

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=10_24    ):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   [], []

					SCREAMING_SNAKE_CASE__   =   list(zip(__A				,		__A    )    )
					SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   sorted_examples[0]

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

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

    # cleanup
					if new_src:
										assert new_tgt
										finished_src.append(__A    )
										finished_tgt.append(__A    )
					return finished_src, finished_tgt
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





					'''simple docstring'''




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





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__   =   argparse.ArgumentParser()
					parser.add_argument('''--tok_name'''				,		type=__A				,		help='''like facebook/bart-large-cnn,t5-base, etc.'''    )
					parser.add_argument('''--max_seq_len'''				,		type=__A				,		default=1_28    )
					parser.add_argument('''--data_dir'''				,		type=__A    )
					parser.add_argument('''--save_path'''				,		type=__A    )
					SCREAMING_SNAKE_CASE__   =   parser.parse_args()
					SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained(args.tok_name    )
					return pack_data_dir(__A				,		Path(args.data_dir    )				,		args.max_seq_len				,		args.save_path    )


if __name__ == "__main__":
							packer_cli()





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


