

import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
     from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
     _lowerCamelCase    :       List[str]        =							True
     from torch.cuda.amp import autocast


_lowerCamelCase    :       Any        =							logging.getLogger(__name__)





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to log verbose messages or not."""}      ,  )
   UpperCamelCase       					=					field(
       default=2.0      ,  metadata={"""help""": """Maximum temperature for gumbel softmax."""}						)
   UpperCamelCase       					=					field(
       default=0.5      ,  metadata={"""help""": """Minimum temperature for gumbel softmax."""}						)
   UpperCamelCase       					=					field(
       default=0.9_9_9_9_9_5      ,  metadata={"""help""": """Decay of gumbel temperature during training."""}						)






def       a__					(		UpperCAmelCase							:      ModelArguments    ,			UpperCAmelCase							:      TrainingArguments      )  ->						Any:
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,			datefmt='''%m/%d/%Y %H:%M:%S'''    ,			handlers=[logging.StreamHandler(sys.stdout      )]    ,			)
  UpperCAmelCase	:							Any						=			logging.WARNING
  if model_args.verbose_logging:
    UpperCAmelCase	:							Any						=			logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank      ):
    UpperCAmelCase	:							Any						=			logging.INFO
  logger.setLevel(UpperCAmelCase      )





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
   UpperCamelCase       					=					field(
       default="""train"""      ,  metadata={
           """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
       }      ,  )
   UpperCamelCase       					=					field(
       default="""validation"""      ,  metadata={
           """help""": (
               """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
           )
       }      ,  )
   UpperCamelCase       					=					field(
       default="""file"""      ,  metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
   UpperCamelCase       					=					field(
       default=1      ,  metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       }      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,  )
   UpperCamelCase       					=					field(
       default=2_0.0      ,  metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}						)






@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					42
   UpperCamelCase       					=					42
   UpperCamelCase       					=					"longest"
   UpperCamelCase       					=					None
   UpperCamelCase       					=					None



   def __call__(							self							:					int,		__A							:					List[Dict[str, Union[List[int], torch.Tensor]]]						):
     # reformat list to dict and set to pytorch format
     UpperCAmelCase	:							List[Any]						=			self.feature_extractor.pad(
         __A,		max_length=self.max_length,		padding=self.padding,		pad_to_multiple_of=self.pad_to_multiple_of,		return_tensors='''pt''',		)
     UpperCAmelCase	:							int						=			self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]						)

     UpperCAmelCase	:							Tuple						=			batch['''input_values'''].shape[0]

     # make sure that no loss is computed on padded inputs
     if batch["attention_mask"] is not None:
       # compute real output lengths according to convolution formula
       UpperCAmelCase	:							Tuple						=			self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1						)						).to(
           torch.long						)

       UpperCAmelCase	:							Dict						=			torch.zeros(
           (batch_size, mask_indices_seq_length),		dtype=torch.long,		device=batch['''input_values'''].device						)

       # these two operations makes sure that all values
       # before the output lengths indices are attended to
       UpperCAmelCase	:							Tuple						=			1
       UpperCAmelCase	:							int						=			attention_mask.flip([-1]						).cumsum(-1						).flip([-1]						).bool()

     # sample randomly masked indices
     UpperCAmelCase	:							Dict						=			_compute_mask_indices(
         (batch_size, mask_indices_seq_length),		self.model.config.mask_time_prob,		self.model.config.mask_time_length,		attention_mask=__A,		min_masks=2,		)

     return batch






class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Union[str, Any],		*__A							:					int,		__A							:					Dict=1,		__A							:					Any=0,		__A							:					Optional[Any]=1.0,		**__A							:					Any						):
     super().__init__(*__A,		**__A						)
     UpperCAmelCase	:							Any						=			0
     UpperCAmelCase	:							Any						=			max_gumbel_temp
     UpperCAmelCase	:							Optional[Any]						=			min_gumbel_temp
     UpperCAmelCase	:							str						=			gumbel_temp_decay





   def 	__magic_name__   (							self							:					Dict,		__A							:					nn.Module,		__A							:					Dict[str, Union[torch.Tensor, Any]]						):
     model.train()
     UpperCAmelCase	:							List[Any]						=			self._prepare_inputs(__A						)

     if self.use_amp:
       with autocast():
         UpperCAmelCase	:							Optional[Any]						=			self.compute_loss(__A,		__A						)
     else:
       UpperCAmelCase	:							Optional[int]						=			self.compute_loss(__A,		__A						)

     if self.args.n_gpu > 1 or self.deepspeed:
       if model.module.config.ctc_loss_reduction == "mean":
         UpperCAmelCase	:							Optional[Any]						=			loss.mean()
       elif model.module.config.ctc_loss_reduction == "sum":
         UpperCAmelCase	:							str						=			loss.sum() / (inputs['''mask_time_indices''']).sum()
       else:
         raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''						)

     if self.args.gradient_accumulation_steps > 1:
       UpperCAmelCase	:							Any						=			loss / self.args.gradient_accumulation_steps

     if self.use_amp:
       self.scaler.scale(__A						).backward()
     elif self.use_apex:
       with amp.scale_loss(__A,		self.optimizer						) as scaled_loss:
         scaled_loss.backward()
     elif self.deepspeed:
       self.deepspeed.backward(__A						)
     else:
       loss.backward()

     self.num_update_step += 1
     # make sure gumbel softmax temperature is decayed
     if self.args.n_gpu > 1 or self.deepspeed:
       model.module.set_gumbel_temperature(
           max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,		self.min_gumbel_temp						)						)
     else:
       model.set_gumbel_temperature(
           max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,		self.min_gumbel_temp						)						)

     return loss.detach()






def       a__					(		)  ->						Union[str, Any]:
  # 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.

  UpperCAmelCase	:							Optional[Any]						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

  UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			parser.parse_args_into_dataclasses()
  configure_logger(UpperCAmelCase    ,			UpperCAmelCase      )

  # Downloading and loading a dataset from the hub.
  UpperCAmelCase	:							int						=			load_dataset(data_args.dataset_name    ,			data_args.dataset_config_name    ,			cache_dir=model_args.cache_dir      )

  if "validation" not in datasets.keys():
    # make sure only "validation" and "train" keys remain"
    UpperCAmelCase	:							Union[str, Any]						=			DatasetDict()
    UpperCAmelCase	:							Any						=			load_dataset(
        data_args.dataset_name    ,			data_args.dataset_config_name    ,			split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''    ,			cache_dir=model_args.cache_dir    ,			)
    UpperCAmelCase	:							Tuple						=			load_dataset(
        data_args.dataset_name    ,			data_args.dataset_config_name    ,			split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''    ,			cache_dir=model_args.cache_dir    ,			)
  else:
    # make sure only "validation" and "train" keys remain"
    UpperCAmelCase	:							Optional[Any]						=			DatasetDict()
    UpperCAmelCase	:							List[Any]						=			load_dataset(
        data_args.dataset_name    ,			data_args.dataset_config_name    ,			split='''validation'''    ,			cache_dir=model_args.cache_dir    ,			)
    UpperCAmelCase	:							int						=			load_dataset(
        data_args.dataset_name    ,			data_args.dataset_config_name    ,			split=f'''{data_args.train_split_name}'''    ,			cache_dir=model_args.cache_dir    ,			)

  # only normalized-inputs-training is supported
  UpperCAmelCase	:							Optional[Any]						=			WavaVecaFeatureExtractor.from_pretrained(
      model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			do_normalize=UpperCAmelCase      )

  def prepare_dataset(UpperCAmelCase							:      Dict      ):
    # check that all files have the correct sampling rate
    UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			librosa.load(batch[data_args.speech_file_column]    ,			sr=feature_extractor.sampling_rate      )
    return batch

  # load audio files into numpy arrays
  UpperCAmelCase	:							str						=			datasets.map(
      UpperCAmelCase    ,			num_proc=data_args.preprocessing_num_workers    ,			remove_columns=datasets['''train'''].column_names      )

  # filter audio files that are too long
  UpperCAmelCase	:							int						=			vectorized_datasets.filter(
      lambda UpperCAmelCase      : len(data['''speech''']      ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate      )      )

  def normalize(UpperCAmelCase							:      Dict      ):
    return feature_extractor(batch['''speech''']    ,			sampling_rate=feature_extractor.sampling_rate      )

  # normalize and transform to `BatchFeatures`
  UpperCAmelCase	:							Any						=			vectorized_datasets.map(
      UpperCAmelCase    ,			batched=UpperCAmelCase    ,			num_proc=data_args.preprocessing_num_workers    ,			load_from_cache_file=not data_args.overwrite_cache    ,			remove_columns=vectorized_datasets['''train'''].column_names    ,			)

  # pretraining is only supported for "newer" stable layer norm architecture
  # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
  UpperCAmelCase	:							Optional[int]						=			WavaVecaConfig.from_pretrained(
      model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			gradient_checkpointing=training_args.gradient_checkpointing    ,			)

  if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
    raise ValueError(
        '''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
        ''' ``config.feat_extract_norm=\'layer\''''      )

  UpperCAmelCase	:							Any						=			WavaVecaForPreTraining(UpperCAmelCase      )

  UpperCAmelCase	:							int						=			DataCollatorForWavaVecaPretraining(model=UpperCAmelCase    ,			feature_extractor=UpperCAmelCase      )

  UpperCAmelCase	:							Any						=			WavaVecaPreTrainer(
      model=UpperCAmelCase    ,			data_collator=UpperCAmelCase    ,			args=UpperCAmelCase    ,			train_dataset=vectorized_datasets['''train''']    ,			eval_dataset=vectorized_datasets['''validation''']    ,			tokenizer=UpperCAmelCase    ,			max_gumbel_temp=model_args.max_gumbel_temperature    ,			min_gumbel_temp=model_args.min_gumbel_temperature    ,			gumbel_temp_decay=model_args.gumbel_temperature_decay    ,			)
  trainer.train()


if __name__ == "__main__":
     main()








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    :       Optional[Any]        =							get_logger(__name__)





class  __UpperCAmelCase					:



   def __init__(							self							:					Any,		__A							:					Optional[str] = None						):
     UpperCAmelCase	:							str						=			(
         os.path.join(__A,		config.EXTRACTED_DATASETS_DIR						) if cache_dir else config.EXTRACTED_DATASETS_PATH
     )
     UpperCAmelCase	:							str						=			Extractor



   def 	__magic_name__   (							self							:					str,		__A							:					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"
     UpperCAmelCase	:							int						=			os.path.abspath(__A						)
     return os.path.join(self.extract_dir,		hash_url_to_filename(__A						)						)



   def 	__magic_name__   (							self							:					int,		__A							:					str,		__A							:					bool						):
     return force_extract or (
         not os.path.isfile(__A						) and not (os.path.isdir(__A						) and os.listdir(__A						))
     )





   def 	__magic_name__   (							self							:					str,		__A							:					str,		__A							:					bool = False						):
     UpperCAmelCase	:							Any						=			self.extractor.infer_extractor_format(__A						)
     if not extractor_format:
       return input_path
     UpperCAmelCase	:							Tuple						=			self._get_output_path(__A						)
     if self._do_extract(__A,		__A						):
       self.extractor.extract(__A,		__A,		__A						)
     return output_path






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @classmethod
   @abstractmethod
   def 	__magic_name__   (							cls							:					int,		__A							:					Union[Path, str],		**__A							:					List[Any]						):
     ...





   @staticmethod
   @abstractmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     ...






class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__						):
   UpperCamelCase       					=					[]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					int						):
     with open(__A,		'''rb'''						) as f:
       return f.read(__A						)





   @classmethod
   def 	__magic_name__   (							cls							:					List[str],		__A							:					Union[Path, str],		__A							:					bytes = b""						):
     if not magic_number:
       UpperCAmelCase	:							int						=			max(len(__A						) for cls_magic_number in cls.magic_numbers						)
       try:
         UpperCAmelCase	:							Any						=			cls.read_magic_number(__A,		__A						)
       except OSError:
         return False
     return any(magic_number.startswith(__A						) for cls_magic_number in cls.magic_numbers						)






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @classmethod
   def 	__magic_name__   (							cls							:					Union[str, Any],		__A							:					Union[Path, str],		**__A							:					int						):
     return tarfile.is_tarfile(__A						)



   @staticmethod
   def 	__magic_name__   (							__A							:					List[Any],		__A							:					Any						):

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

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

     def badlink(__A							:					List[Any],		__A							:					str						) -> bool:
       # Links are interpreted relative to the directory containing the link
       UpperCAmelCase	:							Dict						=			resolved(os.path.join(__A,		os.path.dirname(info.name						)						)						)
       return badpath(info.linkname,		base=__A						)

     UpperCAmelCase	:							Any						=			resolved(__A						)

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





   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     os.makedirs(__A,		exist_ok=__A						)
     UpperCAmelCase	:							Any						=			tarfile.open(__A						)
     tar_file.extractall(__A,		members=TarExtractor.safemembers(__A,		__A						)						)
     tar_file.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x1F\x8B"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with gzip.open(__A,		'''rb'''						) as gzip_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[
       B"""PK\x03\x04""",
       B"""PK\x05\x06""",  # empty archive
       B"""PK\x07\x08""",  # spanned archive
   ]



   @classmethod
   def 	__magic_name__   (							cls							:					Union[str, Any],		__A							:					Union[Path, str],		__A							:					bytes = b""						):
     if super().is_extractable(__A,		magic_number=__A						):
       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(__A,		'''rb'''						) as fp:
         UpperCAmelCase	:							Union[str, Any]						=			_EndRecData(__A						)
         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:
               UpperCAmelCase	:							Optional[int]						=			fp.read(__A						)  # CD is where we expect it to be
               if len(__A						) == sizeCentralDir:
                 UpperCAmelCase	:							List[Any]						=			struct.unpack(__A,		__A						)  # 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 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     os.makedirs(__A,		exist_ok=__A						)
     with zipfile.ZipFile(__A,		'''r'''						) as zip_file:
       zip_file.extractall(__A						)
       zip_file.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\xFD\x37\x7A\x58\x5A\x00"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with lzma.open(__A						) as compressed_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""]  # RAR_ID  # RAR5_ID



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.RARFILE_AVAILABLE:
       raise ImportError('''Please pip install rarfile'''						)
     import rarfile

     os.makedirs(__A,		exist_ok=__A						)
     UpperCAmelCase	:							Tuple						=			rarfile.RarFile(__A						)
     rf.extractall(__A						)
     rf.close()






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x28\xb5\x2F\xFD"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.ZSTANDARD_AVAILABLE:
       raise ImportError('''Please pip install zstandard'''						)
     import zstandard as zstd

     UpperCAmelCase	:							List[str]						=			zstd.ZstdDecompressor()
     with open(__A,		'''rb'''						) as ifh, open(__A,		'''wb'''						) as ofh:
       dctx.copy_stream(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x42\x5A\x68"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     with bza.open(__A,		'''rb'''						) as compressed_file:
       with open(__A,		'''wb'''						) as extracted_file:
         shutil.copyfileobj(__A,		__A						)






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x37\x7A\xBC\xAF\x27\x1C"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.PY7ZR_AVAILABLE:
       raise ImportError('''Please pip install py7zr'''						)
     import pyazr

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






class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					[B"""\x04\x22\x4D\x18"""]



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					Union[Path, str]						):
     if not config.LZ4_AVAILABLE:
       raise ImportError('''Please pip install lz4'''						)
     import lza.frame

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






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)
   UpperCamelCase       					=					{
       "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 	__magic_name__   (							cls							:					Dict						):
     return max(
         len(__A						)
         for extractor in cls.extractors.values()
         if issubclass(__A,		__A						)
         for extractor_magic_number in extractor.magic_numbers						)



   @staticmethod
   def 	__magic_name__   (							__A							:					Union[Path, str],		__A							:					int						):
     try:
       return MagicNumberBaseExtractor.read_magic_number(__A,		magic_number_length=__A						)
     except OSError:
       return b""



   @classmethod
   def 	__magic_name__   (							cls							:					Dict,		__A							:					Union[Path, str],		__A							:					bool = False						):
     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=__A,		)
     UpperCAmelCase	:							Dict						=			cls.infer_extractor_format(__A						)
     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 	__magic_name__   (							cls							:					Optional[Any],		__A							:					Union[Path, str]						):  # <Added version="2.4.0"/>
     UpperCAmelCase	:							Tuple						=			cls._get_magic_number_max_length()
     UpperCAmelCase	:							Tuple						=			cls._read_magic_number(__A,		__A						)
     for extractor_format, extractor in cls.extractors.items():
       if extractor.is_extractable(__A,		magic_number=__A						):
         return extractor_format





   @classmethod
   def 	__magic_name__   (							cls							:					Optional[Any],		__A							:					Union[Path, str],		__A							:					Union[Path, str],		__A							:					Optional[str] = None,		__A							:					Optional[BaseExtractor] = "deprecated",		):
     os.makedirs(os.path.dirname(__A						),		exist_ok=__A						)
     # Prevent parallel extractions
     UpperCAmelCase	:							Optional[int]						=			str(Path(__A						).with_suffix('''.lock'''						)						)
     with FileLock(__A						):
       shutil.rmtree(__A,		ignore_errors=__A						)
       if extractor_format or extractor != "deprecated":
         if extractor != "deprecated" or not isinstance(__A,		__A						):  # 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=__A,		)
           UpperCAmelCase	:							List[str]						=			extractor if extractor != '''deprecated''' else extractor_format
         else:
           UpperCAmelCase	:							Optional[int]						=			cls.extractors[extractor_format]
         return extractor.extract(__A,		__A						)
       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=__A,		)
         for extractor in cls.extractors.values():
           if extractor.is_extractable(__A						):
             return extractor.extract(__A,		__A						)





