





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


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={'vocab_file': 'spiece.model'}

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





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=False    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase="<s>"    ,						__lowerCamelCase="</s>"    ,						__lowerCamelCase="<unk>"    ,						__lowerCamelCase="<sep>"    ,						__lowerCamelCase="<pad>"    ,						__lowerCamelCase="<cls>"    ,						__lowerCamelCase="<mask>"    ,						__lowerCamelCase=["<eop>", "<eod>"]    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     None:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							AddedToken(__lowerCamelCase    ,						lstrip=__lowerCamelCase    ,						rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase    ,						__lowerCamelCase							) else mask_token

            _SCREAMING_SNAKE_CASE       :	int						    =							{} if sp_model_kwargs is None else sp_model_kwargs

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

            _SCREAMING_SNAKE_CASE       :	Dict						    =							3

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							do_lower_case
            _SCREAMING_SNAKE_CASE       :	int						    =							remove_space
            _SCREAMING_SNAKE_CASE       :	int						    =							keep_accents
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							vocab_file

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
            self.sp_model.Load(__lowerCamelCase							)

            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."							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							jieba
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							str.maketrans(" \n"    ,						"\u2582\u2583"							)




       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def     UpperCamelCase_			(      self							)			->     Tuple:
            return len(self.sp_model							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
            vocab.update(self.added_tokens_encoder							)
            return vocab




       def __getstate__(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.__dict__.copy()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							None
            return state




       def __setstate__(      self    ,						__lowerCamelCase							)			->     int:
            _SCREAMING_SNAKE_CASE       :	str						    =							d

            # for backward compatibility
            if not hasattr(self    ,						"sp_model_kwargs"							):
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{}

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
            self.sp_model.Load(self.vocab_file							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     List[str]:
            if self.remove_space:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							" ".join(inputs.strip().split()							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							outputs.replace("``"    ,						"\""							).replace("''"    ,						"\""							)

            if not self.keep_accents:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							unicodedata.normalize("NFKD"    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							"".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase							)]							)
            if self.do_lower_case:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							outputs.lower()

            return outputs




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.preprocess_text(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.sp_model.encode(__lowerCamelCase    ,						out_type=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[]
            for piece in pieces:
                 if len(__lowerCamelCase							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
                      _SCREAMING_SNAKE_CASE       :	int						    =							self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase    ,						""							)							)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0]							) == 1:
                                _SCREAMING_SNAKE_CASE       :	Tuple						    =							cur_pieces[1:]
                           else:
                                _SCREAMING_SNAKE_CASE       :	Dict						    =							cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]							)
                      new_pieces.extend(__lowerCamelCase							)
                 else:
                      new_pieces.append(__lowerCamelCase							)

            return new_pieces




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     int:
            return self.sp_model.PieceToId(__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Tuple:
            return self.sp_model.IdToPiece(__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"".join(__lowerCamelCase							).replace(__lowerCamelCase    ,						" "							).strip()
            return out_string




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     List[int]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[self.sep_token_id]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[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     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None    ,						__lowerCamelCase = False							)			->     List[int]:

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

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




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     List[int]:
            _SCREAMING_SNAKE_CASE       :	str						    =							[self.sep_token_id]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[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     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     Tuple[str]:
            if not os.path.isdir(__lowerCamelCase							):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
                 return
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							os.path.join(
                __lowerCamelCase    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

            if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
                 copyfile(self.vocab_file    ,						__lowerCamelCase							)
            elif not os.path.isfile(self.vocab_file							):
                 with open(__lowerCamelCase    ,						"wb"							) as fi:
                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase							)

            return (out_vocab_file,)







       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     int:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							super()._decode(*__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							text.replace(" "    ,						""							).replace("\u2582"    ,						" "							).replace("\u2583"    ,						"\n"							)
            return text






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     # Construct model
     if gpta_config_file == "":
          _SCREAMING_SNAKE_CASE       :	str						    =							GPTaConfig()
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig.from_json_file(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							GPTaModel(__lowerCamelCase	)

     # Load weights from numpy
     load_tf_weights_in_gpta(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
     torch.save(model.state_dict(),       __lowerCamelCase	)
     print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
     with open(__lowerCamelCase,       "w",       encoding="utf-8"	) as f:
          f.write(config.to_json_string()	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--gpt2_config_file',
           default='',
           type=str,
           help=(
               'An optional config json file corresponding to the pre-trained OpenAI model. \n'
               'This specifies the model architecture.'
           ),
       )
       UpperCamelCase__      =parser.parse_args()
       convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)