


'''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 	a_					( ):
   lowerCAmelCase     =			10
   lowerCAmelCase     =			datasets.Features(
       {
           'tokens': datasets.Sequence(datasets.Value('string'				)				),
           'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive']				)				),
           'answers': datasets.Sequence(
               {
                   'text': datasets.Value('string'				),
                   'answer_start': datasets.Value('int32'				),
               }				),
           'id': datasets.Value('int64'				),
       }				)
   lowerCAmelCase     =			datasets.Dataset.from_dict(
       {
           'tokens': [['foo'] * 5] * n,
           'labels': [[1] * 5] * n,
           'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
           'id': list(range(lowerCamelCase				)				),
       }	, features=lowerCamelCase	, )
   return dataset





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Tuple				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'file.arrow'				)
   dataset.map(cache_file_name=lowerCamelCase				)
   return filename


# FILE_CONTENT + files


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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[str]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'file.txt'
   lowerCAmelCase     =			FILE_CONTENT
   with open(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase				)
   return filename





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Union[str, Any]				):
   import bza

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Union[str, Any]				):
   import gzip

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Any				):
   if datasets.config.LZ4_AVAILABLE:
      import lza.frame

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     List[Any]				):
   if datasets.config.PY7ZR_AVAILABLE:
      import pyazr

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     List[str]				):
   import tarfile

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Any				):
   import lzma

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     int				):
   import zipfile

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[Any]				):
   if datasets.config.ZSTANDARD_AVAILABLE:
      import zstandard as zstd

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'file.xml'
   lowerCAmelCase     =			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


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

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

__snake_case    						=[
    {"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
    {"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
    {"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
    {"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]





@pytest.fixture(scope='session'				)
def 	a_					( ):
   return DATA_DICT_OF_LISTS





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			datasets.Dataset.from_dict(lowerCamelCase				)
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.arrow'				)
   dataset.map(cache_file_name=lowerCamelCase				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[int]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.sqlite'				)
   with contextlib.closing(sqlitea.connect(lowerCamelCase				)				) as con:
      lowerCAmelCase     =			con.cursor()
      cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)'				)
      for item in DATA:
         cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)'	, tuple(item.values()				)				)
      con.commit()
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Any				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.csv'				)
   with open(lowerCamelCase	, 'w'	, newline=''				) as f:
      lowerCAmelCase     =			csv.DictWriter(lowerCamelCase	, fieldnames=['col_1', 'col_2', 'col_3']				)
      writer.writeheader()
      for item in DATA:
         writer.writerow(lowerCamelCase				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[Any]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset2.csv'				)
   with open(lowerCamelCase	, 'w'	, newline=''				) as f:
      lowerCAmelCase     =			csv.DictWriter(lowerCamelCase	, fieldnames=['col_1', 'col_2', 'col_3']				)
      writer.writeheader()
      for item in DATA:
         writer.writerow(lowerCamelCase				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]				):
   import bza

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[int]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Any				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename(csv_path.replace('.csv'	, '.CSV'				)				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename(csva_path.replace('.csv'	, '.CSV'				)				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     int	, lowerCamelCase	:     Union[str, Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.csv.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.parquet'				)
   lowerCAmelCase     =			pa.schema(
       {
           'col_1': pa.string(),
           'col_2': pa.intaa(),
           'col_3': pa.floataa(),
       }				)
   with open(lowerCamelCase	, 'wb'				) as f:
      lowerCAmelCase     =			pq.ParquetWriter(lowerCamelCase	, schema=lowerCamelCase				)
      lowerCAmelCase     =			pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowerCamelCase				)				)] for k in DATA[0]}	, schema=lowerCamelCase				)
      writer.write_table(lowerCamelCase				)
      writer.close()
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Union[str, Any]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
   lowerCAmelCase     =			{'data': DATA}
   with open(lowerCamelCase	, 'w'				) as f:
      json.dump(lowerCamelCase	, lowerCamelCase				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[int]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
   lowerCAmelCase     =			{'data': DATA_DICT_OF_LISTS}
   with open(lowerCamelCase	, 'w'				) as f:
      json.dump(lowerCamelCase	, lowerCamelCase				)
   return path





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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset2.jsonl'				)
   with open(lowerCamelCase	, 'w'				) as f:
      for item in DATA:
         f.write(json.dumps(lowerCamelCase				) + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset_312.jsonl'				)
   with open(lowerCamelCase	, 'w'				) as f:
      for item in DATA_312:
         f.write(json.dumps(lowerCamelCase				) + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[str]				):
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset-str.jsonl'				)
   with open(lowerCamelCase	, 'w'				) as f:
      for item in DATA_STR:
         f.write(json.dumps(lowerCamelCase				) + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     Tuple				):
   import gzip

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     int				):
   import gzip

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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     str	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Any				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset_nested.jsonl.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.join('nested'	, os.path.basename(lowerCamelCase				)				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     Any	, lowerCamelCase	:     Union[str, Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.jsonl.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     str	, lowerCamelCase	:     Optional[Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.tar'
   with tarfile.TarFile(lowerCamelCase	, 'w'				) as f:
      f.add(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
      f.add(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
   return path





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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			['0', '1', '2', '3']
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset.txt'				)
   with open(lowerCamelCase	, 'w'				) as f:
      for item in data:
         f.write(item + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     List[str]				):
   lowerCAmelCase     =			['0', '1', '2', '3']
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset2.txt'				)
   with open(lowerCamelCase	, 'w'				) as f:
      for item in data:
         f.write(item + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			['0', '1', '2', '3']
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.abc'
   with open(lowerCamelCase	, 'w'				) as f:
      for item in data:
         f.write(item + '\n'				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Tuple				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.text.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Any	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.text.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
      f.write(lowerCamelCase	, arcname=os.path.join('main_dir'	, os.path.basename(lowerCamelCase				)				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Tuple				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.ext.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename('unsupported.ext'				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename('unsupported_2.ext'				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			'\n'.join(['First', 'Second\u2029with Unicode new line', 'Third']				)
   lowerCAmelCase     =			str(tmp_path_factory.mktemp('data'				) / 'dataset_with_unicode_new_lines.txt'				)
   with open(lowerCamelCase	, 'w'	, encoding='utf-8'				) as f:
      f.write(lowerCamelCase				)
   return path





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





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





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Union[str, Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data'				) / 'dataset.img.zip'
   with zipfile.ZipFile(lowerCamelCase	, 'w'				) as f:
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				)				)
      f.write(lowerCamelCase	, arcname=os.path.basename(lowerCamelCase				).replace('.jpg'	, '2.jpg'				)				)
   return path





@pytest.fixture(scope='session'				)
def 	a_					( lowerCamelCase	:     Union[str, Any]				):
   lowerCAmelCase     =			tmp_path_factory.mktemp('data_dir'				)

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

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

   return data_dir









'''simple docstring'''



import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A__		:					Optional[int]												=  logging.get_logger(__name__)

A__		:					List[Any]												=  {
    """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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
A__		:					Tuple												=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Tuple:
     for attribute in key.split('.'							):
          __lowerCamelCase   :	List[Any]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)

     if weight_type is not None:
          __lowerCamelCase   :	Any								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							).shape
     else:
          __lowerCamelCase   :	Any								=		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":
          __lowerCamelCase   :	Tuple								=		value
     elif weight_type == "weight_g":
          __lowerCamelCase   :	Optional[int]								=		value
     elif weight_type == "weight_v":
          __lowerCamelCase   :	str								=		value
     elif weight_type == "bias":
          __lowerCamelCase   :	List[Any]								=		value
     else:
          __lowerCamelCase   :	List[str]								=		value

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[Any]								=		[]
     __lowerCamelCase   :	Dict								=		fairseq_model.state_dict()

     __lowerCamelCase   :	List[str]								=		hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCamelCase   :	Union[str, Any]								=		False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
               __lowerCamelCase   :	str								=		True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                         __lowerCamelCase   :	int								=		True
                         if "*" in mapped_key:
                              __lowerCamelCase   :	Optional[int]								=		name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                              __lowerCamelCase   :	List[str]								=		mapped_key.replace('*'      ,    UpperCAmelCase_							)
                         if "weight_g" in name:
                              __lowerCamelCase   :	Dict								=		'weight_g'
                         elif "weight_v" in name:
                              __lowerCamelCase   :	Any								=		'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __lowerCamelCase   :	Any								=		'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __lowerCamelCase   :	List[Any]								=		'weight'
                         else:
                              __lowerCamelCase   :	str								=		None

                         set_recursively(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase_							)

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     __lowerCamelCase   :	List[str]								=		full_name.split('conv_layers.'							)[-1]
     __lowerCamelCase   :	List[Any]								=		name.split('.'							)
     __lowerCamelCase   :	Any								=		int(items[0]							)
     __lowerCamelCase   :	Optional[Any]								=		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.'
               )
               __lowerCamelCase   :	Union[str, Any]								=		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.'
               )
               __lowerCamelCase   :	Tuple								=		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."
               )
               __lowerCamelCase   :	str								=		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.'
               )
               __lowerCamelCase   :	List[Any]								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(UpperCAmelCase_							)



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]=None							)     ->		Optional[int]:
     # load the pre-trained checkpoints
     __lowerCamelCase   :	Optional[Any]								=		torch.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		WavLMConfigOrig(checkpoint['cfg']							)
     __lowerCamelCase   :	Union[str, Any]								=		WavLMOrig(UpperCAmelCase_							)
     model.load_state_dict(checkpoint['model']							)
     model.eval()

     if config_path is not None:
          __lowerCamelCase   :	Optional[int]								=		WavLMConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Any								=		WavLMConfig()

     __lowerCamelCase   :	Optional[int]								=		WavLMModel(UpperCAmelCase_							)

     recursively_load_weights(UpperCAmelCase_      ,    UpperCAmelCase_							)

     hf_wavlm.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					List[Any]												=  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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A__		:					List[str]												=  parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






