


import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   ()->  Union[str, Any]:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

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


# FILE_CONTENT + files


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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      import bza

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      import gzip

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      if datasets.config.LZ4_AVAILABLE:
            import lza.frame

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      if datasets.config.PY7ZR_AVAILABLE:
            import pyazr

            snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      import tarfile

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      import lzma

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      import zipfile

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      if datasets.config.ZSTANDARD_AVAILABLE:
            import zstandard as zstd

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				tmp_path_factory.mktemp('''data''' ) / """file.xml"""
      snake_case_	=				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


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

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

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      return DATA_DICT_OF_LISTS






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
      with contextlib.closing(sqlitea.connect(_a ) ) as con:
            snake_case_	=				con.cursor()
            cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)''' )
            for item in DATA:
                  cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)''' , tuple(item.values() ) )
            con.commit()
      return path






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
      with open(_a , '''w''' , newline='''''' ) as f:
            snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
      with open(_a , '''w''' , newline='''''' ) as f:
            snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      import bza

      snake_case_	=				tmp_path_factory.mktemp('''data''' ) / """dataset.csv.bz2"""
      with open(_a , '''rb''' ) as f:
            snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
      snake_case_	=				pa.schema(
          {
              '''col_1''': pa.string(),
              '''col_2''': pa.intaa(),
              '''col_3''': pa.floataa(),
          } )
      with open(_a , '''wb''' ) as f:
            snake_case_	=				pq.ParquetWriter(_a , schema=_a )
            snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      import gzip

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      import gzip

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				["""0""", """1""", """2""", """3"""]
      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				["""0""", """1""", """2""", """3"""]
      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      snake_case_	=				["""0""", """1""", """2""", """3"""]
      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   ()->  Optional[Any]:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   ()->  Dict:




      """simple docstring"""

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






@pytest.fixture(scope='''session''' )
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				tmp_path_factory.mktemp('''data_dir''' )

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

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

      return data_dir



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				self.get_dummy_inputs(_UpperCAmelCase )
       snake_case_	=				sd_pipe(**_UpperCAmelCase ).images
       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







@slow
@require_torch_gpu
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9