




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__ (   )  ->       Tuple:
   snake_case				:    Tuple           =	10
   snake_case				:    Optional[int]           =	datasets.Features(
       {
           """tokens""": datasets.Sequence(datasets.Value("""string"""					)					),
           """labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""]					)					),
           """answers""": datasets.Sequence(
               {
                   """text""": datasets.Value("""string"""					),
                   """answer_start""": datasets.Value("""int32"""					),
               }					),
           """id""": datasets.Value("""int64"""					),
       }					)
   snake_case				:    List[Any]           =	datasets.Dataset.from_dict(
       {
           """tokens""": [["""foo"""] * 5] * n,
           """labels""": [[1] * 5] * n,
           """answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
           """id""": list(range(_a					)					),
       }						,features=_a						,)
   return dataset




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   snake_case				:    Union[str, Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """file.arrow"""					)
   dataset.map(cache_file_name=_a					)
   return filename


# FILE_CONTENT + files


lowerCamelCase	:	List[str]				=   '\\n    Text data.\n    Second line of data.'




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    List[Any]           =	tmp_path_factory.mktemp("""data"""					) / """file.txt"""
   snake_case				:    List[str]           =	FILE_CONTENT
   with open(_a						,"""w"""					) as f:
      f.write(_a					)
   return filename




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   import bza

   snake_case				:    Union[str, Any]           =	tmp_path_factory.mktemp("""data"""					) / """file.txt.bz2"""
   snake_case				:    List[str]           =	bytes(_a						,"""utf-8"""					)
   with bza.open(_a						,"""wb"""					) as f:
      f.write(_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   import gzip

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:




   if datasets.config.LZ4_AVAILABLE:
      import lza.frame

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:




   if datasets.config.PY7ZR_AVAILABLE:
      import pyazr

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[Any]:
   import tarfile

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
   import lzma

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   import zipfile

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:




   if datasets.config.ZSTANDARD_AVAILABLE:
      import zstandard as zstd

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   snake_case				:    Optional[Any]           =	tmp_path_factory.mktemp("""data"""					) / """file.xml"""
   snake_case				:    List[str]           =	textwrap.dedent(
       """\
    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>
    <tmx version=\"1.4\">
      <header segtype=\"sentence\" srclang=\"ca\" />
      <body>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
        </tu>
      </body>
    </tmx>"""					)
   with open(_a						,"""w"""					) as f:
      f.write(_a					)
   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	:	Any				=   [
    {'col_1': '4', 'col_2': 4, 'col_3': 4.0},
    {'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
lowerCamelCase	:	List[str]				=   {
    '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	:	Union[str, Any]				=   [
    {'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
    {'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
    {'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
    {'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   )  ->       Dict:
   return DATA_DICT_OF_LISTS




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   snake_case				:    Dict           =	datasets.Dataset.from_dict(_a					)
   snake_case				:    Any           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.arrow"""					)
   dataset.map(cache_file_name=_a					)
   return path




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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
   snake_case				:    List[str]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.csv"""					)
   with open(_a						,"""w"""						,newline=""""""					) as f:
      snake_case				:    List[str]           =	csv.DictWriter(_a						,fieldnames=["""col_1""", """col_2""", """col_3"""]					)
      writer.writeheader()
      for item in DATA:
         writer.writerow(_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    int           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset2.csv"""					)
   with open(_a						,"""w"""						,newline=""""""					) as f:
      snake_case				:    str           =	csv.DictWriter(_a						,fieldnames=["""col_1""", """col_2""", """col_3"""]					)
      writer.writeheader()
      for item in DATA:
         writer.writerow(_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   import bza

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Optional[int]           =	tmp_path_factory.mktemp("""data"""					) / """dataset.csv.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename(_a					)					)
      f.write(_a						,arcname=os.path.basename(_a					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       int:
   snake_case				:    Optional[int]           =	tmp_path_factory.mktemp("""data"""					) / """dataset.csv.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename(csv_path.replace(""".csv"""						,""".CSV"""					)					)					)
      f.write(_a						,arcname=os.path.basename(csva_path.replace(""".csv"""						,""".CSV"""					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    List[Any]           =	tmp_path_factory.mktemp("""data"""					) / """dataset_with_dir.csv.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    int           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.parquet"""					)
   snake_case				:    int           =	pa.schema(
       {
           """col_1""": pa.string(),
           """col_2""": pa.intaa(),
           """col_3""": pa.floataa(),
       }					)
   with open(_a						,"""wb"""					) as f:
      snake_case				:    Union[str, Any]           =	pq.ParquetWriter(_a						,schema=_a					)
      snake_case				:    Any           =	pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_a					)					)] for k in DATA[0]}						,schema=_a					)
      writer.write_table(_a					)
      writer.close()
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
   snake_case				:    Optional[int]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.json"""					)
   snake_case				:    Optional[Any]           =	{"""data""": DATA}
   with open(_a						,"""w"""					) as f:
      json.dump(_a						,_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    List[str]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.json"""					)
   snake_case				:    Dict           =	{"""data""": DATA_DICT_OF_LISTS}
   with open(_a						,"""w"""					) as f:
      json.dump(_a						,_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   snake_case				:    Dict           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.jsonl"""					)
   with open(_a						,"""w"""					) as f:
      for item in DATA:
         f.write(json.dumps(_a					) + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Tuple           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset2.jsonl"""					)
   with open(_a						,"""w"""					) as f:
      for item in DATA:
         f.write(json.dumps(_a					) + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
   snake_case				:    Union[str, Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset_312.jsonl"""					)
   with open(_a						,"""w"""					) as f:
      for item in DATA_312:
         f.write(json.dumps(_a					) + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    List[Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset-str.jsonl"""					)
   with open(_a						,"""w"""					) as f:
      for item in DATA_STR:
         f.write(json.dumps(_a					) + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Union[str, Any]:
   import gzip

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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   import gzip

   snake_case				:    List[Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.jsonl.gz"""					)
   with open(_a						,"""rb"""					) as orig_file:
      with gzip.open(_a						,"""wb"""					) as zipped_file:
         zipped_file.writelines(_a					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	tmp_path_factory.mktemp("""data"""					) / """dataset.jsonl.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename(_a					)					)
      f.write(_a						,arcname=os.path.basename(_a					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       Union[str, Any]:
   snake_case				:    Any           =	tmp_path_factory.mktemp("""data"""					) / """dataset_nested.jsonl.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.join("""nested"""						,os.path.basename(_a					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    Optional[int]           =	tmp_path_factory.mktemp("""data"""					) / """dataset_with_dir.jsonl.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Any:
   snake_case				:    Tuple           =	tmp_path_factory.mktemp("""data"""					) / """dataset.jsonl.tar"""
   with tarfile.TarFile(_a						,"""w"""					) as f:
      f.add(_a						,arcname=os.path.basename(_a					)					)
      f.add(_a						,arcname=os.path.basename(_a					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Dict           =	tmp_path_factory.mktemp("""data"""					) / """dataset_nested.jsonl.tar"""
   with tarfile.TarFile(_a						,"""w"""					) as f:
      f.add(_a						,arcname=os.path.join("""nested"""						,os.path.basename(_a					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    Union[str, Any]           =	["""0""", """1""", """2""", """3"""]
   snake_case				:    Optional[Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset.txt"""					)
   with open(_a						,"""w"""					) as f:
      for item in data:
         f.write(item + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:
   snake_case				:    Union[str, Any]           =	["""0""", """1""", """2""", """3"""]
   snake_case				:    List[Any]           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset2.txt"""					)
   with open(_a						,"""w"""					) as f:
      for item in data:
         f.write(item + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    List[str]           =	["""0""", """1""", """2""", """3"""]
   snake_case				:    List[str]           =	tmp_path_factory.mktemp("""data"""					) / """dataset.abc"""
   with open(_a						,"""w"""					) as f:
      for item in data:
         f.write(item + """\n"""					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Any:
   snake_case				:    Tuple           =	tmp_path_factory.mktemp("""data"""					) / """dataset.text.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename(_a					)					)
      f.write(_a						,arcname=os.path.basename(_a					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   snake_case				:    str           =	tmp_path_factory.mktemp("""data"""					) / """dataset_with_dir.text.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
      f.write(_a						,arcname=os.path.join("""main_dir"""						,os.path.basename(_a					)					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   snake_case				:    Union[str, Any]           =	tmp_path_factory.mktemp("""data"""					) / """dataset.ext.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename("""unsupported.ext"""					)					)
      f.write(_a						,arcname=os.path.basename("""unsupported_2.ext"""					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
   snake_case				:    Dict           =	"""\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""]					)
   snake_case				:    str           =	str(tmp_path_factory.mktemp("""data"""					) / """dataset_with_unicode_new_lines.txt"""					)
   with open(_a						,"""w"""						,encoding="""utf-8"""					) as f:
      f.write(_a					)
   return path




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




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   )  ->       List[Any]:
   return os.path.join("""tests"""						,"""features"""						,"""data"""						,"""test_audio_44100.wav"""					)




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   snake_case				:    List[str]           =	tmp_path_factory.mktemp("""data"""					) / """dataset.img.zip"""
   with zipfile.ZipFile(_a						,"""w"""					) as f:
      f.write(_a						,arcname=os.path.basename(_a					)					)
      f.write(_a						,arcname=os.path.basename(_a					).replace(""".jpg"""						,"""2.jpg"""					)					)
   return path




@pytest.fixture(scope="""session"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:
   snake_case				:    str           =	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 List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase	:	Tuple				=   logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase	:	Dict				=   '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				)		->    int:
         super().__init__()

         self.register_modules(
             unet=A    ,				scheduler=A    ,				movq=A    ,				)
         snake_case				:    Tuple           =	2 ** (len(self.movq.config.block_out_channels     ) - 1)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[str]           =	randn_tensor(A    ,				generator=A    ,				device=A    ,				dtype=A     )
         else:
            if latents.shape != shape:
               raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

         snake_case				:    Optional[Any]           =	latents * scheduler.init_noise_sigma
         return latents

      def      UpperCAmelCase				(				self    ,				A=0     )		->    List[str]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

         snake_case				:    Tuple           =	[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
               cpu_offload(A    ,				A     )

      def      UpperCAmelCase				(				self    ,				A=0     )		->    Optional[Any]:
         if is_accelerate_available() and is_accelerate_version(""">="""    ,				"""0.17.0.dev0"""     ):
            from accelerate import cpu_offload_with_hook
         else:
            raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""     )

         snake_case				:    int           =	torch.device(f"""cuda:{gpu_id}"""     )

         if self.device.type != "cpu":
            self.to("""cpu"""    ,				silence_dtype_warnings=A     )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

         # We'll offload the last model manually.
         snake_case				:    Tuple           =	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    str:
         if not hasattr(self.unet    ,				"""_hf_hook"""     ):
            return self.device
         for module in self.unet.modules():
            if (
                hasattr(A    ,				"""_hf_hook"""     )
                and hasattr(module._hf_hook    ,				"""execution_device"""     )
                and module._hf_hook.execution_device is not None
            ):
               return torch.device(module._hf_hook.execution_device     )
         return self.device

      @torch.no_grad()
      @replace_example_docstring(A     )
      def __call__(				self    ,				A    ,				A    ,				A    ,				A = 5_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

         snake_case				:    List[Any]           =	image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
            snake_case				:    Optional[int]           =	image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Dict           =	negative_image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Optional[Any]           =	hint.repeat_interleave(A    ,				dim=0     )

         snake_case				:    List[str]           =	torch.cat([negative_image_embeds, image_embeds]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         snake_case				:    Optional[Any]           =	torch.cat([hint, hint]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         self.scheduler.set_timesteps(A    ,				device=A     )
         snake_case				:    Tuple           =	self.scheduler.timesteps

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

         # create initial latent
         snake_case				:    str           =	self.prepare_latents(
             (batch_size, num_channels_latents, height, width)    ,				image_embeds.dtype    ,				A    ,				A    ,				A    ,				self.scheduler    ,				)

         for i, t in enumerate(self.progress_bar(A     )     ):
            # expand the latents if we are doing classifier free guidance
            snake_case				:    str           =	torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

            snake_case				:    Any           =	{"""image_embeds""": image_embeds, """hint""": hint}
            snake_case				:    List[Any]           =	self.unet(
                sample=A    ,				timestep=A    ,				encoder_hidden_states=A    ,				added_cond_kwargs=A    ,				return_dict=A    ,				)[0]

            if do_classifier_free_guidance:
               snake_case     , snake_case				:    Union[str, Any]           =	noise_pred.split(latents.shape[1]    ,				dim=1     )
               snake_case     , snake_case				:    Tuple           =	noise_pred.chunk(2     )
               snake_case     , snake_case				:    Optional[Any]           =	variance_pred.chunk(2     )
               snake_case				:    Optional[Any]           =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               snake_case				:    Dict           =	torch.cat([noise_pred, variance_pred_text]    ,				dim=1     )

            if not (
                hasattr(self.scheduler.config    ,				"""variance_type"""     )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
               snake_case     , snake_case				:    int           =	noise_pred.split(latents.shape[1]    ,				dim=1     )

            # compute the previous noisy sample x_t -> x_t-1
            snake_case				:    Dict           =	self.scheduler.step(
                A    ,				A    ,				A    ,				generator=A    ,				)[0]
         # post-processing
         snake_case				:    Tuple           =	self.movq.decode(A    ,				force_not_quantize=A     )["""sample"""]

         if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

         if output_type in ["np", "pil"]:
            snake_case				:    Optional[int]           =	image * 0.5 + 0.5
            snake_case				:    Union[str, Any]           =	image.clamp(0    ,				1     )
            snake_case				:    List[str]           =	image.cpu().permute(0    ,				2    ,				3    ,				1     ).float().numpy()

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





