
"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    if (
        (cp >= 0X4E00 and cp <= 0X9FFF)
        or (cp >= 0X3400 and cp <= 0X4DBF)  #
        or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
        or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
        or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
        or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
        or (cp >= 0XF900 and cp <= 0XFAFF)
        or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
    ):  #
        return True

    return False


def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    for char in word:
        _UpperCAmelCase          =   ord(_A      )
        if not _is_chinese_char(_A      ):
            return 0
    return 1


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   set()

    for token in tokens:
        _UpperCAmelCase          =   len(_A      ) > 1 and is_chinese(_A      )
        if chinese_word:
            word_set.add(_A      )
    _UpperCAmelCase          =   list(_A      )
    return word_list


def  _UpperCamelCase	(						_A					,      _A      )    ->  List[str]:





    """simple docstring"""

    if not chinese_word_set:
        return bert_tokens
    _UpperCAmelCase          =   max([len(_A      ) for w in chinese_word_set]      )

    _UpperCAmelCase          =   bert_tokens
    _UpperCAmelCase    ,_UpperCAmelCase          =   0, len(_A      )
    while start < end:
        _UpperCAmelCase          =   True
        if is_chinese(bert_word[start]      ):
            _UpperCAmelCase          =   min(end - start					,      _A      )
            for i in range(_A					,      1					,      -1      ):
                _UpperCAmelCase          =   """""".join(bert_word[start : start + i]      )
                if whole_word in chinese_word_set:
                    for j in range(start + 1					,      start + i      ):
                        _UpperCAmelCase          =   """##""" + bert_word[j]
                    _UpperCAmelCase          =   start + i
                    _UpperCAmelCase          =   False
                    break
        if single_word:
            start += 1
    return bert_word


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []

    for i in range(0					,      len(_A      )					,      1_0_0      ):
        _UpperCAmelCase          =   ltp_tokenizer.pipeline(lines[i : i + 1_0_0]					,      tasks=["""cws"""]      ).cws
        _UpperCAmelCase          =   [get_chinese_word(_A      ) for r in res]
        ltp_res.extend(_A      )
    assert len(_A      ) == len(_A      )

    _UpperCAmelCase          =   []
    for i in range(0					,      len(_A      )					,      1_0_0      ):
        _UpperCAmelCase          =   bert_tokenizer(lines[i : i + 1_0_0]					,      add_special_tokens=_A					,      truncation=_A					,      max_length=5_1_2      )
        bert_res.extend(res["""input_ids"""]      )
    assert len(_A      ) == len(_A      )

    _UpperCAmelCase          =   []
    for input_ids, chinese_word in zip(_A					,      _A      ):
        _UpperCAmelCase          =   []
        for id in input_ids:
            _UpperCAmelCase          =   bert_tokenizer._convert_id_to_token(_A      )
            input_tokens.append(_A      )
        _UpperCAmelCase          =   add_sub_symbol(_A					,      _A      )
        _UpperCAmelCase          =   []
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_A      ):
            if token[:2] == "##":
                _UpperCAmelCase          =   token[2:]
                # save chinese tokens' pos
                if len(_A      ) == 1 and _is_chinese_char(ord(_A      )      ):
                    ref_id.append(_A      )
        ref_ids.append(_A      )

    assert len(_A      ) == len(_A      )

    return ref_ids


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    with open(args.file_name					,      """r"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   f.readlines()
    _UpperCAmelCase          =   [line.strip() for line in data if len(_A      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    _UpperCAmelCase          =   LTP(args.ltp      )  # faster in GPU device
    _UpperCAmelCase          =   BertTokenizer.from_pretrained(args.bert      )

    _UpperCAmelCase          =   prepare_ref(_A					,      _A					,      _A      )

    with open(args.save_path					,      """w"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   [json.dumps(_A      ) + """\n""" for ref in ref_ids]
        f.writelines(_A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser(description='''prepare_chinese_ref''')
 parser.add_argument(
     '''--file_name''',
     required=False,
     type=str,
     default='''./resources/chinese-demo.txt''',
     help='''file need process, same as training data in lm''',
 )
 parser.add_argument(
     '''--ltp''',
     required=False,
     type=str,
     default='''./resources/ltp''',
     help='''resources for LTP tokenizer, usually a path''',
 )
 parser.add_argument(
     '''--bert''',
     required=False,
     type=str,
     default='''./resources/robert''',
     help='''resources for Bert tokenizer''',
 )
 parser.add_argument(
     '''--save_path''',
     required=False,
     type=str,
     default='''./resources/ref.txt''',
     help='''path to save res''',
 )

 a					:	Union[str, Any]							=		parser.parse_args()
 main(args)

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)