
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowercase  :					Tuple						    =							logging.getLogger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__=2					,	SCREAMING_SNAKE_CASE__=3					,	SCREAMING_SNAKE_CASE__=16					,	SCREAMING_SNAKE_CASE__ = 10					,	SCREAMING_SNAKE_CASE__ = 2				) ->    Union[str, Any]:

       def get_dataset(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Union[str, Any]       	=      torch.randn(batch_size * n_batches					,	1				)
              return TensorDataset(SCREAMING_SNAKE_CASE__					,	a * x + b + 0.1 * torch.randn(batch_size * n_batches					,	1				)				)

       lowercase							:     Dict       	=      get_dataset(SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      get_dataset(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      DataLoader(SCREAMING_SNAKE_CASE__					,	shuffle=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	num_workers=4				)
       lowercase							:     str       	=      DataLoader(SCREAMING_SNAKE_CASE__					,	shuffle=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	num_workers=4				)
       return (train_dataloader, valid_dataloader)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    int:
       lowercase							:     Dict       	=      []
       for epoch in range(SCREAMING_SNAKE_CASE__				):
              # Train quickly
              model.train()
              for batch in dataloader:
                     lowercase	,			lowercase							:     Tuple       	=      batch
                     lowercase							:     str       	=      model(SCREAMING_SNAKE_CASE__				)
                     lowercase							:     int       	=      torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     accelerator.backward(SCREAMING_SNAKE_CASE__				)
                     optimizer.step()
                     optimizer.zero_grad()
              rands.append(random.random()				)  # Introduce some randomness
              if scheduler is not None:
                     scheduler.step()
       return rands

class __snake_case		(      nn.Module     ):
    def __init__(  self      ):



           '''simple docstring'''







           super().__init__()
           lowercase							:     Optional[int]       	=      nn.Parameter(torch.randn(1      )      )
           lowercase							:     List[str]       	=      nn.Parameter(torch.randn(1      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           return x * self.a + self.b






class __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Any       	=      DummyModel()
                  lowercase							:     List[Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     int       	=      dummy_dataloaders()
                  lowercase							:     List[str]       	=      ProjectConfiguration(total_limit=1		,project_dir=snake_case		,automatic_checkpoint_naming=snake_case      )
                  # Train baseline
                  lowercase							:     Tuple       	=      Accelerator(project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  accelerator.save_state()

                  # Save second state
                  accelerator.save_state()
                  self.assertEqual(len(os.listdir(accelerator.project_dir      )      )		,1      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Optional[Any]       	=      DummyModel()
                  lowercase							:     Any       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Optional[Any]       	=      dummy_dataloaders()
                  # Train baseline
                  lowercase							:     Tuple       	=      Accelerator()
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[str]       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  lowercase							:     List[str]       	=      os.path.join(snake_case		,"""initial"""      )
                  accelerator.save_state(snake_case      )
                  ((lowercase)	,			(lowercase))							:     Optional[Any]       	=      model.a.item(), model.b.item()
                  lowercase							:     Optional[int]       	=      optimizer.state_dict()
                  lowercase							:     Tuple       	=      train(3		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     List[str]       	=      model.a.item(), model.b.item()
                  lowercase							:     List[str]       	=      optimizer.state_dict()

                  # Train partially
                  set_seed(42      )
                  lowercase							:     Tuple       	=      DummyModel()
                  lowercase							:     Dict       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Union[str, Any]       	=      dummy_dataloaders()
                  lowercase							:     Union[str, Any]       	=      Accelerator()
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     int       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  accelerator.load_state(snake_case      )
                  ((lowercase)	,			(lowercase))							:     int       	=      model.a.item(), model.b.item()
                  lowercase							:     Optional[Any]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )

                  lowercase							:     Any       	=      train(2		,snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save everything
                  lowercase							:     List[Any]       	=      os.path.join(snake_case		,"""checkpoint"""      )
                  accelerator.save_state(snake_case      )

                  # Load everything back in and make sure all states work
                  accelerator.load_state(snake_case      )
                  test_rands += train(1		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     int       	=      model.a.item(), model.b.item()
                  lowercase							:     List[str]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Dict       	=      DummyModel()
                  lowercase							:     List[Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Union[str, Any]       	=      dummy_dataloaders()
                  lowercase							:     Any       	=      ProjectConfiguration(automatic_checkpoint_naming=snake_case      )

                  # Train baseline
                  lowercase							:     int       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  accelerator.save_state()
                  ((lowercase)	,			(lowercase))							:     Union[str, Any]       	=      model.a.item(), model.b.item()
                  lowercase							:     int       	=      optimizer.state_dict()
                  lowercase							:     Optional[Any]       	=      train(3		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     Any       	=      model.a.item(), model.b.item()
                  lowercase							:     List[str]       	=      optimizer.state_dict()

                  # Train partially
                  set_seed(42      )
                  lowercase							:     Optional[int]       	=      DummyModel()
                  lowercase							:     Union[str, Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Any       	=      dummy_dataloaders()
                  lowercase							:     Tuple       	=      ProjectConfiguration(iteration=1		,automatic_checkpoint_naming=snake_case      )
                  lowercase							:     Tuple       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  accelerator.load_state(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_0"""      )      )
                  ((lowercase)	,			(lowercase))							:     Optional[int]       	=      model.a.item(), model.b.item()
                  lowercase							:     List[Any]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )

                  lowercase							:     int       	=      train(2		,snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save everything
                  accelerator.save_state()

                  # Load everything back in and make sure all states work
                  accelerator.load_state(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_1"""      )      )
                  test_rands += train(1		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     Tuple       	=      model.a.item(), model.b.item()
                  lowercase							:     Optional[int]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      torch.tensor([1, 2, 3]      )
           lowercase							:     int       	=      torch.tensor([2, 3, 4]      )
           lowercase							:     str       	=      DummyModel()
           lowercase							:     Optional[Any]       	=      torch.optim.Adam(net.parameters()      )
           lowercase							:     Any       	=      Accelerator()
           with self.assertRaises(snake_case      ) as ve:
                  accelerator.register_for_checkpointing(snake_case		,snake_case		,snake_case		,snake_case      )
           lowercase							:     int       	=      str(ve.exception      )
           self.assertTrue("""Item at index 0""" in message      )
           self.assertTrue("""Item at index 1""" in message      )
           self.assertFalse("""Item at index 2""" in message      )
           self.assertFalse("""Item at index 3""" in message      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Dict       	=      DummyModel()
                  lowercase							:     Optional[int]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase							:     Union[str, Any]       	=      torch.optim.lr_scheduler.StepLR(snake_case		,step_size=1		,gamma=0.99      )
                  lowercase	,			lowercase							:     List[Any]       	=      dummy_dataloaders()
                  lowercase							:     List[str]       	=      ProjectConfiguration(automatic_checkpoint_naming=snake_case      )
                  # Train baseline
                  lowercase							:     List[str]       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     Any       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  accelerator.save_state()
                  lowercase							:     Optional[int]       	=      scheduler.state_dict()
                  train(3		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      )
                  self.assertNotEqual(snake_case		,scheduler.state_dict()      )

                  # Load everything back in and make sure all states work
                  accelerator.load_state(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_0"""      )      )
                  self.assertEqual(snake_case		,scheduler.state_dict()      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Union[str, Any]       	=      DummyModel()
                  lowercase							:     Union[str, Any]       	=      ProjectConfiguration(automatic_checkpoint_naming=snake_case		,total_limit=2      )
                  # Train baseline
                  lowercase							:     Dict       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase							:     Any       	=      accelerator.prepare(snake_case      )
                  # Save 3 states:
                  for _ in range(11      ):
                         accelerator.save_state()
                  self.assertTrue(not os.path.exists(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_0"""      )      )      )
                  self.assertTrue(os.path.exists(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_9"""      )      )      )
                  self.assertTrue(os.path.exists(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_10"""      )      )      )
    @require_cuda
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
           execute_subprocess_async(snake_case		,env=os.environ.copy()      )


if __name__ == "__main__":
  lowercase  :					int						    =							"""/tmp/accelerate/state_checkpointing"""
  lowercase  :					List[str]						    =							DummyModel()
  lowercase  :					Tuple						    =							torch.optim.Adam(params=model.parameters(), lr=1e-3)
  lowercase  :					List[str]						    =							torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
  lowercase    ,  lowercase  :					str						    =							dummy_dataloaders()
  lowercase  :					str						    =							ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  lowercase  :					Tuple						    =							Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
  if accelerator.process_index == 0:
    if os.path.exists(savedir):
      shutil.rmtree(savedir)
    os.makedirs(savedir)
  lowercase    ,  lowercase    ,  lowercase    ,  lowercase    ,  lowercase  :					Optional[Any]						    =							accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  lowercase    ,  lowercase  :					Union[str, Any]						    =							accelerator.prepare(model, optimizer)
  train(3, model, train_dataloader, optimizer, accelerator, scheduler)
  # Check that the intial optimizer is loaded on the GPU
  for group in optimizer.param_groups:
    lowercase  :					Dict						    =							group["""params"""][0].device
    break
  assert param_device.type == accelerator.device.type
  lowercase  :					int						    =							model.cpu()
  accelerator.wait_for_everyone()
  accelerator.save_state()
  accelerator.wait_for_everyone()

  # Check CPU state
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
  for group in optimizer.param_groups:
    lowercase  :					Tuple						    =							group["""params"""][0].device
    break
  assert (
      param_device.type == torch.device("""cpu""").type
  ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

  # Check device state
  model.to(accelerator.device)
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
  for group in optimizer.param_groups:
    lowercase  :					List[str]						    =							group["""params"""][0].device
    break
  assert (
      param_device.type == accelerator.device.type
  ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

  # Check error
  with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
    accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
  accelerator.wait_for_everyone()
  if accelerator.process_index == 0:
    shutil.rmtree(savedir)
  accelerator.wait_for_everyone()


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard







@pytest.mark.parametrize(
    """compression_format, is_archive"""					,	[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Any:
       lowercase							:     Dict       	=      {
           """7z""": (seven_zip_file, SevenZipExtractor),
           """bz2""": (bza_file, BzipaExtractor),
           """gzip""": (gz_file, GzipExtractor),
           """lz4""": (lza_file, LzaExtractor),
           """tar""": (tar_file, TarExtractor),
           """xz""": (xz_file, XzExtractor),
           """zip""": (zip_file, ZipExtractor),
           """zstd""": (zstd_file, ZstdExtractor),
       }
       lowercase	,			lowercase							:     Optional[Any]       	=      input_paths_and_base_extractors[compression_format]
       if input_path is None:
              lowercase							:     Dict       	=      f"for '{compression_format}' compression_format, "
              if compression_format == "7z":
                     reason += require_pyazr.kwargs["reason"]
              elif compression_format == "lz4":
                     reason += require_lza.kwargs["reason"]
              elif compression_format == "zstd":
                     reason += require_zstandard.kwargs["reason"]
              pytest.skip(SCREAMING_SNAKE_CASE__				)
       assert base_extractor.is_extractable(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
       base_extractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if is_archive:
              assert output_path.is_dir()
              for file_path in output_path.iterdir():
                     assert file_path.name == text_file.name
                     lowercase							:     str       	=      file_path.read_text(encoding="""utf-8"""				)
       else:
              lowercase							:     Optional[Any]       	=      output_path.read_text(encoding="""utf-8"""				)
       lowercase							:     Tuple       	=      text_file.read_text(encoding="""utf-8"""				)
       assert extracted_file_content == expected_file_content







@pytest.mark.parametrize(
    """compression_format, is_archive"""					,	[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Dict:
       lowercase							:     str       	=      {
           """7z""": seven_zip_file,
           """bz2""": bza_file,
           """gzip""": gz_file,
           """lz4""": lza_file,
           """tar""": tar_file,
           """xz""": xz_file,
           """zip""": zip_file,
           """zstd""": zstd_file,
       }
       lowercase							:     Optional[Any]       	=      input_paths[compression_format]
       if input_path is None:
              lowercase							:     int       	=      f"for '{compression_format}' compression_format, "
              if compression_format == "7z":
                     reason += require_pyazr.kwargs["reason"]
              elif compression_format == "lz4":
                     reason += require_lza.kwargs["reason"]
              elif compression_format == "zstd":
                     reason += require_zstandard.kwargs["reason"]
              pytest.skip(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      Extractor.infer_extractor_format(SCREAMING_SNAKE_CASE__				)
       assert extractor_format is not None
       lowercase							:     Any       	=      tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
       Extractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if is_archive:
              assert output_path.is_dir()
              for file_path in output_path.iterdir():
                     assert file_path.name == text_file.name
                     lowercase							:     Dict       	=      file_path.read_text(encoding="""utf-8"""				)
       else:
              lowercase							:     int       	=      output_path.read_text(encoding="""utf-8"""				)
       lowercase							:     Optional[Any]       	=      text_file.read_text(encoding="""utf-8"""				)
       assert extracted_file_content == expected_file_content







@pytest.fixture
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       import tarfile

       lowercase							:     Tuple       	=      tmp_path / """data_dot_dot"""
       directory.mkdir()
       lowercase							:     str       	=      directory / """tar_file_with_dot_dot.tar"""
       with tarfile.TarFile(SCREAMING_SNAKE_CASE__					,	"""w"""				) as f:
              f.add(SCREAMING_SNAKE_CASE__					,	arcname=os.path.join(""".."""					,	text_file.name				)				)
       return path







@pytest.fixture
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       import tarfile

       lowercase							:     Tuple       	=      tmp_path / """data_sym_link"""
       directory.mkdir()
       lowercase							:     int       	=      directory / """tar_file_with_sym_link.tar"""
       os.symlink(""".."""					,	directory / """subdir"""					,	target_is_directory=SCREAMING_SNAKE_CASE__				)
       with tarfile.TarFile(SCREAMING_SNAKE_CASE__					,	"""w"""				) as f:
              f.add(str(directory / """subdir"""				)					,	arcname="""subdir"""				)  # str required by os.readlink on Windows and Python < 3.8
       return path







@pytest.mark.parametrize(
    """insecure_tar_file, error_log"""					,	[("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     List[Any]       	=      {
           """tar_file_with_dot_dot""": tar_file_with_dot_dot,
           """tar_file_with_sym_link""": tar_file_with_sym_link,
       }
       lowercase							:     Optional[int]       	=      insecure_tar_files[insecure_tar_file]
       lowercase							:     List[str]       	=      tmp_path / """extracted"""
       TarExtractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert caplog.text
       for record in caplog.records:
              assert record.levelname == "ERROR"
              assert error_log in record.msg







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       # We should have less false positives than zipfile.is_zipfile
       # We do that by checking only the magic number
       lowercase							:     Any       	=      tmpdir / """not_a_zip_file"""
       # From: https://github.com/python/cpython/pull/5053
       lowercase							:     str       	=      (
           B"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
           B"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"""
           B"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
           B"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
       )
       with not_a_zip_file.open("""wb"""				) as f:
              f.write(SCREAMING_SNAKE_CASE__				)
       assert zipfile.is_zipfile(str(SCREAMING_SNAKE_CASE__				)				)  # is a false positive for `zipfile`
       assert not ZipExtractor.is_extractable(SCREAMING_SNAKE_CASE__				)  # but we're right
