




"""simple docstring"""

import numpy as np






def 						_snake_case					( _snake_case							: np.ndarray    ,  _snake_case							: np.ndarray    ,  _snake_case							: float = 1E-12    ,  _snake_case							: int = 1_00    ,  )   -> tuple[float, np.ndarray]:






      '''simple docstring'''


      assert np.shape(_snake_case							)[0] == np.shape(_snake_case							)[1]
      # Ensure proper dimensionality.
      assert np.shape(_snake_case							)[0] == np.shape(_snake_case							)[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(_snake_case							) == np.iscomplexobj(_snake_case							)
      _A        =       np.iscomplexobj(_snake_case							)
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(_snake_case    ,  input_matrix.conj().T							)

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _A        =       False
      _A        =       0
      _A        =       0
      _A        =       1E12

      while not convergence:
            # Multiple matrix by the vector.
            _A        =       np.dot(_snake_case    ,  _snake_case							)
            # Normalize the resulting output vector.
            _A        =       w / np.linalg.norm(_snake_case							)
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _A        =       vector.conj().T if is_complex else vector.T
            _A        =       np.dot(_snake_case    ,  np.dot(_snake_case    ,  _snake_case							)							)

            # Check convergence.
            _A        =       np.abs(lambda_ - lambda_previous							) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _A        =       True

            _A        =       lambda_

      if is_complex:
            _A        =       np.real(lambda_							)

      return lambda_, vector






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
      _A        =       np.array([41, 4, 20]							)
      _A        =       real_input_matrix.astype(np.complexaaa							)
      _A        =       np.triu(1J * complex_input_matrix    ,  1							)
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _A        =       np.array([41, 4, 20]							).astype(np.complexaaa							)

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _A        =       real_input_matrix
                  _A        =       real_vector
            elif problem_type == "complex":
                  _A        =       complex_input_matrix
                  _A        =       complex_vector

            # Our implementation.
            _A       ,   _A        =       power_iteration(_snake_case    ,  _snake_case							)

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _A       ,   _A        =       np.linalg.eigh(_snake_case							)
            # Last eigenvalue is the maximum one.
            _A        =       eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _A        =       eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max							) <= 1E-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(_snake_case							) - np.abs(_snake_case							)							) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_power_iteration()







"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 						_snake_case					( _snake_case							: Dict							)   -> Any:






      '''simple docstring'''


      if (
          (cp >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
          or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
          or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
          or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
      ):  #
            return True

      return False






def 						_snake_case					( _snake_case							: str							)   -> Tuple:






      '''simple docstring'''


      for char in word:
            _A        =       ord(_snake_case							)
            if not _is_chinese_char(_snake_case							):
                  return 0
      return 1






def 						_snake_case					( _snake_case							: List[str]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       set()

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






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: set()							)   -> Optional[Any]:






      '''simple docstring'''


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

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






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: LTP    ,  _snake_case							: BertTokenizer							)   -> str:






      '''simple docstring'''


      _A        =       []

      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       ltp_tokenizer.seg(lines[i : i + 1_00]							)[0]
            _A        =       [get_chinese_word(_snake_case							) for r in res]
            ltp_res.extend(_snake_case							)
      assert len(_snake_case							) == len(_snake_case							)

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

      _A        =       []
      for input_ids, chinese_word in zip(_snake_case    ,  _snake_case							):
            _A        =       []
            for id in input_ids:
                  _A        =       bert_tokenizer._convert_id_to_token(_snake_case							)
                  input_tokens.append(_snake_case							)
            _A        =       add_sub_symbol(_snake_case    ,  _snake_case							)
            _A        =       []
            # 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] == "##":
                        _A        =       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 						_snake_case					( _snake_case							: List[str]							)   -> Dict:






      '''simple docstring'''


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

      _A        =       prepare_ref(_snake_case    ,  _snake_case    ,  _snake_case							)

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


if __name__ == "__main__":
       a         =      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         =      parser.parse_args()
       main(args)

