




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.exp(lowercase  )
      lowerCamelCase_	  =					torch.sum(lowercase			,							dim=1  )  # sum of exp(x_i)
      lowerCamelCase_	  =					torch.sum(x * exp_x			,							dim=1  )  # sum of x_i * exp(x_i)
      return torch.log(lowercase  ) - B / A







class 						A(		nn.Module     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[Any]		,							A_      :						Union[str, Any]						)     ->			str:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_	  =					config.output_attentions
       lowerCamelCase_	  =					config.output_hidden_states
       lowerCamelCase_	  =					nn.ModuleList([BertLayer(A_						) for _ in range(config.num_hidden_layers						)]						)
       lowerCamelCase_	  =					nn.ModuleList([BertHighway(A_						) for _ in range(config.num_hidden_layers						)]						)

       lowerCamelCase_	  =					[-1 for _ in range(config.num_hidden_layers						)]

 def        a__				(       self      :						Tuple		,							A_      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       if (type(A_						) is float) or (type(A_						) is int):
             for i in range(len(self.early_exit_entropy						)						):
                   lowerCamelCase_	  =					x
       else:
             lowerCamelCase_	  =					x

 def        a__				(       self      :						int		,							A_      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					pooler.state_dict()
       for highway in self.highway:
             for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]						)





 def        a__				(       self      :						Tuple		,							A_      :						Dict		,							A_      :						List[str]=None		,							A_      :						Union[str, Any]=None		,							A_      :						Optional[int]=None		,							A_      :						Any=None		,							)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					()
       lowerCamelCase_	  =					()
       lowerCamelCase_	  =					()
       for i, layer_module in enumerate(self.layer						):
             if self.output_hidden_states:
                   lowerCamelCase_	  =					all_hidden_states + (hidden_states,)

             lowerCamelCase_	  =					layer_module(
                 A_		,							A_		,							head_mask[i]		,							A_		,							A_						)
             lowerCamelCase_	  =					layer_outputs[0]

             if self.output_attentions:
                   lowerCamelCase_	  =					all_attentions + (layer_outputs[1],)

             lowerCamelCase_	  =					(hidden_states,)
             if self.output_hidden_states:
                   lowerCamelCase_	  =					current_outputs + (all_hidden_states,)
             if self.output_attentions:
                   lowerCamelCase_	  =					current_outputs + (all_attentions,)

             lowerCamelCase_	  =					self.highway[i](A_						)
             # logits, pooled_output

             if not self.training:
                   lowerCamelCase_	  =					highway_exit[0]
                   lowerCamelCase_	  =					entropy(A_						)
                   lowerCamelCase_	  =					highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                   lowerCamelCase_	  =					all_highway_exits + (highway_exit,)

                   if highway_entropy < self.early_exit_entropy[i]:
                         lowerCamelCase_	  =					(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                         raise HighwayException(A_		,							i + 1						)
             else:
                   lowerCamelCase_	  =					all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
             lowerCamelCase_	  =					all_hidden_states + (hidden_states,)

       lowerCamelCase_	  =					(hidden_states,)
       if self.output_hidden_states:
             lowerCamelCase_	  =					outputs + (all_hidden_states,)
       if self.output_attentions:
             lowerCamelCase_	  =					outputs + (all_attentions,)

       lowerCamelCase_	  =					outputs + (all_highway_exits,)
       return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''							,   UpperCamelCase							,   )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						List[str]		,							A_      :						List[Any]						)     ->			str:

       """simple docstring"""






       super().__init__(A_						)
       lowerCamelCase_	  =					config

       lowerCamelCase_	  =					BertEmbeddings(A_						)
       lowerCamelCase_	  =					DeeBertEncoder(A_						)
       lowerCamelCase_	  =					BertPooler(A_						)

       self.init_weights()

 def        a__				(       self      :						Dict						)     ->			Optional[Any]:

       """simple docstring"""






       self.encoder.init_highway_pooler(self.pooler						)

 def        a__				(       self      :						Tuple						)     ->			Optional[int]:

       """simple docstring"""






       return self.embeddings.word_embeddings

 def        a__				(       self      :						List[str]		,							A_      :						Any						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					value

 def        a__				(       self      :						Tuple		,							A_      :						Union[str, Any]						)     ->			str:

       """simple docstring"""






       for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(A_						)





 @add_start_docstrings_to_model_forward(A_						)
 def        a__				(       self      :						Optional[Any]		,							A_      :						Union[str, Any]=None		,							A_      :						List[str]=None		,							A_      :						Tuple=None		,							A_      :						Tuple=None		,							A_      :						Optional[int]=None		,							A_      :						str=None		,							A_      :						Any=None		,							A_      :						Optional[int]=None		,							)     ->			int:

       """simple docstring"""






       if input_ids is not None and inputs_embeds is not None:
             raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'						)
       elif input_ids is not None:
             lowerCamelCase_	  =					input_ids.size()
       elif inputs_embeds is not None:
             lowerCamelCase_	  =					inputs_embeds.size()[:-1]
       else:
             raise ValueError('You have to specify either input_ids or inputs_embeds'						)

       lowerCamelCase_	  =					input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
             lowerCamelCase_	  =					torch.ones(A_		,							device=A_						)
       if encoder_attention_mask is None:
             lowerCamelCase_	  =					torch.ones(A_		,							device=A_						)
       if token_type_ids is None:
             lowerCamelCase_	  =					torch.zeros(A_		,							dtype=torch.long		,							device=A_						)

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       lowerCamelCase_	  =					self.get_extended_attention_mask(A_		,							A_		,							A_						)

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if encoder_attention_mask.dim() == 3:
             lowerCamelCase_	  =					encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
             lowerCamelCase_	  =					encoder_attention_mask[:, None, None, :]

       lowerCamelCase_	  =					encoder_extended_attention_mask.to(
           dtype=next(self.parameters()						).dtype						)  # fp16 compatibility
       lowerCamelCase_	  =					(1.0 - encoder_extended_attention_mask) * -10000.0

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       lowerCamelCase_	  =					self.get_head_mask(A_		,							self.config.num_hidden_layers						)

       lowerCamelCase_	  =					self.embeddings(
           input_ids=A_		,							position_ids=A_		,							token_type_ids=A_		,							inputs_embeds=A_						)
       lowerCamelCase_	  =					self.encoder(
           A_		,							attention_mask=A_		,							head_mask=A_		,							encoder_hidden_states=A_		,							encoder_attention_mask=A_		,							)
       lowerCamelCase_	  =					encoder_outputs[0]
       lowerCamelCase_	  =					self.pooler(A_						)

       lowerCamelCase_	  =					(
           sequence_output,
           pooled_output,
       ) + encoder_outputs[
           1:
       ]  # add hidden_states and attentions if they are here
       return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						str		,							A_      :						Tuple		,							A_      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					message
       lowerCamelCase_	  =					exit_layer  # start from 1!







class 						A(		nn.Module     ):



 '''simple docstring'''


 def __init__(       self      :						List[Any]		,							A_      :						str						)     ->			Union[str, Any]:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_	  =					BertPooler(A_						)
       lowerCamelCase_	  =					nn.Dropout(config.hidden_dropout_prob						)
       lowerCamelCase_	  =					nn.Linear(config.hidden_size		,							config.num_labels						)





 def        a__				(       self      :						Tuple		,							A_      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					encoder_outputs[0]
       lowerCamelCase_	  =					self.pooler(A_						)
       # "return" pooler_output

       # BertModel
       lowerCamelCase_	  =					(pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       lowerCamelCase_	  =					bmodel_output[1]

       lowerCamelCase_	  =					self.dropout(A_						)
       lowerCamelCase_	  =					self.classifier(A_						)

       return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''							,   UpperCamelCase							,   )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[Any]		,							A_      :						str						)     ->			int:

       """simple docstring"""






       super().__init__(A_						)
       lowerCamelCase_	  =					config.num_labels
       lowerCamelCase_	  =					config.num_hidden_layers

       lowerCamelCase_	  =					DeeBertModel(A_						)
       lowerCamelCase_	  =					nn.Dropout(config.hidden_dropout_prob						)
       lowerCamelCase_	  =					nn.Linear(config.hidden_size		,							self.config.num_labels						)

       self.init_weights()





 @add_start_docstrings_to_model_forward(A_						)
 def        a__				(       self      :						Union[str, Any]		,							A_      :						int=None		,							A_      :						Dict=None		,							A_      :						List[str]=None		,							A_      :						Dict=None		,							A_      :						int=None		,							A_      :						Dict=None		,							A_      :						Union[str, Any]=None		,							A_      :						List[Any]=-1		,							A_      :						Optional[Any]=False		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_layers
       try:
             lowerCamelCase_	  =					self.bert(
                 A_		,							attention_mask=A_		,							token_type_ids=A_		,							position_ids=A_		,							head_mask=A_		,							inputs_embeds=A_		,							)
             # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

             lowerCamelCase_	  =					outputs[1]

             lowerCamelCase_	  =					self.dropout(A_						)
             lowerCamelCase_	  =					self.classifier(A_						)
             lowerCamelCase_	  =					(logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
             lowerCamelCase_	  =					e.message
             lowerCamelCase_	  =					e.exit_layer
             lowerCamelCase_	  =					outputs[0]

       if not self.training:
             lowerCamelCase_	  =					entropy(A_						)
             lowerCamelCase_	  =					[]
             lowerCamelCase_	  =					[]
       if labels is not None:
             if self.num_labels == 1:
                   #  We are doing regression
                   lowerCamelCase_	  =					MSELoss()
                   lowerCamelCase_	  =					loss_fct(logits.view(-1						)		,							labels.view(-1						)						)
             else:
                   lowerCamelCase_	  =					CrossEntropyLoss()
                   lowerCamelCase_	  =					loss_fct(logits.view(-1		,							self.num_labels						)		,							labels.view(-1						)						)

             # work with highway exits
             lowerCamelCase_	  =					[]
             for highway_exit in outputs[-1]:
                   lowerCamelCase_	  =					highway_exit[0]
                   if not self.training:
                         highway_logits_all.append(A_						)
                         highway_entropy.append(highway_exit[2]						)
                   if self.num_labels == 1:
                         #  We are doing regression
                         lowerCamelCase_	  =					MSELoss()
                         lowerCamelCase_	  =					loss_fct(highway_logits.view(-1						)		,							labels.view(-1						)						)
                   else:
                         lowerCamelCase_	  =					CrossEntropyLoss()
                         lowerCamelCase_	  =					loss_fct(highway_logits.view(-1		,							self.num_labels						)		,							labels.view(-1						)						)
                   highway_losses.append(A_						)

             if train_highway:
                   lowerCamelCase_	  =					(sum(highway_losses[:-1]						),) + outputs
                   # exclude the final highway, of course
             else:
                   lowerCamelCase_	  =					(loss,) + outputs
       if not self.training:
             lowerCamelCase_	  =					outputs + ((original_entropy, highway_entropy), exit_layer)
             if output_layer >= 0:
                   lowerCamelCase_	  =					(
                       (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                   )  # use the highway of the last layer

       return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)









import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

lowerCamelCase       :	str									=     logging.get_logger(__name__)


lowerCamelCase       :	Optional[Any]									=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCamelCase       :	int									=     {
    "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"
    },
}

lowerCamelCase       :	Tuple									=     {"facebook/blenderbot-3B": 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					(
          list(range(ord('!'  )			,							ord('~'  ) + 1  )  ) + list(range(ord('¡'  )			,							ord('¬'  ) + 1  )  ) + list(range(ord('®'  )			,							ord('ÿ'  ) + 1  )  )
      )
      lowerCamelCase_	  =					bs[:]
      lowerCamelCase_	  =					0
      for b in range(2**8  ):
            if b not in bs:
                  bs.append(lowercase  )
                  cs.append(2**8 + n  )
                  n += 1
      lowerCamelCase_	  =					[chr(lowercase  ) for n in cs]
      return dict(zip(lowercase			,							lowercase  )  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					set()
      lowerCamelCase_	  =					word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)  )
            lowerCamelCase_	  =					char
      return pairs







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		VOCAB_FILES_NAMES
 UpperCamelCase		     =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		     =		['''input_ids''', '''attention_mask''']

 def __init__(       self      :						Optional[Any]		,							A_      :						List[Any]		,							A_      :						List[Any]		,							A_      :						Union[str, Any]="replace"		,							A_      :						Dict="<s>"		,							A_      :						Optional[int]="</s>"		,							A_      :						Optional[Any]="</s>"		,							A_      :						Dict="<s>"		,							A_      :						Dict="<unk>"		,							A_      :						Any="<pad>"		,							A_      :						Dict="<mask>"		,							A_      :						Union[str, Any]=False		,							**A_      :						List[str]		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else bos_token
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else eos_token
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else sep_token
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else cls_token
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else unk_token
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else mask_token

       super().__init__(
           errors=A_		,							bos_token=A_		,							eos_token=A_		,							unk_token=A_		,							sep_token=A_		,							cls_token=A_		,							pad_token=A_		,							mask_token=A_		,							add_prefix_space=A_		,							**A_		,							)

       with open(A_		,							encoding='utf-8'						) as vocab_handle:
             lowerCamelCase_	  =					json.load(A_						)
       lowerCamelCase_	  =					{v: k for k, v in self.encoder.items()}
       lowerCamelCase_	  =					errors  # how to handle errors in decoding
       lowerCamelCase_	  =					bytes_to_unicode()
       lowerCamelCase_	  =					{v: k for k, v in self.byte_encoder.items()}
       with open(A_		,							encoding='utf-8'						) as merges_handle:
             lowerCamelCase_	  =					merges_handle.read().split('\n'						)[1:-1]
       lowerCamelCase_	  =					[tuple(merge.split()						) for merge in bpe_merges]
       lowerCamelCase_	  =					dict(zip(A_		,							range(len(A_						)						)						)						)
       lowerCamelCase_	  =					{}
       lowerCamelCase_	  =					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowerCamelCase_	  =					re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'						)

 @property
 # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
 def        a__				(       self      :						Optional[Any]						)     ->			Dict:

       """simple docstring"""






       return len(self.encoder						)

 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       return dict(self.encoder		,							**self.added_tokens_encoder						)

 def        a__				(       self      :						Tuple		,							A_      :						Tuple						)     ->			Optional[Any]:

       """simple docstring"""






       if token in self.cache:
             return self.cache[token]
       lowerCamelCase_	  =					tuple(A_						)
       lowerCamelCase_	  =					get_pairs(A_						)

       if not pairs:
             return token

       while True:
             lowerCamelCase_	  =					min(A_		,							key=lambda A_						: self.bpe_ranks.get(A_		,							float('inf'						)						)						)
             if bigram not in self.bpe_ranks:
                   break
             lowerCamelCase_  ,       lowerCamelCase_	  =					bigram
             lowerCamelCase_	  =					[]
             lowerCamelCase_	  =					0
             while i < len(A_						):
                   try:
                         lowerCamelCase_	  =					word.index(A_		,							A_						)
                   except ValueError:
                         new_word.extend(word[i:]						)
                         break
                   else:
                         new_word.extend(word[i:j]						)
                         lowerCamelCase_	  =					j

                   if word[i] == first and i < len(A_						) - 1 and word[i + 1] == second:
                         new_word.append(first + second						)
                         i += 2
                   else:
                         new_word.append(word[i]						)
                         i += 1
             lowerCamelCase_	  =					tuple(A_						)
             lowerCamelCase_	  =					new_word
             if len(A_						) == 1:
                   break
             else:
                   lowerCamelCase_	  =					get_pairs(A_						)
       lowerCamelCase_	  =					' '.join(A_						)
       lowerCamelCase_	  =					word
       return word

 def        a__				(       self      :						str		,							A_      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       for token in re.findall(self.pat		,							A_						):
             lowerCamelCase_	  =					''.join(
                 self.byte_encoder[b] for b in token.encode('utf-8'						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_						).split(' '						)						)
       return bpe_tokens

 def        a__				(       self      :						Tuple		,							A_      :						str						)     ->			Optional[Any]:

       """simple docstring"""






       return self.encoder.get(A_		,							self.encoder.get(self.unk_token						)						)

 def        a__				(       self      :						Tuple		,							A_      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       return self.decoder.get(A_						)

 def        a__				(       self      :						Optional[int]		,							A_      :						List[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					''.join(A_						)
       lowerCamelCase_	  =					bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'		,							errors=self.errors						)
       return text

 def        a__				(       self      :						Tuple		,							A_      :						str		,							A_      :						Optional[str] = None						)     ->			Tuple[str]:

       """simple docstring"""






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

       with open(A_		,							'w'		,							encoding='utf-8'						) as f:
             f.write(json.dumps(self.encoder		,							indent=2		,							sort_keys=A_		,							ensure_ascii=A_						) + '\n'						)

       lowerCamelCase_	  =					0
       with open(A_		,							'w'		,							encoding='utf-8'						) as writer:
             writer.write('#version: 0.2\n'						)
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,							key=lambda A_						: kv[1]						):
                   if index != token_index:
                         logger.warning(
                             f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                             ' Please check that the tokenizer is not corrupted!'						)
                         lowerCamelCase_	  =					token_index
                   writer.write(' '.join(A_						) + '\n'						)
                   index += 1

       return vocab_file, merge_file

 def        a__				(       self      :						str		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None		,							A_      :						bool = False						)     ->			List[int]:

       """simple docstring"""






       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A_		,							token_ids_a=A_		,							already_has_special_tokens=A_						)

       if token_ids_a is None:
             return [1] + ([0] * len(A_						)) + [1]
       return [1] + ([0] * len(A_						)) + [1, 1] + ([0] * len(A_						)) + [1]

 def        a__				(       self      :						int		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None						)     ->			List[int]:

       """simple docstring"""






       lowerCamelCase_	  =					[self.sep_token_id]
       lowerCamelCase_	  =					[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        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]=False		,							**A_      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					kwargs.pop('add_prefix_space'		,							self.add_prefix_space						)
       if (is_split_into_words or add_prefix_space) and (len(A_						) > 0 and not text[0].isspace()):
             lowerCamelCase_	  =					' ' + text
       return (text, kwargs)

 def        a__				(       self      :						List[Any]		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None						)     ->			Dict:

       """simple docstring"""






       return token_ids_a + [self.eos_token_id]





 def        a__				(       self      :						Optional[int]		,							A_      :						"Conversation"						)     ->			List[int]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       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(A_						)

       lowerCamelCase_	  =					'  '.join(A_						)
       lowerCamelCase_	  =					self.encode(A_						)
       if len(A_						) > self.model_max_length:
             lowerCamelCase_	  =					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



