





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

UpperCamelCase_												=	TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

UpperCamelCase_												=	TaTokenizerFast

UpperCamelCase_												=	{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	[
        'MT5EncoderModel',
        'MT5ForConditionalGeneration',
        'MT5ForQuestionAnswering',
        'MT5Model',
        'MT5PreTrainedModel',
        'MT5Stack',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    UpperCamelCase_												=	_LazyModule(
        __name__,
        globals()['__file__'],
        _import_structure,
        extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
        module_spec=__spec__,
    )










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=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:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=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     )
         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(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=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"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=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'''

   UpperCAmelCase__						=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=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=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()



