

"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def  _snake_case     (    UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					List[Any]=1024			):
     A__			,				A__										=  [], []

     A__										=  list(zip(UpperCAmelCase_					,   UpperCAmelCase_			)			)
     A__			,				A__										=  sorted_examples[0]

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

     for src, tgt in tqdm(sorted_examples[1:]			):
          A__										=  new_src + """ """ + src
          A__										=  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__			,				A__										=  src, tgt
          else:  # can fit, keep adding
               A__			,				A__										=  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  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Path					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Optional[int]			):
     A__										=  Path(UpperCAmelCase_			)
     save_path.mkdir(exist_ok=UpperCAmelCase_			)
     for split in ["train"]:
          A__			,				A__										=  data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
          A__										=  [x.rstrip() for x in Path(UpperCAmelCase_			).open().readlines()]
          A__										=  [x.rstrip() for x in Path(UpperCAmelCase_			).open().readlines()]
          A__			,				A__										=  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__			,				A__										=  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  _snake_case     (    ):
     A__										=  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__										=  parser.parse_args()
     A__										=  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"""



# 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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_					:	List[str]							  =						{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 SCREAMING_SNAKE_CASE_					:	List[str]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure)






