			tmp_path / """cache"""
     __UpperCamelCase =			{"""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 =			ParquetDatasetReader(
              {'train': parquet_path}					,  cache_dir=_lowercase					,  keep_in_memory=_lowercase ).read()
     _check_parquet_datasetdict(_lowercase					,  _lowercase )






@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		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


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






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


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase )					-> List[str]:


     """simple docstring"""


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

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

     __UpperCamelCase =			ParquetDatasetReader(str(tmp_path / 'foo.parquet' )					,  streaming=_lowercase ).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		(		_lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     assert get_writer_batch_size(_lowercase ) == expected









"""simple docstring"""


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



