


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 						a__			(				A__,      A__,      A__,      A__=1_0_2_4				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					[], []

			SCREAMING_SNAKE_CASE_		:     str					=					list(zip(A__,      A__				)				)
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					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_		:     int					=					new_src + ' ' + src
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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_		:     int					=					src, tgt
						else:  # can fit, keep adding
									SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					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 						a__			(				A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					Path(A__				)
			save_path.mkdir(exist_ok=A__				)
			for split in ["train"]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
						SCREAMING_SNAKE_CASE_		:     int					=					[x.rstrip() for x in Path(A__				).open().readlines()]
						SCREAMING_SNAKE_CASE_		:     Dict					=					[x.rstrip() for x in Path(A__				).open().readlines()]
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					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_		:     List[Any]					=					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 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     str					=					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_2_8				)
			parser.add_argument('--data_dir',      type=A__				)
			parser.add_argument('--save_path',      type=A__				)
			SCREAMING_SNAKE_CASE_		:     str					=					parser.parse_args()
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      Union[str, Any]								={
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Dict								=[
				    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Blip2Model',
				    'Blip2QFormerModel',
				    'Blip2PreTrainedModel',
				    'Blip2ForConditionalGeneration',
				    'Blip2VisionModel',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      Union[str, Any]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





