'''simple docstring'''
import pickle

import numpy as np
from matplotlib import pyplot as plt






class 			__lowercase		:







     """simple docstring"""






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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # save model dict with pickle
         __UpperCamelCase	:	Dict		    =  {
             '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(_lowerCAmelCase     ,			'wb'    ) as f:
             pickle.dump(_lowerCAmelCase     ,			_lowerCAmelCase    )

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







     @classmethod
     def lowerCAmelCase     (		cls     ,			_lowerCamelCase    ):
         # read saved model
         with open(_lowerCAmelCase     ,			'rb'    ) as f:
             __UpperCamelCase	:	Dict		    =  pickle.load(_lowerCAmelCase    )  # noqa: S301

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return 1 / (1 + np.exp(-1 * x    ))







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return round(_lowerCAmelCase     ,			3    )







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

         # expanding the data slice to One dimenssion
         __UpperCamelCase	:	Union[str, Any]		    =  []
         for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(_lowerCAmelCase    )    )
         __UpperCamelCase	:	List[str]		    =  np.asarray(_lowerCAmelCase    )
         return focus_list, data_featuremap







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="average_pool"    ):
         # pooling process
         __UpperCamelCase	:	Union[str, Any]		    =  len(featuremaps[0]    )
         __UpperCamelCase	:	Dict		    =  int(size_map / size_pooling    )
         __UpperCamelCase	:	str		    =  []
         for i_map in range(len(_lowerCAmelCase    )    ):
             __UpperCamelCase	:	Union[str, Any]		    =  featuremaps[i_map]
             __UpperCamelCase	:	Dict		    =  []
             for i_focus in range(0     ,			_lowerCAmelCase     ,			_lowerCAmelCase    ):
                 for j_focus in range(0     ,			_lowerCAmelCase     ,			_lowerCAmelCase    ):
                     __UpperCamelCase	:	Any		    =  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(_lowerCAmelCase    )    )
                     elif pooling_type == "max_pooling":
                         # max pooling
                         map_pooled.append(np.max(_lowerCAmelCase    )    )
             __UpperCamelCase	:	List[str]		    =  np.asmatrix(_lowerCAmelCase    ).reshape(_lowerCAmelCase     ,			_lowerCAmelCase    )
             featuremap_pooled.append(_lowerCAmelCase    )
         return featuremap_pooled







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # expanding three dimension data to one dimension list
         __UpperCamelCase	:	Any		    =  []
         for i in range(len(_lowerCAmelCase    )    ):
             __UpperCamelCase	:	Optional[int]		    =  np.shape(data[i]    )
             __UpperCamelCase	:	int		    =  data[i].reshape(1     ,			shapes[0] * shapes[1]    )
             __UpperCamelCase	:	List[str]		    =  data_listed.getA().tolist()[0]
             data_expanded.extend(_lowerCAmelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(_lowerCAmelCase    )
         return data_expanded







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # expanding matrix to one dimension list
         __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(_lowerCAmelCase    )
         __UpperCamelCase	:	Any		    =  np.shape(_lowerCAmelCase    )
         __UpperCamelCase	:	Dict		    =  data_mat.reshape(1     ,			shapes[0] * shapes[1]    )
         return data_expanded







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  []
         __UpperCamelCase	:	Optional[Any]		    =  0
         for i_map in range(_lowerCAmelCase    ):
             __UpperCamelCase	:	Any		    =  np.ones((size_map, size_map)    )
             for i in range(0     ,			_lowerCAmelCase     ,			_lowerCAmelCase    ):
                 for j in range(0     ,			_lowerCAmelCase     ,			_lowerCAmelCase    ):
                     __UpperCamelCase	:	str		    =  pd_pool[
                         i_pool
                     ]
                     __UpperCamelCase	:	Dict		    =  i_pool + 1
             __UpperCamelCase	:	str		    =  np.multiply(
                 _lowerCAmelCase     ,			np.multiply(out_map[i_map]     ,			(1 - out_map[i_map])    )    )
             pd_all.append(_lowerCAmelCase    )
         return pd_all







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=bool    ):
         # model traning
         print('----------------------Start Training-------------------------'    )
         print((' - - Shape: Train_Data  ', np.shape(_lowerCAmelCase    ))    )
         print((' - - Shape: Teach_Data  ', np.shape(_lowerCAmelCase    ))    )
         __UpperCamelCase	:	Optional[int]		    =  0
         __UpperCamelCase	:	int		    =  []
         __UpperCamelCase	:	str		    =  1_0_0_0_0
         while rp < n_repeat and mse >= error_accuracy:
             __UpperCamelCase	:	List[Any]		    =  0
             print(f"""-------------Learning Time {rp}--------------"""    )
             for p in range(len(_lowerCAmelCase    )    ):
                 # print('------------Learning Image: %d--------------'%p)
                 __UpperCamelCase	:	List[Any]		    =  np.asmatrix(datas_train[p]    )
                 __UpperCamelCase	:	List[str]		    =  np.asarray(datas_teach[p]    )
                 __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  self.convolute(
                     _lowerCAmelCase     ,			self.conva     ,			self.w_conva     ,			self.thre_conva     ,			conv_step=self.step_conva     ,			)
                 __UpperCamelCase	:	List[Any]		    =  self.pooling(_lowerCAmelCase     ,			self.size_poolinga    )
                 __UpperCamelCase	:	Union[str, Any]		    =  np.shape(_lowerCAmelCase    )
                 __UpperCamelCase	:	Optional[Any]		    =  self._expand(_lowerCAmelCase    )
                 __UpperCamelCase	:	List[Any]		    =  data_bp_input

                 __UpperCamelCase	:	Dict		    =  np.dot(_lowerCAmelCase     ,			self.vji.T    ) - self.thre_bpa
                 __UpperCamelCase	:	Optional[Any]		    =  self.sig(_lowerCAmelCase    )
                 __UpperCamelCase	:	Union[str, Any]		    =  np.dot(_lowerCAmelCase     ,			self.wkj.T    ) - self.thre_bpa
                 __UpperCamelCase	:	List[str]		    =  self.sig(_lowerCAmelCase    )

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

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

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

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

         def draw_error():
             __UpperCamelCase	:	List[Any]		    =  [error_accuracy for i in range(int(n_repeat * 1.2    )    )]
             plt.plot(_lowerCAmelCase     ,			'+-'    )
             plt.plot(_lowerCAmelCase     ,			'r--'    )
             plt.xlabel('Learning Times'    )
             plt.ylabel('All_mse'    )
             plt.grid(_lowerCAmelCase     ,			alpha=0.5    )
             plt.show()

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # model predict
         __UpperCamelCase	:	Optional[Any]		    =  []
         print('-------------------Start Testing-------------------------'    )
         print((' - - Shape: Test_Data  ', np.shape(_lowerCAmelCase    ))    )
         for p in range(len(_lowerCAmelCase    )    ):
             __UpperCamelCase	:	Union[str, Any]		    =  np.asmatrix(datas_test[p]    )
             __UpperCamelCase    ,					__UpperCamelCase	:	Optional[Any]		    =  self.convolute(
                 _lowerCAmelCase     ,			self.conva     ,			self.w_conva     ,			self.thre_conva     ,			conv_step=self.step_conva     ,			)
             __UpperCamelCase	:	int		    =  self.pooling(_lowerCAmelCase     ,			self.size_poolinga    )
             __UpperCamelCase	:	Tuple		    =  self._expand(_lowerCAmelCase    )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # return the data of image after convoluting process so we can check it out
         __UpperCamelCase	:	Any		    =  np.asmatrix(_lowerCAmelCase    )
         __UpperCamelCase    ,					__UpperCamelCase	:	Dict		    =  self.convolute(
             _lowerCAmelCase     ,			self.conva     ,			self.w_conva     ,			self.thre_conva     ,			conv_step=self.step_conva     ,			)
         __UpperCamelCase	:	Optional[int]		    =  self.pooling(_lowerCAmelCase     ,			self.size_poolinga    )

         return data_conveda, data_pooleda


if __name__ == "__main__":
     pass




'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="replace"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(
             _lowerCamelCase     ,			_lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			errors=_lowerCamelCase     ,			bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			sep_token=_lowerCamelCase     ,			cls_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			mask_token=_lowerCamelCase     ,			add_prefix_space=_lowerCamelCase     ,			trim_offsets=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  json.loads(tokenizer_component_instance.__getstate__()    )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'    )
             return None
         return str(self._mask_token    )







     @mask_token.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[Any]		    =  [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 + sep + token_ids_a + sep    ) * [0]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for is_user, text in conversation.iter_texts():
             if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(' ' + text    )
             else:
                 # Generated responses should contain them already.
                 inputs.append(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
         return input_ids



