import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Optional[int]:  # picklable for multiprocessing







  """simple docstring"""





  return i + 1



@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  with parallel_backend("""spark"""			):
    assert ParallelBackendConfig.backend_name == "spark"

  SCREAMING_SNAKE_CASE__    							=   [1, 2, 3]
  with pytest.raises(__UpperCamelCase			):
    with parallel_backend("""unsupported backend"""			):
      map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=2			)

  with pytest.raises(__UpperCamelCase			):
    with parallel_backend("""unsupported backend"""			):
      map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=-1			)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""				,  [2, -1]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1, 2]
  SCREAMING_SNAKE_CASE__    							=   {"""a""": 1, """b""": 2}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": [1, 2], """b""": [3, 4]}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": {"""1""": 1}, """b""": 2}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
  SCREAMING_SNAKE_CASE__    							=   [2, 3]
  SCREAMING_SNAKE_CASE__    							=   {"""a""": 2, """b""": 3}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": [2, 3], """b""": [4, 5]}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": {"""1""": 2}, """b""": 3}
  SCREAMING_SNAKE_CASE__    							=   {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

  with parallel_backend("""spark"""			):
    assert map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=__UpperCamelCase			) == expected_map_nested_sa
    assert map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=__UpperCamelCase			) == expected_map_nested_sa
    assert map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=__UpperCamelCase			) == expected_map_nested_sa
    assert map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=__UpperCamelCase			) == expected_map_nested_sa
    assert map_nested(__UpperCamelCase				,  __UpperCamelCase				,  num_proc=__UpperCamelCase			) == expected_map_nested_sa

import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3]   ) ,			type=pa.intaa()   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""bool"""   ) ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=Value("""int64"""   )   )   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([1, 2, 3] ,			try_type=Value("""int32"""   )   )   )
     self.assertEqual(arr.type ,			pa.intaa()   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			try_type=Value("""int64"""   )   )   )
     self.assertEqual(arr.type ,			pa.string()   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			type=ArrayaD((1, 3) ,			"""int64"""   )   )   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([[[1, 2, 3]]] ,			try_type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			ArrayaDExtensionType((1, 3) ,			"""int64"""   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(["""foo""", """bar"""] ,			try_type=ArrayaD((1, 3) ,			"""int64"""   )   )   )
     self.assertEqual(arr.type ,			pa.string()   )




   @require_pil
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     import PIL.Image

     SCREAMING_SNAKE_CASE__    							=   PIL.Image.fromarray(np.arange(10 ,			dtype=np.uinta   ).reshape(2 ,			5   )   )
     with patch(
         """datasets.arrow_writer.cast_to_python_objects""" ,			side_effect=_lowercase   ) as mock_cast_to_python_objects:
       SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] ,			type=Image()   )   )
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   mock_cast_to_python_objects.call_args_list[-1]
       self.assertIn("""optimize_list_casting""" ,			_lowercase   )
       self.assertFalse(kwargs["""optimize_list_casting"""]   )





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: int			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(__UpperCamelCase			) if isinstance(__UpperCamelCase				,  pa.Buffer			) else pa.memory_map(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   f.read_all()
  assert len(pa_table.to_batches()			) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: int			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]			)}			)
  with ArrowWriter(stream=__UpperCamelCase				,  features=__UpperCamelCase			) as writer:
    writer.write({"""labels""": 0}			)
    writer.write({"""labels""": 1}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pa.ipc.open_stream(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   f.read_all()
  SCREAMING_SNAKE_CASE__    							=   pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(__UpperCamelCase			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    with pytest.raises(__UpperCamelCase			):
      writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=[1, 2]			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 2, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    with pytest.raises(__UpperCamelCase			):
      writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=10			)
      writer.write({"""col_1""": """bar""", """col_2""": 2}				,  key=10			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 2, 10]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(
      stream=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase				,  hash_salt="""split_name"""				,  check_duplicates=__UpperCamelCase				,  ) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}				,  key=1			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}				,  key=2			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)
    writer.write_batch({"""col_1""": [], """col_2""": []}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: Union[str, Any]			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



@pytest.mark.parametrize("""writer_batch_size"""				,  [None, 1, 10]			)
@pytest.mark.parametrize(
    """fields"""				,  [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  SCREAMING_SNAKE_CASE__    							=   pa.schema(__UpperCamelCase			) if fields else None
  with ArrowWriter(stream=__UpperCamelCase				,  schema=__UpperCamelCase				,  writer_batch_size=__UpperCamelCase			) as writer:
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}			)			)
    writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}			)			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
  _check_output(output.getvalue()				,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__    							=   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  """test.arrow"""			)
    with ArrowWriter(path=__UpperCamelCase				,  schema=pa.schema(__UpperCamelCase			)			) as writer:
      writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(__UpperCamelCase				,  metadata=writer._schema.metadata			)
    _check_output(__UpperCamelCase				,  1			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      Tuple:







  """simple docstring"""





  if pa.types.is_list(__UpperCamelCase			):
    return get_base_dtype(arr_type.value_type			)
  else:
    return arr_type



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Union[str, Any]			)							->      Tuple:







  """simple docstring"""





  if isinstance(lst[0]				,  __UpperCamelCase			):
    change_first_primitive_element_in_list(lst[0]				,  __UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   value



@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""				,  [(None, pa.intaa()), (Value("""int32"""			), pa.intaa())]			)
@pytest.mark.parametrize("""sequence"""				,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Dict			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(TypedSequence(__UpperCamelCase				,  optimized_int_type=__UpperCamelCase			)			)
  assert get_base_dtype(arr.type			) == expected_dtype



@pytest.mark.parametrize(
    """col, expected_dtype"""				,  [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]				,  )
@pytest.mark.parametrize("""sequence"""				,  [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: List[str]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.array(OptimizedTypedSequence(__UpperCamelCase				,  col=__UpperCamelCase			)			)
  assert get_base_dtype(arr.type			) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.iinfo(expected_dtype.to_pandas_dtype()			).max + 1
    change_first_primitive_element_in_list(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   pa.array(OptimizedTypedSequence(__UpperCamelCase				,  col=__UpperCamelCase			)			)
    assert get_base_dtype(arr.type			) == pa.intaa()



@pytest.mark.parametrize("""raise_exception"""				,  [False, True]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   str(tmp_path / """dataset-train.arrow"""			)
  try:
    with ArrowWriter(path=__UpperCamelCase			) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """mock://dataset-train.arrow"""
  with ArrowWriter(path=__UpperCamelCase				,  storage_options=mockfs.storage_options			) as writer:
    assert isinstance(writer._fs				,  type(__UpperCamelCase			)			)
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(__UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ParquetWriter(stream=__UpperCamelCase			) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}			)
    writer.write({"""col_1""": """bar""", """col_2""": 2}			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pq.read_table(__UpperCamelCase			)
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("""embed_local_files"""				,  [False, True]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Tuple			)							->      Optional[Any]:







  """simple docstring"""





  import PIL.Image

  SCREAMING_SNAKE_CASE__    							=   str(tmp_path / """test_image_rgb.jpg"""			)
  PIL.Image.fromarray(np.zeros((5, 5)				,  dtype=np.uinta			)			).save(__UpperCamelCase				,  format="""png"""			)
  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ParquetWriter(
      stream=__UpperCamelCase				,  features=Features({"""image""": Image()}			)				,  embed_local_files=__UpperCamelCase			) as writer:
    writer.write({"""image""": image_path}			)
    writer.finalize()
  SCREAMING_SNAKE_CASE__    							=   pa.BufferReader(output.getvalue()			)
  SCREAMING_SNAKE_CASE__    							=   pq.read_table(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]				,  __UpperCamelCase			)
    with open(__UpperCamelCase				,  """rb"""			) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None






def 			__SCREAMING_SNAKE_CASE						(      )							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   pa.schema([pa.field("""col_1"""				,  pa.string()				,  nullable=__UpperCamelCase			)]			)
  SCREAMING_SNAKE_CASE__    							=   pa.BufferOutputStream()
  with ArrowWriter(stream=__UpperCamelCase			) as writer:
    writer._build_writer(inferred_schema=__UpperCamelCase			)
    assert writer._schema == pa.schema([pa.field("""col_1"""				,  pa.string()			)]			)
