





"""simple docstring"""

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=1024	):




	"""simple docstring"""






	A_  :				Optional[int]	   =		[], []

	A_  :				Any	   =		list(zip(_UpperCAmelCase  ,      _UpperCAmelCase	)	)
	A_  :				Union[str, Any]	   =		sorted_examples[0]

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

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

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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






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

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		argparse.ArgumentParser()
	parser.add_argument('''--tok_name'''  ,      type=_UpperCAmelCase  ,      help='''like facebook/bart-large-cnn,t5-base, etc.'''	)
	parser.add_argument('''--max_seq_len'''  ,      type=_UpperCAmelCase  ,      default=128	)
	parser.add_argument('''--data_dir'''  ,      type=_UpperCAmelCase	)
	parser.add_argument('''--save_path'''  ,      type=_UpperCAmelCase	)
	A_  :				Tuple	   =		parser.parse_args()
	A_  :				List[Any]	   =		AutoTokenizer.from_pretrained(args.tok_name	)
	return pack_data_dir(_UpperCAmelCase  ,      Path(args.data_dir	)  ,      args.max_seq_len  ,      args.save_path	)


if __name__ == "__main__":
		packer_cli()











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

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

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					if self.task == "multiple-choice":
						A_  :				Union[str, Any]	   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
						A_  :				Dict	   =		{0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]       )





