"""simple docstring"""






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=1_0_2_4       ):
				UpperCAmelCase__      ,	UpperCAmelCase__             =							[], []

				UpperCAmelCase__             =							list(zip(lowerCamelCase					,			lowerCamelCase       )       )
				UpperCAmelCase__      ,	UpperCAmelCase__             =							sorted_examples[0]

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

				for src, tgt in tqdm(sorted_examples[1:]       ):
								UpperCAmelCase__             =							new_src + ' ' + src
								UpperCAmelCase__             =							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       )
												UpperCAmelCase__      ,	UpperCAmelCase__             =							src, tgt
								else:  # can fit, keep adding
												UpperCAmelCase__      ,	UpperCAmelCase__             =							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					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							Path(lowerCamelCase       )
				save_path.mkdir(exist_ok=lowerCamelCase       )
				for split in ["train"]:
								UpperCAmelCase__      ,	UpperCAmelCase__             =							data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
								UpperCAmelCase__             =							[x.rstrip() for x in Path(lowerCamelCase       ).open().readlines()]
								UpperCAmelCase__             =							[x.rstrip() for x in Path(lowerCamelCase       ).open().readlines()]
								UpperCAmelCase__      ,	UpperCAmelCase__             =							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"]:
								UpperCAmelCase__      ,	UpperCAmelCase__             =							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_						(					):
				UpperCAmelCase__             =							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       )
				UpperCAmelCase__             =							parser.parse_args()
				UpperCAmelCase__             =							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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

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




