


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowerCAmelCase	: Dict  				=1_6
__lowerCAmelCase	: List[str]  				=3_2


def        _UpperCamelCase							(      lowercase__    ,       lowercase__ = 16      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		load_dataset('''glue'''    ,       '''mrpc'''      )

						def tokenize_function(lowercase__      ):
												# max_length=None => use the model max length (it's actually the default)
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Dict					=		datasets.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__SCREAMING_SNAKE_CASE						:     List[str]					=		128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__SCREAMING_SNAKE_CASE						:     str					=		16
												elif accelerator.mixed_precision != "no":
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		8
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		None

												return tokenizer.pad(
												    lowercase__    ,       padding='''longest'''    ,       max_length=lowercase__    ,       pad_to_multiple_of=lowercase__    ,       return_tensors='''pt'''    ,       )

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE						:     Tuple					=		DataLoader(
						    tokenized_datasets['''train''']    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		DataLoader(
						    tokenized_datasets['''validation''']    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=lowercase__      )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						__lowerCAmelCase	: Dict  				=mocked_dataloaders  # noqa: F811


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,       lowercase__      ) == "1":
												__SCREAMING_SNAKE_CASE						:     Dict					=		2
						# Initialize accelerator
						__SCREAMING_SNAKE_CASE						:     List[str]					=		Accelerator(cpu=args.cpu    ,       mixed_precision=args.mixed_precision      )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__SCREAMING_SNAKE_CASE						:     str					=		config['''lr''']
						__SCREAMING_SNAKE_CASE						:     Tuple					=		int(config['''num_epochs''']      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		int(config['''seed''']      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		int(config['''batch_size''']      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		evaluate.load('''glue'''    ,       '''mrpc'''      )

						# If the batch size is too big we use gradient accumulation
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size // MAX_GPU_BATCH_SIZE
												__SCREAMING_SNAKE_CASE						:     List[str]					=		MAX_GPU_BATCH_SIZE

						set_seed(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_dataloaders(lowercase__    ,       lowercase__      )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,       return_dict=lowercase__      )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model.to(accelerator.device      )

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		AdamW(params=model.parameters()    ,       lr=lowercase__      )

						# Instantiate scheduler
						__SCREAMING_SNAKE_CASE						:     str					=		get_linear_schedule_with_warmup(
						    optimizer=lowercase__    ,       num_warmup_steps=100    ,       num_training_steps=(len(lowercase__      ) * num_epochs) // gradient_accumulation_steps    ,       )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		accelerator.prepare(
						    lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )

						# Now we train the model
						for epoch in range(lowercase__      ):
												model.train()
												for step, batch in enumerate(lowercase__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(**lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Any					=		outputs.loss
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		loss / gradient_accumulation_steps
																		accelerator.backward(lowercase__      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												__SCREAMING_SNAKE_CASE						:     Dict					=		0
												for step, batch in enumerate(lowercase__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		with torch.no_grad():
																								__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(**lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		outputs.logits.argmax(dim=-1      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		accelerator.gather((predictions, batch['''labels'''])      )
																		# New Code #
																		# First we check if it's a distributed system
																		if accelerator.use_distributed:
																								# Then see if we're on the last batch of our eval dataloader
																								if step == len(lowercase__      ) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														__SCREAMING_SNAKE_CASE						:     int					=		predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																														__SCREAMING_SNAKE_CASE						:     Any					=		references[: len(eval_dataloader.dataset      ) - samples_seen]
																								else:
																														# Otherwise we add the number of samples seen
																														samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																		metric.add_batch(
																		    predictions=lowercase__    ,       references=lowercase__    ,       )

												__SCREAMING_SNAKE_CASE						:     Dict					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''    ,       lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     int					=		argparse.ArgumentParser(description='''Simple example of training script.'''      )
						parser.add_argument(
						    '''--mixed_precision'''    ,       type=lowercase__    ,       default=lowercase__    ,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,       help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''    ,       )
						parser.add_argument('''--cpu'''    ,       action='''store_true'''    ,       help='''If passed, will train on the CPU.'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:     Dict					=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__    ,       lowercase__      )


if __name__ == "__main__":
						main()

'''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


__SCREAMING_SNAKE_CASE       :     Dict     =  get_logger(__name__)

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[str]						,					A						:				Optional[str] = None       ):
             _UpperCAmelCase     :  Dict							      =    (
                 os.path.join(A						,					config.EXTRACTED_DATASETS_DIR       ) if cache_dir else config.EXTRACTED_DATASETS_PATH
             )
             _UpperCAmelCase     :  Union[str, Any]							      =    Extractor




      def      _A	(       self						:				Tuple						,					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     :  Dict							      =    os.path.abspath(A       )
             return os.path.join(self.extract_dir						,					hash_url_to_filename(A       )       )




      def      _A	(       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      _A	(       self						:				Optional[int]						,					A						:				str						,					A						:				bool = False       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.extractor.infer_extractor_format(A       )
             if not extractor_format:
                    return input_path
             _UpperCAmelCase     :  Optional[Any]							      =    self._get_output_path(A       )
             if self._do_extract(A						,					A       ):
                    self.extractor.extract(A						,					A						,					A       )
             return output_path

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @classmethod
      @abstractmethod
      def      _A	(       cls						:				str						,					A						:				Union[Path, str]						,					**A						:				Dict       ):
             ...





      @staticmethod
      @abstractmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             ...

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[bytes]						=						[]




      @staticmethod
      def      _A	(       A						:				Union[Path, str]						,					A						:				int       ):
             with open(A						,					"rb"       ) as f:
                    return f.read(A       )





      @classmethod
      def      _A	(       cls						:				Any						,					A						:				Union[Path, str]						,					A						:				bytes = b""       ):
             if not magic_number:
                    _UpperCAmelCase     :  Any							      =    max(len(A       ) for cls_magic_number in cls.magic_numbers       )
                    try:
                           _UpperCAmelCase     :  int							      =    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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @classmethod
      def      _A	(       cls						:				str						,					A						:				Union[Path, str]						,					**A						:				List[Any]       ):
             return tarfile.is_tarfile(A       )




      @staticmethod
      def      _A	(       A						:				Union[str, Any]						,					A						:				str       ):

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

             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       A						:				Union[Path, str]						,					A						:				Union[Path, str]       ):
             os.makedirs(A						,					exist_ok=A       )
             _UpperCAmelCase     :  int							      =    tarfile.open(A       )
             tar_file.extractall(A						,					members=TarExtractor.safemembers(A						,					A       )       )
             tar_file.close()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						[b"\x1F\x8B"]




      @staticmethod
      def      _A	(       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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						[
          b"PK\x03\x04",
          b"PK\x05\x06",  # empty archive
          b"PK\x07\x08",  # spanned archive
      ]




      @classmethod
      def      _A	(       cls						:				Dict						,					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     :  Tuple							      =    _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     :  Dict							      =    fp.read(A       )  # CD is where we expect it to be
                                                if len(A       ) == sizeCentralDir:
                                                       _UpperCAmelCase     :  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      _A	(       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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						[b"\xFD\x37\x7A\x58\x5A\x00"]




      @staticmethod
      def      _A	(       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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						[b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"]  # RAR_ID  # RAR5_ID




      @staticmethod
      def      _A	(       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     :  List[str]							      =    rarfile.RarFile(A       )
             rf.extractall(A       )
             rf.close()

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						[b"\x28\xb5\x2F\xFD"]




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

             _UpperCAmelCase     :  Optional[Any]							      =    zstd.ZstdDecompressor()
             with open(A						,					"rb"       ) as ifh, open(A						,					"wb"       ) as ofh:
                    dctx.copy_stream(A						,					A       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						[b"\x42\x5A\x68"]




      @staticmethod
      def      _A	(       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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						[b"\x37\x7A\xBC\xAF\x27\x1C"]




      @staticmethod
      def      _A	(       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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						[b"\x04\x22\x4D\x18"]




      @staticmethod
      def      _A	(       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 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  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      _A	(       cls						:				List[Any]       ):
             return max(
                 len(A       )
                 for extractor in cls.extractors.values()
                 if issubclass(A						,					A       )
                 for extractor_magic_number in extractor.magic_numbers       )




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




      @classmethod
      def      _A	(       cls						:				Optional[Any]						,					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     :  Union[str, Any]							      =    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      _A	(       cls						:				Dict						,					A						:				Union[Path, str]       ):  # <Added version="2.4.0"/>
             _UpperCAmelCase     :  Optional[int]							      =    cls._get_magic_number_max_length()
             _UpperCAmelCase     :  str							      =    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      _A	(       cls						:				List[str]						,					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     :  Tuple							      =    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     :  Tuple							      =    extractor if extractor != "deprecated" else extractor_format
                           else:
                                  _UpperCAmelCase     :  Tuple							      =    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       )

