





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]  )   ->    str:
  _lowercase						    =			set()

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:set()  )   ->    Dict:
  if not chinese_word_set:
    return bert_tokens
  _lowercase						    =			max([len(snake_case__  ) for w in chinese_word_set]  )

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[str]      ,				snake_case__	:LTP      ,				snake_case__	:BertTokenizer  )   ->    Optional[Any]:
  _lowercase						    =			[]

  for i in range(0      ,				len(snake_case__  )      ,				100  ):
    _lowercase						    =			ltp_tokenizer.pipeline(lines[i : i + 100]      ,				tasks=['cws']  ).cws
    _lowercase						    =			[get_chinese_word(snake_case__  ) for r in res]
    ltp_res.extend(snake_case__  )
  assert len(snake_case__  ) == len(snake_case__  )

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

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

  assert len(snake_case__  ) == len(snake_case__  )

  return ref_ids

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int  )   ->    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:
    _lowercase						    =			f.readlines()
  _lowercase						    =			[line.strip() for line in data if len(snake_case__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  _lowercase						    =			LTP(args.ltp  )  # faster in GPU device
  _lowercase						    =			BertTokenizer.from_pretrained(args.bert  )

  _lowercase						    =			prepare_ref(snake_case__      ,				snake_case__      ,				snake_case__  )

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


if __name__ == "__main__":
      snake_case								=							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""",
      )

      snake_case								=							parser.parse_args()
      main(args)




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
