




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


__magic_name__  :  List[Any]					=       logging.getLogger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      str				= "summarization"
     lowercase_ :      Union[str, Any]				= ["loss"]
     lowercase_ :      str				= ROUGE_KEYS
     lowercase_ :      List[Any]				= "rouge2"
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Dict       ,						**__lowerCamelCase	:		Any						):



       """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        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict[str, torch.Tensor]						):



       """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        A__      (						self	:		Dict       ,						__lowerCamelCase	:		Union[str, Any]       ,						**__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       return self.model(__lowerCamelCase       ,						**__lowerCamelCase						)
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		List[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.tokenizer.batch_decode(
           __lowerCamelCase       ,						skip_special_tokens=__lowerCamelCase       ,						clean_up_tokenization_spaces=__lowerCamelCase						)
       return lmap(str.strip       ,						__lowerCamelCase						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		dict						):



       """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        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       return self.tokenizer.pad_token_id
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		str						):



       """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        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       return self._generative_step(__lowerCamelCase						)
     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Tuple="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        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       return calculate_rouge(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		dict						):



       """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        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       return self._generative_step(__lowerCamelCase						)
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict						):



       """simple docstring"""
       return self.validation_epoch_end(__lowerCamelCase       ,						prefix='''test'''						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any						):



       """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        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		bool = 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        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_dataloader('''train'''       ,						batch_size=self.hparams.train_batch_size       ,						shuffle=__lowerCamelCase						)
       return dataloader
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       return self.get_dataloader('''val'''       ,						batch_size=self.hparams.eval_batch_size						)
     def        A__      (						self	:		str						):



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



     @staticmethod
     def        A__      (						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Dict						):



       """simple docstring"""
       BaseTransformer.add_model_specific_args(__lowerCamelCase       ,						__lowerCamelCase						)
       add_generic_args(__lowerCamelCase       ,						__lowerCamelCase						)
       parser.add_argument(
           '''--max_source_length'''       ,						default=10_24       ,						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=56       ,						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_42       ,						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_42       ,						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_00       ,						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     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= "translation"
     lowercase_ :      List[Any]				= ["loss"]
     lowercase_ :      Union[str, Any]				= ["bleu"]
     lowercase_ :      Optional[int]				= "bleu"
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		Any       ,						**__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						**__lowerCamelCase						)
       lowerCAmelCase__										=      hparams.src_lang
       lowerCAmelCase__										=      hparams.tgt_lang



     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       return calculate_bleu(__lowerCamelCase       ,						__lowerCamelCase						)



def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=None				):
  Path(args.output_dir				).mkdir(exist_ok=__lowerCAmelCase				)
  check_output_dir(__lowerCAmelCase   , expected_items=3				)

  if model is None:
    if "summarization" in args.task:
      lowerCAmelCase__										=      SummarizationModule(__lowerCAmelCase				)
    else:
      lowerCAmelCase__										=      TranslationModule(__lowerCAmelCase				)
  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'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      WandbLogger(name=model.output_dir.name   , project=__lowerCAmelCase				)

  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(
      __lowerCAmelCase   , __lowerCAmelCase   , logging_callback=SeqaSeqLoggingCallback()   , checkpoint_callback=get_checkpoint_callback(
          args.output_dir   , model.val_metric   , args.save_top_k   , __lowerCAmelCase				)   , early_stopping_callback=__lowerCAmelCase   , logger=__lowerCAmelCase   , )
  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=__lowerCAmelCase				)				)
  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__":
      __magic_name__  :  str					=       argparse.ArgumentParser()
      __magic_name__  :  Optional[Any]					=       pl.Trainer.add_argparse_args(parser)
      __magic_name__  :  Optional[int]					=       SummarizationModule.add_model_specific_args(parser, os.getcwd())

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      main(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__  :  Any					=       {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       ["""LayoutLMv2TokenizerFast"""]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       ["""LayoutLMv2FeatureExtractor"""]
      __magic_name__  :  Any					=       ["""LayoutLMv2ImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       [
          """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """LayoutLMv2ForQuestionAnswering""",
          """LayoutLMv2ForSequenceClassification""",
          """LayoutLMv2ForTokenClassification""",
          """LayoutLMv2Layer""",
          """LayoutLMv2Model""",
          """LayoutLMv2PreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

