'''simple docstring'''




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}

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

      def __init__(       self						:				Any						,					A						:				Optional[int]=None						,					A						:				Tuple=None						,					*A						:				Tuple						,					**A						:				List[str]       ):
             super().__init__(*A						,					**A       )

             if config is None:
                    assert isinstance(self.model						,					A       ), (
                        "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                        F""" {self.model.__class__}"""
                    )
                    _UpperCAmelCase     :  str							      =    self.model.config
             else:
                    _UpperCAmelCase     :  List[str]							      =    config

             _UpperCAmelCase     :  List[Any]							      =    data_args
             _UpperCAmelCase     :  str							      =    self.config.tgt_vocab_size if isinstance(self.config						,					A       ) else self.config.vocab_size

             if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                    assert self.config.pad_token_id is not None, (
                        "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                        " calculation or doing label smoothing."
                    )

             if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                    logger.warning(
                        F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
                        " padding.."       )

             if self.args.label_smoothing == 0:
                    _UpperCAmelCase     :  Optional[Any]							      =    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
             else:
                    # dynamically import label_smoothed_nll_loss
                    from utils import label_smoothed_nll_loss

                    _UpperCAmelCase     :  Dict							      =    label_smoothed_nll_loss




      def      _A	(       self						:				Tuple						,					A						:				int       ):
             if self.optimizer is None:
                    _UpperCAmelCase     :  Tuple							      =    ["bias", "LayerNorm.weight"]
                    _UpperCAmelCase     :  str							      =    [
                        {
                            "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                            "weight_decay": self.args.weight_decay,
                        },
                        {
                            "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                            "weight_decay": 0.0,
                        },
                    ]
                    _UpperCAmelCase     :  int							      =    Adafactor if self.args.adafactor else AdamW
                    if self.args.adafactor:
                           _UpperCAmelCase     :  List[str]							      =    Adafactor
                           _UpperCAmelCase     :  List[Any]							      =    {"scale_parameter": False, "relative_step": False}
                    else:
                           _UpperCAmelCase     :  List[str]							      =    AdamW
                           _UpperCAmelCase     :  List[str]							      =    {
                               "betas": (self.args.adam_betaa, self.args.adam_betaa),
                               "eps": self.args.adam_epsilon,
                           }
                    _UpperCAmelCase     :  List[Any]							      =    self.args.learning_rate
                    if self.sharded_ddp:
                           _UpperCAmelCase     :  List[Any]							      =    OSS(
                               params=A						,					optim=A						,					**A						,					)
                    else:
                           _UpperCAmelCase     :  Union[str, Any]							      =    optimizer_cls(A						,					**A       )

             if self.lr_scheduler is None:
                    _UpperCAmelCase     :  List[str]							      =    self._get_lr_scheduler(A       )
             else:  # ignoring --lr_scheduler
                    logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."       )




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    arg_to_scheduler[self.args.lr_scheduler]
             if self.args.lr_scheduler == "constant":
                    _UpperCAmelCase     :  Optional[Any]							      =    schedule_func(self.optimizer       )
             elif self.args.lr_scheduler == "constant_w_warmup":
                    _UpperCAmelCase     :  str							      =    schedule_func(self.optimizer						,					num_warmup_steps=self.args.warmup_steps       )
             else:
                    _UpperCAmelCase     :  str							      =    schedule_func(
                        self.optimizer						,					num_warmup_steps=self.args.warmup_steps						,					num_training_steps=A       )
             return scheduler




      def      _A	(       self						:				Tuple       ):
             if isinstance(self.train_dataset						,					torch.utils.data.IterableDataset       ):
                    return None
             elif is_torch_tpu_available():
                    return get_tpu_sampler(self.train_dataset       )
             else:
                    if self.args.sortish_sampler:
                           self.train_dataset.make_sortish_sampler(
                               self.args.per_device_train_batch_size						,					distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						,					)

                    return (
                        RandomSampler(self.train_dataset       )
                        if self.args.local_rank == -1
                        else DistributedSampler(self.train_dataset       )
                    )




      def      _A	(       self						:				Any						,					A						:				Union[str, Any]						,					A						:				Union[str, Any]						,					A						:				List[Any]       ):
             if self.args.label_smoothing == 0:
                    if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                           # force training to ignore pad token
                           _UpperCAmelCase     :  List[str]							      =    model(**A						,					use_cache=A       )[0]
                           _UpperCAmelCase     :  int							      =    self.loss_fn(logits.view(-1						,					logits.shape[-1]       )						,					labels.view(-1       )       )
                    else:
                           # compute usual loss via models
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    model(**A						,					labels=A						,					use_cache=A       )[:2]
             else:
                    # compute label smoothed loss
                    _UpperCAmelCase     :  Optional[int]							      =    model(**A						,					use_cache=A       )[0]
                    _UpperCAmelCase     :  List[str]							      =    torch.nn.functional.log_softmax(A						,					dim=-1       )
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  Union[str, Any]							      =    self.loss_fn(A						,					A						,					self.args.label_smoothing						,					ignore_index=self.config.pad_token_id       )
             return loss, logits




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    inputs.pop("labels"       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    self._compute_loss(A						,					A						,					A       )
             return loss




      def      _A	(       self						:				List[str]						,					A						:				nn.Module						,					A						:				Dict[str, Union[torch.Tensor, Any]]						,					A						:				bool						,					A						:				Optional[List[str]] = None						,					):
             _UpperCAmelCase     :  List[str]							      =    self._prepare_inputs(A       )

             _UpperCAmelCase     :  Dict							      =    {
                 "max_length": self.data_args.val_max_target_length
                 if self.data_args is not None
                 else self.config.max_length,
                 "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
             }

             if self.args.predict_with_generate and not self.args.prediction_loss_only:
                    _UpperCAmelCase     :  Dict							      =    self.model.generate(
                        inputs["input_ids"]						,					attention_mask=inputs["attention_mask"]						,					**A						,					)
                    # in case the batch is shorter than max length, the output should be padded
                    if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                           _UpperCAmelCase     :  int							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

             _UpperCAmelCase     :  Any							      =    inputs.pop("labels"       )
             with torch.no_grad():
                    # compute loss on predict data
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    self._compute_loss(A						,					A						,					A       )

             _UpperCAmelCase     :  List[str]							      =    loss.mean().detach()
             if self.args.prediction_loss_only:
                    return (loss, None, None)

             _UpperCAmelCase     :  str							      =    generated_tokens if self.args.predict_with_generate else logits

             if labels.shape[-1] < gen_kwargs["max_length"]:
                    _UpperCAmelCase     :  Optional[Any]							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

             return (loss, logits, labels)





      def      _A	(       self						:				Dict						,					A						:				int						,					A						:				List[str]       ):
             # If PAD token is not defined at least EOS token has to be defined
             _UpperCAmelCase     :  Union[str, Any]							      =    self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

             if pad_token_id is None:
                    raise ValueError(
                        "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                        F""" padded to `max_length`={max_length}"""       )

             _UpperCAmelCase     :  Tuple							      =    pad_token_id * torch.ones(
                 (tensor.shape[0], max_length)						,					dtype=tensor.dtype						,					device=tensor.device       )
             _UpperCAmelCase     :  Tuple							      =    tensor
             return padded_tensor


'''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       )

