
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 							a		(				snake_case__:   int		,  snake_case__:   List[Any]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Dict=1_024   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   [], []

				lowercase_	    =   list(zip(snake_case__		,  snake_case__   )   )
				lowercase_    ,      lowercase_	    =   sorted_examples[0]

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

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

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




def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Path		,  snake_case__:   int		,  snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   Path(snake_case__   )
				save_path.mkdir(exist_ok=snake_case__   )
				for split in ["train"]:
								lowercase_    ,      lowercase_	    =   data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
								lowercase_	    =   [x.rstrip() for x in Path(snake_case__   ).open().readlines()]
								lowercase_	    =   [x.rstrip() for x in Path(snake_case__   ).open().readlines()]
								lowercase_    ,      lowercase_	    =   pack_examples(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								print(F'''packed {split} split from {len(snake_case__   )} examples -> {len(snake_case__   )}.'''   )
								Path(save_path / F'''{split}.source'''   ).open('''w'''   ).write('''\n'''.join(snake_case__   )   )
								Path(save_path / F'''{split}.target'''   ).open('''w'''   ).write('''\n'''.join(snake_case__   )   )
				for split in ["val", "test"]:
								lowercase_    ,      lowercase_	    =   data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
								shutil.copyfile(snake_case__		,  save_path / F'''{split}.source'''   )
								shutil.copyfile(snake_case__		,  save_path / F'''{split}.target'''   )




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   argparse.ArgumentParser()
				parser.add_argument('''--tok_name'''		,  type=snake_case__		,  help='''like facebook/bart-large-cnn,t5-base, etc.'''   )
				parser.add_argument('''--max_seq_len'''		,  type=snake_case__		,  default=128   )
				parser.add_argument('''--data_dir'''		,  type=snake_case__   )
				parser.add_argument('''--save_path'''		,  type=snake_case__   )
				lowercase_	    =   parser.parse_args()
				lowercase_	    =   AutoTokenizer.from_pretrained(args.tok_name   )
				return pack_data_dir(snake_case__		,  Path(args.data_dir   )		,  args.max_seq_len		,  args.save_path   )


if __name__ == "__main__":
						packer_cli()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        UpperCamelCase						(				__a				):
						a__		:List[str]      =			'''megatron-bert'''





						def __init__(self			,     __UpperCamelCase=29_056			,     __UpperCamelCase=1_024			,     __UpperCamelCase=24			,     __UpperCamelCase=16			,     __UpperCamelCase=4_096			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0			,     __UpperCamelCase="absolute"			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(pad_token_id=__UpperCamelCase			,     **__UpperCamelCase						)

												UpperCamelCase_		:							Union[str, Any]  =		vocab_size
												UpperCamelCase_		:							Union[str, Any]  =		hidden_size
												UpperCamelCase_		:							Any  =		num_hidden_layers
												UpperCamelCase_		:							str  =		num_attention_heads
												UpperCamelCase_		:							List[Any]  =		hidden_act
												UpperCamelCase_		:							Dict  =		intermediate_size
												UpperCamelCase_		:							Optional[int]  =		hidden_dropout_prob
												UpperCamelCase_		:							Dict  =		attention_probs_dropout_prob
												UpperCamelCase_		:							Any  =		max_position_embeddings
												UpperCamelCase_		:							str  =		type_vocab_size
												UpperCamelCase_		:							Tuple  =		initializer_range
												UpperCamelCase_		:							List[str]  =		layer_norm_eps
												UpperCamelCase_		:							Tuple  =		position_embedding_type
												UpperCamelCase_		:							Tuple  =		use_cache






