





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  UpperCamelCase__     (   _A:    str							,    _A:    Union[str, Any]				):




      '''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  UpperCamelCase__     (   _A:    Optional[Any]							,    _A:    Tuple							,    _A:    List[str]				):




      '''simple docstring'''







      __lowerCamelCase				   =       tmp_path / """cache"""
      __lowerCamelCase				   =       {"""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				   =       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  UpperCamelCase__     (   _A:    Optional[int]							,    _A:    Optional[int]							,    _A:    Optional[Any]				):




      '''simple docstring'''







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



@pytest.mark.parametrize("""split"""							,    [None, NamedSplit("""train"""				), """train""", """test"""]				)
def  UpperCamelCase__     (   _A:    Tuple							,    _A:    Any							,    _A:    Any				):




      '''simple docstring'''







      __lowerCamelCase				   =       tmp_path / """cache"""
      __lowerCamelCase				   =       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      __lowerCamelCase				   =       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  UpperCamelCase__     (   _A:    Optional[Any]							,    _A:    Tuple							,    _A:    List[str]				):




      '''simple docstring'''







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



def  UpperCamelCase__     (   _A:    Any							,    _A:    Any							,    _A:    Any=("train",)				):




      '''simple docstring'''







      assert isinstance(__lowerCAmelCase							,    __lowerCAmelCase				)
      for split in splits:
            __lowerCamelCase				   =       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  UpperCamelCase__     (   _A:    Tuple							,    _A:    Union[str, Any]							,    _A:    Dict				):




      '''simple docstring'''







      __lowerCamelCase				   =       tmp_path / """cache"""
      __lowerCamelCase				   =       {"""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				   =       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  UpperCamelCase__     (   _A:    str							,    _A:    Any							,    _A:    Optional[Any]				):




      '''simple docstring'''







      __lowerCamelCase				   =       tmp_path / """cache"""
      __lowerCamelCase				   =       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      __lowerCamelCase				   =       features.copy() if features else default_expected_features
      __lowerCamelCase				   =       (
          Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      __lowerCamelCase				   =       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  UpperCamelCase__     (   _A:    Optional[int]							,    _A:    Union[str, Any]							,    _A:    Union[str, Any]				):




      '''simple docstring'''







      if split:
            __lowerCamelCase				   =       {split: parquet_path}
      else:
            __lowerCamelCase				   =       """train"""
            __lowerCamelCase				   =       {"""train""": parquet_path, """test""": parquet_path}
      __lowerCamelCase				   =       tmp_path / """cache"""
      __lowerCamelCase				   =       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      __lowerCamelCase				   =       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  UpperCamelCase__     (   _A:    List[Any]							,    _A:    Optional[int]				):




      '''simple docstring'''







      __lowerCamelCase				   =       ParquetDatasetWriter(__lowerCAmelCase							,    tmp_path / """foo.parquet"""				)
      assert writer.write() > 0
      __lowerCamelCase				   =       pq.ParquetFile(tmp_path / """foo.parquet"""				)
      __lowerCamelCase				   =       pf.read()
      assert dataset.data.table == output_table



def  UpperCamelCase__     (   _A:    Dict							,    _A:    Tuple				):




      '''simple docstring'''







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

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

      __lowerCamelCase				   =       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  UpperCamelCase__     (   _A:    Optional[int]							,    _A:    str				):




      '''simple docstring'''







      assert get_writer_batch_size(__lowerCAmelCase				) == expected












from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__magic_name__  :  Any					=       TypeVar("""KEY""")
__magic_name__  :  Dict					=       TypeVar("""VAL""")





@dataclass(frozen=_a    ,					slots=_a			)
class     SCREAMING_SNAKE_CASE__		(Generic[KEY, VAL]			):
     lowercase_ :      KEY
     lowercase_ :      VAL





class     SCREAMING_SNAKE_CASE__		(_Item			):
     def __init__(						self	:		Tuple						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						__lowerCamelCase						)



     def __bool__(						self	:		Tuple						):



       """simple docstring"""
       return False



__magic_name__  :  str					=       _DeletedItem()





class     SCREAMING_SNAKE_CASE__		(MutableMapping[KEY, VAL]			):
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		int = 8       ,						__lowerCamelCase	:		float = 0.75						):



       """simple docstring"""
       lowerCAmelCase__										=      initial_block_size
       lowerCAmelCase__										=      [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCAmelCase__										=      capacity_factor
       lowerCAmelCase__										=      0
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       return hash(__lowerCamelCase						) % len(self._buckets						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       return (ind + 1) % len(self._buckets						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets[ind]
       if not stored:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         self._len += 1
         return True
       elif stored.key == key:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         return True
       else:
         return False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if len(self._buckets						) <= self._initial_block_size:
         return False
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets
       lowerCAmelCase__										=      [None] * new_size
       lowerCAmelCase__										=      0
       for item in old_buckets:
         if item:
           self._add_item(item.key       ,						item.val						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self._resize(len(self._buckets						) * 2						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self._resize(len(self._buckets						) // 2						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       lowerCAmelCase__										=      self._get_bucket_index(__lowerCamelCase						)
       for _ in range(len(self._buckets						)						):
         yield ind
         lowerCAmelCase__										=      self._get_next_ind(__lowerCamelCase						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         if self._try_set(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						):
           break
     def __setitem__(						self	:		str       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       if self._is_full():
         self._size_up()

       self._add_item(__lowerCamelCase       ,						__lowerCamelCase						)
     def __delitem__(						self	:		Tuple       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           raise KeyError(__lowerCamelCase						)
         if item is _deleted:
           continue
         if item.key == key:
           lowerCAmelCase__										=      _deleted
           self._len -= 1
           break
       if self._is_sparse():
         self._size_down()
     def __getitem__(						self	:		Optional[int]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           break
         if item is _deleted:
           continue
         if item.key == key:
           return item.val
       raise KeyError(__lowerCamelCase						)
     def __len__(						self	:		int						):



       """simple docstring"""
       return self._len
     def __iter__(						self	:		Union[str, Any]						):



       """simple docstring"""
       yield from (item.key for item in self._buckets if item)



     def __repr__(						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''' ,'''.join(
           F"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return F"""HashMap({val_string})"""

