
"""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  _UpperCamelCase	(						)    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   1_0
    _UpperCAmelCase          =   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"""      ),
        }      )
    _UpperCAmelCase          =   datasets.Dataset.from_dict(
        {
            """tokens""": [["""foo"""] * 5] * n,
            """labels""": [[1] * 5] * n,
            """answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
            """id""": list(range(_A      )      ),
        }					,      features=_A					,      )
    return dataset


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """file.arrow"""      )
    dataset.map(cache_file_name=_A      )
    return filename


# FILE_CONTENT + files


a					:	Dict							=		'''\
    Text data.
    Second line of data.'''


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt"""
    _UpperCAmelCase          =   FILE_CONTENT
    with open(_A					,      """w"""      ) as f:
        f.write(_A      )
    return filename


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    import bza

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.bz2"""
    _UpperCAmelCase          =   bytes(_A					,      """utf-8"""      )
    with bza.open(_A					,      """wb"""      ) as f:
        f.write(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    import gzip

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


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    if datasets.config.LZ4_AVAILABLE:
        import lza.frame

        _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.lz4"""
        _UpperCAmelCase          =   bytes(_A					,      """utf-8"""      )
        with lza.frame.open(_A					,      """wb"""      ) as f:
            f.write(_A      )
        return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    if datasets.config.PY7ZR_AVAILABLE:
        import pyazr

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


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    import tarfile

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.tar"""
    with tarfile.TarFile(_A					,      """w"""      ) as f:
        f.add(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    import lzma

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.xz"""
    _UpperCAmelCase          =   bytes(_A					,      """utf-8"""      )
    with lzma.open(_A					,      """wb"""      ) as f:
        f.write(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    import zipfile

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    if datasets.config.ZSTANDARD_AVAILABLE:
        import zstandard as zstd

        _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.txt.zst"""
        _UpperCAmelCase          =   bytes(_A					,      """utf-8"""      )
        with zstd.open(_A					,      """wb"""      ) as f:
            f.write(_A      )
        return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """file.xml"""
    _UpperCAmelCase          =   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(_A					,      """w"""      ) as f:
        f.write(_A      )
    return filename


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

a					:	Optional[int]							=		[
    {'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
    {'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]

a					:	str							=		[
    {'''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  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    return DATA_DICT_OF_LISTS


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   datasets.Dataset.from_dict(_A      )
    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.arrow"""      )
    dataset.map(cache_file_name=_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.sqlite"""      )
    with contextlib.closing(sqlitea.connect(_A      )      ) as con:
        _UpperCAmelCase          =   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  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.csv"""      )
    with open(_A					,      """w"""					,      newline=""""""      ) as f:
        _UpperCAmelCase          =   csv.DictWriter(_A					,      fieldnames=["""col_1""", """col_2""", """col_3"""]      )
        writer.writeheader()
        for item in DATA:
            writer.writerow(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset2.csv"""      )
    with open(_A					,      """w"""					,      newline=""""""      ) as f:
        _UpperCAmelCase          =   csv.DictWriter(_A					,      fieldnames=["""col_1""", """col_2""", """col_3"""]      )
        writer.writeheader()
        for item in DATA:
            writer.writerow(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    import bza

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.csv.bz2"""
    with open(_A					,      """rb"""      ) as f:
        _UpperCAmelCase          =   f.read()
    # data = bytes(FILE_CONTENT, "utf-8")
    with bza.open(_A					,      """wb"""      ) as f:
        f.write(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.csv.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(_A      )      )
        f.write(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.csv.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(csv_path.replace(""".csv"""					,      """.CSV"""      )      )      )
        f.write(_A					,      arcname=os.path.basename(csva_path.replace(""".csv"""					,      """.CSV"""      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset_with_dir.csv.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.parquet"""      )
    _UpperCAmelCase          =   pa.schema(
        {
            """col_1""": pa.string(),
            """col_2""": pa.intaa(),
            """col_3""": pa.floataa(),
        }      )
    with open(_A					,      """wb"""      ) as f:
        _UpperCAmelCase          =   pq.ParquetWriter(_A					,      schema=_A      )
        _UpperCAmelCase          =   pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_A      )      )] for k in DATA[0]}					,      schema=_A      )
        writer.write_table(_A      )
        writer.close()
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.json"""      )
    _UpperCAmelCase          =   {"""data""": DATA}
    with open(_A					,      """w"""      ) as f:
        json.dump(_A					,      _A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.json"""      )
    _UpperCAmelCase          =   {"""data""": DATA_DICT_OF_LISTS}
    with open(_A					,      """w"""      ) as f:
        json.dump(_A					,      _A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.jsonl"""      )
    with open(_A					,      """w"""      ) as f:
        for item in DATA:
            f.write(json.dumps(_A      ) + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset2.jsonl"""      )
    with open(_A					,      """w"""      ) as f:
        for item in DATA:
            f.write(json.dumps(_A      ) + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset_312.jsonl"""      )
    with open(_A					,      """w"""      ) as f:
        for item in DATA_312:
            f.write(json.dumps(_A      ) + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset-str.jsonl"""      )
    with open(_A					,      """w"""      ) as f:
        for item in DATA_STR:
            f.write(json.dumps(_A      ) + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  str:





    """simple docstring"""

    import gzip

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


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    import gzip

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


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.jsonl.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(_A      )      )
        f.write(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset_nested.jsonl.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.join("""nested"""					,      os.path.basename(_A      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset_with_dir.jsonl.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.jsonl.tar"""
    with tarfile.TarFile(_A					,      """w"""      ) as f:
        f.add(_A					,      arcname=os.path.basename(_A      )      )
        f.add(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset_nested.jsonl.tar"""
    with tarfile.TarFile(_A					,      """w"""      ) as f:
        f.add(_A					,      arcname=os.path.join("""nested"""					,      os.path.basename(_A      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   ["""0""", """1""", """2""", """3"""]
    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset.txt"""      )
    with open(_A					,      """w"""      ) as f:
        for item in data:
            f.write(item + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   ["""0""", """1""", """2""", """3"""]
    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset2.txt"""      )
    with open(_A					,      """w"""      ) as f:
        for item in data:
            f.write(item + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   ["""0""", """1""", """2""", """3"""]
    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.abc"""
    with open(_A					,      """w"""      ) as f:
        for item in data:
            f.write(item + """\n"""      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.text.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(_A      )      )
        f.write(_A					,      arcname=os.path.basename(_A      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset_with_dir.text.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
        f.write(_A					,      arcname=os.path.join("""main_dir"""					,      os.path.basename(_A      )      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.ext.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename("""unsupported.ext"""      )      )
        f.write(_A					,      arcname=os.path.basename("""unsupported_2.ext"""      )      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]      )
    _UpperCAmelCase          =   str(tmp_path_factory.mktemp("""data"""      ) / """dataset_with_unicode_new_lines.txt"""      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""      ) as f:
        f.write(_A      )
    return path


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    return os.path.join("""tests"""					,      """features"""					,      """data"""					,      """test_image_rgb.jpg"""      )


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						)    ->  str:





    """simple docstring"""

    return os.path.join("""tests"""					,      """features"""					,      """data"""					,      """test_audio_44100.wav"""      )


@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data"""      ) / """dataset.img.zip"""
    with zipfile.ZipFile(_A					,      """w"""      ) as f:
        f.write(_A					,      arcname=os.path.basename(_A      )      )
        f.write(_A					,      arcname=os.path.basename(_A      ).replace(""".jpg"""					,      """2.jpg"""      )      )
    return path







@pytest.fixture(scope="""session"""      )
def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   tmp_path_factory.mktemp("""data_dir"""      )

    (data_dir / "subdir").mkdir()
    with open(data_dir / """subdir""" / """train.txt"""					,      """w"""      ) as f:
        f.write("""foo\n""" * 1_0      )
    with open(data_dir / """subdir""" / """test.txt"""					,      """w"""      ) as f:
        f.write("""bar\n""" * 1_0      )
    # hidden file
    with open(data_dir / """subdir""" / """.test.txt"""					,      """w"""      ) as f:
        f.write("""bar\n""" * 1_0      )

    # hidden directory
    (data_dir / ".subdir").mkdir()
    with open(data_dir / """.subdir""" / """train.txt"""					,      """w"""      ) as f:
        f.write("""foo\n""" * 1_0      )
    with open(data_dir / """.subdir""" / """test.txt"""					,      """w"""      ) as f:
        f.write("""bar\n""" * 1_0      )

    return data_dir

"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 		a_						:







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       List[str]=7     ,					__UpperCamelCase			:       List[Any]=9     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       Optional[int]=4     ,					__UpperCamelCase			:       Any=37     ,					__UpperCamelCase			:       List[Any]=8     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       str=0.0_0_2     ,					__UpperCamelCase			:       Union[str, Any]=1     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       Any=None     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   encoder_seq_length
        _UpperCAmelCase          =   decoder_seq_length
        # For common tests
        _UpperCAmelCase          =   self.decoder_seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_attention_mask
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   d_ff
        _UpperCAmelCase          =   relative_attention_num_buckets
        _UpperCAmelCase          =   dropout_rate
        _UpperCAmelCase          =   initializer_factor
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   decoder_start_token_id
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   decoder_layers







    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        return TaConfig.from_pretrained("""google/umt5-base"""     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       str=None     ,					)		->int:






        '''simple docstring'''






        if attention_mask is None:
            _UpperCAmelCase          =   input_ids.ne(config.pad_token_id     )
        if decoder_attention_mask is None:
            _UpperCAmelCase          =   decoder_input_ids.ne(config.pad_token_id     )
        if head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_hidden_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if decoder_head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if cross_attn_head_mask is None:
            _UpperCAmelCase          =   torch.ones(
                config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        return {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
        }







    def      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.encoder_seq_length]     ,					self.vocab_size     )
        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.decoder_seq_length]     ,					self.vocab_size     )

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _UpperCAmelCase          =   input_ids.clamp(self.pad_token_id + 1     )
        _UpperCAmelCase          =   decoder_input_ids.clamp(self.pad_token_id + 1     )

        _UpperCAmelCase          =   self.get_config()
        _UpperCAmelCase          =   config.num_attention_heads
        _UpperCAmelCase          =   self.prepare_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, input_dict







    def      _snake_case					(			self			:       Union[str, Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_config_and_inputs()
        return config, inputs_dict







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        return TaConfig(
            vocab_size=1_66     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        return TaConfig(
            vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   model(input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   result.last_hidden_state
        _UpperCAmelCase          =   result.past_key_values
        _UpperCAmelCase          =   result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()     ,					(self.batch_size, self.encoder_seq_length, self.hidden_size)     )
        self.parent.assertEqual(decoder_output.size()     ,					(self.batch_size, self.decoder_seq_length, self.hidden_size)     )
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__UpperCamelCase     )     ,					config.num_layers     )
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]     )     ,					4     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).get_decoder().to(__UpperCamelCase     ).eval()
        # first forward pass
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )

        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     )     )
        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     ) + 1     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCAmelCase          =   ids_tensor((self.batch_size, 1)     ,					config.vocab_size     )

        # append to next input_ids and
        _UpperCAmelCase          =   torch.cat([input_ids, next_tokens]     ,					dim=-1     )

        _UpperCAmelCase          =   model(__UpperCamelCase     )["""last_hidden_state"""]
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					past_key_values=__UpperCamelCase     )["""last_hidden_state"""]

        # select random slice
        _UpperCAmelCase          =   ids_tensor((1,)     ,					output_from_past.shape[-1]     ).item()
        _UpperCAmelCase          =   output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCAmelCase          =   output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).to(__UpperCamelCase     ).half().eval()
        _UpperCAmelCase          =   model(**__UpperCamelCase     )["""last_hidden_state"""]
        self.parent.assertFalse(torch.isnan(__UpperCamelCase     ).any().item()     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    a      :				Tuple     =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
    a      :				Optional[Any]     =				(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    a      :				Any     =				True
    a      :				Optional[int]     =				False
    a      :				Any     =				False
    a      :				Optional[int]     =				True
    a      :				Optional[Any]     =				True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    a      :				int     =				[0.8, 0.9]







    def      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModelTester(self     )







    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""     )
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   UMTaModel(config_and_inputs[0]     ).to(__UpperCamelCase     )
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                __UpperCamelCase     ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,					f"""{tmpdirname}/t5_test.onnx"""     ,					export_params=__UpperCamelCase     ,					opset_version=9     ,					input_names=["""input_ids""", """decoder_input_ids"""]     ,					)







    @unittest.skipIf(torch_device == """cpu"""     ,					"""Cant do half precision"""     )
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   config_and_inputs[0]
        _UpperCAmelCase          =   UMTaForConditionalGeneration(__UpperCamelCase     ).eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   {
            """head_mask""": torch.zeros(config.num_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """decoder_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
        }

        for attn_name, (name, mask) in zip(__UpperCamelCase     ,					head_masking.items()     ):
            _UpperCAmelCase          =   {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                _UpperCAmelCase          =   torch.ones(
                    config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     )

            _UpperCAmelCase          =   model.generate(
                config_and_inputs[1]["""input_ids"""]     ,					num_beams=1     ,					max_length=3     ,					output_attentions=__UpperCamelCase     ,					return_dict_in_generate=__UpperCamelCase     ,					**__UpperCamelCase     ,					)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            _UpperCAmelCase          =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]     )     ,					0.0     )







    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""     )
    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        pass







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







    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""     )
    def      _snake_case					(			self			:       Optional[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""     ,					return_dict=__UpperCamelCase     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""google/umt5-small"""     ,					use_fast=__UpperCamelCase     ,					legacy=__UpperCamelCase     )
        _UpperCAmelCase          =   [
            """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
            """No se como puedo <extra_id_0>.""",
            """This is the reason why we <extra_id_0> them.""",
            """The <extra_id_0> walks in <extra_id_1>, seats""",
            """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
        ]
        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""pt"""     ,					padding=__UpperCamelCase     ).input_ids
        # fmt: off
        _UpperCAmelCase          =   torch.tensor(
            [
                [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
            ]     )
        # fmt: on
        torch.testing.assert_allclose(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   model.generate(input_ids.to(__UpperCamelCase     )     )
        _UpperCAmelCase          =   [
            """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
            """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
        ]
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )
        self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )