

'''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__      ( )     ->     int:
       A_		   = 10
       A_		   = 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"""			),
           }			)
       A_		   = datasets.Dataset.from_dict(
           {
               """tokens""": [["""foo"""] * 5] * n,
               """labels""": [[1] * 5] * n,
               """answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
               """id""": list(range(UpperCAmelCase__			)			),
           },    features=UpperCAmelCase__,    )
       return dataset
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Union[str, Any]:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """file.arrow"""			)
       dataset.map(cache_file_name=UpperCAmelCase__			)
       return filename


# FILE_CONTENT + files


__lowerCamelCase						    =      '''\
    Text data.
    Second line of data.'''
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt"""
       A_		   = FILE_CONTENT
       with open(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__			)
       return filename
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       import bza

       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.bz2"""
       A_		   = bytes(UpperCAmelCase__,    """utf-8"""			)
       with bza.open(UpperCAmelCase__,    """wb"""			) as f:
              f.write(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       import gzip

       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """file.txt.gz"""			)
       A_		   = bytes(UpperCAmelCase__,    """utf-8"""			)
       with gzip.open(UpperCAmelCase__,    """wb"""			) as f:
              f.write(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     List[str]:
       if datasets.config.LZ4_AVAILABLE:
              import lza.frame

              A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.lz4"""
              A_		   = bytes(UpperCAmelCase__,    """utf-8"""			)
              with lza.frame.open(UpperCAmelCase__,    """wb"""			) as f:
                     f.write(UpperCAmelCase__			)
              return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       if datasets.config.PY7ZR_AVAILABLE:
              import pyazr

              A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.7z"""
              with pyazr.SevenZipFile(UpperCAmelCase__,    """w"""			) as archive:
                     archive.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[Any]:
       import tarfile

       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.tar"""
       with tarfile.TarFile(UpperCAmelCase__,    """w"""			) as f:
              f.add(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       import lzma

       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.xz"""
       A_		   = bytes(UpperCAmelCase__,    """utf-8"""			)
       with lzma.open(UpperCAmelCase__,    """wb"""			) as f:
              f.write(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       import zipfile

       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       if datasets.config.ZSTANDARD_AVAILABLE:
              import zstandard as zstd

              A_		   = tmp_path_factory.mktemp("""data"""			) / """file.txt.zst"""
              A_		   = bytes(UpperCAmelCase__,    """utf-8"""			)
              with zstd.open(UpperCAmelCase__,    """wb"""			) as f:
                     f.write(UpperCAmelCase__			)
              return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """file.xml"""
       A_		   = 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(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__			)
       return filename


__lowerCamelCase						    =      [
    {'''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},
]
__lowerCamelCase						    =      [
    {'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
    {'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCamelCase						    =      {
    '''col_1''': ['''0''', '''1''', '''2''', '''3'''],
    '''col_2''': [0, 1, 2, 3],
    '''col_3''': [0.0, 1.0, 2.0, 3.0],
}

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

__lowerCamelCase						    =      [
    {'''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__      ( )     ->     List[str]:
       return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       A_		   = datasets.Dataset.from_dict(UpperCAmelCase__			)
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.arrow"""			)
       dataset.map(cache_file_name=UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.sqlite"""			)
       with contextlib.closing(sqlitea.connect(UpperCAmelCase__			)			) as con:
              A_		   = 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__      ( UpperCAmelCase__			)     ->     Any:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.csv"""			)
       with open(UpperCAmelCase__,    """w""",    newline=""""""			) as f:
              A_		   = csv.DictWriter(UpperCAmelCase__,    fieldnames=["""col_1""", """col_2""", """col_3"""]			)
              writer.writeheader()
              for item in DATA:
                     writer.writerow(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset2.csv"""			)
       with open(UpperCAmelCase__,    """w""",    newline=""""""			) as f:
              A_		   = csv.DictWriter(UpperCAmelCase__,    fieldnames=["""col_1""", """col_2""", """col_3"""]			)
              writer.writeheader()
              for item in DATA:
                     writer.writerow(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Tuple:
       import bza

       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.csv.bz2"""
       with open(UpperCAmelCase__,    """rb"""			) as f:
              A_		   = f.read()
       # data = bytes(FILE_CONTENT, "utf-8")
       with bza.open(UpperCAmelCase__,    """wb"""			) as f:
              f.write(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[Any]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.csv.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.csv.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(csv_path.replace(""".csv""",    """.CSV"""			)			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename(csva_path.replace(""".csv""",    """.CSV"""			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset_with_dir.csv.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Union[str, Any]:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.parquet"""			)
       A_		   = pa.schema(
           {
               """col_1""": pa.string(),
               """col_2""": pa.intaa(),
               """col_3""": pa.floataa(),
           }			)
       with open(UpperCAmelCase__,    """wb"""			) as f:
              A_		   = pq.ParquetWriter(UpperCAmelCase__,    schema=UpperCAmelCase__			)
              A_		   = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCAmelCase__			)			)] for k in DATA[0]},    schema=UpperCAmelCase__			)
              writer.write_table(UpperCAmelCase__			)
              writer.close()
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.json"""			)
       A_		   = {"""data""": DATA}
       with open(UpperCAmelCase__,    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.json"""			)
       A_		   = {"""data""": DATA_DICT_OF_LISTS}
       with open(UpperCAmelCase__,    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.jsonl"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in DATA:
                     f.write(json.dumps(UpperCAmelCase__			) + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset2.jsonl"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in DATA:
                     f.write(json.dumps(UpperCAmelCase__			) + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Union[str, Any]:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset_312.jsonl"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in DATA_312:
                     f.write(json.dumps(UpperCAmelCase__			) + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     List[Any]:
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset-str.jsonl"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in DATA_STR:
                     f.write(json.dumps(UpperCAmelCase__			) + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[Any]:
       import gzip

       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.txt.gz"""			)
       with open(UpperCAmelCase__,    """rb"""			) as orig_file:
              with gzip.open(UpperCAmelCase__,    """wb"""			) as zipped_file:
                     zipped_file.writelines(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       import gzip

       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.jsonl.gz"""			)
       with open(UpperCAmelCase__,    """rb"""			) as orig_file:
              with gzip.open(UpperCAmelCase__,    """wb"""			) as zipped_file:
                     zipped_file.writelines(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[Any]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.jsonl.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset_nested.jsonl.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.join("""nested""",    os.path.basename(UpperCAmelCase__			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Any:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset_with_dir.jsonl.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Tuple:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.jsonl.tar"""
       with tarfile.TarFile(UpperCAmelCase__,    """w"""			) as f:
              f.add(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              f.add(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset_nested.jsonl.tar"""
       with tarfile.TarFile(UpperCAmelCase__,    """w"""			) as f:
              f.add(UpperCAmelCase__,    arcname=os.path.join("""nested""",    os.path.basename(UpperCAmelCase__			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       A_		   = ["""0""", """1""", """2""", """3"""]
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset.txt"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in data:
                     f.write(item + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = ["""0""", """1""", """2""", """3"""]
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset2.txt"""			)
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in data:
                     f.write(item + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = ["""0""", """1""", """2""", """3"""]
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.abc"""
       with open(UpperCAmelCase__,    """w"""			) as f:
              for item in data:
                     f.write(item + """\n"""			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[str]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.text.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Tuple:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset_with_dir.text.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.join("""main_dir""",    os.path.basename(UpperCAmelCase__			)			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.ext.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename("""unsupported.ext"""			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename("""unsupported_2.ext"""			)			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]			)
       A_		   = str(tmp_path_factory.mktemp("""data"""			) / """dataset_with_unicode_new_lines.txt"""			)
       with open(UpperCAmelCase__,    """w""",    encoding="""utf-8"""			) as f:
              f.write(UpperCAmelCase__			)
       return path
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( )     ->     int:
       return os.path.join("""tests""",    """features""",    """data""",    """test_image_rgb.jpg"""			)
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( )     ->     Optional[int]:
       return os.path.join("""tests""",    """features""",    """data""",    """test_audio_44100.wav"""			)
@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = tmp_path_factory.mktemp("""data"""			) / """dataset.img.zip"""
       with zipfile.ZipFile(UpperCAmelCase__,    """w"""			) as f:
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			)			)
              f.write(UpperCAmelCase__,    arcname=os.path.basename(UpperCAmelCase__			).replace(""".jpg""",    """2.jpg"""			)			)
       return path







@pytest.fixture(scope="""session"""			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       A_		   = 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 gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 3
           A_		   = (32, 32)

           A_		   = floats_tensor((batch_size, num_channels) + sizes						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           return image



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = UNetaDConditionModel(
               block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=4						,			out_channels=4						,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,			cross_attention_dim=32						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = AutoencoderKL(
               block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,			latent_channels=4						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = RobertaSeriesConfig(
               hidden_size=32						,			project_dim=32						,			intermediate_size=37						,			layer_norm_eps=1e-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=5006						,			)
           return RobertaSeriesModelWithTransformation(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           def extract(*UpperCamelCase__						,			**UpperCamelCase__    ):


                  class    A__ :



                      def __init__(   self    )  -> Dict:





                             '''simple docstring'''
                             A_		   = torch.ones([0]    )







                      def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





                             '''simple docstring'''
                             self.pixel_values.to(UpperCamelCase__    )
                             return self

                  return Out()

           return extract



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )
           A_		   = init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			)

           A_		   = output.images

           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[0]

           A_		   = image[0, -3:, -3:, -1]
           A_		   = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_		   = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3



    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )

           # put models in fp16
           A_		   = unet.half()
           A_		   = vae.half()
           A_		   = bert.half()

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			).images

           assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           # resize to resolution that is divisible by 8 but not 16 or 32
           A_		   = init_image.resize((760, 504)    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           A_		   = image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           A_		   = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


@slow
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           A_		   = init_image.resize((768, 512)    )
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           assert image.shape == (512, 768, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image    ).max() < 1e-2
