




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__       (						):







	'''simple docstring'''





	snake_case_						       =	10
	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"				),
	    }				)
	snake_case_						       =	datasets.Dataset.from_dict(
	    {
	        "tokens": [["foo"] * 5] * n,
	        "labels": [[1] * 5] * n,
	        "answers": [{"answer_start": [97], "text": ["1976"]}] * 10,
	        "id": list(range(UpperCAmelCase__				)				),
	    }	,      features=UpperCAmelCase__	,      )
	return dataset


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





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


# FILE_CONTENT + files


lowercase__							:	List[Any]											=		"\\n    Text data.\n    Second line of data."


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	import bza

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	import gzip

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if datasets.config.LZ4_AVAILABLE:
		import lza.frame

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	if datasets.config.PY7ZR_AVAILABLE:
		import pyazr

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import tarfile

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	import lzma

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import zipfile

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if datasets.config.ZSTANDARD_AVAILABLE:
		import zstandard as zstd

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


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

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

lowercase__							:	Union[str, Any]											=		[
    {"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__       (						):







	'''simple docstring'''





	return DATA_DICT_OF_LISTS


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	str(tmp_path_factory.mktemp("data"				) / "dataset.sqlite"				)
	with contextlib.closing(sqlitea.connect(UpperCAmelCase__				)				) as con:
		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 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import bza

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import gzip

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import gzip

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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						):







	'''simple docstring'''





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


@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





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






@pytest.fixture(scope="session"				)
def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	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" * 10				)
	with open(data_dir / "subdir" / "test.txt"	,      "w"				) as f:
		f.write("bar\n" * 10				)
	# hidden file
	with open(data_dir / "subdir" / ".test.txt"	,      "w"				) as f:
		f.write("bar\n" * 10				)

	# hidden directory
	(data_dir / ".subdir").mkdir()
	with open(data_dir / ".subdir" / "train.txt"	,      "w"				) as f:
		f.write("foo\n" * 10				)
	with open(data_dir / ".subdir" / "test.txt"	,      "w"				) as f:
		f.write("bar\n" * 10				)

	return data_dir



'''simple docstring'''







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   VideoMAEConfig()

       set_architecture_configs(UpperCAmelCase__,							UpperCAmelCase__					)

       if "finetuned" not in model_name:
              A_						       =   False

       if "finetuned" in model_name:
              A_						       =   """huggingface/label-files"""
              if "kinetics" in model_name:
                     A_						       =   4_00
                     A_						       =   """kinetics400-id2label.json"""
              elif "ssv2" in model_name:
                     A_						       =   1_74
                     A_						       =   """something-something-v2-id2label.json"""
              else:
                     raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""					)
              A_						       =   json.load(open(hf_hub_download(UpperCAmelCase__,							UpperCAmelCase__,							repo_type="""dataset"""					),							"""r"""					)					)
              A_						       =   {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
              A_						       =   idalabel
              A_						       =   {v: k for k, v in idalabel.items()}

       return config
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if "small" in model_name:
              A_						       =   3_84
              A_						       =   15_36
              A_						       =   12
              A_						       =   16
              A_						       =   12
              A_						       =   3
              A_						       =   1_92
              A_						       =   7_68
       elif "large" in model_name:
              A_						       =   10_24
              A_						       =   40_96
              A_						       =   24
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   5_12
              A_						       =   20_48
       elif "huge" in model_name:
              A_						       =   12_80
              A_						       =   51_20
              A_						       =   32
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   6_40
              A_						       =   25_60
       elif "base" not in model_name:
              raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       if "encoder." in name:
              A_						       =   name.replace("""encoder.""",							""""""					)
       if "cls_token" in name:
              A_						       =   name.replace("""cls_token""",							"""videomae.embeddings.cls_token"""					)
       if "decoder_pos_embed" in name:
              A_						       =   name.replace("""decoder_pos_embed""",							"""decoder.decoder_pos_embed"""					)
       if "pos_embed" in name and "decoder" not in name:
              A_						       =   name.replace("""pos_embed""",							"""videomae.embeddings.position_embeddings"""					)
       if "patch_embed.proj" in name:
              A_						       =   name.replace("""patch_embed.proj""",							"""videomae.embeddings.patch_embeddings.projection"""					)
       if "patch_embed.norm" in name:
              A_						       =   name.replace("""patch_embed.norm""",							"""videomae.embeddings.norm"""					)
       if "decoder.blocks" in name:
              A_						       =   name.replace("""decoder.blocks""",							"""decoder.decoder_layers"""					)
       if "blocks" in name:
              A_						       =   name.replace("""blocks""",							"""videomae.encoder.layer"""					)
       if "attn.proj" in name:
              A_						       =   name.replace("""attn.proj""",							"""attention.output.dense"""					)
       if "attn" in name and "bias" not in name:
              A_						       =   name.replace("""attn""",							"""attention.self"""					)
       if "attn" in name:
              A_						       =   name.replace("""attn""",							"""attention.attention"""					)
       if "norm1" in name:
              A_						       =   name.replace("""norm1""",							"""layernorm_before"""					)
       if "norm2" in name:
              A_						       =   name.replace("""norm2""",							"""layernorm_after"""					)
       if "mlp.fc1" in name:
              A_						       =   name.replace("""mlp.fc1""",							"""intermediate.dense"""					)
       if "mlp.fc2" in name:
              A_						       =   name.replace("""mlp.fc2""",							"""output.dense"""					)
       if "decoder_embed" in name:
              A_						       =   name.replace("""decoder_embed""",							"""decoder.decoder_embed"""					)
       if "decoder_norm" in name:
              A_						       =   name.replace("""decoder_norm""",							"""decoder.decoder_norm"""					)
       if "decoder_pred" in name:
              A_						       =   name.replace("""decoder_pred""",							"""decoder.decoder_pred"""					)
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.weight""",							"""videomae.layernorm.weight"""					)
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.bias""",							"""videomae.layernorm.bias"""					)
       if "head" in name and "decoder" not in name:
              A_						       =   name.replace("""head""",							"""classifier"""					)

       return name
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[int]:
       for key in orig_state_dict.copy().keys():
              A_						       =   orig_state_dict.pop(UpperCAmelCase__					)

              if key.startswith("""encoder."""					):
                     A_						       =   key.replace("""encoder.""",							""""""					)

              if "qkv" in key:
                     A_						       =   key.split("""."""					)
                     if key.startswith("""decoder.blocks"""					):
                            A_						       =   config.decoder_hidden_size
                            A_						       =   int(key_split[2]					)
                            A_						       =   """decoder.decoder_layers."""
                            if "weight" in key:
                                   A_						       =   val[:dim, :]
                                   A_						       =   val[dim : dim * 2, :]
                                   A_						       =   val[-dim:, :]
                     else:
                            A_						       =   config.hidden_size
                            A_						       =   int(key_split[1]					)
                            A_						       =   """videomae.encoder.layer."""
                            if "weight" in key:
                                   A_						       =   val[:dim, :]
                                   A_						       =   val[dim : dim * 2, :]
                                   A_						       =   val[-dim:, :]
              else:
                     A_						       =   val

       return orig_state_dict
def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video""",							filename="""eating_spaghetti.npy""",							repo_type="""dataset"""					)
       A_						       =   np.load(UpperCAmelCase__					)
       return list(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       A_						       =   get_videomae_config(UpperCAmelCase__					)

       if "finetuned" in model_name:
              A_						       =   VideoMAEForVideoClassification(UpperCAmelCase__					)
       else:
              A_						       =   VideoMAEForPreTraining(UpperCAmelCase__					)

       # download original checkpoint, hosted on Google Drive
       A_						       =   """pytorch_model.bin"""
       gdown.cached_download(UpperCAmelCase__,							UpperCAmelCase__,							quiet=UpperCAmelCase__					)
       A_						       =   torch.load(UpperCAmelCase__,							map_location="""cpu"""					)
       if "model" in files:
              A_						       =   files["""model"""]
       else:
              A_						       =   files["""module"""]
       A_						       =   convert_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)

       model.load_state_dict(UpperCAmelCase__					)
       model.eval()

       # verify model on basic input
       A_						       =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],							image_std=[0.5, 0.5, 0.5]					)
       A_						       =   prepare_video()
       A_						       =   image_processor(UpperCAmelCase__,							return_tensors="""pt"""					)

       if "finetuned" not in model_name:
              A_						       =   hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""",							filename="""bool_masked_pos.pt"""					)
              A_						       =   torch.load(UpperCAmelCase__					)

       A_						       =   model(**UpperCAmelCase__					)
       A_						       =   outputs.logits

       A_						       =   [
           """videomae-small-finetuned-kinetics""",
           """videomae-small-finetuned-ssv2""",
           # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
           """videomae-base-short""",
           """videomae-base-short-finetuned-kinetics""",
           """videomae-base""",
           """videomae-base-finetuned-kinetics""",
           """videomae-large""",
           """videomae-large-finetuned-kinetics""",
           """videomae-huge-finetuned-kinetics""",
           # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
           """videomae-base-short-ssv2""",
           """videomae-base-short-finetuned-ssv2""",
           """videomae-base-ssv2""",
           """videomae-base-finetuned-ssv2""",
       ]

       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "videomae-small-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([-0.9_291, -0.4_061, -0.9_307]					)
       elif model_name == "videomae-small-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.2_671, -0.4_689, -0.8_235]					)
       elif model_name == "videomae-base":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]					)
       elif model_name == "videomae-base-short":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]					)
              # we verified the loss both for normalized and unnormalized targets for this one
              A_						       =   torch.tensor([0.5_142]					) if config.norm_pix_loss else torch.tensor([0.6_469]					)
       elif model_name == "videomae-large":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]					)
       elif model_name == "videomae-large-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.0_771, 0.0_011, -0.3_625]					)
       elif model_name == "videomae-huge-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.2_433, 0.1_632, -0.4_894]					)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.6_588, 0.0_990, -0.2_493]					)
       elif model_name == "videomae-base-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.3_669, -0.0_688, -0.2_421]					)
       elif model_name == "videomae-base-short-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]					)
       elif model_name == "videomae-base-short-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([-0.0_537, -0.1_539, -0.3_266]					)
       elif model_name == "videomae-base-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]					)
       elif model_name == "videomae-base-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.1_961, -0.8_337, -0.6_389]					)
       else:
              raise ValueError(F'''Model name not supported. Should be one of {model_names}'''					)

       # verify logits
       assert logits.shape == expected_shape
       if "finetuned" in model_name:
              assert torch.allclose(logits[0, :3],							UpperCAmelCase__,							atol=1e-4					)
       else:
              print("""Logits:""",							logits[0, :3, :3]					)
              assert torch.allclose(logits[0, :3, :3],							UpperCAmelCase__,							atol=1e-4					)
       print("""Logits ok!"""					)

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              A_						       =   outputs.loss
              assert torch.allclose(UpperCAmelCase__,							UpperCAmelCase__,							atol=1e-4					)
              print("""Loss ok!"""					)

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''					)
              image_processor.save_pretrained(UpperCAmelCase__					)
              model.save_pretrained(UpperCAmelCase__					)

       if push_to_hub:
              print("""Pushing to the hub..."""					)
              model.push_to_hub(UpperCAmelCase__,							organization="""nielsr"""					)


if __name__ == "__main__":
      __lowerCamelCase									=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
          type=str,
          help=(
              '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
              ''' download link.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
          type=str,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      __lowerCamelCase									=     parser.parse_args()
      convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
