



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        __lowerCAmelCase		(	)	->    Dict:
					__lowerCAmelCase						 =10
					__lowerCAmelCase						 =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						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    Dict:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """file.arrow"""						)
					dataset.map(cache_file_name=__lowerCamelCase						)
					return filename


# FILE_CONTENT + files


lowercase_													=      '''\
    Text data.
    Second line of data.'''






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






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

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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    int:
					import gzip

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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]						)	->    str:
					if datasets.config.LZ4_AVAILABLE:
										import lza.frame

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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    str:
					if datasets.config.PY7ZR_AVAILABLE:
										import pyazr

										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Optional[Any]						)	->    Any:
					import tarfile

					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]						)	->    int:
					import lzma

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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     int						)	->    Optional[Any]:
					import zipfile

					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    List[Any]:
					if datasets.config.ZSTANDARD_AVAILABLE:
										import zstandard as zstd

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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path_factory.mktemp("""data"""						) / """file.xml"""
					__lowerCAmelCase						 =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


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

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

lowercase_													=      [
    {'''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        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATA_DICT_OF_LISTS






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple						)	->    Tuple:
					__lowerCAmelCase						 =datasets.Dataset.from_dict(__lowerCamelCase						)
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset.arrow"""						)
					dataset.map(cache_file_name=__lowerCamelCase						)
					return path






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]						)	->    str:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset.sqlite"""						)
					with contextlib.closing(sqlitea.connect(__lowerCamelCase						)						) as con:
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict						)	->    Any:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset.csv"""						)
					with open(__lowerCamelCase					, """w"""					, newline=""""""						) as f:
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]						)	->    Tuple:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset2.csv"""						)
					with open(__lowerCamelCase					, """w"""					, newline=""""""						) as f:
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     int						)	->    int:
					import bza

					__lowerCAmelCase						 =tmp_path_factory.mktemp("""data"""						) / """dataset.csv.bz2"""
					with open(__lowerCamelCase					, """rb"""						) as f:
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[int]						)	->    List[str]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     int					, __lowerCamelCase						:     Union[str, Any]						)	->    int:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[str]						)	->    List[Any]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple						)	->    Dict:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset.parquet"""						)
					__lowerCAmelCase						 =pa.schema(
					    {
					        """col_1""": pa.string(),
					        """col_2""": pa.intaa(),
					        """col_3""": pa.floataa(),
					    }						)
					with open(__lowerCamelCase					, """wb"""						) as f:
										__lowerCAmelCase						 =pq.ParquetWriter(__lowerCamelCase					, schema=__lowerCamelCase						)
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]						)	->    str:
					__lowerCAmelCase						 =str(tmp_path_factory.mktemp("""data"""						) / """dataset.json"""						)
					__lowerCAmelCase						 ={"""data""": DATA}
					with open(__lowerCamelCase					, """w"""						) as f:
										json.dump(__lowerCamelCase					, __lowerCamelCase						)
					return path






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






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

					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int						)	->    Dict:
					import gzip

					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]						)	->    List[str]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Optional[int]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Union[str, Any]						)	->    List[str]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[Any]						)	->    Optional[int]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Dict						)	->    Optional[int]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]						)	->    Union[str, Any]:
					__lowerCAmelCase						 =["""0""", """1""", """2""", """3"""]
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any						)	->    List[str]:
					__lowerCAmelCase						 =["""0""", """1""", """2""", """3"""]
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any						)	->    List[str]:
					__lowerCAmelCase						 =["""0""", """1""", """2""", """3"""]
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int						)	->    Optional[Any]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict						)	->    Dict:
					__lowerCAmelCase						 ="""\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]						)
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	)	->    Tuple:
					return os.path.join("""tests"""					, """features"""					, """data"""					, """test_image_rgb.jpg"""						)






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






@pytest.fixture(scope="""session"""						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[Any]						)	->    Any:
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]						)	->    int:
					__lowerCAmelCase						 =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









import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
				import numpy as np
				import torch

				from . import (
				    AlbertForPreTraining,
				    BartForConditionalGeneration,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    CamembertForMaskedLM,
				    CTRLLMHeadModel,
				    DistilBertForMaskedLM,
				    DistilBertForQuestionAnswering,
				    DPRContextEncoder,
				    DPRQuestionEncoder,
				    DPRReader,
				    ElectraForPreTraining,
				    FlaubertWithLMHeadModel,
				    GPTaLMHeadModel,
				    LayoutLMForMaskedLM,
				    LxmertForPreTraining,
				    LxmertVisualFeatureEncoder,
				    OpenAIGPTLMHeadModel,
				    RobertaForMaskedLM,
				    RobertaForSequenceClassification,
				    TaForConditionalGeneration,
				    TransfoXLLMHeadModel,
				    XLMRobertaForMaskedLM,
				    XLMWithLMHeadModel,
				    XLNetLMHeadModel,
				)


logging.set_verbosity_info()

lowercase_													=      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Dict=False					, __lowerCamelCase						:     int=True						)	->    Dict:
					if model_type not in MODEL_CLASSES:
										raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()						)}."""						)

					__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =MODEL_CLASSES[model_type]

					# Initialise TF model
					if config_file in aws_config_map:
										__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
					__lowerCAmelCase						 =config_class.from_json_file(__lowerCamelCase						)
					__lowerCAmelCase						 =True
					__lowerCAmelCase						 =True
					print(f"""Building TensorFlow model from configuration: {config}"""						)
					__lowerCAmelCase						 =model_class(__lowerCamelCase						)

					# Load weights from tf checkpoint
					if pytorch_checkpoint_path in aws_config_map.keys():
										__lowerCAmelCase						 =cached_file(
										    __lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
					# Load PyTorch checkpoint in tf2 model:
					__lowerCAmelCase						 =load_pytorch_checkpoint_in_tfa_model(__lowerCamelCase					, __lowerCamelCase						)

					if compare_with_pt_model:
										__lowerCAmelCase						 =tf_model(tf_model.dummy_inputs					, training=__lowerCamelCase						)  # build the network

										__lowerCAmelCase						 =torch.load(__lowerCamelCase					, map_location="""cpu"""						)
										__lowerCAmelCase						 =pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=__lowerCamelCase					, config=__lowerCamelCase					, state_dict=__lowerCamelCase						)

										with torch.no_grad():
															__lowerCAmelCase						 =pt_model(**pt_model.dummy_inputs						)

										__lowerCAmelCase						 =pto[0].numpy()
										__lowerCAmelCase						 =tfo[0].numpy()
										__lowerCAmelCase						 =np.amax(np.abs(np_pt - np_tf						)						)
										print(f"""Max absolute difference between models outputs {diff}"""						)
										assert diff <= 2E-2, f"""Error, model absolute difference is >2e-2: {diff}"""

					# Save pytorch-model
					print(f"""Save TensorFlow model to {tf_dump_path}"""						)
					tf_model.save_weights(__lowerCamelCase					, save_format="""h5"""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Tuple=None					, __lowerCamelCase						:     Any=None					, __lowerCamelCase						:     Any=False					, __lowerCamelCase						:     str=False					, __lowerCamelCase						:     List[str]=False					, __lowerCamelCase						:     Tuple=False					, )	->    Dict:
					if args_model_type is None:
										__lowerCAmelCase						 =list(MODEL_CLASSES.keys()						)
					else:
										__lowerCAmelCase						 =[args_model_type]

					for j, model_type in enumerate(__lowerCamelCase					, start=1						):
										print("""=""" * 100						)
										print(f""" Converting model type {j}/{len(__lowerCamelCase						)}: {model_type}"""						)
										print("""=""" * 100						)
										if model_type not in MODEL_CLASSES:
															raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()						)}."""						)

										__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =MODEL_CLASSES[model_type]

										if model_shortcut_names_or_path is None:
															__lowerCAmelCase						 =list(aws_model_maps.keys()						)
										if config_shortcut_names_or_path is None:
															__lowerCAmelCase						 =model_shortcut_names_or_path

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(__lowerCamelCase					, __lowerCamelCase						)					, start=1						):
															print("""-""" * 100						)
															if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																				if not only_convert_finetuned_models:
																									print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""						)
																									continue
																				__lowerCAmelCase						 =model_shortcut_name
															elif only_convert_finetuned_models:
																				print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""						)
																				continue
															print(
															    f"""    Converting checkpoint {i}/{len(__lowerCamelCase						)}: {model_shortcut_name} - model_type {model_type}"""						)
															print("""-""" * 100						)

															if config_shortcut_name in aws_config_map:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				__lowerCAmelCase						 =cached_file(__lowerCamelCase					, __lowerCamelCase					, force_download=not use_cached_models						)
															else:
																				__lowerCAmelCase						 =model_shortcut_name

															if os.path.isfile(__lowerCamelCase						):
																				__lowerCAmelCase						 ="""converted_model"""

															convert_pt_checkpoint_to_tf(
															    model_type=__lowerCamelCase					, pytorch_checkpoint_path=__lowerCamelCase					, config_file=__lowerCamelCase					, tf_dump_path=os.path.join(__lowerCamelCase					, model_shortcut_name + """-tf_model.h5"""						)					, compare_with_pt_model=__lowerCamelCase					, )
															if remove_cached_files:
																				os.remove(__lowerCamelCase						)
																				os.remove(__lowerCamelCase						)


if __name__ == "__main__":
				lowercase_													=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
				)
				parser.add_argument(
				    '''--model_type''',
				    default=None,
				    type=str,
				    help=(
				        F"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
				        '''convert all the models from AWS.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_checkpoint_path''',
				    default=None,
				    type=str,
				    help=(
				        '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
				        '''If not given, will download and convert all the checkpoints from AWS.'''
				    ),
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    help=(
				        '''The config json file corresponding to the pre-trained model. \n'''
				        '''This specifies the model architecture. If not given and '''
				        '''--pytorch_checkpoint_path is not given or is a shortcut name '''
				        '''use the configuration associated to the shortcut name on the AWS'''
				    ),
				)
				parser.add_argument(
				    '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
				)
				parser.add_argument(
				    '''--use_cached_models''',
				    action='''store_true''',
				    help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
				)
				parser.add_argument(
				    '''--remove_cached_files''',
				    action='''store_true''',
				    help='''Remove pytorch models after conversion (save memory when converting in batches).''',
				)
				parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
				lowercase_													=      parser.parse_args()

				# if args.pytorch_checkpoint_path is not None:
				#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
				#                                 args.pytorch_checkpoint_path,
				#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
				#                                 args.tf_dump_path,
				#                                 compare_with_pt_model=args.compare_with_pt_model,
				#                                 use_cached_models=args.use_cached_models)
				# else:
				convert_all_pt_checkpoints_to_tf(
				    args.model_type.lower() if args.model_type is not None else None,
				    args.tf_dump_path,
				    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
				    if args.pytorch_checkpoint_path is not None
				    else None,
				    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
				    compare_with_pt_model=args.compare_with_pt_model,
				    use_cached_models=args.use_cached_models,
				    remove_cached_files=args.remove_cached_files,
				    only_convert_finetuned_models=args.only_convert_finetuned_models,
				)




