
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


lowercase_											=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowercase_											=	{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase_											=	{
    """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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase_											=	sorted(arg_to_scheduler.keys())
lowercase_											=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class   __UpperCamelCase				(    pl.LightningModule   ):






     """simple docstring"""







     def __init__(							self    :    List[str]      ,		_A    :    argparse.Namespace      ,		_A    :    str=None      ,		_A    :    Optional[Any]="base"      ,		_A    :    str=None      ,		_A    :    int=None      ,		_A    :    Union[str, Any]=None      ,		**_A    :    str      ,		):







       """simple docstring"""




       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						0
       __SCREAMING_SNAKE_CASE     :					int			=						Path(self.hparams.output_dir				)
       __SCREAMING_SNAKE_CASE     :					str			=						self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
         __SCREAMING_SNAKE_CASE     :					Any			=						AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path      ,		**({'''num_labels''': num_labels} if num_labels is not None else {})      ,		cache_dir=_A      ,		**_A      ,		)
       else:
         __SCREAMING_SNAKE_CASE     :					PretrainedConfig			=						config

       __SCREAMING_SNAKE_CASE     :					Any			=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
       for p in extra_model_params:
         if getattr(self.hparams      ,		_A      ,		_A				):
           assert hasattr(self.config      ,		_A				), F'''model config doesn\'t have a `{p}` attribute'''
           setattr(self.config      ,		_A      ,		getattr(self.hparams      ,		_A				)				)

       if tokenizer is None:
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,		cache_dir=_A      ,		)
       else:
         __SCREAMING_SNAKE_CASE     :					PreTrainedTokenizer			=						tokenizer
       __SCREAMING_SNAKE_CASE     :					Tuple			=						MODEL_MODES[mode]
       if model is None:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						self.model_type.from_pretrained(
             self.hparams.model_name_or_path      ,		from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path				)      ,		config=self.config      ,		cache_dir=_A      ,		)
       else:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		*_A    :    Dict      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.model_type.from_pretrained(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						arg_to_scheduler[self.hparams.lr_scheduler]
       __SCREAMING_SNAKE_CASE     :					str			=						get_schedule_func(
           self.opt      ,		num_warmup_steps=self.hparams.warmup_steps      ,		num_training_steps=self.total_steps()				)
       __SCREAMING_SNAKE_CASE     :					str			=						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
       return scheduler



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.model
       __SCREAMING_SNAKE_CASE     :					Tuple			=						['''bias''', '''LayerNorm.weight''']
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[
           {
               '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
               '''weight_decay''': self.hparams.weight_decay,
           },
           {
               '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
               '''weight_decay''': 0.0,
           },
       ]
       if self.hparams.adafactor:
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						Adafactor(
             _A      ,		lr=self.hparams.learning_rate      ,		scale_parameter=_A      ,		relative_step=_A				)

       else:
         __SCREAMING_SNAKE_CASE     :					Any			=						AdamW(
             _A      ,		lr=self.hparams.learning_rate      ,		eps=self.hparams.adam_epsilon				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						optimizer

       __SCREAMING_SNAKE_CASE     :					str			=						self.get_lr_scheduler()

       return [optimizer], [scheduler]



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    int      ,		_A    :    Any				):







       """simple docstring"""




       return self.validation_step(_A      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    Tuple				):







       """simple docstring"""




       return self.validation_end(_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						max(1      ,		self.hparams.gpus				)  # TODO: consider num_tpu_cores
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    str				):







       """simple docstring"""




       if mode == "test":
         __SCREAMING_SNAKE_CASE     :					int			=						len(self.test_dataloader().dataset				)
       else:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.get_dataloader('''train'''      ,		self.hparams.train_batch_size      ,		shuffle=_A				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						len(self.train_dataloader().dataset				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    str      ,		_A    :    int      ,		_A    :    bool = False				):







       """simple docstring"""




       raise NotImplementedError('''You must implement this for your task'''				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       return self.train_loader



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       return self.get_dataloader('''dev'''      ,		self.hparams.eval_batch_size      ,		shuffle=_A				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       return self.get_dataloader('''test'''      ,		self.hparams.eval_batch_size      ,		shuffle=_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Optional[Any]				):







       """simple docstring"""




       return os.path.join(
           self.hparams.data_dir      ,		'''cached_{}_{}_{}'''.format(
               _A      ,		list(filter(_A      ,		self.hparams.model_name_or_path.split('''/'''				)				)				).pop()      ,		str(self.hparams.max_seq_length				)      ,		)      ,		)



     @pl.utilities.rank_zero_only
     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    Dict[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.output_dir.joinpath('''best_tfmr'''				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.step_count
       self.model.save_pretrained(_A				)
       self.tokenizer.save_pretrained(_A				)




     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    int      ,		_A    :    Any				):







       """simple docstring"""




       parser.add_argument(
           '''--model_name_or_path'''      ,		default=_A      ,		type=_A      ,		required=_A      ,		help='''Path to pretrained model or model identifier from huggingface.co/models'''      ,		)
       parser.add_argument(
           '''--config_name'''      ,		default=''''''      ,		type=_A      ,		help='''Pretrained config name or path if not the same as model_name'''				)
       parser.add_argument(
           '''--tokenizer_name'''      ,		default=_A      ,		type=_A      ,		help='''Pretrained tokenizer name or path if not the same as model_name'''      ,		)
       parser.add_argument(
           '''--cache_dir'''      ,		default=''''''      ,		type=_A      ,		help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''      ,		)
       parser.add_argument(
           '''--encoder_layerdrop'''      ,		type=_A      ,		help='''Encoder layer dropout probability (Optional). Goes into model.config'''      ,		)
       parser.add_argument(
           '''--decoder_layerdrop'''      ,		type=_A      ,		help='''Decoder layer dropout probability (Optional). Goes into model.config'''      ,		)
       parser.add_argument(
           '''--dropout'''      ,		type=_A      ,		help='''Dropout probability (Optional). Goes into model.config'''      ,		)
       parser.add_argument(
           '''--attention_dropout'''      ,		type=_A      ,		help='''Attention dropout probability (Optional). Goes into model.config'''      ,		)
       parser.add_argument('''--learning_rate'''      ,		default=5e-5      ,		type=_A      ,		help='''The initial learning rate for Adam.'''				)
       parser.add_argument(
           '''--lr_scheduler'''      ,		default='''linear'''      ,		choices=_A      ,		metavar=_A      ,		type=_A      ,		help='''Learning rate scheduler'''      ,		)
       parser.add_argument('''--weight_decay'''      ,		default=0.0      ,		type=_A      ,		help='''Weight decay if we apply some.'''				)
       parser.add_argument('''--adam_epsilon'''      ,		default=1e-8      ,		type=_A      ,		help='''Epsilon for Adam optimizer.'''				)
       parser.add_argument('''--warmup_steps'''      ,		default=0      ,		type=_A      ,		help='''Linear warmup over warmup_steps.'''				)
       parser.add_argument('''--num_workers'''      ,		default=4      ,		type=_A      ,		help='''kwarg passed to DataLoader'''				)
       parser.add_argument('''--num_train_epochs'''      ,		dest='''max_epochs'''      ,		default=3      ,		type=_A				)
       parser.add_argument('''--train_batch_size'''      ,		default=32      ,		type=_A				)
       parser.add_argument('''--eval_batch_size'''      ,		default=32      ,		type=_A				)
       parser.add_argument('''--adafactor'''      ,		action='''store_true'''				)






class   __UpperCamelCase				(    pl.Callback   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    Union[str, Any]      ,		_A    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						trainer.lr_schedulers[0]['''scheduler''']
       __SCREAMING_SNAKE_CASE     :					Tuple			=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
       pl_module.logger.log_metrics(_A				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    pl.Trainer      ,		_A    :    pl.LightningModule				):







       """simple docstring"""




       rank_zero_info('''***** Validation results *****'''				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						trainer.callback_metrics
       # Log results
       for key in sorted(_A				):
         if key not in ["log", "progress_bar"]:
           rank_zero_info('''{} = {}\n'''.format(_A      ,		str(metrics[key]				)				)				)




     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    pl.Trainer      ,		_A    :    pl.LightningModule				):







       """simple docstring"""




       rank_zero_info('''***** Test results *****'''				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						trainer.callback_metrics
       # Log and save results to file
       __SCREAMING_SNAKE_CASE     :					Any			=						os.path.join(pl_module.hparams.output_dir      ,		'''test_results.txt'''				)
       with open(_A      ,		'''w'''				) as writer:
         for key in sorted(_A				):
           if key not in ["log", "progress_bar"]:
             rank_zero_info('''{} = {}\n'''.format(_A      ,		str(metrics[key]				)				)				)
             writer.write('''{} = {}\n'''.format(_A      ,		str(metrics[key]				)				)				)






def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      '''--output_dir'''   ,	default=snake_case   ,	type=snake_case   ,	required=snake_case   ,	help='''The output directory where the model predictions and checkpoints will be written.'''   ,	)
  parser.add_argument(
      '''--fp16'''   ,	action='''store_true'''   ,	help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,	)

  parser.add_argument(
      '''--fp16_opt_level'''   ,	type=snake_case   ,	default='''O2'''   ,	help=(
          '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
          '''See details at https://nvidia.github.io/apex/amp.html'''
      )   ,	)
  parser.add_argument('''--n_tpu_cores'''   ,	dest='''tpu_cores'''   ,	type=snake_case    )
  parser.add_argument('''--max_grad_norm'''   ,	dest='''gradient_clip_val'''   ,	default=1.0   ,	type=snake_case   ,	help='''Max gradient norm'''    )
  parser.add_argument('''--do_train'''   ,	action='''store_true'''   ,	help='''Whether to run training.'''    )
  parser.add_argument('''--do_predict'''   ,	action='''store_true'''   ,	help='''Whether to run predictions on the test set.'''    )
  parser.add_argument(
      '''--gradient_accumulation_steps'''   ,	dest='''accumulate_grad_batches'''   ,	type=snake_case   ,	default=1   ,	help='''Number of updates steps to accumulate before performing a backward/update pass.'''   ,	)
  parser.add_argument('''--seed'''   ,	type=snake_case   ,	default=42   ,	help='''random seed for initialization'''    )
  parser.add_argument(
      '''--data_dir'''   ,	default=snake_case   ,	type=snake_case   ,	required=snake_case   ,	help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''   ,	)


def     a__			(    snake_case   ,	snake_case   ,	snake_case=None   ,	snake_case=True   ,	snake_case=[]   ,	snake_case=None   ,	snake_case=None   ,	**snake_case   ,	):





  """simple docstring"""
  pl.seed_everything(args.seed    )

  # init model
  __SCREAMING_SNAKE_CASE     :					Any			=						Path(model.hparams.output_dir    )
  odir.mkdir(exist_ok=snake_case    )

  # add custom checkpoints
  if checkpoint_callback is None:
    __SCREAMING_SNAKE_CASE     :					int			=						pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir   ,	prefix='''checkpoint'''   ,	monitor='''val_loss'''   ,	mode='''min'''   ,	save_top_k=1    )
  if early_stopping_callback:
    extra_callbacks.append(snake_case    )
  if logging_callback is None:
    __SCREAMING_SNAKE_CASE     :					int			=						LoggingCallback()

  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    __SCREAMING_SNAKE_CASE     :					int			=						16
    __SCREAMING_SNAKE_CASE     :					str			=						args.fpaa_opt_level

  if args.gpus > 1:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						'''ddp'''

  __SCREAMING_SNAKE_CASE     :					Tuple			=						args.accumulate_grad_batches
  __SCREAMING_SNAKE_CASE     :					str			=						extra_train_kwargs.get('''accelerator'''   ,	snake_case    )
  __SCREAMING_SNAKE_CASE     :					Any			=						extra_train_kwargs.get('''profiler'''   ,	snake_case    )

  __SCREAMING_SNAKE_CASE     :					List[str]			=						pl.Trainer.from_argparse_args(
      snake_case   ,	weights_summary=snake_case   ,	callbacks=[logging_callback] + extra_callbacks   ,	logger=snake_case   ,	checkpoint_callback=snake_case   ,	**snake_case   ,	)

  if args.do_train:
    trainer.fit(snake_case    )

  return trainer


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



     def __init__(							self    :    Optional[Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''				)
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''				)

       super().__init__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

       if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

       # first, apply the image processor
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						self.tokenizer(
           text=text if text is not None else features['''words''']      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features['''boxes''']      ,		word_labels=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

       if len(_A				) != len(_A				):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F''' {len(_A				)} and {len(_A				)}'''				)

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




       return self.tokenizer.decode(*_A      ,		**_A				)



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,		_A      ,		)
       return self.image_processor
