



'''simple docstring'''





import pickle

import numpy as np
from matplotlib import pyplot as plt






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=0.2  ,__UpperCAmelCase=0.2      )    ->		str:
      lowerCAmelCase__ :  Dict							      =					bp_numa
      lowerCAmelCase__ :  Optional[int]							      =					bp_numa
      lowerCAmelCase__ :  Dict							      =					bp_numa
      lowerCAmelCase__ :  List[Any]							      =					conva_get[:2]
      lowerCAmelCase__ :  Tuple							      =					conva_get[2]
      lowerCAmelCase__ :  int							      =					size_pa
      lowerCAmelCase__ :  str							      =					rate_w
      lowerCAmelCase__ :  Optional[int]							      =					rate_t
      lowerCAmelCase__ :  int							      =					[
          np.mat(-1 * np.random.rand(self.conva[0]  ,self.conva[0]      ) + 0.5      )
          for i in range(self.conva[1]      )
      ]
      lowerCAmelCase__ :  Union[str, Any]							      =					np.mat(-1 * np.random.rand(self.num_bpa  ,self.num_bpa      ) + 0.5      )
      lowerCAmelCase__ :  Dict							      =					np.mat(-1 * np.random.rand(self.num_bpa  ,self.num_bpa      ) + 0.5      )
      lowerCAmelCase__ :  Union[str, Any]							      =					-2 * np.random.rand(self.conva[1]      ) + 1
      lowerCAmelCase__ :  Union[str, Any]							      =					-2 * np.random.rand(self.num_bpa      ) + 1
      lowerCAmelCase__ :  str							      =					-2 * np.random.rand(self.num_bpa      ) + 1




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      # save model dict with pickle
      lowerCAmelCase__ :  Optional[Any]							      =					{
          """num_bp1""": self.num_bpa,
          """num_bp2""": self.num_bpa,
          """num_bp3""": self.num_bpa,
          """conv1""": self.conva,
          """step_conv1""": self.step_conva,
          """size_pooling1""": self.size_poolinga,
          """rate_weight""": self.rate_weight,
          """rate_thre""": self.rate_thre,
          """w_conv1""": self.w_conva,
          """wkj""": self.wkj,
          """vji""": self.vji,
          """thre_conv1""": self.thre_conva,
          """thre_bp2""": self.thre_bpa,
          """thre_bp3""": self.thre_bpa,
      }
      with open(__UpperCAmelCase  ,"""wb"""      ) as f:
          pickle.dump(__UpperCAmelCase  ,__UpperCAmelCase      )

      print(F"""Model saved： {save_path}"""      )




  @classmethod
  def 							UpperCAmelCase_	(    cls  ,__UpperCAmelCase      )    ->		List[Any]:
      # read saved model
      with open(__UpperCAmelCase  ,"""rb"""      ) as f:
          lowerCAmelCase__ :  List[str]							      =					pickle.load(__UpperCAmelCase      )  # noqa: S301

      lowerCAmelCase__ :  Any							      =					model_dic.get("""conv1"""      )
      conv_get.append(model_dic.get("""step_conv1"""      )      )
      lowerCAmelCase__ :  Optional[int]							      =					model_dic.get("""size_pooling1"""      )
      lowerCAmelCase__ :  Union[str, Any]							      =					model_dic.get("""num_bp1"""      )
      lowerCAmelCase__ :  Dict							      =					model_dic.get("""num_bp2"""      )
      lowerCAmelCase__ :  Optional[int]							      =					model_dic.get("""num_bp3"""      )
      lowerCAmelCase__ :  List[Any]							      =					model_dic.get("""rate_weight"""      )
      lowerCAmelCase__ :  Any							      =					model_dic.get("""rate_thre"""      )
      # create model instance
      lowerCAmelCase__ :  Optional[int]							      =					CNN(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      # modify model parameter
      lowerCAmelCase__ :  List[str]							      =					model_dic.get("""w_conv1"""      )
      lowerCAmelCase__ :  Dict							      =					model_dic.get("""wkj"""      )
      lowerCAmelCase__ :  str							      =					model_dic.get("""vji"""      )
      lowerCAmelCase__ :  Dict							      =					model_dic.get("""thre_conv1"""      )
      lowerCAmelCase__ :  Dict							      =					model_dic.get("""thre_bp2"""      )
      lowerCAmelCase__ :  Any							      =					model_dic.get("""thre_bp3"""      )
      return conv_ins




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      return 1 / (1 + np.exp(-1 * x      ))




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      return round(__UpperCAmelCase  ,3      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[str]:
      # convolution process
      lowerCAmelCase__ :  int							      =					convs[0]
      lowerCAmelCase__ :  Union[str, Any]							      =					convs[1]
      lowerCAmelCase__ :  Optional[Any]							      =					np.shape(__UpperCAmelCase      )[0]
      # get the data slice of original image data, data_focus
      lowerCAmelCase__ :  Tuple							      =					[]
      for i_focus in range(0  ,size_data - size_conv + 1  ,__UpperCAmelCase      ):
          for j_focus in range(0  ,size_data - size_conv + 1  ,__UpperCAmelCase      ):
              lowerCAmelCase__ :  str							      =					data[
                  i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
              ]
              data_focus.append(__UpperCAmelCase      )
        # calculate the feature map of every single kernel, and saved as list of matrix
      lowerCAmelCase__ :  Optional[int]							      =					[]
      lowerCAmelCase__ :  str							      =					int((size_data - size_conv) / conv_step + 1      )
      for i_map in range(__UpperCAmelCase      ):
          lowerCAmelCase__ :  List[str]							      =					[]
          for i_focus in range(len(__UpperCAmelCase      )      ):
              lowerCAmelCase__ :  Tuple							      =					(
                  np.sum(np.multiply(data_focus[i_focus]  ,w_convs[i_map]      )      )
                  - thre_convs[i_map]
              )
              featuremap.append(self.sig(__UpperCAmelCase      )      )
          lowerCAmelCase__ :  List[str]							      =					np.asmatrix(__UpperCAmelCase      ).reshape(
              __UpperCAmelCase  ,__UpperCAmelCase      )
          data_featuremap.append(__UpperCAmelCase      )

      # expanding the data slice to One dimenssion
      lowerCAmelCase__ :  int							      =					[]
      for each_focus in data_focus:
          focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
      lowerCAmelCase__ :  int							      =					np.asarray(__UpperCAmelCase      )
      return focus_list, data_featuremap




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase="average_pool"      )    ->		List[Any]:
      # pooling process
      lowerCAmelCase__ :  str							      =					len(featuremaps[0]      )
      lowerCAmelCase__ :  List[Any]							      =					int(size_map / size_pooling      )
      lowerCAmelCase__ :  Any							      =					[]
      for i_map in range(len(__UpperCAmelCase      )      ):
          lowerCAmelCase__ :  Tuple							      =					featuremaps[i_map]
          lowerCAmelCase__ :  Union[str, Any]							      =					[]
          for i_focus in range(0  ,__UpperCAmelCase  ,__UpperCAmelCase      ):
              for j_focus in range(0  ,__UpperCAmelCase  ,__UpperCAmelCase      ):
                  lowerCAmelCase__ :  str							      =					feature_map[
                      i_focus : i_focus + size_pooling,
                      j_focus : j_focus + size_pooling,
                  ]
                  if pooling_type == "average_pool":
                      # average pooling
                      map_pooled.append(np.average(__UpperCAmelCase      )      )
                  elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(__UpperCAmelCase      )      )
          lowerCAmelCase__ :  Tuple							      =					np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase  ,__UpperCAmelCase      )
          featuremap_pooled.append(__UpperCAmelCase      )
      return featuremap_pooled




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      # expanding three dimension data to one dimension list
      lowerCAmelCase__ :  List[str]							      =					[]
      for i in range(len(__UpperCAmelCase      )      ):
          lowerCAmelCase__ :  List[Any]							      =					np.shape(data[i]      )
          lowerCAmelCase__ :  Optional[Any]							      =					data[i].reshape(1  ,shapes[0] * shapes[1]      )
          lowerCAmelCase__ :  List[Any]							      =					data_listed.getA().tolist()[0]
          data_expanded.extend(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					np.asarray(__UpperCAmelCase      )
      return data_expanded




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      # expanding matrix to one dimension list
      lowerCAmelCase__ :  Optional[Any]							      =					np.asarray(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					np.shape(__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					data_mat.reshape(1  ,shapes[0] * shapes[1]      )
      return data_expanded




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  int							      =					[]
      lowerCAmelCase__ :  Optional[Any]							      =					0
      for i_map in range(__UpperCAmelCase      ):
          lowerCAmelCase__ :  Tuple							      =					np.ones((size_map, size_map)      )
          for i in range(0  ,__UpperCAmelCase  ,__UpperCAmelCase      ):
              for j in range(0  ,__UpperCAmelCase  ,__UpperCAmelCase      ):
                  lowerCAmelCase__ :  Optional[Any]							      =					pd_pool[
                      i_pool
                  ]
                  lowerCAmelCase__ :  Optional[Any]							      =					i_pool + 1
          lowerCAmelCase__ :  Dict							      =					np.multiply(
              __UpperCAmelCase  ,np.multiply(out_map[i_map]  ,(1 - out_map[i_map])      )      )
          pd_all.append(__UpperCAmelCase      )
      return pd_all




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=bool      )    ->		Tuple:
      # model traning
      print("""----------------------Start Training-------------------------"""      )
      print((""" - - Shape: Train_Data  """, np.shape(__UpperCAmelCase      ))      )
      print((""" - - Shape: Teach_Data  """, np.shape(__UpperCAmelCase      ))      )
      lowerCAmelCase__ :  Optional[Any]							      =					0
      lowerCAmelCase__ :  Union[str, Any]							      =					[]
      lowerCAmelCase__ :  Optional[int]							      =					1_0000
      while rp < n_repeat and mse >= error_accuracy:
          lowerCAmelCase__ :  int							      =					0
          print(F"""-------------Learning Time {rp}--------------"""      )
          for p in range(len(__UpperCAmelCase      )      ):
              # print('------------Learning Image: %d--------------'%p)
              lowerCAmelCase__ :  Any							      =					np.asmatrix(datas_train[p]      )
              lowerCAmelCase__ :  List[str]							      =					np.asarray(datas_teach[p]      )
              lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.convolute(
                  __UpperCAmelCase  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
              lowerCAmelCase__ :  Union[str, Any]							      =					self.pooling(__UpperCAmelCase  ,self.size_poolinga      )
              lowerCAmelCase__ :  List[Any]							      =					np.shape(__UpperCAmelCase      )
              lowerCAmelCase__ :  List[Any]							      =					self._expand(__UpperCAmelCase      )
              lowerCAmelCase__ :  List[str]							      =					data_bp_input

              lowerCAmelCase__ :  int							      =					np.dot(__UpperCAmelCase  ,self.vji.T      ) - self.thre_bpa
              lowerCAmelCase__ :  Tuple							      =					self.sig(__UpperCAmelCase      )
              lowerCAmelCase__ :  Tuple							      =					np.dot(__UpperCAmelCase  ,self.wkj.T      ) - self.thre_bpa
              lowerCAmelCase__ :  str							      =					self.sig(__UpperCAmelCase      )

              # --------------Model Leaning ------------------------
              # calculate error and gradient---------------
              lowerCAmelCase__ :  Optional[Any]							      =					np.multiply(
                  (data_teach - bp_outa)  ,np.multiply(__UpperCAmelCase  ,(1 - bp_outa)      )      )
              lowerCAmelCase__ :  List[str]							      =					np.multiply(
                  np.dot(__UpperCAmelCase  ,self.wkj      )  ,np.multiply(__UpperCAmelCase  ,(1 - bp_outa)      )      )
              lowerCAmelCase__ :  Dict							      =					np.dot(__UpperCAmelCase  ,self.vji      )

              lowerCAmelCase__ :  List[Any]							      =					pd_i_all / (self.size_poolinga * self.size_poolinga)
              lowerCAmelCase__ :  Dict							      =					pd_conva_pooled.T.getA().tolist()
              lowerCAmelCase__ :  Any							      =					self._calculate_gradient_from_pool(
                  __UpperCAmelCase  ,__UpperCAmelCase  ,shape_featuremapa[0]  ,shape_featuremapa[1]  ,self.size_poolinga  ,)
              # weight and threshold learning process---------
              # convolution layer
              for k_conv in range(self.conva[1]      ):
                  lowerCAmelCase__ :  Dict							      =					self._expand_mat(pd_conva_all[k_conv]      )
                  lowerCAmelCase__ :  Optional[Any]							      =					self.rate_weight * np.dot(__UpperCAmelCase  ,__UpperCAmelCase      )

                  lowerCAmelCase__ :  Any							      =					self.w_conva[k_conv] + delta_w.reshape(
                      (self.conva[0], self.conva[0])      )

                  lowerCAmelCase__ :  int							      =					(
                      self.thre_conva[k_conv]
                      - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
                  )
              # all connected layer
              lowerCAmelCase__ :  str							      =					self.wkj + pd_k_all.T * bp_outa * self.rate_weight
              lowerCAmelCase__ :  Tuple							      =					self.vji + pd_j_all.T * bp_outa * self.rate_weight
              lowerCAmelCase__ :  List[Any]							      =					self.thre_bpa - pd_k_all * self.rate_thre
              lowerCAmelCase__ :  str							      =					self.thre_bpa - pd_j_all * self.rate_thre
              # calculate the sum error of all single image
              lowerCAmelCase__ :  Union[str, Any]							      =					np.sum(abs(data_teach - bp_outa      )      )
              error_count += errors
              # print('   ----Teach      ',data_teach)
              # print('   ----BP_output  ',bp_out3)
          lowerCAmelCase__ :  List[Any]							      =					rp + 1
          lowerCAmelCase__ :  List[Any]							      =					error_count / patterns
          all_mse.append(__UpperCAmelCase      )

      def draw_error():
          lowerCAmelCase__ :  Optional[int]							      =					[error_accuracy for i in range(int(n_repeat * 1.2      )      )]
          plt.plot(__UpperCAmelCase  ,"""+-"""      )
          plt.plot(__UpperCAmelCase  ,"""r--"""      )
          plt.xlabel("""Learning Times"""      )
          plt.ylabel("""All_mse"""      )
          plt.grid(__UpperCAmelCase  ,alpha=0.5      )
          plt.show()

      print("""------------------Training Complished---------------------"""      )
      print((""" - - Training epoch: """, rp, F"""     - - Mse: {mse:.6f}""")      )
      if draw_e:
          draw_error()
      return mse




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      # model predict
      lowerCAmelCase__ :  str							      =					[]
      print("""-------------------Start Testing-------------------------"""      )
      print((""" - - Shape: Test_Data  """, np.shape(__UpperCAmelCase      ))      )
      for p in range(len(__UpperCAmelCase      )      ):
          lowerCAmelCase__ :  List[str]							      =					np.asmatrix(datas_test[p]      )
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					self.convolute(
              __UpperCAmelCase  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
          lowerCAmelCase__ :  str							      =					self.pooling(__UpperCAmelCase  ,self.size_poolinga      )
          lowerCAmelCase__ :  Tuple							      =					self._expand(__UpperCAmelCase      )

          lowerCAmelCase__ :  Dict							      =					data_bp_input
          lowerCAmelCase__ :  Optional[int]							      =					bp_outa * self.vji.T - self.thre_bpa
          lowerCAmelCase__ :  Dict							      =					self.sig(__UpperCAmelCase      )
          lowerCAmelCase__ :  Optional[int]							      =					bp_outa * self.wkj.T - self.thre_bpa
          lowerCAmelCase__ :  Any							      =					self.sig(__UpperCAmelCase      )
          produce_out.extend(bp_outa.getA().tolist()      )
      lowerCAmelCase__ :  int							      =					[list(map(self.do_round  ,__UpperCAmelCase      )      ) for each in produce_out]
      return np.asarray(__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      # return the data of image after convoluting process so we can check it out
      lowerCAmelCase__ :  str							      =					np.asmatrix(__UpperCAmelCase      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[Any]							      =					self.convolute(
          __UpperCAmelCase  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
      lowerCAmelCase__ :  List[Any]							      =					self.pooling(__UpperCAmelCase  ,self.size_poolinga      )

      return data_conveda, data_pooleda


if __name__ == "__main__":
 pass







'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase      						=	logging.get_logger(__name__)
_lowerCAmelCase      						=	{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase      						=	{
    '''AI-Sweden/gpt-sw3-126m''': 2048,
    '''AI-Sweden/gpt-sw3-350m''': 2048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2048,
    '''AI-Sweden/gpt-sw3-20b''': 2048,
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      VOCAB_FILES_NAMES
  __lowercase							:      str				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Dict				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Optional[int]				=      ['''input_ids''', '''attention_mask''']




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      lowerCAmelCase__ :  Optional[int]							      =					{} if sp_model_kwargs is None else sp_model_kwargs

      lowerCAmelCase__ :  Dict							      =					kwargs.get("""name_or_path"""      )
      if name_or_path is None:
          logger.warning(
              """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
              """ you are testing the model, this can safely be ignored"""      )
          lowerCAmelCase__ :  Tuple							      =					"""None"""

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      lowerCAmelCase__ :  Union[str, Any]							      =					"""<|endoftext|>""" if eos_token is None else eos_token
      lowerCAmelCase__ :  Dict							      =					"""<unk>""" if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
          lowerCAmelCase__ :  Any							      =					unk_token if pad_token is None else pad_token
          lowerCAmelCase__ :  Dict							      =					eos_token if bos_token is None else bos_token
      else:
          lowerCAmelCase__ :  List[str]							      =					"""<pad>""" if pad_token is None else pad_token
          lowerCAmelCase__ :  Optional[int]							      =					"""<s>""" if bos_token is None else bos_token

      super().__init__(
          do_lower_case=__UpperCAmelCase  ,remove_space=__UpperCAmelCase  ,keep_accents=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,sp_model_kwargs=self.sp_model_kwargs  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__ :  Optional[int]							      =					do_lower_case
      lowerCAmelCase__ :  Dict							      =					remove_space
      lowerCAmelCase__ :  Optional[Any]							      =					keep_accents
      lowerCAmelCase__ :  int							      =					vocab_file

      lowerCAmelCase__ :  str							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(__UpperCAmelCase      )

      # Used for whitespace normalization in input texts
      # fmt : off
      lowerCAmelCase__ :  int							      =					{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      lowerCAmelCase__ :  List[str]							      =					re.compile(
          F"""[{''.join(map(__UpperCAmelCase  ,list(range(0  ,9      )      ) + list(range(11  ,32      )      ) + list(range(127  ,160      )      ) + [160, 173, 8203]      )      )}]"""      )




  def __getstate__(    self      )    ->		Any:
      lowerCAmelCase__ :  int							      =					self.__dict__.copy()
      lowerCAmelCase__ :  Optional[int]							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					d

      # for backward compatibility
      if not hasattr(self  ,"""sp_model_kwargs"""      ):
          lowerCAmelCase__ :  Tuple							      =					{}

      lowerCAmelCase__ :  Optional[int]							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 							UpperCAmelCase_	(    self      )    ->		int:
      return len(self.sp_model      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  Tuple							      =					self.non_printing_characters_re.sub(""""""  ,__UpperCAmelCase      )

      # Normalize whitespaces
      lowerCAmelCase__ :  List[Any]							      =					"""""".join([char if char not in self.whitespaces else """ """ for char in text]      )

      # NFC Unicode normalization
      lowerCAmelCase__ :  List[Any]							      =					unicodedata.normalize("""NFC"""  ,__UpperCAmelCase      )
      return text




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[Any]							      =					self.preprocess_text(__UpperCAmelCase      )
      return self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return self.sp_model.PieceToId(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.IdToPiece(__UpperCAmelCase      )




  @staticmethod
  def 							UpperCAmelCase_	(    __UpperCAmelCase      )    ->		str:
      return out_string




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  int							      =					[]
      lowerCAmelCase__ :  Optional[int]							      =					""""""
      lowerCAmelCase__ :  Tuple							      =					False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
              if not prev_is_special:
                  out_string += " "

              out_string += self.sp_model.decode(__UpperCAmelCase      ) + token
              lowerCAmelCase__ :  Union[str, Any]							      =					True
              lowerCAmelCase__ :  Optional[Any]							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
              lowerCAmelCase__ :  Any							      =					False
      out_string += self.sp_model.decode(__UpperCAmelCase      )

      return out_string




  def 							UpperCAmelCase_	(    self      )    ->		Dict[str, int]:
      lowerCAmelCase__ :  Optional[int]							      =					{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  Optional[int]							      =					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      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file  ,__UpperCAmelCase      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(__UpperCAmelCase  ,"""wb"""      ) as fi:
              lowerCAmelCase__ :  str							      =					self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase      )

      return (out_vocab_file,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Union[List[int], List[List[int]], "torch.Tensor"]:

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  Tuple							      =					self.preprocess_text(__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					self.sp_model.encode(__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  int							      =					[self.preprocess_text(__UpperCAmelCase      ) for t in text]
          lowerCAmelCase__ :  Any							      =					self.sp_model.encode(__UpperCAmelCase      )

      if return_tensors is True or return_tensors == "pt":
          lowerCAmelCase__ :  Tuple							      =					torch.tensor(__UpperCAmelCase      )

      return token_ids




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.decode(__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[int]:
      lowerCAmelCase__ :  List[Any]							      =					[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
      lowerCAmelCase__ :  Any							      =					(
          F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase      ) + F"""{self.bos_token}Bot:"""
      )
      return self.encode(text=__UpperCAmelCase      )


