




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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''file.arrow'''     )
							dataset.map(cache_file_name=_lowerCAmelCase     )
							return filename


# FILE_CONTENT + files


__SCREAMING_SNAKE_CASE 						="""\
    Text data.
    Second line of data."""

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    tmp_path_factory.mktemp('''data'''     ) / '''file.txt'''
							SCREAMING_SNAKE_CASE_   	=    FILE_CONTENT
							with open(_lowerCAmelCase  ,    '''w'''     ) as f:
														f.write(_lowerCAmelCase     )
							return filename

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							import bza

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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							import gzip

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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							if datasets.config.LZ4_AVAILABLE:
														import lza.frame

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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							if datasets.config.PY7ZR_AVAILABLE:
														import pyazr

														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							import tarfile

							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							import lzma

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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							import zipfile

							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							if datasets.config.ZSTANDARD_AVAILABLE:
														import zstandard as zstd

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

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    tmp_path_factory.mktemp('''data'''     ) / '''file.xml'''
							SCREAMING_SNAKE_CASE_   	=    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


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

__SCREAMING_SNAKE_CASE 						=[
    {"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
    {"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]

__SCREAMING_SNAKE_CASE 						=[
    {"""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 			a   ():
							return DATA_DICT_OF_LISTS

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    datasets.Dataset.from_dict(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.arrow'''     )
							dataset.map(cache_file_name=_lowerCAmelCase     )
							return path

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.sqlite'''     )
							with contextlib.closing(sqlitea.connect(_lowerCAmelCase     )     ) as con:
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.csv'''     )
							with open(_lowerCAmelCase  ,    '''w'''  ,    newline=''''''     ) as f:
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset2.csv'''     )
							with open(_lowerCAmelCase  ,    '''w'''  ,    newline=''''''     ) as f:
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							import bza

							SCREAMING_SNAKE_CASE_   	=    tmp_path_factory.mktemp('''data'''     ) / '''dataset.csv.bz2'''
							with open(_lowerCAmelCase  ,    '''rb'''     ) as f:
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.parquet'''     )
							SCREAMING_SNAKE_CASE_   	=    pa.schema(
							    {
							        '''col_1''': pa.string(),
							        '''col_2''': pa.intaa(),
							        '''col_3''': pa.floataa(),
							    }     )
							with open(_lowerCAmelCase  ,    '''wb'''     ) as f:
														SCREAMING_SNAKE_CASE_   	=    pq.ParquetWriter(_lowerCAmelCase  ,    schema=_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.json'''     )
							SCREAMING_SNAKE_CASE_   	=    {'''data''': DATA}
							with open(_lowerCAmelCase  ,    '''w'''     ) as f:
														json.dump(_lowerCAmelCase  ,    _lowerCAmelCase     )
							return path

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    str(tmp_path_factory.mktemp('''data'''     ) / '''dataset.json'''     )
							SCREAMING_SNAKE_CASE_   	=    {'''data''': DATA_DICT_OF_LISTS}
							with open(_lowerCAmelCase  ,    '''w'''     ) as f:
														json.dump(_lowerCAmelCase  ,    _lowerCAmelCase     )
							return path

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							import gzip

							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							import gzip

							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    ['''0''', '''1''', '''2''', '''3''']
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    ['''0''', '''1''', '''2''', '''3''']
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    ['''0''', '''1''', '''2''', '''3''']
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third''']     )
							SCREAMING_SNAKE_CASE_   	=    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 			a   ():
							return os.path.join('''tests'''  ,    '''features'''  ,    '''data'''  ,    '''test_image_rgb.jpg'''     )

@pytest.fixture(scope='''session'''     )
def 			a   ():
							return os.path.join('''tests'''  ,    '''features'''  ,    '''data'''  ,    '''test_audio_44100.wav'''     )

@pytest.fixture(scope='''session'''     )
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    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





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    fname.split(os.path.sep     )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''  ,    _lowerCAmelCase     ).groups()[0]






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    file_names
											SCREAMING_SNAKE_CASE_   	=    image_transform
											SCREAMING_SNAKE_CASE_   	=    label_to_id


				def __len__(							self:   Any		):
											return len(self.file_names		)





				def __getitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.file_names[idx]
											SCREAMING_SNAKE_CASE_   	=    PIL.Image.open(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    raw_image.convert('''RGB'''		)
											if self.image_transform is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.image_transform(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    extract_label(_lowerCamelCase		)
											if self.label_to_id is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.label_to_id[label]
											return {"image": image, "label": label}






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(
														    cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='''all'''  ,    project_dir=args.project_dir     )
							else:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(cpu=args.cpu  ,    mixed_precision=args.mixed_precision     )

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_   	=    config['''lr''']
							SCREAMING_SNAKE_CASE_   	=    int(config['''num_epochs''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''seed''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''batch_size''']     )
							SCREAMING_SNAKE_CASE_   	=    config['''image_size''']
							if not isinstance(_lowerCAmelCase  ,    (list, tuple)     ):
														SCREAMING_SNAKE_CASE_   	=    (image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps  ,    '''isdigit'''     ):
														if args.checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					SCREAMING_SNAKE_CASE_   	=    int(args.checkpointing_steps     )
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    os.path.split(_lowerCAmelCase     )[-1].split('''.'''     )[0]
														accelerator.init_trackers(_lowerCAmelCase  ,    _lowerCAmelCase     )

							# Grab all the image filenames
							SCREAMING_SNAKE_CASE_   	=    [os.path.join(args.data_dir  ,    _lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('''.jpg'''     )]

							# Build the label correspondences
							SCREAMING_SNAKE_CASE_   	=    [extract_label(_lowerCAmelCase     ) for fname in file_names]
							SCREAMING_SNAKE_CASE_   	=    list(set(_lowerCAmelCase     )     )
							id_to_label.sort()
							SCREAMING_SNAKE_CASE_   	=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase     )}

							# Set the seed before splitting the data.
							np.random.seed(_lowerCAmelCase     )
							torch.manual_seed(_lowerCAmelCase     )
							torch.cuda.manual_seed_all(_lowerCAmelCase     )

							# Split our filenames between train and validation
							SCREAMING_SNAKE_CASE_   	=    np.random.permutation(len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    int(0.8 * len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    random_perm[:cut]
							SCREAMING_SNAKE_CASE_   	=    random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							SCREAMING_SNAKE_CASE_   	=    Compose([RandomResizedCrop(_lowerCAmelCase  ,    scale=(0.5, 1.0)     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset(
							    [file_names[i] for i in train_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# For evaluation, we use a deterministic Resize
							SCREAMING_SNAKE_CASE_   	=    Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset([file_names[i] for i in eval_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_   	=    create_model('''resnet50d'''  ,    pretrained=_lowerCAmelCase  ,    num_classes=len(_lowerCAmelCase     )     )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							SCREAMING_SNAKE_CASE_   	=    model.to(accelerator.device     )

							# Freezing the base model
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_   	=    False
							for param in model.get_classifier().parameters():
														SCREAMING_SNAKE_CASE_   	=    True

							# We normalize the batches of images to be a bit faster.
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''mean''']     )[None, :, None, None].to(accelerator.device     )
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''std''']     )[None, :, None, None].to(accelerator.device     )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_   	=    torch.optim.Adam(params=model.parameters()  ,    lr=lr / 2_5     )

							# Instantiate learning rate scheduler
							SCREAMING_SNAKE_CASE_   	=    OneCycleLR(optimizer=_lowerCAmelCase  ,    max_lr=_lowerCAmelCase  ,    epochs=_lowerCAmelCase  ,    steps_per_epoch=len(_lowerCAmelCase     )     )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE_   	=    0
							# We also need to keep track of the starting epoch so files are named properly
							SCREAMING_SNAKE_CASE_   	=    0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
																					accelerator.load_state(args.resume_from_checkpoint     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.basename(args.resume_from_checkpoint     )
														else:
																					# Get the most recent checkpoint
																					SCREAMING_SNAKE_CASE_   	=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime     )
																					SCREAMING_SNAKE_CASE_   	=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														SCREAMING_SNAKE_CASE_   	=    os.path.splitext(_lowerCAmelCase     )[0]

														if "epoch" in training_difference:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''epoch_'''  ,    ''''''     )     ) + 1
																					SCREAMING_SNAKE_CASE_   	=    None
														else:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''step_'''  ,    ''''''     )     )
																					SCREAMING_SNAKE_CASE_   	=    resume_step // len(_lowerCAmelCase     )
																					resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
							for epoch in range(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														model.train()
														if args.with_tracking:
																					SCREAMING_SNAKE_CASE_   	=    0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					SCREAMING_SNAKE_CASE_   	=    accelerator.skip_first_batches(_lowerCAmelCase  ,    _lowerCAmelCase     )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					SCREAMING_SNAKE_CASE_   	=    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.cross_entropy(_lowerCAmelCase  ,    batch['''label''']     )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase     )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																																			accelerator.save_state(_lowerCAmelCase     )
														model.eval()
														SCREAMING_SNAKE_CASE_   	=    0
														SCREAMING_SNAKE_CASE_   	=    0
														for step, batch in enumerate(_lowerCAmelCase     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    outputs.argmax(dim=-1     )
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather_for_metrics((predictions, batch['''label'''])     )
																					SCREAMING_SNAKE_CASE_   	=    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														SCREAMING_SNAKE_CASE_   	=    accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"     )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 1_0_0 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(_lowerCAmelCase     ),
																					        '''epoch''': epoch,
																					    }  ,    step=_lowerCAmelCase  ,    )
														if checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																					accelerator.save_state(_lowerCAmelCase     )

							if args.with_tracking:
														accelerator.end_training()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    argparse.ArgumentParser(description='''Simple example of training script.'''     )
							parser.add_argument('''--data_dir'''  ,    required=_lowerCAmelCase  ,    help='''The data folder on disk.'''     )
							parser.add_argument('''--fp16'''  ,    action='''store_true'''  ,    help='''If passed, will use FP16 training.'''     )
							parser.add_argument(
							    '''--mixed_precision'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''  ,    )
							parser.add_argument('''--cpu'''  ,    action='''store_true'''  ,    help='''If passed, will train on the CPU.'''     )
							parser.add_argument(
							    '''--checkpointing_steps'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,    )
							parser.add_argument(
							    '''--output_dir'''  ,    type=_lowerCAmelCase  ,    default='''.'''  ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,    )
							parser.add_argument(
							    '''--resume_from_checkpoint'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''If the training should continue from a checkpoint folder.'''  ,    )
							parser.add_argument(
							    '''--with_tracking'''  ,    action='''store_true'''  ,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,    )
							parser.add_argument(
							    '''--project_dir'''  ,    type=_lowerCAmelCase  ,    default='''logs'''  ,    help='''Location on where to store experiment tracking logs` and relevent project information'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(_lowerCAmelCase  ,    _lowerCAmelCase     )


if __name__ == "__main__":
			main()


