


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      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    lowercase_	(	_A    :					str		):

      """simple docstring"""




      for char in word:
            lowerCamelCase__	:       Union[str, Any]					=						ord(_A		)
            if not _is_chinese_char(_A		):
                  return 0
      return 1

def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						set()

      for token in tokens:
            lowerCamelCase__	:       Optional[int]					=						len(_A		) > 1 and is_chinese(_A		)
            if chinese_word:
                  word_set.add(_A		)
      lowerCamelCase__	:       Dict					=						list(_A		)
      return word_list

def    lowercase_	(	_A    :					List[str]					,      _A    :					set()		):

      """simple docstring"""




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

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

def    lowercase_	(	_A    :					List[str]					,      _A    :					LTP					,      _A    :					BertTokenizer		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						[]

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

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

      lowerCamelCase__	:       List[str]					=						[]
      for input_ids, chinese_word in zip(_A					,      _A		):
            lowerCamelCase__	:       Union[str, Any]					=						[]
            for id in input_ids:
                  lowerCamelCase__	:       List[Any]					=						bert_tokenizer._convert_id_to_token(_A		)
                  input_tokens.append(_A		)
            lowerCamelCase__	:       int					=						add_sub_symbol(_A					,      _A		)
            lowerCamelCase__	:       List[str]					=						[]
            # 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] == "##":
                        lowerCamelCase__	:       Any					=						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    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




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

      lowerCamelCase__	:       Dict					=						prepare_ref(_A					,      _A					,      _A		)

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


if __name__ == "__main__":
    A   :					Optional[int]	=      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")

    A   :					Any	=      parser.parse_args()
    main(args)






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


