



'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 						__snake_case(				_lowerCAmelCase		)		-> Any:




      if (
          (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
          or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
          or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
          or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
          or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
          or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
      ):  #
            return True

      return False





def 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:




      for char in word:
            snake_case__  :    List[str]    						=      ord(lowerCAmelCase__		)
            if not _is_chinese_char(lowerCAmelCase__		):
                  return 0
      return 1





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    Dict    						=      set()

      for token in tokens:
            snake_case__  :    List[str]    						=      len(lowerCAmelCase__		) > 1 and is_chinese(lowerCAmelCase__		)
            if chinese_word:
                  word_set.add(lowerCAmelCase__		)
      snake_case__  :    Dict    						=      list(lowerCAmelCase__		)
      return word_list





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:




      if not chinese_word_set:
            return bert_tokens
      snake_case__  :    Any    						=      max([len(lowerCAmelCase__		) for w in chinese_word_set]		)

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    str    						=      []

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

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

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

      assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)

      return ref_ids





def 						__snake_case(				_lowerCAmelCase		)		-> str:




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

      snake_case__  :    Any    						=      prepare_ref(lowerCAmelCase__     ,   lowerCAmelCase__     ,   lowerCAmelCase__		)

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


if __name__ == "__main__":
       __a          =		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          =		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


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




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




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int = -1						)						->   Tuple:

  """simple docstring"""




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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = -1						)						->   str:

  """simple docstring"""




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

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				str								= {}
  lowerCAmelCase_   :				Optional[Any]								= step_rules.split(','						)
  for rule_str in rule_list[:-1]:
    lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= rule_str.split(':'						)
    lowerCAmelCase_   :				List[str]								= int(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= float(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= value
  lowerCAmelCase_   :				str								= float(rule_list[-1]						)

  def create_rules_function(lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int						):
    def rule_func(lowerCAmelCase__      :				int						) -> float:
      lowerCAmelCase_   :				str								= sorted(rules_dict.keys()						)
      for i, sorted_step in enumerate(lowerCAmelCase__						):
        if steps < sorted_step:
          return rules_dict[sorted_steps[i]]
      return last_lr_multiple

    return rule_func

  lowerCAmelCase_   :				Dict								= create_rules_function(lowerCAmelCase__    ,   lowerCAmelCase__						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]=-1						)						->   Any:

  """simple docstring"""




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

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float = 0.5    ,   lowerCAmelCase__      :				int = -1						)						->   Union[str, Any]:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				Any						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				List[Any]								= 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(lowerCAmelCase__						) * 2.0 * progress						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				str						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				Dict								= 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(lowerCAmelCase__						) * progress) % 1.0)						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=1e-7    ,   lowerCAmelCase__      :				Union[str, Any]=1.0    ,   lowerCAmelCase__      :				int=-1						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= 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(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    elif current_step > num_training_steps:
      return lr_end / lr_init  # as LambdaLR multiplies by lr_init
    else:
      lowerCAmelCase_   :				Any								= lr_init - lr_end
      lowerCAmelCase_   :				int								= num_training_steps - num_warmup_steps
      lowerCAmelCase_   :				Dict								= 1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_   :				Dict								= lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)


lowercase__		:				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 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, SchedulerType]    ,   lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				Optional[str] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				float = 1.0    ,   lowerCAmelCase__      :				int = -1    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= SchedulerType(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

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

  # 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(lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  # 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(
        lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   num_cycles=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__    ,   )

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

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





