"""simple docstring"""





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def 				a_     (      _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : str   , _lowerCAmelCase  : str=1024     ):





       '''simple docstring'''

       lowercase__    ,      lowercase__			:		Any       =	[], []

       lowercase__			:		str       =	list(zip(_lowerCAmelCase   , _lowerCAmelCase     )     )
       lowercase__    ,      lowercase__			:		List[str]       =	sorted_examples[0]

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

       for src, tgt in tqdm(sorted_examples[1:]     ):
              lowercase__			:		Union[str, Any]       =	new_src + ' ' + src
              lowercase__			:		str       =	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     )
                     lowercase__    ,      lowercase__			:		Any       =	src, tgt
              else:  # can fit, keep adding
                     lowercase__    ,      lowercase__			:		Dict       =	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 				a_     (      _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : Path   , _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Union[str, Any]     ):





       '''simple docstring'''

       lowercase__			:		Dict       =	Path(_lowerCAmelCase     )
       save_path.mkdir(exist_ok=_lowerCAmelCase     )
       for split in ["train"]:
              lowercase__    ,      lowercase__			:		Dict       =	data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
              lowercase__			:		Tuple       =	[x.rstrip() for x in Path(_lowerCAmelCase     ).open().readlines()]
              lowercase__			:		Union[str, Any]       =	[x.rstrip() for x in Path(_lowerCAmelCase     ).open().readlines()]
              lowercase__    ,      lowercase__			:		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"]:
              lowercase__    ,      lowercase__			:		List[str]       =	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 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	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=128     )
       parser.add_argument('--data_dir'   , type=_lowerCAmelCase     )
       parser.add_argument('--save_path'   , type=_lowerCAmelCase     )
       lowercase__			:		List[str]       =	parser.parse_args()
       lowercase__			:		Tuple       =	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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				"convbert"






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     str=3_05_22	,						lowercase_				:     Any=7_68	,						lowercase_				:     Tuple=12	,						lowercase_				:     List[str]=12	,						lowercase_				:     Optional[int]=30_72	,						lowercase_				:     Union[str, Any]="gelu"	,						lowercase_				:     str=0.1	,						lowercase_				:     List[str]=0.1	,						lowercase_				:     Optional[Any]=5_12	,						lowercase_				:     Dict=2	,						lowercase_				:     Union[str, Any]=0.02	,						lowercase_				:     Optional[Any]=1E-12	,						lowercase_				:     Optional[int]=1	,						lowercase_				:     List[Any]=0	,						lowercase_				:     Optional[int]=2	,						lowercase_				:     str=7_68	,						lowercase_				:     Dict=2	,						lowercase_				:     Optional[Any]=9	,						lowercase_				:     Union[str, Any]=1	,						lowercase_				:     Any=None	,						**lowercase_				:     Optional[Any]	,						)					->						Dict:
       super().__init__(
           pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						**lowercase_	,						)

       lowercase__ :   List[str]    					=				vocab_size
       lowercase__ :   Union[str, Any]    					=				hidden_size
       lowercase__ :   Any    					=				num_hidden_layers
       lowercase__ :   List[str]    					=				num_attention_heads
       lowercase__ :   Union[str, Any]    					=				intermediate_size
       lowercase__ :   Optional[Any]    					=				hidden_act
       lowercase__ :   int    					=				hidden_dropout_prob
       lowercase__ :   str    					=				attention_probs_dropout_prob
       lowercase__ :   Union[str, Any]    					=				max_position_embeddings
       lowercase__ :   Optional[int]    					=				type_vocab_size
       lowercase__ :   Tuple    					=				initializer_range
       lowercase__ :   List[str]    					=				layer_norm_eps
       lowercase__ :   List[Any]    					=				embedding_size
       lowercase__ :   Optional[Any]    					=				head_ratio
       lowercase__ :   Dict    					=				conv_kernel_size
       lowercase__ :   Tuple    					=				num_groups
       lowercase__ :   Optional[int]    					=				classifier_dropout



class   snake_case_       (		__A       ):






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           lowercase__ :   Tuple    					=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
           lowercase__ :   str    					=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]  )



