

import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_lowerCamelCase    :       Any        =							logging.getLogger(__name__)

_lowerCamelCase    :       Tuple        =							"pytorch_model.bin"





@dataclasses.dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					dataclasses.field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}						)
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}      ,  )






@dataclasses.dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}						)
   UpperCamelCase       					=					dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}						)
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """A csv or a json file containing the validation data."""}						)
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The name of the task to train on."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The list of labels for the task."""}						)






@dataclasses.dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					dataclasses.field(
       metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}						)
   UpperCamelCase       					=					dataclasses.field(
       default="""accuracy"""      ,  metadata={"""help""": """The evaluation metric used for the task."""}						)
   UpperCamelCase       					=					dataclasses.field(
       default="""no"""      ,  metadata={
           """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
       }      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=1_0      ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=0.0      ,  metadata={
           """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
       }      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=0.0      ,  metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=1_0_0      ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,  )
   UpperCamelCase       					=					dataclasses.field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Random seed for initialization."""}      ,  )






def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Tuple      )  ->						Dict:
  UpperCAmelCase	:							Tuple						=			datasets.concatenate_datasets([infer_input, infer_output]    ,			axis=1      )

  if args.do_filter_by_confidence:
    UpperCAmelCase	:							int						=			dataset.filter(lambda UpperCAmelCase      : example["probability"] > args.confidence_threshold      )

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    UpperCAmelCase	:							List[str]						=			int(eval_result * len(UpperCAmelCase      )      )
    print(UpperCAmelCase      )
    UpperCAmelCase	:							Optional[int]						=			dataset.sort('''probability'''    ,			reverse=UpperCAmelCase      )
    UpperCAmelCase	:							str						=			dataset.select(range(UpperCAmelCase      )      )

  UpperCAmelCase	:							Optional[int]						=			dataset.remove_columns(['''label''', '''probability''']      )
  UpperCAmelCase	:							Union[str, Any]						=			dataset.rename_column('''prediction'''    ,			'''label'''      )
  UpperCAmelCase	:							Dict						=			dataset.map(lambda UpperCAmelCase      : {"label": idalabel[example["label"]]}      )
  UpperCAmelCase	:							Any						=			dataset.shuffle(seed=args.seed      )

  UpperCAmelCase	:							Optional[Any]						=			os.path.join(UpperCAmelCase    ,			f'''train_pseudo.{args.data_file_extension}'''      )
  if args.data_file_extension == "csv":
    dataset.to_csv(UpperCAmelCase    ,			index=UpperCAmelCase      )
  else:
    dataset.to_json(UpperCAmelCase      )






def       a__					(		UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[Any]    ,			**UpperCAmelCase							:      List[str]      )  ->						str:
  UpperCAmelCase	:							Union[str, Any]						=			Accelerator()
  # Make one log on every process with the configuration for debugging.
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,			datefmt='''%m/%d/%Y %H:%M:%S'''    ,			level=logging.INFO    ,			)
  logger.info(accelerator.state      )

  # Setup logging, we only want one process per machine to log things on the
  # screen. accelerator.is_local_main_process is only True for one process per
  # machine.
  logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR      )

  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()

  UpperCAmelCase	:							Optional[Any]						=			STModelArguments(model_name_or_path=UpperCAmelCase      )
  UpperCAmelCase	:							str						=			STDataArguments(train_file=UpperCAmelCase    ,			infer_file=UpperCAmelCase      )
  UpperCAmelCase	:							Optional[int]						=			STTrainingArguments(output_dir=UpperCAmelCase      )
  UpperCAmelCase	:							str						=			argparse.Namespace()

  for arg_class in (model_args, data_args, training_args):
    for key, value in vars(UpperCAmelCase      ).items():
      setattr(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  for key, value in kwargs.items():
    if hasattr(UpperCAmelCase    ,			UpperCAmelCase      ):
      setattr(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

    # Sanity checks
  UpperCAmelCase	:							Optional[Any]						=			{}
  UpperCAmelCase	:							Tuple						=			None

  # You need to provide the training data and the data to predict on
  assert args.train_file is not None
  assert args.infer_file is not None
  UpperCAmelCase	:							Optional[int]						=			args.train_file
  UpperCAmelCase	:							Optional[int]						=			args.infer_file

  if args.evaluation_strategy != IntervalStrategy.NO.value:
    assert args.eval_file is not None
    UpperCAmelCase	:							Optional[int]						=			args.eval_file

  for key in data_files:
    UpperCAmelCase	:							Any						=			data_files[key].split('''.'''      )[-1]
    assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
    if args.data_file_extension is None:
      UpperCAmelCase	:							Any						=			extension
    else:
      assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

  assert (
      args.eval_metric in datasets.list_metrics()
  ), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

  # If passed along, set the training seed now.
  if args.seed is not None:
    set_seed(args.seed      )

  logger.info('''Creating the initial data directory for self-training...'''      )
  UpperCAmelCase	:							int						=			f'''{args.output_dir}/self-train_iter-{{}}'''.format
  UpperCAmelCase	:							Dict						=			data_dir_format(0      )

  if accelerator.is_main_process:
    if args.output_dir is not None:
      os.makedirs(args.output_dir    ,			exist_ok=UpperCAmelCase      )
      os.makedirs(UpperCAmelCase    ,			exist_ok=UpperCAmelCase      )
  accelerator.wait_for_everyone()

  UpperCAmelCase	:							Union[str, Any]						=			None
  UpperCAmelCase	:							Optional[int]						=			None
  UpperCAmelCase	:							Any						=			0
  UpperCAmelCase	:							Optional[Any]						=			False
  # Show the progress bar
  UpperCAmelCase	:							str						=			tqdm(range(args.max_selftrain_iterations      )    ,			disable=not accelerator.is_local_main_process      )

  # Self-train
  for iteration in range(0    ,			int(args.max_selftrain_iterations      )      ):
    UpperCAmelCase	:							Optional[Any]						=			data_dir_format(UpperCAmelCase      )
    assert os.path.exists(UpperCAmelCase      )

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    UpperCAmelCase	:							List[str]						=			os.path.join(UpperCAmelCase    ,			'''stage-1'''      )
    UpperCAmelCase	:							Dict						=			{
        '''accelerator''': accelerator,
        '''model_name_or_path''': args.model_name_or_path,
        '''cache_dir''': args.cache_dir,
        '''do_train''': True,
        '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
        '''do_eval''': True if args.eval_file is not None else False,
        '''eval_file''': data_files['''eval'''],
        '''do_predict''': True,
        '''infer_file''': data_files['''infer'''],
        '''task_name''': args.task_name,
        '''label_list''': args.label_list,
        '''output_dir''': current_output_dir,
        '''eval_metric''': args.eval_metric,
        '''evaluation_strategy''': args.evaluation_strategy,
        '''early_stopping_patience''': args.early_stopping_patience,
        '''early_stopping_threshold''': args.early_stopping_threshold,
        '''seed''': args.seed,
    }
    # Add additional training arguments
    for key, value in kwargs.items():
      if key not in arguments_dict and not hasattr(UpperCAmelCase    ,			UpperCAmelCase      ):
        arguments_dict.update({key: value}      )

    UpperCAmelCase	:							List[Any]						=			os.path.join(UpperCAmelCase    ,			'''best-checkpoint'''    ,			UpperCAmelCase      )
    if os.path.exists(UpperCAmelCase      ):
      logger.info(
          '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			)
    else:
      logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''    ,			UpperCAmelCase      )
      finetune(**UpperCAmelCase      )
      accelerator.wait_for_everyone()
      assert os.path.exists(UpperCAmelCase      )
      logger.info('''Self-training job completed: iteration: %d, stage: 1.'''    ,			UpperCAmelCase      )

    if iteration > 0 and args.finetune_on_labeled_data:
      # Stage 2 (optional): fine-tuning on the original labeled data
      UpperCAmelCase	:							List[Any]						=			os.path.join(UpperCAmelCase    ,			'''best-checkpoint'''      )
      UpperCAmelCase	:							int						=			os.path.join(UpperCAmelCase    ,			'''stage-2'''      )
      # Update arguments_dict
      UpperCAmelCase	:							List[str]						=			model_path
      UpperCAmelCase	:							Tuple						=			data_files['''train''']
      UpperCAmelCase	:							Optional[Any]						=			current_output_dir

      UpperCAmelCase	:							List[str]						=			os.path.join(UpperCAmelCase    ,			'''best-checkpoint'''    ,			UpperCAmelCase      )
      if os.path.exists(UpperCAmelCase      ):
        logger.info(
            '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			)
      else:
        logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''    ,			UpperCAmelCase      )
        finetune(**UpperCAmelCase      )
        accelerator.wait_for_everyone()
        assert os.path.exists(UpperCAmelCase      )
        logger.info('''Self-training job completed: iteration: %d, stage: 2.'''    ,			UpperCAmelCase      )

    UpperCAmelCase	:							Dict						=			iteration
    UpperCAmelCase	:							int						=			data_dir_format(iteration + 1      )

    UpperCAmelCase	:							Optional[int]						=			AutoConfig.from_pretrained(os.path.join(UpperCAmelCase    ,			'''best-checkpoint'''      )      )
    UpperCAmelCase	:							List[Any]						=			config.idalabel
    UpperCAmelCase	:							str						=			os.path.join(UpperCAmelCase    ,			'''eval_results_best-checkpoint.json'''      )
    UpperCAmelCase	:							int						=			os.path.join(UpperCAmelCase    ,			'''test_results_best-checkpoint.json'''      )
    assert os.path.exists(UpperCAmelCase      )

    with open(UpperCAmelCase    ,			'''r'''      ) as f:
      UpperCAmelCase	:							Union[str, Any]						=			float(json.load(UpperCAmelCase      )[args.eval_metric]      )
    UpperCAmelCase	:							int						=			os.path.join(UpperCAmelCase    ,			'''infer_output_best-checkpoint.csv'''      )
    assert os.path.exists(UpperCAmelCase      )
    # Loading the dataset from local csv or json files.
    UpperCAmelCase	:							Optional[Any]						=			load_dataset(args.data_file_extension    ,			data_files={'''data''': data_files['''infer''']}      )['''data''']
    UpperCAmelCase	:							List[Any]						=			load_dataset('''csv'''    ,			data_files={'''data''': infer_output_file}      )['''data''']

    if accelerator.is_main_process:
      os.makedirs(UpperCAmelCase    ,			exist_ok=UpperCAmelCase      )
      shutil.copy(UpperCAmelCase    ,			os.path.join(UpperCAmelCase    ,			f'''eval_results_iter-{iteration}.json'''      )      )
      if os.path.exists(UpperCAmelCase      ):
        shutil.copy(UpperCAmelCase    ,			os.path.join(UpperCAmelCase    ,			f'''test_results_iter-{iteration}.json'''      )      )
      create_pseudo_labeled_data(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
    accelerator.wait_for_everyone()

    UpperCAmelCase	:							List[str]						=			os.path.join(UpperCAmelCase    ,			f'''train_pseudo.{args.data_file_extension}'''      )

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      UpperCAmelCase	:							int						=			eval_result

      if best_iteration is None:
        UpperCAmelCase	:							Tuple						=			new_iteration
        UpperCAmelCase	:							Optional[int]						=			new_eval_result
      else:
        if new_eval_result - best_eval_result > args.early_stopping_threshold:
          UpperCAmelCase	:							Optional[int]						=			new_iteration
          UpperCAmelCase	:							Tuple						=			new_eval_result
          UpperCAmelCase	:							Optional[int]						=			0
        else:
          if new_eval_result == best_eval_result:
            UpperCAmelCase	:							Tuple						=			new_iteration
            UpperCAmelCase	:							str						=			new_eval_result
          early_stopping_patience_counter += 1

        if early_stopping_patience_counter >= args.early_stopping_patience:
          UpperCAmelCase	:							Tuple						=			True

    progress_bar.update(1      )

    if should_training_stop:
      break

  if best_iteration is not None:
    # Save the best iteration
    logger.info('''Best iteration: %d'''    ,			UpperCAmelCase      )
    logger.info('''Best evaluation result: %s = %f'''    ,			args.eval_metric    ,			UpperCAmelCase      )
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(UpperCAmelCase    ,			f'''eval_results_iter-{iteration}.json'''      )    ,			os.path.join(UpperCAmelCase    ,			'''eval_results_best-iteration.json'''      )    ,			)
  else:
    # Assume that the last iteration is the best
    logger.info('''Best iteration: %d'''    ,			args.max_selftrain_iterations - 1      )
    logger.info('''Best evaluation result: %s = %f'''    ,			args.eval_metric    ,			UpperCAmelCase      )
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(UpperCAmelCase    ,			f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''      )    ,			os.path.join(UpperCAmelCase    ,			'''eval_results_best-iteration.json'''      )    ,			)








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						)





