

'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {'''vocab_file''': '''sentencepiece.model'''}

__lowerCamelCase									=     {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__lowerCamelCase									=     {
    '''google/rembert''': 256,
}


class        A__   (   _snake_case      ):
    lowercase						       =		VOCAB_FILES_NAMES
    lowercase						       =		PRETRAINED_VOCAB_FILES_MAP
    lowercase						       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__="[CLS]"		,					UpperCamelCase__="[SEP]"		,					UpperCamelCase__="[UNK]"		,					UpperCamelCase__="[SEP]"		,					UpperCamelCase__="[PAD]"		,					UpperCamelCase__="[CLS]"		,					UpperCamelCase__="[MASK]"		,					**UpperCamelCase__		,					)    ->   List[Any]:





           '''simple docstring'''
           super().__init__(
               do_lower_case=UpperCamelCase__		,					remove_space=UpperCamelCase__		,					keep_accents=UpperCamelCase__		,					bos_token=UpperCamelCase__		,					eos_token=UpperCamelCase__		,					unk_token=UpperCamelCase__		,					sep_token=UpperCamelCase__		,					pad_token=UpperCamelCase__		,					cls_token=UpperCamelCase__		,					mask_token=UpperCamelCase__		,					**UpperCamelCase__		,					)

           A_						       =   do_lower_case
           A_						       =   remove_space
           A_						       =   keep_accents
           A_						       =   vocab_file

           A_						       =   spm.SentencePieceProcessor()
           self.sp_model.Load(UpperCamelCase__							)



    @property
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           return len(self.sp_model							)



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   {self.convert_ids_to_tokens(UpperCamelCase__							): i for i in range(self.vocab_size							)}
           vocab.update(self.added_tokens_encoder							)
           return vocab



    def __getstate__(       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.__dict__.copy()
           A_						       =   None
           return state



    def __setstate__(       self		,					UpperCamelCase__							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   d
           A_						       =   spm.SentencePieceProcessor()
           self.sp_model.Load(self.vocab_file							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__=False							)    ->   str:





           '''simple docstring'''
           A_						       =   self.sp_model.EncodeAsPieces(UpperCamelCase__							)
           return pieces



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           return self.sp_model.PieceToId(UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           return self.sp_model.IdToPiece(UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           A_						       =   self.sp_model.decode_pieces(UpperCamelCase__							)
           return out_string



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   List[int]:





           '''simple docstring'''
           A_						       =   [self.sep_token_id]
           A_						       =   [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					UpperCamelCase__ = False							)    ->   List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             """You should not supply a second sequence if the provided sequence of """
                             """ids is already formatted with special tokens for the model."""							)
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCamelCase__							)) + [1] + ([0] * len(UpperCamelCase__							)) + [1]
           return [1] + ([0] * len(UpperCamelCase__							)) + [1]



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   List[int]:





           '''simple docstring'''
           A_						       =   [self.sep_token_id]
           A_						       =   [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep							) * [0]
           return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   Tuple[str]:





           '''simple docstring'''
           if not os.path.isdir(UpperCamelCase__							):
                  logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase__							)							)
                  return
           A_						       =   os.path.join(
               UpperCamelCase__		,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

           if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase__							):
                  copyfile(self.vocab_file		,					UpperCamelCase__							)

           return (out_vocab_file,)



'''simple docstring'''







import numpy as np
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = 1e-12,							UpperCAmelCase__ = 1_00,							)      ->      tuple[float, np.ndarray]:
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase__					) == np.iscomplexobj(UpperCAmelCase__					)
       A_						       =   np.iscomplexobj(UpperCAmelCase__					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase__,							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(UpperCAmelCase__,							UpperCAmelCase__					)
              # Normalize the resulting output vector.
              A_						       =   w / np.linalg.norm(UpperCAmelCase__					)
              # 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(UpperCAmelCase__,							np.dot(UpperCAmelCase__,							UpperCAmelCase__					)					)

              # 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    UpperCAmelCase__     (   )      ->      None:
       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(UpperCAmelCase__,							UpperCAmelCase__					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              A_       ,      A_						       =   np.linalg.eigh(UpperCAmelCase__					)
              # 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(UpperCAmelCase__					) - np.abs(UpperCAmelCase__					)					) <= 1e-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()
