
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      )      ->   List[str]:
   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      )      ->   Optional[int]:
   _A:  List[Any]					=       tmp_path / '''cache'''
   _A:  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():
      _A:  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      )      ->   Tuple:
   _A:  str					=       tmp_path / '''cache'''
   _A:  Dict					=       {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
   _A:  Any					=       features.copy() if features else default_expected_features
   _A:  Optional[int]					=       (
       Features({feature: Value(a      ) for feature, dtype in features.items()}      ) if features is not None else None
   )
   _A:  List[Any]					=       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      )      ->   List[Any]:
   _A:  List[Any]					=       tmp_path / '''cache'''
   _A:  Optional[Any]					=       {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
   _A:  Union[str, Any]					=       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      )      ->   Tuple:
   if issubclass(a		,		a      ):
      _A:  Dict					=       parquet_path
   elif issubclass(a		,		a      ):
      _A:  Optional[Any]					=       [parquet_path]
   _A:  Tuple					=       tmp_path / '''cache'''
   _A:  Tuple					=       {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
   _A:  Tuple					=       ParquetDatasetReader(a		,		cache_dir=a      ).read()
   _check_parquet_dataset(a		,		a      )






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






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

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

   _A:  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      )      ->   Dict:
   assert get_writer_batch_size(a      ) == expected





import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__		:     Any     			=      3






def   lowerCamelCase__							(					a      )      ->   int:
   print('''Generating primitive root of p'''      )
   while True:
      _A:  Union[str, Any]					=       random.randrange(3		,		a      )
      if pow(a		,		2		,		a      ) == 1:
         continue
      if pow(a		,		a		,		a      ) == 1:
         continue
      return g






def   lowerCamelCase__							(					a      )      ->   tuple[tuple[int, int, int, int], tuple[int, int]]:
   print('''Generating prime p...'''      )
   _A:  Dict					=       rabin_miller.generate_large_prime(a      )  # select large prime number.
   _A:  Any					=       primitive_root(a      )  # one primitive root on modulo p.
   _A:  Optional[Any]					=       random.randrange(3		,		a      )  # private_key -> have to be greater than 2 for safety.
   _A:  Dict					=       cryptomath.find_mod_inverse(pow(a		,		a		,		a      )		,		a      )

   _A:  Union[str, Any]					=       (key_size, e_a, e_a, p)
   _A:  Union[str, Any]					=       (key_size, d)

   return public_key, private_key






def   lowerCamelCase__							(					a		,		a      )      ->   None:
   if os.path.exists(f"""{name}_pubkey.txt"""      ) or os.path.exists(f"""{name}_privkey.txt"""      ):
      print('''\nWARNING:'''      )
      print(
          f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
          '''Use a different name or delete these files and re-run this program.'''      )
      sys.exit()

   _A			,  _A:  Any					=       generate_key(a      )
   print(f"""\nWriting public key to file {name}_pubkey.txt..."""      )
   with open(f"""{name}_pubkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""      )

   print(f"""Writing private key to file {name}_privkey.txt..."""      )
   with open(f"""{name}_privkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{private_key[0]},{private_key[1]}"""      )






def   lowerCamelCase__							(					)      ->   None:
   print('''Making key files...'''      )
   make_key_files('''elgamal'''		,		20_48      )
   print('''Key files generation successful'''      )


if __name__ == "__main__":
   main()



