
'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Any=1024				):



      """simple docstring"""




      _lowerCamelCase    ,		_lowerCamelCase					: int		    = [], []

      _lowerCamelCase					: List[str]		    = list(zip(_lowerCAmelCase					,						_lowerCAmelCase				)				)
      _lowerCamelCase    ,		_lowerCamelCase					: List[str]		    = sorted_examples[0]

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

      for src, tgt in tqdm(sorted_examples[1:]				):
            _lowerCamelCase					: Any		    = new_src + " " + src
            _lowerCamelCase					: Union[str, Any]		    = 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				)
                  _lowerCamelCase    ,		_lowerCamelCase					: Optional[Any]		    = src, tgt
            else:  # can fit, keep adding
                  _lowerCamelCase    ,		_lowerCamelCase					: Any		    = 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 :     Union[str, Any]					,						_lowerCAmelCase :     Path					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = Path(_lowerCAmelCase				)
      save_path.mkdir(exist_ok=_lowerCAmelCase				)
      for split in ["train"]:
            _lowerCamelCase    ,		_lowerCamelCase					: Any		    = data_dir / F'{split}.source', data_dir / F'{split}.target'
            _lowerCamelCase					: Tuple		    = [x.rstrip() for x in Path(_lowerCAmelCase				).open().readlines()]
            _lowerCamelCase					: Dict		    = [x.rstrip() for x in Path(_lowerCAmelCase				).open().readlines()]
            _lowerCamelCase    ,		_lowerCamelCase					: List[str]		    = 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"]:
            _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = 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_							(						):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = 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=128				)
      parser.add_argument("--data_dir"					,						type=_lowerCAmelCase				)
      parser.add_argument("--save_path"					,						type=_lowerCAmelCase				)
      _lowerCamelCase					: str		    = parser.parse_args()
      _lowerCamelCase					: Optional[int]		    = 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()





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






