






'''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      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      1_0
				__magic_name__		:Union[str, Any]        =      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''' ),
				    } )
				__magic_name__		:List[str]        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
				dataset.map(cache_file_name=lowerCamelCase__ )
				return filename


# FILE_CONTENT + files


SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		"""\
    Text data.
    Second line of data."""

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      tmp_path_factory.mktemp('''data''' ) / "file.txt"
				__magic_name__		:List[str]        =      FILE_CONTENT
				with open(lowerCamelCase__,  '''w''' ) as f:
								f.write(lowerCamelCase__ )
				return filename

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				import bza

				__magic_name__		:Dict        =      tmp_path_factory.mktemp('''data''' ) / "file.txt.bz2"
				__magic_name__		:Tuple        =      bytes(lowerCamelCase__,  '''utf-8''' )
				with bza.open(lowerCamelCase__,  '''wb''' ) as f:
								f.write(lowerCamelCase__ )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				import gzip

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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				if datasets.config.LZ4_AVAILABLE:
								import lza.frame

								__magic_name__		:Dict        =      tmp_path_factory.mktemp('''data''' ) / "file.txt.lz4"
								__magic_name__		:Optional[Any]        =      bytes(lowerCamelCase__,  '''utf-8''' )
								with lza.frame.open(lowerCamelCase__,  '''wb''' ) as f:
												f.write(lowerCamelCase__ )
								return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if datasets.config.PY7ZR_AVAILABLE:
								import pyazr

								__magic_name__		:int        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				import tarfile

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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				import lzma

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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				import zipfile

				__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				if datasets.config.ZSTANDARD_AVAILABLE:
								import zstandard as zstd

								__magic_name__		:Any        =      tmp_path_factory.mktemp('''data''' ) / "file.txt.zst"
								__magic_name__		:Optional[Any]        =      bytes(lowerCamelCase__,  '''utf-8''' )
								with zstd.open(lowerCamelCase__,  '''wb''' ) as f:
												f.write(lowerCamelCase__ )
								return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      tmp_path_factory.mktemp('''data''' ) / "file.xml"
				__magic_name__		:Optional[int]        =      textwrap.dedent(
				    '''\\n    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n    <tmx version=\"1.4\">\n      <header segtype=\"sentence\" srclang=\"ca\" />\n      <body>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n        </tu>\n      </body>\n    </tmx>''' )
				with open(lowerCamelCase__,  '''w''' ) as f:
								f.write(lowerCamelCase__ )
				return filename


SCREAMING_SNAKE_CASE__      :			Tuple							=		[
    {"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
    {"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
    {"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
    {"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    {"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
    {"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
SCREAMING_SNAKE_CASE__      :			int							=		{
    """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__      :			Tuple							=		[
    {"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
    {"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]

SCREAMING_SNAKE_CASE__      :			Tuple							=		[
    {"""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      __lowercase					( ):







				"""simple docstring"""






				return DATA_DICT_OF_LISTS

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      datasets.Dataset.from_dict(lowerCamelCase__ )
				__magic_name__		:Any        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
				dataset.map(cache_file_name=lowerCamelCase__ )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
				with contextlib.closing(sqlitea.connect(lowerCamelCase__ ) ) as con:
								__magic_name__		:Union[str, Any]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
				with open(lowerCamelCase__,  '''w''',  newline='''''' ) as f:
								__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:int        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
				with open(lowerCamelCase__,  '''w''',  newline='''''' ) as f:
								__magic_name__		:Union[str, Any]        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				import bza

				__magic_name__		:Any        =      tmp_path_factory.mktemp('''data''' ) / "dataset.csv.bz2"
				with open(lowerCamelCase__,  '''rb''' ) as f:
								__magic_name__		:Optional[Any]        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      tmp_path_factory.mktemp('''data''' ) / "dataset.csv.zip"
				with zipfile.ZipFile(lowerCamelCase__,  '''w''' ) as f:
								f.write(lowerCamelCase__,  arcname=os.path.basename(csv_path.replace('''.csv''',  '''.CSV''' ) ) )
								f.write(lowerCamelCase__,  arcname=os.path.basename(csva_path.replace('''.csv''',  '''.CSV''' ) ) )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
				__magic_name__		:str        =      pa.schema(
				    {
				        '''col_1''': pa.string(),
				        '''col_2''': pa.intaa(),
				        '''col_3''': pa.floataa(),
				    } )
				with open(lowerCamelCase__,  '''wb''' ) as f:
								__magic_name__		:Union[str, Any]        =      pq.ParquetWriter(lowerCamelCase__,  schema=lowerCamelCase__ )
								__magic_name__		:Optional[Any]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
				__magic_name__		:int        =      {"data": DATA}
				with open(lowerCamelCase__,  '''w''' ) as f:
								json.dump(lowerCamelCase__,  lowerCamelCase__ )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
				__magic_name__		:int        =      {"data": DATA_DICT_OF_LISTS}
				with open(lowerCamelCase__,  '''w''' ) as f:
								json.dump(lowerCamelCase__,  lowerCamelCase__ )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				import gzip

				__magic_name__		:int        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				import gzip

				__magic_name__		:Optional[Any]        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      tmp_path_factory.mktemp('''data''' ) / "dataset_nested.jsonl.zip"
				with zipfile.ZipFile(lowerCamelCase__,  '''w''' ) as f:
								f.write(lowerCamelCase__,  arcname=os.path.join('''nested''',  os.path.basename(lowerCamelCase__ ) ) )
				return path

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      ["0", "1", "2", "3"]
				__magic_name__		:Optional[Any]        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      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      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      "\n".join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
				__magic_name__		:List[str]        =      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      __lowercase					( ):







				"""simple docstring"""






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

@pytest.fixture(scope='''session''' )
def      __lowercase					( ):







				"""simple docstring"""






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

@pytest.fixture(scope='''session''' )
def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      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














from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE__      :			Any							=		8

def      __lowercase					( snake_case,  snake_case=BITS ):







				"""simple docstring"""






				__magic_name__		:int        =      x.device

				__magic_name__		:Optional[Any]        =      (x * 2_5_5).int().clamp(0,  2_5_5 )

				__magic_name__		:Dict        =      2 ** torch.arange(bits - 1,  -1,  -1,  device=snake_case )
				__magic_name__		:Any        =      rearrange(snake_case,  '''d -> d 1 1''' )
				__magic_name__		:Dict        =      rearrange(snake_case,  '''b c h w -> b c 1 h w''' )

				__magic_name__		:Optional[Any]        =      ((x & mask) != 0).float()
				__magic_name__		:List[str]        =      rearrange(snake_case,  '''b c d h w -> b (c d) h w''' )
				__magic_name__		:List[Any]        =      bits * 2 - 1
				return bits

def      __lowercase					( snake_case,  snake_case=BITS ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      x.device

				__magic_name__		:Dict        =      (x > 0).int()
				__magic_name__		:int        =      2 ** torch.arange(bits - 1,  -1,  -1,  device=snake_case,  dtype=torch.intaa )

				__magic_name__		:Optional[Any]        =      rearrange(snake_case,  '''d -> d 1 1''' )
				__magic_name__		:Union[str, Any]        =      rearrange(snake_case,  '''b (c d) h w -> b c d h w''',  d=8 )
				__magic_name__		:Optional[Any]        =      reduce(x * mask,  '''b c d h w -> b c h w''',  '''sum''' )
				return (dec / 2_5_5).clamp(0.0,  1.0 )

def      __lowercase					( self,  snake_case,  snake_case,  snake_case,  snake_case = 0.0,  snake_case = True,  snake_case=None,  snake_case = True,  ):







				"""simple docstring"""






				if self.num_inference_steps is None:
								raise ValueError(
								    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				__magic_name__		:Union[str, Any]        =      timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				__magic_name__		:Optional[Any]        =      self.alphas_cumprod[timestep]
				__magic_name__		:List[Any]        =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				__magic_name__		:Dict        =      1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				__magic_name__		:Optional[int]        =      self.bit_scale
				if self.config.clip_sample:
								__magic_name__		:Dict        =      torch.clamp(snake_case,  -scale,  snake_case )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				__magic_name__		:Optional[Any]        =      self._get_variance(snake_case,  snake_case )
				__magic_name__		:Tuple        =      eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								__magic_name__		:int        =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__magic_name__		:str        =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								__magic_name__		:List[str]        =      model_output.device if torch.is_tensor(snake_case ) else '''cpu'''
								__magic_name__		:Tuple        =      torch.randn(model_output.shape,  dtype=model_output.dtype,  generator=snake_case ).to(snake_case )
								__magic_name__		:Optional[Any]        =      self._get_variance(snake_case,  snake_case ) ** 0.5 * eta * noise

								__magic_name__		:List[Any]        =      prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=snake_case,  pred_original_sample=snake_case )

def      __lowercase					( self,  snake_case,  snake_case,  snake_case,  snake_case="epsilon",  snake_case=None,  snake_case = True,  ):







				"""simple docstring"""






				__magic_name__		:int        =      timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								__magic_name__     ,				__magic_name__		:Dict        =      torch.split(snake_case,  sample.shape[1],  dim=1 )
				else:
								__magic_name__		:Any        =      None

				# 1. compute alphas, betas
				__magic_name__		:List[str]        =      self.alphas_cumprod[t]
				__magic_name__		:List[str]        =      self.alphas_cumprod[t - 1] if t > 0 else self.one
				__magic_name__		:Any        =      1 - alpha_prod_t
				__magic_name__		:Tuple        =      1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								__magic_name__		:str        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								__magic_name__		:List[Any]        =      model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )

				# 3. Clip "predicted x_0"
				__magic_name__		:List[str]        =      self.bit_scale
				if self.config.clip_sample:
								__magic_name__		:int        =      torch.clamp(snake_case,  -scale,  snake_case )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__magic_name__		:Any        =      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				__magic_name__		:str        =      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__magic_name__		:List[Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__magic_name__		:int        =      0
				if t > 0:
								__magic_name__		:Any        =      torch.randn(
								    model_output.size(),  dtype=model_output.dtype,  layout=model_output.layout,  generator=snake_case ).to(model_output.device )
								__magic_name__		:int        =      (self._get_variance(snake_case,  predicted_variance=snake_case ) ** 0.5) * noise

				__magic_name__		:int        =      pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=snake_case,  pred_original_sample=snake_case )

class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 1.0       ,					):
								"""simple docstring"""
								super().__init__()
								__magic_name__		:Any        =      bit_scale
								__magic_name__		:List[Any]        =      (
								    ddim_bit_scheduler_step if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) else ddpm_bit_scheduler_step
								)

								self.register_modules(unet=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase     )







				@torch.no_grad()
				def __call__(						self       ,					__lowerCAmelCase = 2_5_6       ,					__lowerCAmelCase = 2_5_6       ,					__lowerCAmelCase = 5_0       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = "pil"       ,					__lowerCAmelCase = True       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)       ,					generator=__lowerCAmelCase       ,					)
								__magic_name__		:List[str]        =      decimal_to_bits(__lowerCAmelCase     ) * self.bit_scale
								__magic_name__		:int        =      latents.to(self.device     )

								self.scheduler.set_timesteps(__lowerCAmelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
												# predict the noise residual
												__magic_name__		:List[Any]        =      self.unet(__lowerCAmelCase       ,					__lowerCAmelCase     ).sample

												# compute the previous noisy sample x_t -> x_t-1
												__magic_name__		:Any        =      self.scheduler.step(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ).prev_sample

								__magic_name__		:str        =      bits_to_decimal(__lowerCAmelCase     )

								if output_type == "pil":
												__magic_name__		:Any        =      self.numpy_to_pil(__lowerCAmelCase     )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=__lowerCAmelCase     )






