



import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
	from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=13			,				UpperCamelCase=32			,				UpperCamelCase=3			,				UpperCamelCase=4			,				UpperCamelCase=[10, 20, 30, 40]			,				UpperCamelCase=[2, 2, 3, 2]			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=37			,				UpperCamelCase="gelu"			,				UpperCamelCase=10			,				UpperCamelCase=0.02			,				UpperCamelCase=["stage2", "stage3", "stage4"]			,				UpperCamelCase=[2, 3, 4]			,				UpperCamelCase=None			,				)     ->			str:
						__lowerCAmelCase												=  parent
						__lowerCAmelCase												=  batch_size
						__lowerCAmelCase												=  image_size
						__lowerCAmelCase												=  num_channels
						__lowerCAmelCase												=  num_stages
						__lowerCAmelCase												=  hidden_sizes
						__lowerCAmelCase												=  depths
						__lowerCAmelCase												=  is_training
						__lowerCAmelCase												=  use_labels
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  num_labels
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  out_features
						__lowerCAmelCase												=  out_indices
						__lowerCAmelCase												=  scope



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

						__lowerCAmelCase												=  None
						if self.use_labels:
							__lowerCAmelCase												=  ids_tensor([self.batch_size]			,				self.num_labels     )

						__lowerCAmelCase												=  self.get_config()
						return config, pixel_values, labels



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						return ConvNextConfig(
						    num_channels=self.num_channels			,				hidden_sizes=self.hidden_sizes			,				depths=self.depths			,				num_stages=self.num_stages			,				hidden_act=self.hidden_act			,				is_decoder=_snake_case			,				initializer_range=self.initializer_range			,				out_features=self.out_features			,				out_indices=self.out_indices			,				num_labels=self.num_labels			,				)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  ConvNextModel(config=_snake_case     )
						model.to(_snake_case     )
						model.eval()
						__lowerCAmelCase												=  model(_snake_case     )
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,				)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  ConvNextForImageClassification(_snake_case     )
						model.to(_snake_case     )
						model.eval()
						__lowerCAmelCase												=  model(_snake_case			,				labels=_snake_case     )
						self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  ConvNextBackbone(config=_snake_case     )
						model.to(_snake_case     )
						model.eval()
						__lowerCAmelCase												=  model(_snake_case     )

						# verify hidden states
						self.parent.assertEqual(len(result.feature_maps     )			,				len(config.out_features     )     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,				[self.batch_size, self.hidden_sizes[1], 4, 4]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )			,				len(config.out_features     )     )
						self.parent.assertListEqual(model.channels			,				config.hidden_sizes[1:]     )

						# verify backbone works with out_features=None
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  ConvNextBackbone(config=_snake_case     )
						model.to(_snake_case     )
						model.eval()
						__lowerCAmelCase												=  model(_snake_case     )

						# verify feature maps
						self.parent.assertEqual(len(result.feature_maps     )			,				1     )
						self.parent.assertListEqual(list(result.feature_maps[0].shape     )			,				[self.batch_size, self.hidden_sizes[-1], 1, 1]     )

						# verify channels
						self.parent.assertEqual(len(model.channels     )			,				1     )
						self.parent.assertListEqual(model.channels			,				[config.hidden_sizes[-1]]     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.prepare_config_and_inputs()
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  config_and_inputs
						__lowerCAmelCase												=  {"pixel_values": pixel_values}
						return config, inputs_dict






@require_torch
class 					UpperCAmelCase__	(		UpperCAmelCase_							,       UpperCAmelCase_							,       unittest.TestCase ):
					a      :     Any        =							(
					    (
					        ConvNextModel,
					        ConvNextForImageClassification,
					        ConvNextBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					a      :     Optional[Any]        =							(
					    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
					    if is_torch_available()
					    else {}
					)

					a      :     Dict        =							True
					a      :     Optional[int]        =							False
					a      :     str        =							False
					a      :     Tuple        =							False
					a      :     List[str]        =							False



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  ConvNextModelTester(self     )
						__lowerCAmelCase												=  ConfigTester(self			,				config_class=_snake_case			,				has_text_modality=_snake_case			,				hidden_size=37     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						return



					@unittest.skip(reason="ConvNext does not use inputs_embeds"     )
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						pass



					@unittest.skip(reason="ConvNext does not support input and output embeddings"     )
					def        UpperCAmelCase_ (    self     )     ->			str:
						pass



					@unittest.skip(reason="ConvNext does not use feedforward chunking"     )
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						pass



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							__lowerCAmelCase												=  model_class(_snake_case     )
							__lowerCAmelCase												=  inspect.signature(model.forward     )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__lowerCAmelCase												=  [*signature.parameters.keys()]

							__lowerCAmelCase												=  ["pixel_values"]
							self.assertListEqual(arg_names[:1]			,				_snake_case     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_snake_case     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*_snake_case     )



					def        UpperCAmelCase_ (    self     )     ->			Any:


						def check_hidden_states_output(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ):
							__lowerCAmelCase												=  model_class(_snake_case     )
							model.to(_snake_case     )
							model.eval()

							with torch.no_grad():
								__lowerCAmelCase												=  model(**self._prepare_for_class(_snake_case			,				_snake_case     )     )

							__lowerCAmelCase												=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							__lowerCAmelCase												=  self.model_tester.num_stages
							self.assertEqual(len(_snake_case     )			,				expected_num_stages + 1     )

							# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]     )			,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,				)

						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							__lowerCAmelCase												=  True
							check_hidden_states_output(_snake_case			,				_snake_case			,				_snake_case     )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__lowerCAmelCase												=  True

							check_hidden_states_output(_snake_case			,				_snake_case			,				_snake_case     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_snake_case     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:


						for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase												=  ConvNextModel.from_pretrained(_snake_case     )
							self.assertIsNotNone(_snake_case     )



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
	return image






@require_torch
@require_vision
class 					UpperCAmelCase__	(		unittest.TestCase ):



					@cached_property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"     ) if is_vision_available() else None



					@slow
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"     ).to(_snake_case     )

						__lowerCAmelCase												=  self.default_image_processor
						__lowerCAmelCase												=  prepare_img()
						__lowerCAmelCase												=  image_processor(images=_snake_case			,				return_tensors="pt"     ).to(_snake_case     )

						# forward pass
						with torch.no_grad():
							__lowerCAmelCase												=  model(**_snake_case     )

						# verify the logits
						__lowerCAmelCase												=  torch.Size((1, 1000)     )
						self.assertEqual(outputs.logits.shape			,				_snake_case     )

						__lowerCAmelCase												=  torch.tensor([-0.02_60, -0.47_39, 0.19_11]     ).to(_snake_case     )

						self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_snake_case			,				atol=1E-4     )     )






@require_torch
class 					UpperCAmelCase__	(		unittest.TestCase							,       UpperCAmelCase_ ):
					a      :     Dict        =							(ConvNextBackbone,) if is_torch_available() else ()
					a      :     Tuple        =							ConvNextConfig

					a      :     Union[str, Any]        =							False



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  ConvNextModelTester(self     )




'''simple docstring'''



import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


lowerCAmelCase	:		List[Any]				=   get_logger(__name__)






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase = None     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  (
						    os.path.join(UpperCamelCase			,				config.EXTRACTED_DATASETS_DIR     ) if cache_dir else config.EXTRACTED_DATASETS_PATH
						)
						__lowerCAmelCase												=  Extractor



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			str:
						from .file_utils import hash_url_to_filename

						# Path where we extract compressed archives
						# We extract in the cache dir, and get the extracted path name by hashing the original path"
						__lowerCAmelCase												=  os.path.abspath(UpperCamelCase     )
						return os.path.join(self.extract_dir			,				hash_url_to_filename(UpperCamelCase     )     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			bool:
						return force_extract or (
						    not os.path.isfile(UpperCamelCase     ) and not (os.path.isdir(UpperCamelCase     ) and os.listdir(UpperCamelCase     ))
						)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = False     )     ->			str:
						__lowerCAmelCase												=  self.extractor.infer_extractor_format(UpperCamelCase     )
						if not extractor_format:
							return input_path
						__lowerCAmelCase												=  self._get_output_path(UpperCamelCase     )
						if self._do_extract(UpperCamelCase			,				UpperCamelCase     ):
							self.extractor.extract(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						return output_path






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					@classmethod
					@abstractmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				**UpperCamelCase     )     ->			bool:
						...



					@staticmethod
					@abstractmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						...






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):
					a      :     List[bytes]        =							[]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			List[Any]:
						with open(UpperCamelCase			,				"rb"     ) as f:
							return f.read(UpperCamelCase     )



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				UpperCamelCase = b""     )     ->			bool:
						if not magic_number:
							__lowerCAmelCase												=  max(len(UpperCamelCase     ) for cls_magic_number in cls.magic_numbers     )
							try:
								__lowerCAmelCase												=  cls.read_magic_number(UpperCamelCase			,				UpperCamelCase     )
							except OSError:
								return False
						return any(magic_number.startswith(UpperCamelCase     ) for cls_magic_number in cls.magic_numbers     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				**UpperCamelCase     )     ->			bool:
						return tarfile.is_tarfile(UpperCamelCase     )



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			Dict:

						def resolved(UpperCamelCase     ) -> str:
							return os.path.realpath(os.path.abspath(UpperCamelCase     )     )

						def badpath(UpperCamelCase			,				UpperCamelCase     ) -> bool:
							# joinpath will ignore base if path is absolute
							return not resolved(os.path.join(UpperCamelCase			,				UpperCamelCase     )     ).startswith(UpperCamelCase     )

						def badlink(UpperCamelCase			,				UpperCamelCase     ) -> bool:
							# Links are interpreted relative to the directory containing the link
							__lowerCAmelCase												=  resolved(os.path.join(UpperCamelCase			,				os.path.dirname(info.name     )     )     )
							return badpath(info.linkname			,				base=UpperCamelCase     )

						__lowerCAmelCase												=  resolved(UpperCamelCase     )

						for finfo in members:
							if badpath(finfo.name			,				UpperCamelCase     ):
								logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)'''     )
							elif finfo.issym() and badlink(UpperCamelCase			,				UpperCamelCase     ):
								logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}'''     )
							elif finfo.islnk() and badlink(UpperCamelCase			,				UpperCamelCase     ):
								logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}'''     )
							else:
								yield finfo



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						os.makedirs(UpperCamelCase			,				exist_ok=UpperCamelCase     )
						__lowerCAmelCase												=  tarfile.open(UpperCamelCase     )
						tar_file.extractall(UpperCamelCase			,				members=TarExtractor.safemembers(UpperCamelCase			,				UpperCamelCase     )     )
						tar_file.close()






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							[B"""\x1F\x8B"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						with gzip.open(UpperCamelCase			,				"rb"     ) as gzip_file:
							with open(UpperCamelCase			,				"wb"     ) as extracted_file:
								shutil.copyfileobj(UpperCamelCase			,				UpperCamelCase     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[Any]        =							[
					    B"""PK\x03\x04""",
					    B"""PK\x05\x06""",  # empty archive
					    B"""PK\x07\x08""",  # spanned archive
					]



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				UpperCamelCase = b""     )     ->			bool:
						if super().is_extractable(UpperCamelCase			,				magic_number=UpperCamelCase     ):
							return True
						try:
							# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
							# From: https://github.com/python/cpython/pull/5053
							from zipfile import (
							    _CD_SIGNATURE,
							    _ECD_DISK_NUMBER,
							    _ECD_DISK_START,
							    _ECD_ENTRIES_TOTAL,
							    _ECD_OFFSET,
							    _ECD_SIZE,
							    _EndRecData,
							    sizeCentralDir,
							    stringCentralDir,
							    structCentralDir,
							)

							with open(UpperCamelCase			,				"rb"     ) as fp:
								__lowerCAmelCase												=  _EndRecData(UpperCamelCase     )
								if endrec:
									if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
										return True  # Empty zipfiles are still zipfiles
									elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
										fp.seek(endrec[_ECD_OFFSET]     )  # Central directory is on the same disk
										if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
											__lowerCAmelCase												=  fp.read(UpperCamelCase     )  # CD is where we expect it to be
											if len(UpperCamelCase     ) == sizeCentralDir:
												__lowerCAmelCase												=  struct.unpack(UpperCamelCase			,				UpperCamelCase     )  # CD is the right size
												if centdir[_CD_SIGNATURE] == stringCentralDir:
													return True  # First central directory entry  has correct magic number
							return False
						except Exception:  # catch all errors in case future python versions change the zipfile internals
							return False



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						os.makedirs(UpperCamelCase			,				exist_ok=UpperCamelCase     )
						with zipfile.ZipFile(UpperCamelCase			,				"r"     ) as zip_file:
							zip_file.extractall(UpperCamelCase     )
							zip_file.close()






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Tuple        =							[B"""\xFD\x37\x7A\x58\x5A\x00"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						with lzma.open(UpperCamelCase     ) as compressed_file:
							with open(UpperCamelCase			,				"wb"     ) as extracted_file:
								shutil.copyfileobj(UpperCamelCase			,				UpperCamelCase     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     str        =							[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						if not config.RARFILE_AVAILABLE:
							raise ImportError("Please pip install rarfile"     )
						import rarfile

						os.makedirs(UpperCamelCase			,				exist_ok=UpperCamelCase     )
						__lowerCAmelCase												=  rarfile.RarFile(UpperCamelCase     )
						rf.extractall(UpperCamelCase     )
						rf.close()






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     int        =							[B"""\x28\xb5\x2F\xFD"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						if not config.ZSTANDARD_AVAILABLE:
							raise ImportError("Please pip install zstandard"     )
						import zstandard as zstd

						__lowerCAmelCase												=  zstd.ZstdDecompressor()
						with open(UpperCamelCase			,				"rb"     ) as ifh, open(UpperCamelCase			,				"wb"     ) as ofh:
							dctx.copy_stream(UpperCamelCase			,				UpperCamelCase     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							[B"""\x42\x5A\x68"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						with bza.open(UpperCamelCase			,				"rb"     ) as compressed_file:
							with open(UpperCamelCase			,				"wb"     ) as extracted_file:
								shutil.copyfileobj(UpperCamelCase			,				UpperCamelCase     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							[B"""\x37\x7A\xBC\xAF\x27\x1C"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						if not config.PY7ZR_AVAILABLE:
							raise ImportError("Please pip install py7zr"     )
						import pyazr

						os.makedirs(UpperCamelCase			,				exist_ok=UpperCamelCase     )
						with pyazr.SevenZipFile(UpperCamelCase			,				"r"     ) as archive:
							archive.extractall(UpperCamelCase     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							[B"""\x04\x22\x4D\x18"""]



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			None:
						if not config.LZ4_AVAILABLE:
							raise ImportError("Please pip install lz4"     )
						import lza.frame

						with lza.frame.open(UpperCamelCase			,				"rb"     ) as compressed_file:
							with open(UpperCamelCase			,				"wb"     ) as extracted_file:
								shutil.copyfileobj(UpperCamelCase			,				UpperCamelCase     )






class 					UpperCAmelCase__	:
					#  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
					a      :     Dict[str, Type[BaseExtractor]]        =							{
					    "tar": TarExtractor,
					    "gzip": GzipExtractor,
					    "zip": ZipExtractor,
					    "xz": XzExtractor,
					    "rar": RarExtractor,
					    "zstd": ZstdExtractor,
					    "bz2": BzipaExtractor,
					    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
					    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
					}



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Optional[Any]:
						return max(
						    len(UpperCamelCase     )
						    for extractor in cls.extractors.values()
						    if issubclass(UpperCamelCase			,				UpperCamelCase     )
						    for extractor_magic_number in extractor.magic_numbers     )



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			Dict:
						try:
							return MagicNumberBaseExtractor.read_magic_number(UpperCamelCase			,				magic_number_length=UpperCamelCase     )
						except OSError:
							return b""



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				UpperCamelCase = False     )     ->			bool:
						warnings.warn(
						    "Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
						    "Use 'infer_extractor_format' instead."			,				category=UpperCamelCase			,				)
						__lowerCAmelCase												=  cls.infer_extractor_format(UpperCamelCase     )
						if extractor_format:
							return True if not return_extractor else (True, cls.extractors[extractor_format])
						return False if not return_extractor else (False, None)



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase     )     ->			str:  # <Added version="2.4.0"/>
						__lowerCAmelCase												=  cls._get_magic_number_max_length()
						__lowerCAmelCase												=  cls._read_magic_number(UpperCamelCase			,				UpperCamelCase     )
						for extractor_format, extractor in cls.extractors.items():
							if extractor.is_extractable(UpperCamelCase			,				magic_number=UpperCamelCase     ):
								return extractor_format



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = "deprecated"			,				)     ->			None:
						os.makedirs(os.path.dirname(UpperCamelCase     )			,				exist_ok=UpperCamelCase     )
						# Prevent parallel extractions
						__lowerCAmelCase												=  str(Path(UpperCamelCase     ).with_suffix(".lock"     )     )
						with FileLock(UpperCamelCase     ):
							shutil.rmtree(UpperCamelCase			,				ignore_errors=UpperCamelCase     )
							if extractor_format or extractor != "deprecated":
								if extractor != "deprecated" or not isinstance(UpperCamelCase			,				UpperCamelCase     ):  # passed as positional arg
									warnings.warn(
									    "Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
									    "Use 'extractor_format' instead."			,				category=UpperCamelCase			,				)
									__lowerCAmelCase												=  extractor if extractor != "deprecated" else extractor_format
								else:
									__lowerCAmelCase												=  cls.extractors[extractor_format]
								return extractor.extract(UpperCamelCase			,				UpperCamelCase     )
							else:
								warnings.warn(
								    "Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
								    "exception in 3.0.0."			,				category=UpperCamelCase			,				)
								for extractor in cls.extractors.values():
									if extractor.is_extractable(UpperCamelCase     ):
										return extractor.extract(UpperCamelCase			,				UpperCamelCase     )