
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    List[Any]=1_0_2_4  ):
 __a    ,	__a			:  int		    =  [], []

 __a			:  Optional[Any]		    =  list(zip(lowerCAmelCase__  ,       lowerCAmelCase__  )  )
 __a    ,	__a			:  Dict		    =  sorted_examples[0]

 def is_too_big(lowerCAmelCase__							:    str  ):
  return tok(lowerCAmelCase__  ,       return_tensors='''pt'''  ).input_ids.shape[1] > max_tokens

 for src, tgt in tqdm(sorted_examples[1:]  ):
  __a			:  Optional[Any]		    =  new_src + ''' ''' + src
  __a			:  List[Any]		    =  new_tgt + ''' ''' + tgt
  if is_too_big(lowerCAmelCase__  ) or is_too_big(lowerCAmelCase__  ):  # cant fit, finalize example
   finished_src.append(lowerCAmelCase__  )
   finished_tgt.append(lowerCAmelCase__  )
   __a    ,	__a			:  List[str]		    =  src, tgt
  else:  # can fit, keep adding
   __a    ,	__a			:  Union[str, Any]		    =  cand_src, cand_tgt

    # cleanup
 if new_src:
  assert new_tgt
  finished_src.append(lowerCAmelCase__  )
  finished_tgt.append(lowerCAmelCase__  )
 return finished_src, finished_tgt




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    str  ):
 __a			:  str		    =  Path(lowerCAmelCase__  )
 save_path.mkdir(exist_ok=lowerCAmelCase__  )
 for split in ["train"]:
  __a    ,	__a			:  Tuple		    =  data_dir / f"{split}.source", data_dir / f"{split}.target"
  __a			:  Union[str, Any]		    =  [x.rstrip() for x in Path(lowerCAmelCase__  ).open().readlines()]
  __a			:  Dict		    =  [x.rstrip() for x in Path(lowerCAmelCase__  ).open().readlines()]
  __a    ,	__a			:  int		    =  pack_examples(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  print(f"packed {split} split from {len(lowerCAmelCase__  )} examples -> {len(lowerCAmelCase__  )}."  )
  Path(save_path / f"{split}.source"  ).open('''w'''  ).write('''\n'''.join(lowerCAmelCase__  )  )
  Path(save_path / f"{split}.target"  ).open('''w'''  ).write('''\n'''.join(lowerCAmelCase__  )  )
 for split in ["val", "test"]:
  __a    ,	__a			:  Optional[Any]		    =  data_dir / f"{split}.source", data_dir / f"{split}.target"
  shutil.copyfile(lowerCAmelCase__  ,       save_path / f"{split}.source"  )
  shutil.copyfile(lowerCAmelCase__  ,       save_path / f"{split}.target"  )




def        __UpperCamelCase					(    ):
 __a			:  Tuple		    =  argparse.ArgumentParser()
 parser.add_argument('''--tok_name'''  ,       type=lowerCAmelCase__  ,       help='''like facebook/bart-large-cnn,t5-base, etc.'''  )
 parser.add_argument('''--max_seq_len'''  ,       type=lowerCAmelCase__  ,       default=1_2_8  )
 parser.add_argument('''--data_dir'''  ,       type=lowerCAmelCase__  )
 parser.add_argument('''--save_path'''  ,       type=lowerCAmelCase__  )
 __a			:  List[str]		    =  parser.parse_args()
 __a			:  int		    =  AutoTokenizer.from_pretrained(args.tok_name  )
 return pack_data_dir(lowerCAmelCase__  ,       Path(args.data_dir  )  ,       args.max_seq_len  ,       args.save_path  )


if __name__ == "__main__":
      packer_cli()


from typing import TYPE_CHECKING

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


lowercase__          ={
    '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:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          '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

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
