






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     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Any							)     ->  str:
   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 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  List[str]:
   __UpperCamelCase	:			str					=					tmp_path / """cache"""
   __UpperCamelCase	:			Union[str, 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	:			List[str]					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					List[str]							)     ->  List[Any]:
   __UpperCamelCase	:			Tuple					=					tmp_path / """cache"""
   __UpperCamelCase	:			Optional[int]					=					{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __UpperCamelCase	:			List[str]					=					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	:			List[Any]					=					ParquetDatasetReader(__lowerCAmelCase					,							features=__lowerCAmelCase					,							cache_dir=__lowerCAmelCase							).read()
   _check_parquet_dataset(__lowerCAmelCase					,							__lowerCAmelCase							)


@pytest.mark.parametrize("""split"""					,							[None, NamedSplit("""train"""							), """train""", """test"""]							)
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  int:
   __UpperCamelCase	:			Optional[Any]					=					tmp_path / """cache"""
   __UpperCamelCase	:			List[str]					=					{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __UpperCamelCase	:			Optional[int]					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int							)     ->  Dict:
   if issubclass(__lowerCAmelCase					,							__lowerCAmelCase							):
      __UpperCamelCase	:			Any					=					parquet_path
   elif issubclass(__lowerCAmelCase					,							__lowerCAmelCase							):
      __UpperCamelCase	:			str					=					[parquet_path]
   __UpperCamelCase	:			List[Any]					=					tmp_path / """cache"""
   __UpperCamelCase	:			str					=					{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __UpperCamelCase	:			List[Any]					=					ParquetDatasetReader(__lowerCAmelCase					,							cache_dir=__lowerCAmelCase							).read()
   _check_parquet_dataset(__lowerCAmelCase					,							__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Optional[int]=("train",)							)     ->  List[Any]:
   assert isinstance(__lowerCAmelCase					,							__lowerCAmelCase							)
   for split in splits:
      __UpperCamelCase	:			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 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					int							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Tuple					=					tmp_path / """cache"""
   __UpperCamelCase	:			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():
      __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 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					str							)     ->  str:
   __UpperCamelCase	:			List[Any]					=					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	:			List[str]					=					(
       Features({feature: Value(__lowerCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
   )
   __UpperCamelCase	:			Union[str, Any]					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Dict							)     ->  Tuple:
   if split:
      __UpperCamelCase	:			List[Any]					=					{split: parquet_path}
   else:
      __UpperCamelCase	:			Optional[int]					=					"""train"""
      __UpperCamelCase	:			str					=					{"""train""": parquet_path, """test""": parquet_path}
   __UpperCamelCase	:			Union[str, Any]					=					tmp_path / """cache"""
   __UpperCamelCase	:			Any					=					{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __UpperCamelCase	:			Dict					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					str							)     ->  str:
   __UpperCamelCase	:			Optional[Any]					=					ParquetDatasetWriter(__lowerCAmelCase					,							tmp_path / """foo.parquet"""							)
   assert writer.write() > 0
   __UpperCamelCase	:			List[Any]					=					pq.ParquetFile(tmp_path / """foo.parquet"""							)
   __UpperCamelCase	:			Tuple					=					pf.read()
   assert dataset.data.table == output_table


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

   __UpperCamelCase	:			List[str]					=					Dataset.from_parquet(str(tmp_path / """foo.parquet"""							)							)
   assert dataset.features == reloaded_dataset.features

   __UpperCamelCase	:			Optional[int]					=					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 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   assert get_writer_batch_size(__lowerCAmelCase							) == expected











import re

from filelock import FileLock


try:
       import nltk

       UpperCamelCase    							=      True
except (ImportError, ModuleNotFoundError):
       UpperCamelCase    							=      False

if NLTK_AVAILABLE:
       with FileLock('.lock') as lock:
              nltk.download('punkt', quiet=True)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  str:
   re.sub("""<n>"""					,							""""""					,							__lowerCAmelCase							)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(__lowerCAmelCase							)							)



