




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     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					str(tmp_path_factory.mktemp('data'  ) / 'file.arrow'  )
      dataset.map(cache_file_name=_lowerCamelCase  )
      return filename


# FILE_CONTENT + files


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

@pytest.fixture(scope='session'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: int  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict  ):


      '''simple docstring'''



      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


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

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

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

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


      '''simple docstring'''



      return DATA_DICT_OF_LISTS

@pytest.fixture(scope='session'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: Tuple			,							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[int]			,							lowercase			: Any  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: str			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: List[Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Union[str, Any]			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: str			,							lowercase			: str			,							lowercase			: Union[str, Any]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict			,							lowercase			: int			,							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Optional[Any]			,							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Optional[int]			,							lowercase			: Optional[Any]			,							lowercase			: str  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Dict			,							lowercase			: Dict  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Optional[int]			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Tuple			,							lowercase			: Any  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

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


      '''simple docstring'''



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

@pytest.fixture(scope='session'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: List[str]  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Dict  ):


      '''simple docstring'''



      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









from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		['''pixel_values''']

 def __init__(       self      :						List[Any]		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						bool = True		,							A_      :						Union[int, float] = 1 / 255		,							A_      :						bool = True		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,							**A_      :						Tuple		,							)     ->			None:

       """simple docstring"""






       super().__init__(**A_						)
       lowerCamelCase_	  =					size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       lowerCamelCase_	  =					resample
       lowerCamelCase_	  =					do_center_crop
       lowerCamelCase_	  =					crop_size
       lowerCamelCase_	  =					do_rescale
       lowerCamelCase_	  =					rescale_factor
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowerCamelCase_	  =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Tuple		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
             lowerCamelCase_	  =					int((256 / 224) * size['shortest_edge']						)
             lowerCamelCase_	  =					get_resize_output_image_size(A_		,							size=A_		,							default_to_square=A_						)
             lowerCamelCase_	  =					{'height': output_size[0], 'width': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""						)
       return resize(
           A_		,							size=(size_dict['height'], size_dict['width'])		,							resample=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Any		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Any		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_						)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""						)
       return center_crop(A_		,							size=(size['height'], size['width'])		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Union[int, float]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Optional[int]		,							)     ->			np.ndarray:

       """simple docstring"""






       return rescale(A_		,							scale=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						List[str]		,							A_      :						np.ndarray		,							A_      :						Union[float, List[float]]		,							A_      :						Union[float, List[float]]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						str		,							)     ->			np.ndarray:

       """simple docstring"""






       return normalize(A_		,							mean=A_		,							std=A_		,							data_format=A_		,							**A_						)





 def        a__				(       self      :						Optional[int]		,							A_      :						ImageInput		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						PILImageResampling = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[float] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[TensorType] = None		,							A_      :						ChannelDimension = ChannelDimension.FIRST		,							**A_      :						List[Any]		,							)     ->			BatchFeature:

       """simple docstring"""






       lowerCamelCase_	  =					do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_	  =					resample if resample is not None else self.resample
       lowerCamelCase_	  =					do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_	  =					do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_	  =					rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_	  =					do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_	  =					image_std if image_std is not None else self.image_std

       lowerCamelCase_	  =					size if size is not None else self.size
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					make_list_of_images(A_						)

       if not valid_images(A_						):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'						)

       if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'						)

       if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'						)

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

       # All transformations expect numpy arrays.
       lowerCamelCase_	  =					[to_numpy_array(A_						) for image in images]

       if do_resize:
             lowerCamelCase_	  =					[self.resize(A_		,							A_		,							A_						) for image in images]

       if do_center_crop:
             lowerCamelCase_	  =					[self.center_crop(A_		,							A_						) for image in images]

       if do_rescale:
             lowerCamelCase_	  =					[self.rescale(A_		,							A_						) for image in images]

       if do_normalize:
             lowerCamelCase_	  =					[self.normalize(A_		,							A_		,							A_						) for image in images]

       lowerCamelCase_	  =					[to_channel_dimension_format(A_		,							A_						) for image in images]

       lowerCamelCase_	  =					{'pixel_values': images}
       return BatchFeature(data=A_		,							tensor_type=A_						)



