"""simple docstring"""




import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

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

import datasets
import datasets.config





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			)       ->      Tuple:
  _lowerCAmelCase						:       List[Any]  			=     10
  _lowerCAmelCase						:       List[str]  			=     datasets.Features(
      {
          """tokens""": datasets.Sequence(datasets.Value("""string"""	)	),
          """labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""]	)	),
          """answers""": datasets.Sequence(
              {
                  """text""": datasets.Value("""string"""	),
                  """answer_start""": datasets.Value("""int32"""	),
              }	),
          """id""": datasets.Value("""int64"""	),
      }	)
  _lowerCAmelCase						:       Optional[Any]  			=     datasets.Dataset.from_dict(
      {
          """tokens""": [["""foo"""] * 5] * n,
          """labels""": [[1] * 5] * n,
          """answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
          """id""": list(range(_lowerCamelCase	)	),
      }			,features=_lowerCamelCase			,)
  return dataset





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int	)       ->      List[Any]:
  _lowerCAmelCase						:       Tuple  			=     str(tmp_path_factory.mktemp("""data"""	) / """file.arrow"""	)
  dataset.map(cache_file_name=_lowerCamelCase	)
  return filename


# FILE_CONTENT + files


_a      :		List[str]           =   '\\n    Text data.\n    Second line of data.'





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Optional[Any]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt"""
  _lowerCAmelCase						:       Union[str, Any]  			=     FILE_CONTENT
  with open(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase	)
  return filename





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      Any:
  import bza

  _lowerCAmelCase						:       Optional[int]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.bz2"""
  _lowerCAmelCase						:       Optional[Any]  			=     bytes(_lowerCamelCase			,"""utf-8"""	)
  with bza.open(_lowerCamelCase			,"""wb"""	) as f:
    f.write(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple	)       ->      Optional[Any]:
  import gzip

  _lowerCAmelCase						:       Dict  			=     str(tmp_path_factory.mktemp("""data"""	) / """file.txt.gz"""	)
  _lowerCAmelCase						:       Dict  			=     bytes(_lowerCamelCase			,"""utf-8"""	)
  with gzip.open(_lowerCamelCase			,"""wb"""	) as f:
    f.write(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict	)       ->      int:
  if datasets.config.LZ4_AVAILABLE:
    import lza.frame

    _lowerCAmelCase						:       List[str]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.lz4"""
    _lowerCAmelCase						:       str  			=     bytes(_lowerCamelCase			,"""utf-8"""	)
    with lza.frame.open(_lowerCamelCase			,"""wb"""	) as f:
      f.write(_lowerCamelCase	)
    return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[str]	)       ->      int:
  if datasets.config.PY7ZR_AVAILABLE:
    import pyazr

    _lowerCAmelCase						:       Dict  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.7z"""
    with pyazr.SevenZipFile(_lowerCamelCase			,"""w"""	) as archive:
      archive.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Dict	)       ->      int:
  import tarfile

  _lowerCAmelCase						:       List[str]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.tar"""
  with tarfile.TarFile(_lowerCamelCase			,"""w"""	) as f:
    f.add(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Tuple:
  import lzma

  _lowerCAmelCase						:       Union[str, Any]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.xz"""
  _lowerCAmelCase						:       Optional[int]  			=     bytes(_lowerCamelCase			,"""utf-8"""	)
  with lzma.open(_lowerCamelCase			,"""wb"""	) as f:
    f.write(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       str	)       ->      Tuple:
  import zipfile

  _lowerCAmelCase						:       List[str]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      List[str]:
  if datasets.config.ZSTANDARD_AVAILABLE:
    import zstandard as zstd

    _lowerCAmelCase						:       List[Any]  			=     tmp_path_factory.mktemp("""data"""	) / """file.txt.zst"""
    _lowerCAmelCase						:       Tuple  			=     bytes(_lowerCamelCase			,"""utf-8"""	)
    with zstd.open(_lowerCamelCase			,"""wb"""	) as f:
      f.write(_lowerCamelCase	)
    return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      Any:
  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """file.xml"""
  _lowerCAmelCase						:       Any  			=     textwrap.dedent(
      """\
    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>
    <tmx version=\"1.4\">
      <header segtype=\"sentence\" srclang=\"ca\" />
      <body>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
        </tu>
      </body>
    </tmx>"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase	)
  return filename


_a      :		Union[str, Any]           =   [
    {'col_1': '0', 'col_2': 0, 'col_3': 0.0},
    {'col_1': '1', 'col_2': 1, 'col_3': 1.0},
    {'col_1': '2', 'col_2': 2, 'col_3': 2.0},
    {'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
_a      :		Any           =   [
    {'col_1': '4', 'col_2': 4, 'col_3': 4.0},
    {'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
_a      :		List[Any]           =   {
    'col_1': ['0', '1', '2', '3'],
    'col_2': [0, 1, 2, 3],
    'col_3': [0.0, 1.0, 2.0, 3.0],
}

_a      :		Any           =   [
    {'col_3': 0.0, 'col_1': '0', 'col_2': 0},
    {'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]

_a      :		Optional[Any]           =   [
    {'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
    {'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
    {'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
    {'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  return DATA_DICT_OF_LISTS





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict	)       ->      str:
  _lowerCAmelCase						:       Union[str, Any]  			=     datasets.Dataset.from_dict(_lowerCamelCase	)
  _lowerCAmelCase						:       int  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.arrow"""	)
  dataset.map(cache_file_name=_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]	)       ->      Dict:
  _lowerCAmelCase						:       List[Any]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.sqlite"""	)
  with contextlib.closing(sqlitea.connect(_lowerCamelCase	)	) as con:
    _lowerCAmelCase						:       int  			=     con.cursor()
    cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)"""	)
    for item in DATA:
      cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)"""			,tuple(item.values()	)	)
    con.commit()
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:
  _lowerCAmelCase						:       Optional[Any]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.csv"""	)
  with open(_lowerCamelCase			,"""w"""			,newline=""""""	) as f:
    _lowerCAmelCase						:       str  			=     csv.DictWriter(_lowerCamelCase			,fieldnames=["""col_1""", """col_2""", """col_3"""]	)
    writer.writeheader()
    for item in DATA:
      writer.writerow(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[str]:
  _lowerCAmelCase						:       str  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset2.csv"""	)
  with open(_lowerCamelCase			,"""w"""			,newline=""""""	) as f:
    _lowerCAmelCase						:       Tuple  			=     csv.DictWriter(_lowerCamelCase			,fieldnames=["""col_1""", """col_2""", """col_3"""]	)
    writer.writeheader()
    for item in DATA:
      writer.writerow(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[Any]	)       ->      Optional[int]:
  import bza

  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.csv.bz2"""
  with open(_lowerCamelCase			,"""rb"""	) as f:
    _lowerCAmelCase						:       List[str]  			=     f.read()
  # data = bytes(FILE_CONTENT, "utf-8")
  with bza.open(_lowerCamelCase			,"""wb"""	) as f:
    f.write(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[Any]	)       ->      Dict:
  _lowerCAmelCase						:       Dict  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.csv.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Tuple	)       ->      Tuple:
  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.csv.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(csv_path.replace(""".csv"""			,""".CSV"""	)	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename(csva_path.replace(""".csv"""			,""".CSV"""	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       List[Any]  			=     tmp_path_factory.mktemp("""data"""	) / """dataset_with_dir.csv.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       int  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.parquet"""	)
  _lowerCAmelCase						:       Any  			=     pa.schema(
      {
          """col_1""": pa.string(),
          """col_2""": pa.intaa(),
          """col_3""": pa.floataa(),
      }	)
  with open(_lowerCamelCase			,"""wb"""	) as f:
    _lowerCAmelCase						:       List[str]  			=     pq.ParquetWriter(_lowerCamelCase			,schema=_lowerCamelCase	)
    _lowerCAmelCase						:       Dict  			=     pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_lowerCamelCase	)	)] for k in DATA[0]}			,schema=_lowerCamelCase	)
    writer.write_table(_lowerCamelCase	)
    writer.close()
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Any  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.json"""	)
  _lowerCAmelCase						:       List[Any]  			=     {"""data""": DATA}
  with open(_lowerCamelCase			,"""w"""	) as f:
    json.dump(_lowerCamelCase			,_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Optional[int]:
  _lowerCAmelCase						:       int  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.json"""	)
  _lowerCAmelCase						:       List[str]  			=     {"""data""": DATA_DICT_OF_LISTS}
  with open(_lowerCamelCase			,"""w"""	) as f:
    json.dump(_lowerCamelCase			,_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]	)       ->      Optional[Any]:
  _lowerCAmelCase						:       List[Any]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.jsonl"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in DATA:
      f.write(json.dumps(_lowerCamelCase	) + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      Optional[int]:
  _lowerCAmelCase						:       List[str]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset2.jsonl"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in DATA:
      f.write(json.dumps(_lowerCamelCase	) + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]	)       ->      str:
  _lowerCAmelCase						:       Optional[int]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset_312.jsonl"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in DATA_312:
      f.write(json.dumps(_lowerCamelCase	) + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset-str.jsonl"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in DATA_STR:
      f.write(json.dumps(_lowerCamelCase	) + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       List[Any]	)       ->      int:
  import gzip

  _lowerCAmelCase						:       str  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.txt.gz"""	)
  with open(_lowerCamelCase			,"""rb"""	) as orig_file:
    with gzip.open(_lowerCamelCase			,"""wb"""	) as zipped_file:
      zipped_file.writelines(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[int]	)       ->      Union[str, Any]:
  import gzip

  _lowerCAmelCase						:       str  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.jsonl.gz"""	)
  with open(_lowerCamelCase			,"""rb"""	) as orig_file:
    with gzip.open(_lowerCamelCase			,"""wb"""	) as zipped_file:
      zipped_file.writelines(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[int]	)       ->      Any:
  _lowerCAmelCase						:       Tuple  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.jsonl.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Union[str, Any]	)       ->      List[str]:
  _lowerCAmelCase						:       List[Any]  			=     tmp_path_factory.mktemp("""data"""	) / """dataset_nested.jsonl.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.join("""nested"""			,os.path.basename(_lowerCamelCase	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       str			,_lowerCamelCase						:       List[Any]	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Dict  			=     tmp_path_factory.mktemp("""data"""	) / """dataset_with_dir.jsonl.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[str]	)       ->      Any:
  _lowerCAmelCase						:       str  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.jsonl.tar"""
  with tarfile.TarFile(_lowerCamelCase			,"""w"""	) as f:
    f.add(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    f.add(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[int]	)       ->      str:
  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """dataset_nested.jsonl.tar"""
  with tarfile.TarFile(_lowerCamelCase			,"""w"""	) as f:
    f.add(_lowerCamelCase			,arcname=os.path.join("""nested"""			,os.path.basename(_lowerCamelCase	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]	)       ->      Optional[Any]:
  _lowerCAmelCase						:       Any  			=     ["""0""", """1""", """2""", """3"""]
  _lowerCAmelCase						:       Any  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset.txt"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in data:
      f.write(item + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       List[Any]  			=     ["""0""", """1""", """2""", """3"""]
  _lowerCAmelCase						:       Optional[Any]  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset2.txt"""	)
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in data:
      f.write(item + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]	)       ->      Tuple:
  _lowerCAmelCase						:       int  			=     ["""0""", """1""", """2""", """3"""]
  _lowerCAmelCase						:       Tuple  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.abc"""
  with open(_lowerCamelCase			,"""w"""	) as f:
    for item in data:
      f.write(item + """\n"""	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       str	)       ->      Any:
  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.text.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       List[Any]	)       ->      List[str]:
  _lowerCAmelCase						:       Any  			=     tmp_path_factory.mktemp("""data"""	) / """dataset_with_dir.text.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
    f.write(_lowerCamelCase			,arcname=os.path.join("""main_dir"""			,os.path.basename(_lowerCamelCase	)	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      Dict:
  _lowerCAmelCase						:       Optional[int]  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.ext.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename("""unsupported.ext"""	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename("""unsupported_2.ext"""	)	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Tuple:
  _lowerCAmelCase						:       List[str]  			=     """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]	)
  _lowerCAmelCase						:       Tuple  			=     str(tmp_path_factory.mktemp("""data"""	) / """dataset_with_unicode_new_lines.txt"""	)
  with open(_lowerCamelCase			,"""w"""			,encoding="""utf-8"""	) as f:
    f.write(_lowerCamelCase	)
  return path





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			)       ->      Dict:
  return os.path.join("""tests"""			,"""features"""			,"""data"""			,"""test_image_rgb.jpg"""	)





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			)       ->      Optional[Any]:
  return os.path.join("""tests"""			,"""features"""			,"""data"""			,"""test_audio_44100.wav"""	)





@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Optional[int]	)       ->      Dict:
  _lowerCAmelCase						:       Tuple  			=     tmp_path_factory.mktemp("""data"""	) / """dataset.img.zip"""
  with zipfile.ZipFile(_lowerCamelCase			,"""w"""	) as f:
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	)	)
    f.write(_lowerCamelCase			,arcname=os.path.basename(_lowerCamelCase	).replace(""".jpg"""			,"""2.jpg"""	)	)
  return path






@pytest.fixture(scope="""session"""	)
def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      Optional[int]:
  _lowerCAmelCase						:       Tuple  			=     tmp_path_factory.mktemp("""data_dir"""	)

  (data_dir / "subdir").mkdir()
  with open(data_dir / """subdir""" / """train.txt"""			,"""w"""	) as f:
    f.write("""foo\n""" * 10	)
  with open(data_dir / """subdir""" / """test.txt"""			,"""w"""	) as f:
    f.write("""bar\n""" * 10	)
  # hidden file
  with open(data_dir / """subdir""" / """.test.txt"""			,"""w"""	) as f:
    f.write("""bar\n""" * 10	)

  # hidden directory
  (data_dir / ".subdir").mkdir()
  with open(data_dir / """.subdir""" / """train.txt"""			,"""w"""	) as f:
    f.write("""foo\n""" * 10	)
  with open(data_dir / """.subdir""" / """test.txt"""			,"""w"""	) as f:
    f.write("""bar\n""" * 10	)

  return data_dir





"""simple docstring"""




import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=True				,     a__=True				,     a__=True				,     a__=99				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=16				,     a__=2				,     a__=0.0_2				,     a__=False				,     a__=True				,     a__="None"				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       Dict  			=     parent
       _lowerCAmelCase						:       str  			=     batch_size
       _lowerCAmelCase						:       List[Any]  			=     seq_length
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       Dict  			=     use_input_mask
       _lowerCAmelCase						:       int  			=     use_token_type_ids
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Optional[int]  			=     vocab_size
       _lowerCAmelCase						:       Tuple  			=     hidden_size
       _lowerCAmelCase						:       Tuple  			=     num_hidden_layers
       _lowerCAmelCase						:       Dict  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     intermediate_size
       _lowerCAmelCase						:       str  			=     hidden_act
       _lowerCAmelCase						:       Any  			=     hidden_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     type_vocab_size
       _lowerCAmelCase						:       Tuple  			=     type_sequence_label_size
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     num_choices
       _lowerCAmelCase						:       Tuple  			=     relative_attention
       _lowerCAmelCase						:       Tuple  			=     position_biased_input
       _lowerCAmelCase						:       Dict  			=     pos_att_type
       _lowerCAmelCase						:       Any  			=     scope






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

       _lowerCAmelCase						:       Optional[Any]  			=     None
       if self.use_input_mask:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

       _lowerCAmelCase						:       str  			=     None
       if self.use_token_type_ids:
         _lowerCAmelCase						:       List[str]  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size  )

       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Any  			=     None
       if self.use_labels:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.type_sequence_label_size  )
         _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels  )
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.num_choices  )

       _lowerCAmelCase						:       List[str]  			=     self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






     def     __A  (		self  ):
       return DebertaVaConfig(
           vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     relative_attention=self.relative_attention				,     position_biased_input=self.position_biased_input				,     pos_att_type=self.pos_att_type				,     )






     def     __A  (		self				,     a__  ):
       self.parent.assertListEqual(list(result.loss.size()  )				,     []  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       Any  			=     model(a__  )[0]

       self.parent.assertListEqual(list(sequence_output.size()  )				,     [self.batch_size, self.seq_length, self.hidden_size]  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     DebertaVaForMaskedLM(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )

       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     self.num_labels
       _lowerCAmelCase						:       int  			=     DebertaVaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )
       self.parent.assertListEqual(list(result.logits.size()  )				,     [self.batch_size, self.num_labels]  )
       self.check_loss_output(a__  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.num_labels
       _lowerCAmelCase						:       str  			=     DebertaVaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     DebertaVaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     start_positions=a__				,     end_positions=a__				,     )
       self.parent.assertEqual(result.start_logits.shape				,     (self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape				,     (self.batch_size, self.seq_length)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaForMultipleChoice(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     input_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       Optional[Any]  			=     token_type_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       str  			=     input_mask.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       List[str]  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__				,     )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       Union[str, Any]  			=     config_and_inputs
       _lowerCAmelCase						:       Union[str, Any]  			=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Union[str, Any]							=		(
         (
             DebertaVaModel,
             DebertaVaForMaskedLM,
             DebertaVaForSequenceClassification,
             DebertaVaForTokenClassification,
             DebertaVaForQuestionAnswering,
             DebertaVaForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			str							=		(
         {
             "feature-extraction": DebertaVaModel,
             "fill-mask": DebertaVaForMaskedLM,
             "question-answering": DebertaVaForQuestionAnswering,
             "text-classification": DebertaVaForSequenceClassification,
             "token-classification": DebertaVaForTokenClassification,
             "zero-shot": DebertaVaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:			Optional[Any]							=		True
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			Tuple							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     DebertaVaModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Tuple  			=     DebertaVaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__A							( unittest.TestCase		):






     @unittest.skip(reason="""Model not available yet"""  )
     def     __A  (		self  ):
       pass






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""  )

       _lowerCAmelCase						:       Dict  			=     torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
       _lowerCAmelCase						:       Any  			=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__  )[0]
       # compare the actual values for a slice.
       _lowerCAmelCase						:       str  			=     torch.tensor(
           [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]  )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,     a__				,     atol=1e-4  )				,     F"{output[:, 1:4, 1:4]}"  )




