

'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def    a__       (  a__						):




     """simple docstring"""


     if (
         (cp >= 0x4_e00 and cp <= 0x9_fff)
         or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
         or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
         or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
         or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
         or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
         or (cp >= 0xf_900 and cp <= 0xf_aff)
         or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
     ):  #
          return True

     return False







def    a__       (  a__						):




     """simple docstring"""


     for char in word:
          __SCREAMING_SNAKE_CASE     =      ord(a__						)
          if not _is_chinese_char(a__						):
               return 0
     return 1







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      set()

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







def    a__       (  a__      ,    a__						):




     """simple docstring"""


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

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







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []

     for i in range(0      ,    len(a__						)      ,    1_00						):
          __SCREAMING_SNAKE_CASE     =      ltp_tokenizer.pipeline(lines[i : i + 1_00]      ,    tasks=["""cws"""]						).cws
          __SCREAMING_SNAKE_CASE     =      [get_chinese_word(a__						) for r in res]
          ltp_res.extend(a__						)
     assert len(a__						) == len(a__						)

     __SCREAMING_SNAKE_CASE     =      []
     for i in range(0      ,    len(a__						)      ,    1_00						):
          __SCREAMING_SNAKE_CASE     =      bert_tokenizer(lines[i : i + 1_00]      ,    add_special_tokens=a__      ,    truncation=a__      ,    max_length=5_12						)
          bert_res.extend(res["""input_ids"""]						)
     assert len(a__						) == len(a__						)

     __SCREAMING_SNAKE_CASE     =      []
     for input_ids, chinese_word in zip(a__      ,    a__						):
          __SCREAMING_SNAKE_CASE     =      []
          for id in input_ids:
               __SCREAMING_SNAKE_CASE     =      bert_tokenizer._convert_id_to_token(a__						)
               input_tokens.append(a__						)
          __SCREAMING_SNAKE_CASE     =      add_sub_symbol(a__      ,    a__						)
          __SCREAMING_SNAKE_CASE     =      []
          # 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] == "##":
                    __SCREAMING_SNAKE_CASE     =      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    a__       (  a__						):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE     =      prepare_ref(a__      ,    a__      ,    a__						)

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


if __name__ == "__main__":
  UpperCAmelCase :       Tuple			   =     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',
  )

  UpperCAmelCase :       Optional[Any]			   =     parser.parse_args()
  main(args)









'''simple docstring'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


     return LambdaLR(a__      ,    lambda a__						: 1      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1.0      ,    a__						)						)
          return 1.0

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      sorted(rules_dict.keys()						)
               for i, sorted_step in enumerate(a__						):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          return max(
              0.0      ,    float(num_training_steps - current_step						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * float(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      float(current_step - num_warmup_steps						) / float(max(1      ,    num_training_steps - num_warmup_steps						)						)
          if progress >= 1.0:
               return 0.0
          return max(0.0      ,    0.5 * (1.0 + math.cos(math.pi * ((float(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      optimizer.defaults["""lr"""]
     if not (lr_init > lr_end):
          raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'						)

     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(a__      ,    step_rules=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'						)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'						)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    power=a__      ,    last_epoch=a__      ,    )

     return schedule_func(
         a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    last_epoch=a__						)






