

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=1024      )							->      Union[str, Any]:





    '''simple docstring'''






    A__      ,						A__          =   [], []

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

    def is_too_big(SCREAMING_SNAKE_CASE__			:	Union[str, Any]      ):
        return tok(SCREAMING_SNAKE_CASE__    ,			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(SCREAMING_SNAKE_CASE__      ) or is_too_big(SCREAMING_SNAKE_CASE__      ):  # cant fit, finalize example
            finished_src.append(SCREAMING_SNAKE_CASE__      )
            finished_tgt.append(SCREAMING_SNAKE_CASE__      )
            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(SCREAMING_SNAKE_CASE__      )
        finished_tgt.append(SCREAMING_SNAKE_CASE__      )
    return finished_src, finished_tgt
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	str      )							->      Optional[int]:





    '''simple docstring'''






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





    '''simple docstring'''






    A__          =   argparse.ArgumentParser()
    parser.add_argument('--tok_name'    ,			type=SCREAMING_SNAKE_CASE__    ,			help='like facebook/bart-large-cnn,t5-base, etc.'      )
    parser.add_argument('--max_seq_len'    ,			type=SCREAMING_SNAKE_CASE__    ,			default=128      )
    parser.add_argument('--data_dir'    ,			type=SCREAMING_SNAKE_CASE__      )
    parser.add_argument('--save_path'    ,			type=SCREAMING_SNAKE_CASE__      )
    A__          =   parser.parse_args()
    A__          =   AutoTokenizer.from_pretrained(args.tok_name      )
    return pack_data_dir(SCREAMING_SNAKE_CASE__    ,			Path(args.data_dir      )    ,			args.max_seq_len    ,			args.save_path      )


if __name__ == "__main__":
     packer_cli()


"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_a      :		List[Any]           =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a      :		Union[str, Any]           =   {
    'vocab_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
        ),
        'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-generator': (
            'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
        ),
    },
}

_a      :		Optional[Any]           =   {
    'google/electra-small-generator': 512,
    'google/electra-base-generator': 512,
    'google/electra-large-generator': 512,
    'google/electra-small-discriminator': 512,
    'google/electra-base-discriminator': 512,
    'google/electra-large-discriminator': 512,
}

_a      :		Any           =   {
    'google/electra-small-generator': {'do_lower_case': True},
    'google/electra-base-generator': {'do_lower_case': True},
    'google/electra-large-generator': {'do_lower_case': True},
    'google/electra-small-discriminator': {'do_lower_case': True},
    'google/electra-base-discriminator': {'do_lower_case': True},
    'google/electra-large-discriminator': {'do_lower_case': True},
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Tuple							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Union[str, Any]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[Any]							=		ElectraTokenizer






     def __init__(		self				,     a__=None				,     a__=None				,     a__=True				,     a__="[UNK]"				,     a__="[SEP]"				,     a__="[PAD]"				,     a__="[CLS]"				,     a__="[MASK]"				,     a__=True				,     a__=None				,     **a__				,     ):
       super().__init__(
           a__				,     tokenizer_file=a__				,     do_lower_case=a__				,     unk_token=a__				,     sep_token=a__				,     pad_token=a__				,     cls_token=a__				,     mask_token=a__				,     tokenize_chinese_chars=a__				,     strip_accents=a__				,     **a__				,     )

       _lowerCAmelCase						:       int  			=     json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get("""lowercase"""				,     a__  ) != do_lower_case
           or normalizer_state.get("""strip_accents"""				,     a__  ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""				,     a__  ) != tokenize_chinese_chars
       ):
         _lowerCAmelCase						:       Dict  			=     getattr(a__				,     normalizer_state.pop("""type"""  )  )
         _lowerCAmelCase						:       int  			=     do_lower_case
         _lowerCAmelCase						:       str  			=     strip_accents
         _lowerCAmelCase						:       Dict  			=     tokenize_chinese_chars
         _lowerCAmelCase						:       str  			=     normalizer_class(**a__  )

       _lowerCAmelCase						:       List[str]  			=     do_lower_case






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       int  			=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       List[str]  			=     [self.sep_token_id]
       _lowerCAmelCase						:       Any  			=     [self.cls_token_id]
       if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._tokenizer.model.save(a__				,     name=a__  )
       return tuple(a__  )




