
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__a  			=					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

__a  			=					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a  			=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:Optional[str]         =	field(
	    default='cifar10' ,						metadata={'help': 'Name of a dataset from the datasets package'}							)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'} ,						)
	a			:Optional[str]         =	field(default=UpperCAmelCase ,						metadata={'help': 'A folder containing the training data.'}							)
	a			:Optional[str]         =	field(default=UpperCAmelCase ,						metadata={'help': 'A folder containing the validation data.'}							)
	a			:Optional[float]         =	field(
	    default=0.15 ,						metadata={'help': 'Percent to split off of train for validation.'}							)
	a			:int         =	field(default=32 ,						metadata={'help': 'The size of the square patches to use for masking.'}							)
	a			:float         =	field(
	    default=0.6 ,						metadata={'help': 'Percentage of patches to mask.'} ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    } ,						)
	def 						_lowercase  (       self  :			List[str]       )       ->     Tuple:
					lowercase_       =					{}
					if self.train_dir is not None:
									lowercase_       =					self.train_dir
					if self.validation_dir is not None:
									lowercase_       =					self.validation_dir
					lowercase_       =					data_files if data_files else None

@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:str         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
	            'checkpoint identifier on the hub. '
	            'Don\'t set if you want to train a model from scratch.'
	        )
	    } ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase							)} ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'Override some existing default config settings when a model is trained from scratch. Example: '
	            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
	        )
	    } ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'} ,						)
	a			:str         =	field(
	    default='main' ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,						)
	a			:str         =	field(default=UpperCAmelCase ,						metadata={'help': 'Name or path of preprocessor config.'}							)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
	            'with private models).'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Stride to use for the encoder.'} ,						)

class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_9_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=3_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=4       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.6       )       ->     List[str]:
					lowercase_       =					input_size
					lowercase_       =					mask_patch_size
					lowercase_       =					model_patch_size
					lowercase_       =					mask_ratio

					if self.input_size % self.mask_patch_size != 0:
									raise ValueError('''Input size must be divisible by mask patch size'''       )
					if self.mask_patch_size % self.model_patch_size != 0:
									raise ValueError('''Mask patch size must be divisible by model patch size'''       )

					lowercase_       =					self.input_size // self.mask_patch_size
					lowercase_       =					self.mask_patch_size // self.model_patch_size

					lowercase_       =					self.rand_size**2
					lowercase_       =					int(np.ceil(self.token_count * self.mask_ratio       )       )




	def __call__(       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					np.random.permutation(self.token_count       )[: self.mask_count]
					lowercase_       =					np.zeros(self.token_count       ,							dtype=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					1

					lowercase_       =					mask.reshape((self.rand_size, self.rand_size)       )
					lowercase_       =					mask.repeat(self.scale       ,							axis=0       ).repeat(self.scale       ,							axis=1       )

					return torch.tensor(mask.flatten()       )




def 				a						(							snake_case__:					str					):


				'''simple docstring'''
				lowercase_       =					torch.stack([example['''pixel_values'''] for example in examples]					)
				lowercase_       =					torch.stack([example['''mask'''] for example in examples]					)
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def 				a						(							):


				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase_       =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
				if len(sys.argv					) == 2 and sys.argv[1].endswith('''.json'''					):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase_  ,	lowercase_  ,	lowercase_       =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								lowercase_  ,	lowercase_  ,	lowercase_       =					parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_mim'''						,   snake_case__						,   snake_case__					)

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,   datefmt='''%m/%d/%Y %H:%M:%S'''						,   handlers=[logging.StreamHandler(sys.stdout					)]						,   )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				lowercase_       =					training_args.get_process_log_level()
				logger.setLevel(snake_case__					)
				transformers.utils.logging.set_verbosity(snake_case__					)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'''					)
				logger.info(F'''Training/evaluation parameters {training_args}'''					)

				# Detecting last checkpoint.
				lowercase_       =					None
				if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase_       =					get_last_checkpoint(training_args.output_dir					)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    '''Use --overwrite_output_dir to overcome.'''					)
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''					)

    # Initialize our dataset.
				lowercase_       =					load_dataset(
				    data_args.dataset_name						,   data_args.dataset_config_name						,   data_files=data_args.data_files						,   cache_dir=model_args.cache_dir						,   use_auth_token=True if model_args.use_auth_token else None						,   )

				# If we don't have a validation split, split off a percentage of train as validation.
				lowercase_       =					None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,   snake_case__					) and data_args.train_val_split > 0.0:
								lowercase_       =					ds['''train'''].train_test_split(data_args.train_val_split					)
								lowercase_       =					split['''train''']
								lowercase_       =					split['''test''']

				# Create config
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowercase_       =					{
				    '''cache_dir''': model_args.cache_dir,
				    '''revision''': model_args.model_revision,
				    '''use_auth_token''': True if model_args.use_auth_token else None,
				}
				if model_args.config_name_or_path:
								lowercase_       =					AutoConfig.from_pretrained(model_args.config_name_or_path						,   **snake_case__					)
				elif model_args.model_name_or_path:
								lowercase_       =					AutoConfig.from_pretrained(model_args.model_name_or_path						,   **snake_case__					)
				else:
								lowercase_       =					CONFIG_MAPPING[model_args.model_type]()
								logger.warning('''You are instantiating a new config instance from scratch.'''					)
								if model_args.config_overrides is not None:
												logger.info(F'''Overriding config: {model_args.config_overrides}'''					)
												config.update_from_string(model_args.config_overrides					)
												logger.info(F'''New config: {config}'''					)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(snake_case__						,   '''decoder_type'''					):
								lowercase_       =					'''simmim'''

				# adapt config
				lowercase_       =					model_args.image_size if model_args.image_size is not None else config.image_size
				lowercase_       =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
				lowercase_       =					(
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        '''image_size''': model_args.image_size,
				        '''patch_size''': model_args.patch_size,
				        '''encoder_stride''': model_args.encoder_stride,
				    }					)

				# create image processor
				if model_args.image_processor_name:
								lowercase_       =					AutoImageProcessor.from_pretrained(model_args.image_processor_name						,   **snake_case__					)
				elif model_args.model_name_or_path:
								lowercase_       =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,   **snake_case__					)
				else:
								lowercase_       =					{
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								lowercase_       =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								lowercase_       =					AutoModelForMaskedImageModeling.from_pretrained(
								    model_args.model_name_or_path						,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path					)						,   config=snake_case__						,   cache_dir=model_args.cache_dir						,   revision=model_args.model_revision						,   use_auth_token=True if model_args.use_auth_token else None						,   )
				else:
								logger.info('''Training new model from scratch'''					)
								lowercase_       =					AutoModelForMaskedImageModeling.from_config(snake_case__					)

				if training_args.do_train:
								lowercase_       =					ds['''train'''].column_names
				else:
								lowercase_       =					ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								lowercase_       =					data_args.image_column_name
				elif "image" in column_names:
								lowercase_       =					'''image'''
				elif "img" in column_names:
								lowercase_       =					'''img'''
				else:
								lowercase_       =					column_names[0]

				# transformations as done in original SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				lowercase_       =					Compose(
				    [
				        Lambda(lambda snake_case__					: img.convert('''RGB'''					) if img.mode != "RGB" else img					),
				        RandomResizedCrop(model_args.image_size						,   scale=(0.6_7, 1.0)						,   ratio=(3.0 / 4.0, 4.0 / 3.0)					),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean						,   std=image_processor.image_std					),
				    ]					)

				# create mask generator
				lowercase_       =					MaskGenerator(
				    input_size=model_args.image_size						,   mask_patch_size=data_args.mask_patch_size						,   model_patch_size=model_args.patch_size						,   mask_ratio=data_args.mask_ratio						,   )

				def preprocess_images(snake_case__:					Optional[int]					):
								lowercase_       =					[transforms(snake_case__					) for image in examples[image_column_name]]
								lowercase_       =					[mask_generator() for i in range(len(examples[image_column_name]					)					)]

								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('''--do_train requires a train dataset'''					)
								if data_args.max_train_samples is not None:
												lowercase_       =					ds['''train'''].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
								# Set the training transforms
								ds["train"].set_transform(snake_case__					)

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('''--do_eval requires a validation dataset'''					)
								if data_args.max_eval_samples is not None:
												lowercase_       =					(
												    ds['''validation'''].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
												)
								# Set the validation transforms
								ds["validation"].set_transform(snake_case__					)

				# Initialize our trainer
				lowercase_       =					Trainer(
				    model=snake_case__						,   args=snake_case__						,   train_dataset=ds['''train'''] if training_args.do_train else None						,   eval_dataset=ds['''validation'''] if training_args.do_eval else None						,   tokenizer=snake_case__						,   data_collator=snake_case__						,   )

				# Training
				if training_args.do_train:
								lowercase_       =					None
								if training_args.resume_from_checkpoint is not None:
												lowercase_       =					training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase_       =					last_checkpoint
								lowercase_       =					trainer.train(resume_from_checkpoint=snake_case__					)
								trainer.save_model()
								trainer.log_metrics('''train'''						,   train_result.metrics					)
								trainer.save_metrics('''train'''						,   train_result.metrics					)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								lowercase_       =					trainer.evaluate()
								trainer.log_metrics('''eval'''						,   snake_case__					)
								trainer.save_metrics('''eval'''						,   snake_case__					)

				# Write model card and (optionally) push to hub
				lowercase_       =					{
				    '''finetuned_from''': model_args.model_name_or_path,
				    '''tasks''': '''masked-image-modeling''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-image-modeling'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**snake_case__					)
				else:
								trainer.create_model_card(**snake_case__					)


if __name__ == "__main__":
						main()






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 				a						(							snake_case__:					int						,   snake_case__:					Tuple						,   snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					str						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					int						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''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_       =					input_paths_and_base_extractors[compression_format]
				if input_path is None:
								lowercase_       =					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(snake_case__					)
				assert base_extractor.is_extractable(snake_case__					)
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				base_extractor.extract(snake_case__						,   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_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					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 				a						(							snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]						,   snake_case__:					Tuple						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					Union[str, Any]						,   snake_case__:					Tuple						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''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_       =					input_paths[compression_format]
				if input_path is None:
								lowercase_       =					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(snake_case__					)
				lowercase_       =					Extractor.infer_extractor_format(snake_case__					)
				assert extractor_format is not None
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				Extractor.extract(snake_case__						,   snake_case__						,   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_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.fixture
def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_dot_dot'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_dot_dot.tar'''
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(snake_case__						,   arcname=os.path.join('''..'''						,   text_file.name					)					)
				return path




@pytest.fixture
def 				a						(							snake_case__:					int					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_sym_link'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_sym_link.tar'''
				os.symlink('''..'''						,   directory / '''subdir'''						,   target_is_directory=snake_case__					)
				with tarfile.TarFile(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 				a						(							snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					List[str]						,   snake_case__:					int						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					{
				    '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
				    '''tar_file_with_sym_link''': tar_file_with_sym_link,
				}
				lowercase_       =					insecure_tar_files[insecure_tar_file]
				lowercase_       =					tmp_path / '''extracted'''
				TarExtractor.extract(snake_case__						,   snake_case__					)
				assert caplog.text
				for record in caplog.records:
								assert record.levelname == "ERROR"
								assert error_log in record.msg




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				# We should have less false positives than zipfile.is_zipfile
				# We do that by checking only the magic number
				lowercase_       =					tmpdir / '''not_a_zip_file'''
				# From: https://github.com/python/cpython/pull/5053
				lowercase_       =					(
				    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(snake_case__					)
				assert zipfile.is_zipfile(str(snake_case__					)					)  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(snake_case__					)  # but we're right




