





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_a 						=      logging.getLogger(__name__)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""summarization"""
			lowerCAmelCase_							     =						["""loss"""]
			lowerCAmelCase_							     =						ROUGE_KEYS
			lowerCAmelCase_							     =						"""rouge2"""





			def __init__(    self       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if hparams.sortish_sampler and hparams.gpus > 1:
					lowerCamelCase__								=		False
				elif hparams.max_tokens_per_batch is not None:
					if hparams.gpus > 1:
						raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''							)
					if hparams.sortish_sampler:
						raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''							)

				super().__init__(__lowerCAmelCase       ,  num_labels=__lowerCAmelCase       ,  mode=self.mode       ,  **__lowerCAmelCase							)
				use_task_specific_params(self.model       ,  '''summarization'''							)
				save_git_info(self.hparams.output_dir							)
				lowerCamelCase__								=		Path(self.output_dir							) / '''metrics.json'''
				lowerCamelCase__								=		Path(self.output_dir							) / '''hparams.pkl'''
				pickle_save(self.hparams       ,  self.hparams_save_path							)
				lowerCamelCase__								=		0
				lowerCamelCase__								=		defaultdict(__lowerCAmelCase							)
				lowerCamelCase__								=		self.config.model_type
				lowerCamelCase__								=		self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

				lowerCamelCase__								=		{
				    "data_dir": self.hparams.data_dir,
				    "max_source_length": self.hparams.max_source_length,
				    "prefix": self.model.config.prefix or "",
				}
				lowerCamelCase__								=		{
				    '''train''': self.hparams.n_train,
				    '''val''': self.hparams.n_val,
				    '''test''': self.hparams.n_test,
				}
				lowerCamelCase__								=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

				lowerCamelCase__								=		{
				    '''train''': self.hparams.max_target_length,
				    '''val''': self.hparams.val_max_target_length,
				    '''test''': self.hparams.test_max_target_length,
				}
				assert self.target_lens["train"] <= self.target_lens["val"], F'target_lens: {self.target_lens}'
				assert self.target_lens["train"] <= self.target_lens["test"], F'target_lens: {self.target_lens}'
				if self.hparams.freeze_embeds:
					freeze_embeds(self.model							)
				if self.hparams.freeze_encoder:
					freeze_params(self.model.get_encoder()							)
					assert_all_frozen(self.model.get_encoder()							)

				lowerCamelCase__								=		get_git_info()['''repo_sha''']
				lowerCamelCase__								=		hparams.num_workers
				lowerCamelCase__								=		None  # default to config
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
					lowerCamelCase__								=		self.decoder_start_token_id
				lowerCamelCase__								=		(
				    SeqaSeqDataset if hasattr(self.tokenizer       ,  '''prepare_seq2seq_batch'''							) else LegacySeqaSeqDataset
				)
				lowerCamelCase__								=		False
				lowerCamelCase__								=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
				if self.hparams.eval_max_gen_length is not None:
					lowerCamelCase__								=		self.hparams.eval_max_gen_length
				else:
					lowerCamelCase__								=		self.model.config.max_length
				lowerCamelCase__								=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    k: self.tokenizer.batch_decode(v.tolist()							) if '''mask''' not in k else v.shape for k, v in batch.items()
				}
				save_json(__lowerCAmelCase       ,  Path(self.output_dir							) / '''text_batch.json'''							)
				save_json({k: v.tolist() for k, v in batch.items()}       ,  Path(self.output_dir							) / '''tok_batch.json'''							)

				lowerCamelCase__								=		True
				return readable_batch





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				return self.model(__lowerCAmelCase       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.tokenizer.batch_decode(
				    __lowerCAmelCase       ,  skip_special_tokens=__lowerCAmelCase       ,  clean_up_tokenization_spaces=__lowerCAmelCase							)
				return lmap(str.strip       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.tokenizer.pad_token_id
				lowerCamelCase__				,	lowerCamelCase__								=		batch['''input_ids'''], batch['''attention_mask''']
				lowerCamelCase__								=		batch['''labels''']
				if isinstance(self.model       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		self.model._shift_right(__lowerCAmelCase							)
				else:
					lowerCamelCase__								=		shift_tokens_right(__lowerCAmelCase       ,  __lowerCAmelCase							)
				if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
					lowerCamelCase__								=		decoder_input_ids
					self.save_readable_batch(__lowerCAmelCase							)

				lowerCamelCase__								=		self(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  decoder_input_ids=__lowerCAmelCase       ,  use_cache=__lowerCAmelCase							)
				lowerCamelCase__								=		outputs['''logits''']
				if self.hparams.label_smoothing == 0:
					# Same behavior as modeling_bart.py, besides ignoring pad_token_id
					lowerCamelCase__								=		nn.CrossEntropyLoss(ignore_index=__lowerCAmelCase							)

					assert lm_logits.shape[-1] == self.vocab_size
					lowerCamelCase__								=		ce_loss_fct(lm_logits.view(-1       ,  lm_logits.shape[-1]							)       ,  tgt_ids.view(-1							)							)
				else:
					lowerCamelCase__								=		nn.functional.log_softmax(__lowerCAmelCase       ,  dim=-1							)
					lowerCamelCase__				,	lowerCamelCase__								=		label_smoothed_nll_loss(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  self.hparams.label_smoothing       ,  ignore_index=__lowerCAmelCase							)
				return (loss,)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.tokenizer.pad_token_id





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self._step(__lowerCAmelCase							)

				lowerCamelCase__								=		dict(zip(self.loss_names       ,  __lowerCAmelCase							)							)
				# tokens per batch
				lowerCamelCase__								=		batch['''input_ids'''].ne(self.pad							).sum() + batch['''labels'''].ne(self.pad							).sum()
				lowerCamelCase__								=		batch['''input_ids'''].shape[0]
				lowerCamelCase__								=		batch['''input_ids'''].eq(self.pad							).sum()
				lowerCamelCase__								=		batch['''input_ids'''].eq(self.pad							).float().mean()
				# TODO(SS): make a wandb summary metric for this
				return {"loss": loss_tensors[0], "log": logs}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self._generative_step(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase="val"							):



				'''simple docstring'''







				self.step_count += 1
				lowerCamelCase__								=		{k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
				lowerCamelCase__								=		losses['''loss''']
				lowerCamelCase__								=		{
				    k: np.array([x[k] for x in outputs]							).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
				}
				lowerCamelCase__								=		(
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							).type_as(__lowerCAmelCase							)
				generative_metrics.update({k: v.item() for k, v in losses.items()}							)
				losses.update(__lowerCAmelCase							)
				lowerCamelCase__								=		{F'{prefix}_avg_{k}': x for k, x in losses.items()}
				lowerCamelCase__								=		self.step_count
				self.metrics[prefix].append(__lowerCAmelCase							)  # callback writes this to self.metrics_save_path
				lowerCamelCase__								=		flatten_list([x['''preds'''] for x in outputs]							)
				return {
				    "log": all_metrics,
				    "preds": preds,
				    F'{prefix}_loss': loss,
				    F'{prefix}_{self.val_metric}': metric_tensor,
				}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return calculate_rouge(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		time.time()

				# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
				lowerCamelCase__								=		self.model.generate(
				    batch['''input_ids''']       ,  attention_mask=batch['''attention_mask''']       ,  use_cache=__lowerCAmelCase       ,  decoder_start_token_id=self.decoder_start_token_id       ,  num_beams=self.eval_beams       ,  max_length=self.eval_max_length       ,  )
				lowerCamelCase__								=		(time.time() - ta) / batch['''input_ids'''].shape[0]
				lowerCamelCase__								=		self.ids_to_clean_text(__lowerCAmelCase							)
				lowerCamelCase__								=		self.ids_to_clean_text(batch['''labels''']							)
				lowerCamelCase__								=		self._step(__lowerCAmelCase							)
				lowerCamelCase__								=		dict(zip(self.loss_names       ,  __lowerCAmelCase							)							)
				lowerCamelCase__								=		self.calc_generative_metrics(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		np.mean(lmap(__lowerCAmelCase       ,  __lowerCAmelCase							)							)
				base_metrics.update(gen_time=__lowerCAmelCase       ,  gen_len=__lowerCAmelCase       ,  preds=__lowerCAmelCase       ,  target=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				return base_metrics





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self._generative_step(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.validation_epoch_end(__lowerCAmelCase       ,  prefix='''test'''							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.n_obs[type_path]
				lowerCamelCase__								=		self.target_lens[type_path]
				lowerCamelCase__								=		self.dataset_class(
				    self.tokenizer       ,  type_path=__lowerCAmelCase       ,  n_obs=__lowerCAmelCase       ,  max_target_length=__lowerCAmelCase       ,  **self.dataset_kwargs       ,  )
				return dataset





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = False							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_dataset(__lowerCAmelCase							)

				if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
					lowerCamelCase__								=		dataset.make_sortish_sampler(__lowerCAmelCase       ,  distributed=self.hparams.gpus > 1							)
					return DataLoader(
					    __lowerCAmelCase       ,  batch_size=__lowerCAmelCase       ,  collate_fn=dataset.collate_fn       ,  shuffle=__lowerCAmelCase       ,  num_workers=self.num_workers       ,  sampler=__lowerCAmelCase       ,  )

				elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
					lowerCamelCase__								=		dataset.make_dynamic_sampler(
					    self.hparams.max_tokens_per_batch       ,  distributed=self.hparams.gpus > 1							)
					return DataLoader(
					    __lowerCAmelCase       ,  batch_sampler=__lowerCAmelCase       ,  collate_fn=dataset.collate_fn       ,  num_workers=self.num_workers       ,  )
				else:
					return DataLoader(
					    __lowerCAmelCase       ,  batch_size=__lowerCAmelCase       ,  collate_fn=dataset.collate_fn       ,  shuffle=__lowerCAmelCase       ,  num_workers=self.num_workers       ,  sampler=__lowerCAmelCase       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_dataloader('''train'''       ,  batch_size=self.hparams.train_batch_size       ,  shuffle=__lowerCAmelCase							)
				return dataloader





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.get_dataloader('''val'''       ,  batch_size=self.hparams.eval_batch_size							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@staticmethod
			def 		__lowerCamelCase				(    __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				BaseTransformer.add_model_specific_args(__lowerCAmelCase       ,  __lowerCAmelCase							)
				add_generic_args(__lowerCAmelCase       ,  __lowerCAmelCase							)
				parser.add_argument(
				    '''--max_source_length'''       ,  default=1_0_2_4       ,  type=__lowerCAmelCase       ,  help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )       ,  )
				parser.add_argument(
				    '''--max_target_length'''       ,  default=5_6       ,  type=__lowerCAmelCase       ,  help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )       ,  )
				parser.add_argument(
				    '''--val_max_target_length'''       ,  default=1_4_2       ,  type=__lowerCAmelCase       ,  help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )       ,  )
				parser.add_argument(
				    '''--test_max_target_length'''       ,  default=1_4_2       ,  type=__lowerCAmelCase       ,  help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )       ,  )
				parser.add_argument('''--freeze_encoder'''       ,  action='''store_true'''							)
				parser.add_argument('''--freeze_embeds'''       ,  action='''store_true'''							)
				parser.add_argument('''--sortish_sampler'''       ,  action='''store_true'''       ,  default=__lowerCAmelCase							)
				parser.add_argument('''--overwrite_output_dir'''       ,  action='''store_true'''       ,  default=__lowerCAmelCase							)
				parser.add_argument('''--max_tokens_per_batch'''       ,  type=__lowerCAmelCase       ,  default=__lowerCAmelCase							)
				parser.add_argument('''--logger_name'''       ,  type=__lowerCAmelCase       ,  choices=['''default''', '''wandb''', '''wandb_shared''']       ,  default='''default'''							)
				parser.add_argument('''--n_train'''       ,  type=__lowerCAmelCase       ,  default=-1       ,  required=__lowerCAmelCase       ,  help='''# examples. -1 means use all.'''							)
				parser.add_argument('''--n_val'''       ,  type=__lowerCAmelCase       ,  default=5_0_0       ,  required=__lowerCAmelCase       ,  help='''# examples. -1 means use all.'''							)
				parser.add_argument('''--n_test'''       ,  type=__lowerCAmelCase       ,  default=-1       ,  required=__lowerCAmelCase       ,  help='''# examples. -1 means use all.'''							)
				parser.add_argument(
				    '''--task'''       ,  type=__lowerCAmelCase       ,  default='''summarization'''       ,  required=__lowerCAmelCase       ,  help='''# examples. -1 means use all.'''							)
				parser.add_argument('''--label_smoothing'''       ,  type=__lowerCAmelCase       ,  default=0.0       ,  required=__lowerCAmelCase							)
				parser.add_argument('''--src_lang'''       ,  type=__lowerCAmelCase       ,  default=''''''       ,  required=__lowerCAmelCase							)
				parser.add_argument('''--tgt_lang'''       ,  type=__lowerCAmelCase       ,  default=''''''       ,  required=__lowerCAmelCase							)
				parser.add_argument('''--eval_beams'''       ,  type=__lowerCAmelCase       ,  default=__lowerCAmelCase       ,  required=__lowerCAmelCase							)
				parser.add_argument(
				    '''--val_metric'''       ,  type=__lowerCAmelCase       ,  default=__lowerCAmelCase       ,  required=__lowerCAmelCase       ,  choices=['''bleu''', '''rouge2''', '''loss''', None]							)
				parser.add_argument('''--eval_max_gen_length'''       ,  type=__lowerCAmelCase       ,  default=__lowerCAmelCase       ,  help='''never generate more than n tokens'''							)
				parser.add_argument('''--save_top_k'''       ,  type=__lowerCAmelCase       ,  default=1       ,  required=__lowerCAmelCase       ,  help='''How many checkpoints to save'''							)
				parser.add_argument(
				    '''--early_stopping_patience'''       ,  type=__lowerCAmelCase       ,  default=-1       ,  required=__lowerCAmelCase       ,  help=(
				        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
				        ''' val_check_interval will effect it.'''
				    )       ,  )
				return parser






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""translation"""
			lowerCAmelCase_							     =						["""loss"""]
			lowerCAmelCase_							     =						["""bleu"""]
			lowerCAmelCase_							     =						"""bleu"""





			def __init__(    self       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase       ,  **__lowerCAmelCase							)
				lowerCamelCase__								=		hparams.src_lang
				lowerCamelCase__								=		hparams.tgt_lang





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return calculate_bleu(__lowerCAmelCase       ,  __lowerCAmelCase							)





def lowerCAmelCase__(__snake_case       ,__snake_case=None							)				->      SummarizationModule:







	'''simple docstring'''







	Path(args.output_dir							).mkdir(exist_ok=__snake_case							)
	check_output_dir(__snake_case       ,expected_items=3							)

	if model is None:
		if "summarization" in args.task:
			lowerCamelCase__								=		SummarizationModule(__snake_case							)
		else:
			lowerCamelCase__								=		TranslationModule(__snake_case							)
	lowerCamelCase__								=		Path(args.data_dir							).name
	if (
	    args.logger_name == "default"
	    or args.fast_dev_run
	    or str(args.output_dir							).startswith('''/tmp'''							)
	    or str(args.output_dir							).startswith('''/var'''							)
	):
		lowerCamelCase__								=		True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		lowerCamelCase__								=		os.environ.get('''WANDB_PROJECT'''       ,__snake_case							)
		lowerCamelCase__								=		WandbLogger(name=model.output_dir.name       ,project=__snake_case							)

	elif args.logger_name == "wandb_shared":
		from pytorch_lightning.loggers import WandbLogger

		lowerCamelCase__								=		WandbLogger(name=model.output_dir.name       ,project=F'hf_{dataset}'							)

	if args.early_stopping_patience >= 0:
		lowerCamelCase__								=		get_early_stopping_callback(model.val_metric       ,args.early_stopping_patience							)
	else:
		lowerCamelCase__								=		False

	lowerCamelCase__								=		args.val_metric == '''loss'''
	lowerCamelCase__								=		generic_train(
	    __snake_case       ,__snake_case       ,logging_callback=SeqaSeqLoggingCallback()       ,checkpoint_callback=get_checkpoint_callback(
	        args.output_dir       ,model.val_metric       ,args.save_top_k       ,__snake_case							)       ,early_stopping_callback=__snake_case       ,logger=__snake_case       ,)
	pickle_save(model.hparams       ,model.output_dir / '''hparams.pkl'''							)
	if not args.do_predict:
		return model

	lowerCamelCase__								=		''''''
	lowerCamelCase__								=		sorted(glob.glob(os.path.join(args.output_dir       ,'''*.ckpt'''							)       ,recursive=__snake_case							)							)
	if checkpoints:
		lowerCamelCase__								=		checkpoints[-1]
		lowerCamelCase__								=		checkpoints[-1]
	trainer.logger.log_hyperparams(model.hparams							)

	# test() without a model tests using the best checkpoint automatically
	trainer.test()
	return model


if __name__ == "__main__":
			_a 						=      argparse.ArgumentParser()
			_a 						=      pl.Trainer.add_argparse_args(parser)
			_a 						=      SummarizationModule.add_model_specific_args(parser, os.getcwd())

			_a 						=      parser.parse_args()

			main(args)










from typing import List, Optional, Union

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






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						["""image_processor""", """tokenizer"""]
			lowerCAmelCase_							     =						"""BlipImageProcessor"""
			lowerCAmelCase_							     =						("""BertTokenizer""", """BertTokenizerFast""")





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		False
				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.image_processor





			def __call__(    self       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = 0       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''							)

				# Get only text
				if images is None:
					lowerCamelCase__								=		self.tokenizer
					lowerCamelCase__								=		self.tokenizer(
					    text=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  stride=__lowerCAmelCase       ,  pad_to_multiple_of=__lowerCAmelCase       ,  return_attention_mask=__lowerCAmelCase       ,  return_overflowing_tokens=__lowerCAmelCase       ,  return_special_tokens_mask=__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase       ,  return_length=__lowerCAmelCase       ,  verbose=__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
					return text_encoding

				# add pixel_values
				lowerCamelCase__								=		self.image_processor(__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase							)

				if text is not None:
					lowerCamelCase__								=		self.tokenizer(
					    text=__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  padding=__lowerCAmelCase       ,  truncation=__lowerCAmelCase       ,  max_length=__lowerCAmelCase       ,  stride=__lowerCAmelCase       ,  pad_to_multiple_of=__lowerCAmelCase       ,  return_attention_mask=__lowerCAmelCase       ,  return_overflowing_tokens=__lowerCAmelCase       ,  return_special_tokens_mask=__lowerCAmelCase       ,  return_offsets_mapping=__lowerCAmelCase       ,  return_token_type_ids=__lowerCAmelCase       ,  return_length=__lowerCAmelCase       ,  verbose=__lowerCAmelCase       ,  return_tensors=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
				else:
					lowerCamelCase__								=		None

				if text_encoding is not None:
					encoding_image_processor.update(__lowerCAmelCase							)

				return encoding_image_processor





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.tokenizer.model_input_names
				lowerCamelCase__								=		self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



