





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[Any]:
       # This defines a "chinese character" as anything in the CJK Unicode block:
       #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
       #
       # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
       # despite its name. The modern Korean Hangul alphabet is a different block,
       # as is Japanese Hiragana and Katakana. Those alphabets are used to write
       # space-separated words, so they are not treated specially and handled
       # like the all of the other languages.
       if (
           (cp >= 0x4e_00 and cp <= 0x9f_ff)
           or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
           or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
           or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
           or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
           or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
           or (cp >= 0xf9_00 and cp <= 0xfa_ff)
           or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
       ):  #
              return True

       return False




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       # word like '180' or '身高' or '神'
       for char in word:
              UpperCamelCase_          =  ord(UpperCamelCase_  )
              if not _is_chinese_char(UpperCamelCase_  ):
                     return 0
       return 1




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  set()

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		str:
       if not chinese_word_set:
              return bert_tokens
       UpperCamelCase_          =  max([len(UpperCamelCase_  ) for w in chinese_word_set]  )

       UpperCamelCase_          =  bert_tokens
       UpperCamelCase_       , UpperCamelCase_          =  0, len(UpperCamelCase_  )
       while start < end:
              UpperCamelCase_          =  True
              if is_chinese(bert_word[start]  ):
                     UpperCamelCase_          =  min(end - start					,		UpperCamelCase_  )
                     for i in range(UpperCamelCase_					,		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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  []

       for i in range(0					,		len(UpperCamelCase_  )					,		100  ):
              UpperCamelCase_          =  ltp_tokenizer.seg(lines[i : i + 100]  )[0]
              UpperCamelCase_          =  [get_chinese_word(UpperCamelCase_  ) for r in res]
              ltp_res.extend(UpperCamelCase_  )
       assert len(UpperCamelCase_  ) == len(UpperCamelCase_  )

       UpperCamelCase_          =  []
       for i in range(0					,		len(UpperCamelCase_  )					,		100  ):
              UpperCamelCase_          =  bert_tokenizer(lines[i : i + 100]					,		add_special_tokens=UpperCamelCase_					,		truncation=UpperCamelCase_					,		max_length=512  )
              bert_res.extend(res["input_ids"]  )
       assert len(UpperCamelCase_  ) == len(UpperCamelCase_  )

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

       assert len(UpperCamelCase_  ) == len(UpperCamelCase_  )

       return ref_ids




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:
       # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
       # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
       with open(args.file_name					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.readlines()
       UpperCamelCase_          =  [line.strip() for line in data if len(UpperCamelCase_  ) > 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(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )

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


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

     _UpperCAmelCase        =				parser.parse_args()
     main(args)








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=None					,		UpperCamelCase_="no"					,		UpperCamelCase_="29500"  )     ->		Optional[Any]:
       UpperCamelCase_          =  False
       UpperCamelCase_          =  False
       if any(key.startswith("KAGGLE"  ) for key in os.environ.keys()  ):
              UpperCamelCase_          =  True
       elif "IPython" in sys.modules:
              UpperCamelCase_          =  "google.colab" in str(sys.modules["IPython"].get_ipython()  )

       try:
              UpperCamelCase_          =  PrecisionType(mixed_precision.lower()  )
       except ValueError:
              raise ValueError(
                  F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

       if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"					,		UpperCamelCase_  ) is not None):
              # TPU launch
              import torch_xla.distributed.xla_multiprocessing as xmp

              if len(AcceleratorState._shared_state  ) > 0:
                     raise ValueError(
                         "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                         "your training function. Restart your notebook and make sure no cells initializes an "
                         "`Accelerator`."  )
              if num_processes is None:
                     UpperCamelCase_          =  8

              UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="TPU"  )
              print(F'''Launching a training on {num_processes} TPU cores.'''  )
              xmp.spawn(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
       elif in_colab:
              # No need for a distributed launch otherwise as it's either CPU or one GPU.
              if torch.cuda.is_available():
                     print("Launching training on one GPU."  )
              else:
                     print("Launching training on one CPU."  )
              function(*UpperCamelCase_  )
       else:
              if num_processes is None:
                     raise ValueError(
                         "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."  )

              if num_processes > 1:
                     # Multi-GPU launch
                     from torch.multiprocessing import start_processes
                     from torch.multiprocessing.spawn import ProcessRaisedException

                     if len(AcceleratorState._shared_state  ) > 0:
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                                "inside your training function. Restart your notebook and make sure no cells initializes an "
                                "`Accelerator`."  )

                     if torch.cuda.is_initialized():
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                                "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                                "function."  )

                     # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                     # process here (the other ones will be set be the launcher).
                     with patch_environment(
                         world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port=UpperCamelCase_					,		mixed_precision=UpperCamelCase_  ):
                            UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="MULTI_GPU"  )
                            print(F'''Launching training on {num_processes} GPUs.'''  )
                            try:
                                   start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
                            except ProcessRaisedException as e:
                                   if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                          raise RuntimeError(
                                              "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                              "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                              "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                              "which one is problematic."  ) from e

              else:
                     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                     if is_mps_available():
                            UpperCamelCase_          =  "1"
                            print("Launching training on MPS."  )
                     elif torch.cuda.is_available():
                            print("Launching training on one GPU."  )
                     else:
                            print("Launching training on CPU."  )
                     function(*UpperCamelCase_  )







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=2  )     ->		Optional[Any]:
       from torch.multiprocessing import start_processes

       with tempfile.NamedTemporaryFile() as tmp_file:
              # torch.distributed will expect a few environment variable to be here. We set the ones common to each
              # process here (the other ones will be set be the launcher).
              with patch_environment(
                  world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port="29500"					,		accelerate_mixed_precision="no"					,		accelerate_debug_rdv_file=tmp_file.name					,		accelerate_use_cpu="yes"					,		):
                     UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		debug=UpperCamelCase_  )
                     start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )

