


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''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}





class 						__SCREAMING_SNAKE_CASE					(      _UpperCAmelCase ):






     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:
          _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		)

          try:
               import jieba
          except ModuleNotFoundError as error:
               raise error.__class__(
                   "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                   "See https://pypi.org/project/jieba/ for installation."		)
          _a						=		jieba
          _a						=		str.maketrans(" \n"     ,							"\u2582\u2583"		)






     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def  a_					(			self		)       ->    List[str]:
          return len(self.sp_model		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _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		)       ->    Union[str, Any]:
          _a						=		self.__dict__.copy()
          _a						=		None
          return state






     def __setstate__(			self     ,							__UpperCamelCase		)       ->    Any:
          _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		)       ->    int:






          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		)       ->    str:
          return self.sp_model.PieceToId(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          return self.sp_model.IdToPiece(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Any:
          _a						=		"".join(__UpperCamelCase		).replace(__UpperCamelCase     ,							" "		).strip()
          return out_string






     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,)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[int]:
          _a						=		super()._decode(*__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		text.replace(" "     ,							""		).replace("\u2582"     ,							" "		).replace("\u2583"     ,							"\n"		)
          return text





'''simple docstring'''

import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase__				     =		data_utils.TransfoXLTokenizer
lowercase__				     =		data_utils.TransfoXLCorpus
lowercase__				     =		data_utils
lowercase__				     =		data_utils






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







     '''simple docstring'''






     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__lowerCamelCase      ,  "rb"				) as fp:
               _a						=		pickle.load(__lowerCamelCase      ,  encoding="latin1"				)
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          _a						=		pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(F"Save vocabulary to {pytorch_vocab_dump_path}"				)
          _a						=		corpus.vocab.__dict__
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

          _a						=		corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"      ,  __lowerCamelCase				)
          _a						=		pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(F"Save dataset to {pytorch_dataset_dump_path}"				)
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _a						=		os.path.abspath(__lowerCamelCase				)
          _a						=		os.path.abspath(__lowerCamelCase				)

          print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."				)
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _a						=		TransfoXLConfig()
          else:
               _a						=		TransfoXLConfig.from_json_file(__lowerCamelCase				)
          print(F"Building PyTorch model from configuration: {config}"				)
          _a						=		TransfoXLLMHeadModel(__lowerCamelCase				)

          _a						=		load_tf_weights_in_transfo_xl(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          # Save pytorch-model
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          print(F"Save PyTorch model to {os.path.abspath(__lowerCamelCase				)}"				)
          torch.save(model.state_dict()      ,  __lowerCamelCase				)
          print(F"Save configuration file to {os.path.abspath(__lowerCamelCase				)}"				)
          with open(__lowerCamelCase      ,  "w"      ,  encoding="utf-8"				) as f:
               f.write(config.to_json_string()				)


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser()
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the folder to store the PyTorch model or dataset/vocab.",
   )
   parser.add_argument(
       "--tf_checkpoint_path",
       default="",
       type=str,
       help="An optional path to a TensorFlow checkpoint path to be converted.",
   )
   parser.add_argument(
       "--transfo_xl_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained BERT model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--transfo_xl_dataset_file",
       default="",
       type=str,
       help="An optional dataset file to be converted in a vocabulary.",
   )
   lowercase__				     =		parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )

