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				():
					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(__snake_case	)	),
					    }   ,			features=__snake_case   ,			)
					return dataset





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "file.arrow"	)
					dataset.map(cache_file_name=__snake_case	)
					return filename


# FILE_CONTENT + files


_lowerCamelCase       : Any      					=		'''\
    Text data.
    Second line of data.'''





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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any	):
					import bza

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):
					import gzip

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[Any]	):





					if datasets.config.LZ4_AVAILABLE:
										import lza.frame

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Dict	):





					if datasets.config.PY7ZR_AVAILABLE:
										import pyazr

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				str	):
					import tarfile

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int	):
					import lzma

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





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

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):





					if datasets.config.ZSTANDARD_AVAILABLE:
										import zstandard as zstd

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "file.xml"
					SCREAMING_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(__snake_case   ,			"w"	) as f:
										f.write(__snake_case	)
					return filename


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

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

_lowerCamelCase       : str      					=		[
    {'''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				():
					return DATA_DICT_OF_LISTS





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							datasets.Dataset.from_dict(__snake_case	)
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.arrow"	)
					dataset.map(cache_file_name=__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Dict	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.sqlite"	)
					with contextlib.closing(sqlitea.connect(__snake_case	)	) 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 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.csv"	)
					with open(__snake_case   ,			"w"   ,			newline=""	) as f:
										SCREAMING_SNAKE_CASE           =							csv.DictWriter(__snake_case   ,			fieldnames=["col_1", "col_2", "col_3"]	)
										writer.writeheader()
										for item in DATA:
															writer.writerow(__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset2.csv"	)
					with open(__snake_case   ,			"w"   ,			newline=""	) as f:
										SCREAMING_SNAKE_CASE           =							csv.DictWriter(__snake_case   ,			fieldnames=["col_1", "col_2", "col_3"]	)
										writer.writeheader()
										for item in DATA:
															writer.writerow(__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				Any	):
					import bza

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				Dict	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.csv.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.csv.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename(csv_path.replace(".csv"   ,			".CSV"	)	)	)
										f.write(__snake_case   ,			arcname=os.path.basename(csva_path.replace(".csv"   ,			".CSV"	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Optional[int]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset_with_dir.csv.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any	):
					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(__snake_case   ,			"wb"	) as f:
										SCREAMING_SNAKE_CASE           =							pq.ParquetWriter(__snake_case   ,			schema=__snake_case	)
										SCREAMING_SNAKE_CASE           =							pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__snake_case	)	)] for k in DATA[0]}   ,			schema=__snake_case	)
										writer.write_table(__snake_case	)
										writer.close()
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.json"	)
					SCREAMING_SNAKE_CASE           =							{"data": DATA}
					with open(__snake_case   ,			"w"	) as f:
										json.dump(__snake_case   ,			__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.json"	)
					SCREAMING_SNAKE_CASE           =							{"data": DATA_DICT_OF_LISTS}
					with open(__snake_case   ,			"w"	) as f:
										json.dump(__snake_case   ,			__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.jsonl"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in DATA:
															f.write(json.dumps(__snake_case	) + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset2.jsonl"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in DATA:
															f.write(json.dumps(__snake_case	) + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset_312.jsonl"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in DATA_312:
															f.write(json.dumps(__snake_case	) + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str	):
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset-str.jsonl"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in DATA_STR:
															f.write(json.dumps(__snake_case	) + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				List[str]	):
					import gzip

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				List[str]	):
					import gzip

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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Union[str, Any]   ,			UpperCAmelCase__  :				List[Any]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.jsonl.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Union[str, Any]   ,			UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset_nested.jsonl.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.join("nested"   ,			os.path.basename(__snake_case	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset_with_dir.jsonl.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Dict	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.jsonl.tar"
					with tarfile.TarFile(__snake_case   ,			"w"	) as f:
										f.add(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
										f.add(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset_nested.jsonl.tar"
					with tarfile.TarFile(__snake_case   ,			"w"	) as f:
										f.add(__snake_case   ,			arcname=os.path.join("nested"   ,			os.path.basename(__snake_case	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							["0", "1", "2", "3"]
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset.txt"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in data:
															f.write(item + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):
					SCREAMING_SNAKE_CASE           =							["0", "1", "2", "3"]
					SCREAMING_SNAKE_CASE           =							str(tmp_path_factory.mktemp("data"	) / "dataset2.txt"	)
					with open(__snake_case   ,			"w"	) as f:
										for item in data:
															f.write(item + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):
					SCREAMING_SNAKE_CASE           =							["0", "1", "2", "3"]
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.abc"
					with open(__snake_case   ,			"w"	) as f:
										for item in data:
															f.write(item + "\n"	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.text.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset_with_dir.text.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
										f.write(__snake_case   ,			arcname=os.path.join("main_dir"   ,			os.path.basename(__snake_case	)	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				Dict	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.ext.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename("unsupported.ext"	)	)
										f.write(__snake_case   ,			arcname=os.path.basename("unsupported_2.ext"	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]	):
					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(__snake_case   ,			"w"   ,			encoding="utf-8"	) as f:
										f.write(__snake_case	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				():
					return os.path.join("tests"   ,			"features"   ,			"data"   ,			"test_image_rgb.jpg"	)





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





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							tmp_path_factory.mktemp("data"	) / "dataset.img.zip"
					with zipfile.ZipFile(__snake_case   ,			"w"	) as f:
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	)	)
										f.write(__snake_case   ,			arcname=os.path.basename(__snake_case	).replace(".jpg"   ,			"2.jpg"	)	)
					return path





@pytest.fixture(scope="session"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int	):
					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






"""simple docstring"""





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_a								=						logging.get_logger(__name__)

_a								=						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
_a								=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





					for attribute in key.split('''.'''      ):
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      )

					if weight_type is not None:
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      ).shape
					else:
										_UpperCamelCase									=    hf_pointer.shape

					assert hf_shape == value.shape, (
					    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
					    F''' {value.shape} for {full_name}'''
					)

					if weight_type == "weight":
										_UpperCamelCase									=    value
					elif weight_type == "weight_g":
										_UpperCamelCase									=    value
					elif weight_type == "weight_v":
										_UpperCamelCase									=    value
					elif weight_type == "bias":
										_UpperCamelCase									=    value
					else:
										_UpperCamelCase									=    value

					logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    fairseq_model.state_dict()

					_UpperCamelCase									=    hf_model.feature_extractor
					_UpperCamelCase									=    hf_model.adapter

					for name, value in fairseq_dict.items():
										_UpperCamelCase									=    False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case,	__snake_case,	__snake_case,	__snake_case,	hf_model.config.feat_extract_norm == '''group''',	)
															_UpperCamelCase									=    True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']      ):
															load_adapter(__snake_case,	__snake_case,	__snake_case,	__snake_case      )
															_UpperCamelCase									=    True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
																									_UpperCamelCase									=    True
																									if "*" in mapped_key:
																														_UpperCamelCase									=    name.split(__snake_case      )[0].split('''.'''      )[-2]
																														_UpperCamelCase									=    mapped_key.replace('''*''',	__snake_case      )
																									if "weight_g" in name:
																														_UpperCamelCase									=    '''weight_g'''
																									elif "weight_v" in name:
																														_UpperCamelCase									=    '''weight_v'''
																									elif "bias" in name:
																														_UpperCamelCase									=    '''bias'''
																									elif "weight" in name:
																														_UpperCamelCase									=    '''weight'''
																									else:
																														_UpperCamelCase									=    None
																									set_recursively(__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
																				continue
										if not is_used:
															unused_weights.append(__snake_case      )

					logger.warning(F'''Unused weights: {unused_weights}'''      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''conv_layers.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )
					_UpperCamelCase									=    int(items[0]      )
					_UpperCamelCase									=    int(items[1]      )

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
															)
															_UpperCamelCase									=    value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
															)
															_UpperCamelCase									=    value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
															    " found."
															)
															_UpperCamelCase									=    value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
															)
															_UpperCamelCase									=    value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''adaptor.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )

					if items[1].isdigit():
										_UpperCamelCase									=    int(items[1]      )
					else:
										_UpperCamelCase									=    None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''      )
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
					elif isinstance(__snake_case,	__snake_case      ):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	)				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    WavaVecaConfig.from_pretrained(
					    __snake_case,	add_adapter=__snake_case,	adapter_stride=__snake_case,	adapter_kernel_size=__snake_case,	use_auth_token=__snake_case,	output_hidden_size=__snake_case,	)
					_UpperCamelCase									=    MBartConfig.from_pretrained(__snake_case      )

					# load model
					_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path],	arg_overrides={
					        '''config_yaml''': config_yaml_path,
					        '''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      ),
					        '''w2v_path''': checkpoint_path,
					        '''load_pretrained_decoder_from''': None,
					    },	)
					_UpperCamelCase									=    model[0].eval()

					# load feature extractor
					_UpperCamelCase									=    WavaVecaFeatureExtractor.from_pretrained(__snake_case,	use_auth_token=__snake_case      )

					# set weights for wav2vec2 encoder
					_UpperCamelCase									=    WavaVecaModel(__snake_case      )

					recursively_load_weights_wavaveca(model.encoder,	__snake_case      )

					# load decoder weights
					_UpperCamelCase									=    MBartForCausalLM(__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),	strict=__snake_case      )
					logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''      )
					logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''      )

					_UpperCamelCase									=    SpeechEncoderDecoderModel(encoder=__snake_case,	decoder=__snake_case      )
					_UpperCamelCase									=    False

					_UpperCamelCase									=    MBartaaTokenizer(__snake_case      )
					tokenizer.save_pretrained(__snake_case      )

					_UpperCamelCase									=    hf_wavavec.config.to_dict()
					_UpperCamelCase									=    tokenizer.pad_token_id
					_UpperCamelCase									=    tokenizer.bos_token_id
					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    '''mbart50'''
					_UpperCamelCase									=    '''wav2vec2'''

					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    25_00_04
					_UpperCamelCase									=    tokenizer.eos_token_id

					_UpperCamelCase									=    SpeechEncoderDecoderConfig.from_dict(__snake_case      )

					hf_wavavec.save_pretrained(__snake_case      )
					feature_extractor.save_pretrained(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
				parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
				parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
				parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-xls-r-1b""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/mbart-large-50-one-to-many-mmt""",
				    type=str,
				    help="""Path to hf decoder checkpoint config""",
				)
				parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
				parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
				parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
				parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

				_a								=						parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)






