


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{"vocab_file": "spiece.model"}

lowercase__				     =		{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

lowercase__				     =		{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
lowercase__				     =		0
lowercase__				     =		1
lowercase__				     =		2
lowercase__				     =		3
lowercase__				     =		4





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				'''left'''






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase="<s>"     ,							__UpperCamelCase="</s>"     ,							__UpperCamelCase="<unk>"     ,							__UpperCamelCase="<sep>"     ,							__UpperCamelCase="<pad>"     ,							__UpperCamelCase="<cls>"     ,							__UpperCamelCase="<mask>"     ,							__UpperCamelCase=["<eop>", "<eod>"]     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    None:
          # Mask token behave like a normal word, i.e. include the space before it
          _a						=		AddedToken(__UpperCamelCase     ,							lstrip=__UpperCamelCase     ,							rstrip=__UpperCamelCase		) if isinstance(__UpperCamelCase     ,							__UpperCamelCase		) else mask_token

          _a						=		{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=__UpperCamelCase     ,							remove_space=__UpperCamelCase     ,							keep_accents=__UpperCamelCase     ,							bos_token=__UpperCamelCase     ,							eos_token=__UpperCamelCase     ,							unk_token=__UpperCamelCase     ,							sep_token=__UpperCamelCase     ,							pad_token=__UpperCamelCase     ,							cls_token=__UpperCamelCase     ,							mask_token=__UpperCamelCase     ,							additional_special_tokens=__UpperCamelCase     ,							sp_model_kwargs=self.sp_model_kwargs     ,							**__UpperCamelCase     ,							)

          _a						=		3

          _a						=		do_lower_case
          _a						=		remove_space
          _a						=		keep_accents
          _a						=		vocab_file

          _a						=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    Optional[int]:
          return len(self.sp_model		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		{self.convert_ids_to_tokens(__UpperCamelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab






     def __getstate__(			self		)       ->    Any:
          _a						=		self.__dict__.copy()
          _a						=		None
          return state






     def __setstate__(			self     ,							__UpperCamelCase		)       ->    int:
          _a						=		d

          # for backward compatibility
          if not hasattr(self     ,							"sp_model_kwargs"		):
               _a						=		{}

          _a						=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          if self.remove_space:
               _a						=		" ".join(inputs.strip().split()		)
          else:
               _a						=		inputs
          _a						=		outputs.replace("``"     ,							"\""		).replace("''"     ,							"\""		)

          if not self.keep_accents:
               _a						=		unicodedata.normalize("NFKD"     ,							__UpperCamelCase		)
               _a						=		"".join([c for c in outputs if not unicodedata.combining(__UpperCamelCase		)]		)
          if self.do_lower_case:
               _a						=		outputs.lower()

          return outputs






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		self.preprocess_text(__UpperCamelCase		)
          _a						=		self.sp_model.encode(__UpperCamelCase     ,							out_type=__UpperCamelCase		)
          _a						=		[]
          for piece in pieces:
               if len(__UpperCamelCase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
                    _a						=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCamelCase     ,							""		)		)
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]		) == 1:
                              _a						=		cur_pieces[1:]
                         else:
                              _a						=		cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]		)
                    new_pieces.extend(__UpperCamelCase		)
               else:
                    new_pieces.append(__UpperCamelCase		)

          return new_pieces






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          return self.sp_model.PieceToId(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          return self.sp_model.IdToPiece(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		"".join(__UpperCamelCase		).replace(__UpperCamelCase     ,							" "		).strip()
          return out_string






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							**__UpperCamelCase     ,							)       ->    str:
          _a						=		kwargs.pop("use_source_tokenizer"     ,							__UpperCamelCase		)

          _a						=		self.convert_ids_to_tokens(__UpperCamelCase     ,							skip_special_tokens=__UpperCamelCase		)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          _a						=		[]
          _a						=		[]
          for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                    continue
               if token in self.added_tokens_encoder:
                    if current_sub_text:
                         sub_texts.append(self.convert_tokens_to_string(__UpperCamelCase		)		)
                         _a						=		[]
                    sub_texts.append(__UpperCamelCase		)
               else:
                    current_sub_text.append(__UpperCamelCase		)
          if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__UpperCamelCase		)		)

          # Mimic the behavior of the Rust tokenizer:
          # By default, there are no spaces between special tokens
          _a						=		"".join(__UpperCamelCase		)

          _a						=		(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
               _a						=		self.clean_up_tokenization(__UpperCamelCase		)
               return clean_text
          else:
               return text






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    List[int]:
          _a						=		[self.sep_token_id]
          _a						=		[self.cls_token_id]
          if token_ids_a is None:
               return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False		)       ->    List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCamelCase     ,							token_ids_a=__UpperCamelCase     ,							already_has_special_tokens=__UpperCamelCase		)

          if token_ids_a is not None:
               return ([0] * len(__UpperCamelCase		)) + [1] + ([0] * len(__UpperCamelCase		)) + [1, 1]
          return ([0] * len(__UpperCamelCase		)) + [1, 1]






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    List[int]:
          _a						=		[self.sep_token_id]
          _a						=		[2]

          if token_ids_a is None:
               return len(token_ids_a + sep		) * [0] + cls_segment_id
          return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    Tuple[str]:
          if not os.path.isdir(__UpperCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          _a						=		os.path.join(
              __UpperCamelCase     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file     ,							__UpperCamelCase		)
          elif not os.path.isfile(self.vocab_file		):
               with open(__UpperCamelCase     ,							"wb"		) as fi:
                    _a						=		self.sp_model.serialized_model_proto()
                    fi.write(__UpperCamelCase		)

          return (out_vocab_file,)





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  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(__lowerCamelCase				) or is_too_big(__lowerCamelCase				):  # cant fit, finalize example
               finished_src.append(__lowerCamelCase				)
               finished_tgt.append(__lowerCamelCase				)
               _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(__lowerCamelCase				)
          finished_tgt.append(__lowerCamelCase				)
     return finished_src, finished_tgt






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		Path(__lowerCamelCase				)
     save_path.mkdir(exist_ok=__lowerCamelCase				)
     for split in ["train"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a			,			_a						=		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"]:
          _a			,			_a						=		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 __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






     _a						=		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				)
     _a						=		parser.parse_args()
     _a						=		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()

