





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			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[Any]   =						pa.array(TypedSequence([1, 2, 3]		)		)
         self.assertEqual(arr.type							,				pa.intaa()		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         with self.assertRaises(A		):
           __snake_case:						Union[str, Any]   =						pa.array(TypedSequence([1, 2, 3]		)							,				type=pa.intaa()		)




       def 							UpperCAmelCase__ (  self :    int		):
         with self.assertRaises(A		):
           __snake_case:						List[Any]   =						pa.array(TypedSequence([1, 2, 3]							,				try_type=Value("""bool"""		)							,				type=Value("""int64"""		)		)		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						int   =						pa.array(TypedSequence([1, 2, 3]							,				type=Value("""int32"""		)		)		)
         self.assertEqual(arr.type							,				pa.intaa()		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         with self.assertRaises((TypeError, pa.lib.ArrowInvalid)		):
           __snake_case:						str   =						pa.array(TypedSequence(["""foo""", """bar"""]							,				type=Value("""int64"""		)		)		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Any   =						pa.array(TypedSequence([1, 2, 3]							,				try_type=Value("""int32"""		)		)		)
         self.assertEqual(arr.type							,				pa.intaa()		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Tuple   =						pa.array(TypedSequence(["""foo""", """bar"""]							,				try_type=Value("""int64"""		)		)		)
         self.assertEqual(arr.type							,				pa.string()		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						str   =						pa.array(TypedSequence([[[1, 2, 3]]]							,				type=ArrayaD((1, 3)							,				"""int64"""		)		)		)
         self.assertEqual(arr.type							,				ArrayaDExtensionType((1, 3)							,				"""int64"""		)		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         with self.assertRaises((TypeError, pa.lib.ArrowInvalid)		):
           __snake_case:						List[Any]   =						pa.array(TypedSequence(["""foo""", """bar"""]							,				type=ArrayaD((1, 3)							,				"""int64"""		)		)		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[Any]   =						pa.array(TypedSequence([[[1, 2, 3]]]							,				try_type=ArrayaD((1, 3)							,				"""int64"""		)		)		)
         self.assertEqual(arr.type							,				ArrayaDExtensionType((1, 3)							,				"""int64"""		)		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Tuple   =						pa.array(TypedSequence(["""foo""", """bar"""]							,				try_type=ArrayaD((1, 3)							,				"""int64"""		)		)		)
         self.assertEqual(arr.type							,				pa.string()		)






       @require_pil
       def 							UpperCAmelCase__ (  self :    str		):
         import PIL.Image

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







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						pa.BufferReader(SCREAMING_SNAKE_CASE__) if isinstance(SCREAMING_SNAKE_CASE__					,					pa.Buffer) else pa.memory_map(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple   =						pa.ipc.open_stream(SCREAMING_SNAKE_CASE__)
  __snake_case:						pa.Table   =						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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    int:
  __snake_case:						List[str]   =						pa.BufferOutputStream()
  __snake_case:						Union[str, Any]   =						pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,					schema=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1})
    writer.write({"""col_1""": """bar""", """col_2""": 2})
    __snake_case  ,   __snake_case:						Union[str, Any]   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __snake_case:						Dict   =						{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,					metadata=writer._schema.metadata)
  _check_output(output.getvalue()					,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1)







def 			A__							(    )   ->    Union[str, Any]:
  __snake_case:						Union[str, Any]   =						pa.BufferOutputStream()
  __snake_case:						Tuple   =						Features({"""labels""": ClassLabel(names=["""neg""", """pos"""])})
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,					features=SCREAMING_SNAKE_CASE__) as writer:
    writer.write({"""labels""": 0})
    writer.write({"""labels""": 1})
    __snake_case  ,   __snake_case:						List[str]   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  __snake_case:						Optional[Any]   =						pa.BufferReader(output.getvalue())
  __snake_case:						Optional[int]   =						pa.ipc.open_stream(SCREAMING_SNAKE_CASE__)
  __snake_case:						pa.Table   =						f.read_all()
  __snake_case:						Tuple   =						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(SCREAMING_SNAKE_CASE__)







@pytest.mark.parametrize("""writer_batch_size"""					,					[None, 1, 10])
def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[str]   =						pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__					,					hash_salt="""split_name"""					,					check_duplicates=SCREAMING_SNAKE_CASE__					,					) as writer:
    with pytest.raises(SCREAMING_SNAKE_CASE__):
      writer.write({"""col_1""": """foo""", """col_2""": 1}					,					key=[1, 2])
      __snake_case  ,   __snake_case:						List[str]   =						writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""					,					[None, 2, 10])
def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Union[str, Any]   =						pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__					,					hash_salt="""split_name"""					,					check_duplicates=SCREAMING_SNAKE_CASE__					,					) as writer:
    with pytest.raises(SCREAMING_SNAKE_CASE__):
      writer.write({"""col_1""": """foo""", """col_2""": 1}					,					key=10)
      writer.write({"""col_1""": """bar""", """col_2""": 2}					,					key=10)
      __snake_case  ,   __snake_case:						Tuple   =						writer.finalize()







@pytest.mark.parametrize("""writer_batch_size"""					,					[None, 2, 10])
def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  __snake_case:						str   =						pa.BufferOutputStream()
  with ArrowWriter(
      stream=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__					,					hash_salt="""split_name"""					,					check_duplicates=SCREAMING_SNAKE_CASE__					,					) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1}					,					key=1)
    writer.write({"""col_1""": """bar""", """col_2""": 2}					,					key=2)
    __snake_case  ,   __snake_case:						Any   =						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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  __snake_case:						Tuple   =						pa.BufferOutputStream()
  __snake_case:						List[str]   =						pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,					schema=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
    writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]})
    writer.write_batch({"""col_1""": [], """col_2""": []})
    __snake_case  ,   __snake_case:						Optional[Any]   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __snake_case:						Optional[int]   =						{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,					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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[str]:
  __snake_case:						Tuple   =						pa.BufferOutputStream()
  __snake_case:						Optional[int]   =						pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,					schema=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
    writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}))
    __snake_case  ,   __snake_case:						str   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __snake_case:						Any   =						{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,					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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						int   =						pa.BufferOutputStream()
  __snake_case:						Optional[int]   =						pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__					,					schema=SCREAMING_SNAKE_CASE__					,					writer_batch_size=SCREAMING_SNAKE_CASE__) 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]}))
    __snake_case  ,   __snake_case:						Union[str, Any]   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    __snake_case:						Tuple   =						{"""col_1""": pa.string(), """col_2""": pa.intaa()}
  assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__					,					metadata=writer._schema.metadata)
  _check_output(output.getvalue()					,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1)







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







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  if pa.types.is_list(SCREAMING_SNAKE_CASE__):
    return get_base_dtype(arr_type.value_type)
  else:
    return arr_type







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    int:
  if isinstance(lst[0]					,					SCREAMING_SNAKE_CASE__):
    change_first_primitive_element_in_list(lst[0]					,					SCREAMING_SNAKE_CASE__)
  else:
    __snake_case:						Optional[Any]   =						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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    str:
  __snake_case:						str   =						pa.array(TypedSequence(SCREAMING_SNAKE_CASE__					,					optimized_int_type=SCREAMING_SNAKE_CASE__))
  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 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  # in range
  __snake_case:						Optional[int]   =						pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE__					,					col=SCREAMING_SNAKE_CASE__))
  assert get_base_dtype(arr.type) == expected_dtype

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







@pytest.mark.parametrize("""raise_exception"""					,					[False, True])
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    List[str]:
  __snake_case:						int   =						str(tmp_path / """dataset-train.arrow""")
  try:
    with ArrowWriter(path=SCREAMING_SNAKE_CASE__) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed







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







def 			A__							(    )   ->    Optional[int]:
  __snake_case:						List[str]   =						pa.BufferOutputStream()
  with ParquetWriter(stream=SCREAMING_SNAKE_CASE__) as writer:
    writer.write({"""col_1""": """foo""", """col_2""": 1})
    writer.write({"""col_1""": """bar""", """col_2""": 2})
    __snake_case  ,   __snake_case:						str   =						writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  __snake_case:						List[Any]   =						pa.BufferReader(output.getvalue())
  __snake_case:						pa.Table   =						pq.read_table(SCREAMING_SNAKE_CASE__)
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("""embed_local_files"""					,					[False, True])
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    str:
  import PIL.Image

  __snake_case:						Any   =						str(tmp_path / """test_image_rgb.jpg""")
  PIL.Image.fromarray(np.zeros((5, 5)					,					dtype=np.uinta)).save(SCREAMING_SNAKE_CASE__					,					format="""png""")
  __snake_case:						List[str]   =						pa.BufferOutputStream()
  with ParquetWriter(
      stream=SCREAMING_SNAKE_CASE__					,					features=Features({"""image""": Image()})					,					embed_local_files=SCREAMING_SNAKE_CASE__) as writer:
    writer.write({"""image""": image_path})
    writer.finalize()
  __snake_case:						Tuple   =						pa.BufferReader(output.getvalue())
  __snake_case:						pa.Table   =						pq.read_table(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple   =						pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out["""image"""][0]["""path"""]					,					SCREAMING_SNAKE_CASE__)
    with open(SCREAMING_SNAKE_CASE__					,					"""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 			A__							(    )   ->    Union[str, Any]:
  __snake_case:						Optional[int]   =						pa.schema([pa.field("""col_1"""					,					pa.string()					,					nullable=SCREAMING_SNAKE_CASE__)])
  __snake_case:						Tuple   =						pa.BufferOutputStream()
  with ArrowWriter(stream=SCREAMING_SNAKE_CASE__) as writer:
    writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE__)
    assert writer._schema == pa.schema([pa.field("""col_1"""					,					pa.string())])













import pytest


__UpperCAmelCase       :   Optional[Any]				       =							"__dummy_dataset1__"

__UpperCAmelCase       :   List[str]				       =							"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"







@pytest.fixture
def 			A__							(    )   ->    Optional[int]:
  return DATASET_LOADING_SCRIPT_NAME







@pytest.fixture
def 			A__							(    )   ->    Tuple:
  return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  __snake_case:						List[Any]   =						dataset_loading_script_name
  __snake_case:						Any   =						tmp_path / """datasets""" / script_name
  script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
  __snake_case:						int   =						script_dir / F'''{script_name}.py'''
  with open(SCREAMING_SNAKE_CASE__					,					"""w""") as f:
    f.write(SCREAMING_SNAKE_CASE__)
  return str(SCREAMING_SNAKE_CASE__)






