



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase	:		Optional[Any]				=   logging.getLogger(__name__)






@dataclass
class 					UpperCAmelCase__	:
					a      :     List[Any]        =							4_2
					a      :     Optional[int]        =							4_2
					a      :     List[Any]        =							4_2






@dataclass
class 					UpperCAmelCase__	:
					a      :     List[Any]        =							4_2
					a      :     str        =							4_2
					a      :     Dict        =							None
					a      :     List[Any]        =							None






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Union[str, Any]        =							"""train"""
					a      :     Dict        =							"""dev"""
					a      :     Any        =							"""test"""






class 					UpperCAmelCase__	:



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase     )     ->			Optional[int]:
						raise NotImplementedError



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase     )     ->			str:
						raise NotImplementedError



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase=False			,				UpperCamelCase="[CLS]"			,				UpperCamelCase=1			,				UpperCamelCase="[SEP]"			,				UpperCamelCase=False			,				UpperCamelCase=False			,				UpperCamelCase=0			,				UpperCamelCase=0			,				UpperCamelCase=-100			,				UpperCamelCase=0			,				UpperCamelCase=True			,				)     ->			str:
						__lowerCAmelCase												=  {label: i for i, label in enumerate(UpperCamelCase     )}

						__lowerCAmelCase												=  []
						for ex_index, example in enumerate(UpperCamelCase     ):
							if ex_index % 1_0000 == 0:
								logger.info("Writing example %d of %d"			,				UpperCamelCase			,				len(UpperCamelCase     )     )

							__lowerCAmelCase												=  []
							__lowerCAmelCase												=  []
							for word, label in zip(example.words			,				example.labels     ):
								__lowerCAmelCase												=  tokenizer.tokenize(UpperCamelCase     )

								# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
								if len(UpperCamelCase     ) > 0:
									tokens.extend(UpperCamelCase     )
									# Use the real label id for the first token of the word, and padding ids for the remaining tokens
									label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase     ) - 1)     )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
							__lowerCAmelCase												=  tokenizer.num_special_tokens_to_add()
							if len(UpperCamelCase     ) > max_seq_length - special_tokens_count:
								__lowerCAmelCase												=  tokens[: (max_seq_length - special_tokens_count)]
								__lowerCAmelCase												=  label_ids[: (max_seq_length - special_tokens_count)]

							# The convention in BERT is:
							# (a) For sequence pairs:
							#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
							#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
							# (b) For single sequences:
							#  tokens:   [CLS] the dog is hairy . [SEP]
							#  type_ids:   0   0   0   0  0     0   0
							#
							# Where "type_ids" are used to indicate whether this is the first
							# sequence or the second sequence. The embedding vectors for `type=0` and
							# `type=1` were learned during pre-training and are added to the wordpiece
							# embedding vector (and position vector). This is not *strictly* necessary
							# since the [SEP] token unambiguously separates the sequences, but it makes
							# it easier for the model to learn the concept of sequences.
							#
							# For classification tasks, the first vector (corresponding to [CLS]) is
							# used as the "sentence vector". Note that this only makes sense because
							# the entire model is fine-tuned.
							tokens += [sep_token]
							label_ids += [pad_token_label_id]
							if sep_token_extra:
								# roberta uses an extra separator b/w pairs of sentences
								tokens += [sep_token]
								label_ids += [pad_token_label_id]
							__lowerCAmelCase												=  [sequence_a_segment_id] * len(UpperCamelCase     )

							if cls_token_at_end:
								tokens += [cls_token]
								label_ids += [pad_token_label_id]
								segment_ids += [cls_token_segment_id]
							else:
								__lowerCAmelCase												=  [cls_token] + tokens
								__lowerCAmelCase												=  [pad_token_label_id] + label_ids
								__lowerCAmelCase												=  [cls_token_segment_id] + segment_ids

							__lowerCAmelCase												=  tokenizer.convert_tokens_to_ids(UpperCamelCase     )

							# The mask has 1 for real tokens and 0 for padding tokens. Only real
							# tokens are attended to.
							__lowerCAmelCase												=  [1 if mask_padding_with_zero else 0] * len(UpperCamelCase     )

							# Zero-pad up to the sequence length.
							__lowerCAmelCase												=  max_seq_length - len(UpperCamelCase     )
							if pad_on_left:
								__lowerCAmelCase												=  ([pad_token] * padding_length) + input_ids
								__lowerCAmelCase												=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
								__lowerCAmelCase												=  ([pad_token_segment_id] * padding_length) + segment_ids
								__lowerCAmelCase												=  ([pad_token_label_id] * padding_length) + label_ids
							else:
								input_ids += [pad_token] * padding_length
								input_mask += [0 if mask_padding_with_zero else 1] * padding_length
								segment_ids += [pad_token_segment_id] * padding_length
								label_ids += [pad_token_label_id] * padding_length

							assert len(UpperCamelCase     ) == max_seq_length
							assert len(UpperCamelCase     ) == max_seq_length
							assert len(UpperCamelCase     ) == max_seq_length
							assert len(UpperCamelCase     ) == max_seq_length

							if ex_index < 5:
								logger.info("*** Example ***"     )
								logger.info("guid: %s"			,				example.guid     )
								logger.info("tokens: %s"			,				" ".join([str(UpperCamelCase     ) for x in tokens]     )     )
								logger.info("input_ids: %s"			,				" ".join([str(UpperCamelCase     ) for x in input_ids]     )     )
								logger.info("input_mask: %s"			,				" ".join([str(UpperCamelCase     ) for x in input_mask]     )     )
								logger.info("segment_ids: %s"			,				" ".join([str(UpperCamelCase     ) for x in segment_ids]     )     )
								logger.info("label_ids: %s"			,				" ".join([str(UpperCamelCase     ) for x in label_ids]     )     )

							if "token_type_ids" not in tokenizer.model_input_names:
								__lowerCAmelCase												=  None

							features.append(
							    InputFeatures(
							        input_ids=UpperCamelCase			,				attention_mask=UpperCamelCase			,				token_type_ids=UpperCamelCase			,				label_ids=UpperCamelCase     )     )
						return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset






	class 					UpperCAmelCase__	(		UpperCamelCase__ ):
						a      :     Union[str, Any]        =							4_2
						a      :     Union[str, Any]        =							nn.CrossEntropyLoss().ignore_index



						def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase=False			,				UpperCamelCase = Split.train			,				)     ->			Optional[Any]:
							# Load data features from cache or dataset file
							__lowerCAmelCase												=  os.path.join(
							    UpperCamelCase			,				"cached_{}_{}_{}".format(mode.value			,				tokenizer.__class__.__name__			,				str(UpperCamelCase     )     )			,				)

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							__lowerCAmelCase												=  cached_features_file + ".lock"
							with FileLock(UpperCamelCase     ):
								if os.path.exists(UpperCamelCase     ) and not overwrite_cache:
									logger.info(F'''Loading features from cached file {cached_features_file}'''     )
									__lowerCAmelCase												=  torch.load(UpperCamelCase     )
								else:
									logger.info(F'''Creating features from dataset file at {data_dir}'''     )
									__lowerCAmelCase												=  token_classification_task.read_examples_from_file(UpperCamelCase			,				UpperCamelCase     )
									# TODO clean up all this to leverage built-in features of tokenizers
									__lowerCAmelCase												=  token_classification_task.convert_examples_to_features(
									    UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				cls_token_at_end=bool(model_type in ["xlnet"]     )			,				cls_token=tokenizer.cls_token			,				cls_token_segment_id=2 if model_type in ["xlnet"] else 0			,				sep_token=tokenizer.sep_token			,				sep_token_extra=UpperCamelCase			,				pad_on_left=bool(tokenizer.padding_side == "left"     )			,				pad_token=tokenizer.pad_token_id			,				pad_token_segment_id=tokenizer.pad_token_type_id			,				pad_token_label_id=self.pad_token_label_id			,				)
									logger.info(F'''Saving features into cached file {cached_features_file}'''     )
									torch.save(self.features			,				UpperCamelCase     )



						def __len__(    self     )     ->			Union[str, Any]:
							return len(self.features     )



						def __getitem__(    self			,				UpperCamelCase     )     ->			Any:
							return self.features[i]


if is_tf_available():
	import tensorflow as tf






	class 					UpperCAmelCase__	:
						a      :     List[Any]        =							4_2
						a      :     Dict        =							-1_0_0



						def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase=False			,				UpperCamelCase = Split.train			,				)     ->			int:
							__lowerCAmelCase												=  token_classification_task.read_examples_from_file(UpperCamelCase			,				UpperCamelCase     )
							# TODO clean up all this to leverage built-in features of tokenizers
							__lowerCAmelCase												=  token_classification_task.convert_examples_to_features(
							    UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				cls_token_at_end=bool(model_type in ["xlnet"]     )			,				cls_token=tokenizer.cls_token			,				cls_token_segment_id=2 if model_type in ["xlnet"] else 0			,				sep_token=tokenizer.sep_token			,				sep_token_extra=UpperCamelCase			,				pad_on_left=bool(tokenizer.padding_side == "left"     )			,				pad_token=tokenizer.pad_token_id			,				pad_token_segment_id=tokenizer.pad_token_type_id			,				pad_token_label_id=self.pad_token_label_id			,				)

							def gen():
								for ex in self.features:
									if ex.token_type_ids is None:
										yield (
										    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
										    ex.label_ids,
										)
									else:
										yield (
										    {
										        "input_ids": ex.input_ids,
										        "attention_mask": ex.attention_mask,
										        "token_type_ids": ex.token_type_ids,
										    },
										    ex.label_ids,
										)

							if "token_type_ids" not in tokenizer.model_input_names:
								__lowerCAmelCase												=  tf.data.Dataset.from_generator(
								    UpperCamelCase			,				({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)			,				(
								        {"input_ids": tf.TensorShape([None]     ), "attention_mask": tf.TensorShape([None]     )},
								        tf.TensorShape([None]     ),
								    )			,				)
							else:
								__lowerCAmelCase												=  tf.data.Dataset.from_generator(
								    UpperCamelCase			,				({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)			,				(
								        {
								            "input_ids": tf.TensorShape([None]     ),
								            "attention_mask": tf.TensorShape([None]     ),
								            "token_type_ids": tf.TensorShape([None]     ),
								        },
								        tf.TensorShape([None]     ),
								    )			,				)



						def        UpperCAmelCase_ (    self     )     ->			Any:
							__lowerCAmelCase												=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features     )     )     )

							return self.dataset



						def __len__(    self     )     ->			Dict:
							return len(self.features     )



						def __getitem__(    self			,				UpperCamelCase     )     ->			int:
							return self.features[i]




'''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     )