





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      lowerCAmelCase_ (       __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =torch.exp(__a		)
  lowerCamelCase__:      List[Any]			  =torch.sum(__a	,    dim=1		)  # sum of exp(x_i)
  lowerCamelCase__:      Union[str, Any]			  =torch.sum(x * exp_x	,    dim=1		)  # sum of x_i * exp(x_i)
  return torch.log(__a		) - B / A



class    _SCREAMING_SNAKE_CASE						(	nn.Module    ):







 '''simple docstring'''

 def __init__(self				:   Dict       , UpperCAmelCase_				:   Optional[int])       ->Any:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:      Dict			  =config.output_attentions
   lowerCamelCase__:      Dict			  =config.output_hidden_states
   lowerCamelCase__:      List[Any]			  =nn.ModuleList([BertLayer(UpperCAmelCase_) for _ in range(config.num_hidden_layers)])
   lowerCamelCase__:      str			  =nn.ModuleList([BertHighway(UpperCAmelCase_) for _ in range(config.num_hidden_layers)])

   lowerCamelCase__:      int			  =[-1 for _ in range(config.num_hidden_layers)]

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Optional[int])       ->Union[str, Any]:



   '''simple docstring'''



   if (type(UpperCAmelCase_) is float) or (type(UpperCAmelCase_) is int):
     for i in range(len(self.early_exit_entropy)):
       lowerCamelCase__:      Optional[int]			  =x
   else:
     lowerCamelCase__:      int			  =x

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Dict)       ->Dict:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Any=None       , UpperCAmelCase_				:   Optional[Any]=None       , UpperCAmelCase_				:   List[Any]=None       , )       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =()
   lowerCamelCase__:      Optional[int]			  =()
   lowerCamelCase__:      Optional[int]			  =()
   for i, layer_module in enumerate(self.layer):
     if self.output_hidden_states:
       lowerCamelCase__:      List[str]			  =all_hidden_states + (hidden_states,)

     lowerCamelCase__:      List[Any]			  =layer_module(
         UpperCAmelCase_       , UpperCAmelCase_       , head_mask[i]       , UpperCAmelCase_       , UpperCAmelCase_)
     lowerCamelCase__:      Optional[Any]			  =layer_outputs[0]

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

     lowerCamelCase__:      str			  =(hidden_states,)
     if self.output_hidden_states:
       lowerCamelCase__:      int			  =current_outputs + (all_hidden_states,)
     if self.output_attentions:
       lowerCamelCase__:      List[Any]			  =current_outputs + (all_attentions,)

     lowerCamelCase__:      List[str]			  =self.highway[i](UpperCAmelCase_)
     # logits, pooled_output

     if not self.training:
       lowerCamelCase__:      List[Any]			  =highway_exit[0]
       lowerCamelCase__:      List[str]			  =entropy(UpperCAmelCase_)
       lowerCamelCase__:      str			  =highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
       lowerCamelCase__:      int			  =all_highway_exits + (highway_exit,)

       if highway_entropy < self.early_exit_entropy[i]:
         lowerCamelCase__:      Dict			  =(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
         raise HighwayException(UpperCAmelCase_       , i + 1)
     else:
       lowerCamelCase__:      Optional[int]			  =all_highway_exits + (highway_exit,)

        # Add last layer
   if self.output_hidden_states:
     lowerCamelCase__:      Union[str, Any]			  =all_hidden_states + (hidden_states,)

   lowerCamelCase__:      List[str]			  =(hidden_states,)
   if self.output_hidden_states:
     lowerCamelCase__:      str			  =outputs + (all_hidden_states,)
   if self.output_attentions:
     lowerCamelCase__:      Dict			  =outputs + (all_attentions,)

   lowerCamelCase__:      Any			  =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). "    ,							__SCREAMING_SNAKE_CASE    ,							)
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

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



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =config

   lowerCamelCase__:      Tuple			  =BertEmbeddings(UpperCAmelCase_)
   lowerCamelCase__:      str			  =DeeBertEncoder(UpperCAmelCase_)
   lowerCamelCase__:      int			  =BertPooler(UpperCAmelCase_)

   self.init_weights()

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



   self.encoder.init_highway_pooler(self.pooler)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   return self.embeddings.word_embeddings

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   List[str])       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =value

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   List[str])       ->List[Any]:



   '''simple docstring'''



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







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



   '''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__:      Union[str, Any]			  =input_ids.size()
   elif inputs_embeds is not None:
     lowerCamelCase__:      Optional[int]			  =inputs_embeds.size()[:-1]
   else:
     raise ValueError("You have to specify either input_ids or inputs_embeds")

   lowerCamelCase__:      Optional[Any]			  =input_ids.device if input_ids is not None else inputs_embeds.device

   if attention_mask is None:
     lowerCamelCase__:      int			  =torch.ones(UpperCAmelCase_       , device=UpperCAmelCase_)
   if encoder_attention_mask is None:
     lowerCamelCase__:      List[Any]			  =torch.ones(UpperCAmelCase_       , device=UpperCAmelCase_)
   if token_type_ids is None:
     lowerCamelCase__:      Dict			  =torch.zeros(UpperCAmelCase_       , dtype=torch.long       , device=UpperCAmelCase_)

   # 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__:      torch.Tensor			  =self.get_extended_attention_mask(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

   # 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__:      Dict			  =encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.dim() == 2:
     lowerCamelCase__:      Optional[int]			  =encoder_attention_mask[:, None, None, :]

   lowerCamelCase__:      List[str]			  =encoder_extended_attention_mask.to(
       dtype=next(self.parameters()).dtype)  # fp16 compatibility
   lowerCamelCase__:      List[str]			  =(1.0 - encoder_extended_attention_mask) * -1_0000.0

   # 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__:      Optional[Any]			  =self.get_head_mask(UpperCAmelCase_       , self.config.num_hidden_layers)

   lowerCamelCase__:      int			  =self.embeddings(
       input_ids=UpperCAmelCase_       , position_ids=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , inputs_embeds=UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =self.encoder(
       UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , head_mask=UpperCAmelCase_       , encoder_hidden_states=UpperCAmelCase_       , encoder_attention_mask=UpperCAmelCase_       , )
   lowerCamelCase__:      List[str]			  =encoder_outputs[0]
   lowerCamelCase__:      Optional[Any]			  =self.pooler(UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =(
       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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =message
   lowerCamelCase__:      int			  =exit_layer  # start from 1!



class    _SCREAMING_SNAKE_CASE						(	nn.Module    ):







 '''simple docstring'''

 def __init__(self				:   Tuple       , UpperCAmelCase_				:   int)       ->Tuple:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:      Tuple			  =BertPooler(UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =nn.Dropout(config.hidden_dropout_prob)
   lowerCamelCase__:      int			  =nn.Linear(config.hidden_size       , config.num_labels)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =encoder_outputs[0]
   lowerCamelCase__:      Optional[int]			  =self.pooler(UpperCAmelCase_)
   # "return" pooler_output

   # BertModel
   lowerCamelCase__:      Union[str, Any]			  =(pooler_input, pooler_output) + encoder_outputs[1:]
   # "return" bmodel_output

   # Dropout and classification
   lowerCamelCase__:      int			  =bmodel_output[1]

   lowerCamelCase__:      Union[str, Any]			  =self.dropout(UpperCAmelCase_)
   lowerCamelCase__:      int			  =self.classifier(UpperCAmelCase_)

   return logits, pooled_output



@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "    ,							__SCREAMING_SNAKE_CASE    ,							)
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

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



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =config.num_labels
   lowerCamelCase__:      Tuple			  =config.num_hidden_layers

   lowerCamelCase__:      Dict			  =DeeBertModel(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =nn.Dropout(config.hidden_dropout_prob)
   lowerCamelCase__:      List[Any]			  =nn.Linear(config.hidden_size       , self.config.num_labels)

   self.init_weights()







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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.num_layers
   try:
     lowerCamelCase__:      str			  =self.bert(
         UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , position_ids=UpperCAmelCase_       , head_mask=UpperCAmelCase_       , inputs_embeds=UpperCAmelCase_       , )
     # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

     lowerCamelCase__:      Union[str, Any]			  =outputs[1]

     lowerCamelCase__:      List[Any]			  =self.dropout(UpperCAmelCase_)
     lowerCamelCase__:      Dict			  =self.classifier(UpperCAmelCase_)
     lowerCamelCase__:      int			  =(logits,) + outputs[2:]  # add hidden states and attention if they are here
   except HighwayException as e:
     lowerCamelCase__:      Any			  =e.message
     lowerCamelCase__:      Dict			  =e.exit_layer
     lowerCamelCase__:      List[str]			  =outputs[0]

   if not self.training:
     lowerCamelCase__:      Optional[Any]			  =entropy(UpperCAmelCase_)
     lowerCamelCase__:      str			  =[]
     lowerCamelCase__:      List[str]			  =[]
   if labels is not None:
     if self.num_labels == 1:
       #  We are doing regression
       lowerCamelCase__:      Optional[Any]			  =MSELoss()
       lowerCamelCase__:      Any			  =loss_fct(logits.view(-1)       , labels.view(-1))
     else:
       lowerCamelCase__:      str			  =CrossEntropyLoss()
       lowerCamelCase__:      Union[str, Any]			  =loss_fct(logits.view(-1       , self.num_labels)       , labels.view(-1))

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

     if train_highway:
       lowerCamelCase__:      Optional[int]			  =(sum(highway_losses[:-1]),) + outputs
       # exclude the final highway, of course
     else:
       lowerCamelCase__:      Any			  =(loss,) + outputs
   if not self.training:
     lowerCamelCase__:      List[str]			  =outputs + ((original_entropy, highway_entropy), exit_layer)
     if output_layer >= 0:
       lowerCamelCase__:      Optional[int]			  =(
           (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 collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__A									=				logging.get_logger(__name__)

__A									=				"▁"

__A									=				{"vocab_file": "prophetnet.tokenizer"}

__A									=				{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =collections.OrderedDict()
  with open(__a	,    "r"	,    encoding="utf-8"		) as reader:
    lowerCamelCase__:      int			  =reader.readlines()
  for index, token in enumerate(__a		):
    lowerCamelCase__:      List[str]			  =token.rstrip("\n"		)
    lowerCamelCase__:      List[Any]			  =index
  return vocab






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   List[Any]="[SEP]"       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   int="[UNK]"       , UpperCAmelCase_				:   Optional[Any]="[PAD]"       , UpperCAmelCase_				:   Dict="[CLS]"       , UpperCAmelCase_				:   Dict="[MASK]"       , UpperCAmelCase_				:   Optional[Dict[str, Any]] = None       , **UpperCAmelCase_				:   Tuple       , )       ->None:



   '''simple docstring'''



   lowerCamelCase__:      int			  ={} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=UpperCAmelCase_       , eos_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , sp_model_kwargs=self.sp_model_kwargs       , **UpperCAmelCase_       , )

   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

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

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # put special tokens and [unused] tokens into the vocab
   lowerCamelCase__:      Optional[int]			  ={"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

   for i in range(10):
     lowerCamelCase__:      Optional[int]			  =F"""[unused{i}]"""
     lowerCamelCase__:      int			  =5 + i

   # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
   lowerCamelCase__:      int			  =12
   lowerCamelCase__:      Optional[Any]			  ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
   for k in self.fairseq_tokens_to_ids.keys():
     self.unique_no_split_tokens.append(UpperCAmelCase_)

 def __getstate__(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.__dict__.copy()
   lowerCamelCase__:      Dict			  =None
   return state

 def __setstate__(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =d
   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

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

   lowerCamelCase__:      Tuple			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)

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



   '''simple docstring'''



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

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =[self.sep_token_id]

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

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Dict:



   '''simple docstring'''



   return len(self.sp_model) + self.fairseq_offset

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      str			  ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

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



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_       , out_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   List[Any])       ->str:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:      str			  =self.sp_model.PieceToId(UpperCAmelCase_)

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

 def 							SCREAMING_SNAKE_CASE_					(self				:   str       , UpperCAmelCase_				:   Optional[Any])       ->Optional[int]:



   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  ="".join(UpperCAmelCase_).replace(UpperCAmelCase_       , " ").strip()
   return out_string

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      List[str]			  =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__:      Dict			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return token_ids_a + [self.sep_token_id]
   lowerCamelCase__:      Union[str, Any]			  =[self.sep_token_id]
   return token_ids_a + sep + token_ids_a + sep

