

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 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






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



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Tuple      , A__				:    int		)   ->					Tuple:
        super().__init__()
        _snake_case						=							config.output_attentions
        _snake_case						=							config.output_hidden_states
        _snake_case						=							nn.ModuleList([BertLayer(A__		) for _ in range(config.num_hidden_layers		)]		)
        _snake_case						=							nn.ModuleList([BertHighway(A__		) for _ in range(config.num_hidden_layers		)]		)

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



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any		)   ->					Any:
        if (type(A__		) is float) or (type(A__		) is int):
          for i in range(len(self.early_exit_entropy		)		):
            _snake_case						=							x
        else:
          _snake_case						=							x



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Tuple		)   ->					int:
        _snake_case						=							pooler.state_dict()
        for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]		)







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[int]      , A__				:    Dict=None      , A__				:    List[str]=None      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , )   ->					Dict:
        _snake_case						=							()
        _snake_case						=							()
        _snake_case						=							()
        for i, layer_module in enumerate(self.layer		):
          if self.output_hidden_states:
            _snake_case						=							all_hidden_states + (hidden_states,)

          _snake_case						=							layer_module(
              A__      , A__      , head_mask[i]      , A__      , A__		)
          _snake_case						=							layer_outputs[0]

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

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

          _snake_case						=							self.highway[i](A__		)
          # logits, pooled_output

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

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

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

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

        _snake_case						=							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). "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , A__				:    Any		)   ->					str:
        super().__init__(A__		)
        _snake_case						=							config

        _snake_case						=							BertEmbeddings(A__		)
        _snake_case						=							DeeBertEncoder(A__		)
        _snake_case						=							BertPooler(A__		)

        self.init_weights()



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
        self.encoder.init_highway_pooler(self.pooler		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        return self.embeddings.word_embeddings



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							value



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    List[Any]		)   ->					Any:
        for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(A__		)







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    int      , A__				:    Tuple=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[Any]=None      , A__				:    Dict=None      , A__				:    Any=None      , A__				:    str=None      , A__				:    Optional[int]=None      , )   ->					Dict:
        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:
          _snake_case						=							input_ids.size()
        elif inputs_embeds is not None:
          _snake_case						=							inputs_embeds.size()[:-1]
        else:
          raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

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

        if attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if encoder_attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if token_type_ids is None:
          _snake_case						=							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.
        _snake_case						=							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:
          _snake_case						=							encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
          _snake_case						=							encoder_attention_mask[:, None, None, :]

        _snake_case						=							encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        _snake_case						=							(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]
        _snake_case						=							self.get_head_mask(A__      , self.config.num_hidden_layers		)

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

        _snake_case						=							(
            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   lowercase_   (  __lowercase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    Dict      , A__				:    Optional[Any]		)   ->					List[str]:
        _snake_case						=							message
        _snake_case						=							exit_layer  # start from 1!



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Any      , A__				:    int		)   ->					Optional[Any]:
        super().__init__()
        _snake_case						=							BertPooler(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , config.num_labels		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					Optional[int]:
        # Pooler
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)
        # "return" pooler_output

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

        # Dropout and classification
        _snake_case						=							bmodel_output[1]

        _snake_case						=							self.dropout(A__		)
        _snake_case						=							self.classifier(A__		)

        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. "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    List[str]      , A__				:    Optional[int]		)   ->					int:
        super().__init__(A__		)
        _snake_case						=							config.num_labels
        _snake_case						=							config.num_hidden_layers

        _snake_case						=							DeeBertModel(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , self.config.num_labels		)

        self.init_weights()







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[Any]=None      , A__				:    List[Any]=None      , A__				:    Optional[int]=None      , A__				:    List[Any]=None      , A__				:    List[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    List[Any]=-1      , A__				:    str=False      , )   ->					Dict:
        _snake_case						=							self.num_layers
        try:
          _snake_case						=							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

          _snake_case						=							outputs[1]

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

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

          # work with highway exits
          _snake_case						=							[]
          for highway_exit in outputs[-1]:
            _snake_case						=							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
              _snake_case						=							MSELoss()
              _snake_case						=							loss_fct(highway_logits.view(-1		)      , labels.view(-1		)		)
            else:
              _snake_case						=							CrossEntropyLoss()
              _snake_case						=							loss_fct(highway_logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)
            highway_losses.append(A__		)

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

import sentencepiece

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


__A      		=     logging.get_logger(__name__)

__A      		=     '''▁'''

__A      		=     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__A      		=     {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

__A      		=     {
    '''facebook/m2m100_418M''': 10_24,
}

# fmt: off
__A      		=     {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		str										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Dict										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		Union[str, Any]										=    ["input_ids", "attention_mask"]

      UpperCamelCase_      :		List[int]										=    []
      UpperCamelCase_      :		List[int]										=    []



      def __init__(      self				:    str      , A__				:    str      , A__				:    Optional[Any]      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , A__				:    Any="<s>"      , A__				:    Union[str, Any]="</s>"      , A__				:    Tuple="</s>"      , A__				:    Dict="<pad>"      , A__				:    List[Any]="<unk>"      , A__				:    str="m2m100"      , A__				:    Optional[Dict[str, Any]] = None      , A__				:    List[Any]=8      , **A__				:    Union[str, Any]      , )   ->					None:
        _snake_case						=							{} if sp_model_kwargs is None else sp_model_kwargs

        _snake_case						=							language_codes
        _snake_case						=							FAIRSEQ_LANGUAGE_CODES[language_codes]
        _snake_case						=							{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

        _snake_case						=							kwargs.get('''additional_special_tokens'''      , []		)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(A__		)
            for lang_code in fairseq_language_code
            if self.get_lang_token(A__		) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=A__      , tgt_lang=A__      , bos_token=A__      , eos_token=A__      , sep_token=A__      , unk_token=A__      , pad_token=A__      , language_codes=A__      , sp_model_kwargs=self.sp_model_kwargs      , num_madeup_words=A__      , **A__      , )

        _snake_case						=							vocab_file
        _snake_case						=							load_json(A__		)
        _snake_case						=							{v: k for k, v in self.encoder.items()}
        _snake_case						=							spm_file
        _snake_case						=							load_spm(A__      , self.sp_model_kwargs		)

        _snake_case						=							len(self.encoder		)

        _snake_case						=							{
            self.get_lang_token(A__		): self.encoder_size + i for i, lang_code in enumerate(A__		)
        }
        _snake_case						=							{lang_code: self.encoder_size + i for i, lang_code in enumerate(A__		)}
        _snake_case						=							{v: k for k, v in self.lang_token_to_id.items()}

        _snake_case						=							src_lang if src_lang is not None else '''en'''
        _snake_case						=							tgt_lang
        _snake_case						=							self.get_lang_id(self._src_lang		)
        self.set_src_lang_special_tokens(self._src_lang		)

        _snake_case						=							num_madeup_words



      @property
      def 						UpperCamelCase_		(      self				:    int		)   ->					int:
        return len(self.encoder		) + len(self.lang_token_to_id		)



      @property
      def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
        return self._src_lang



      @src_lang.setter
      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    str		)   ->					None:
        _snake_case						=							new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    str		)   ->					List[str]:
        return self.sp_model.encode(A__      , out_type=A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Dict		)   ->					str:
        if token in self.lang_token_to_id:
          return self.lang_token_to_id[token]
        return self.encoder.get(A__      , self.encoder[self.unk_token]		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    int		)   ->					str:
        if index in self.id_to_lang_token:
          return self.id_to_lang_token[index]
        return self.decoder.get(A__      , self.unk_token		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[int]		)   ->					List[Any]:
        _snake_case						=							[]
        _snake_case						=							''''''
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            out_string += self.sp_model.decode(A__		) + token
            _snake_case						=							[]
          else:
            current_sub_tokens.append(A__		)
        out_string += self.sp_model.decode(A__		)
        return out_string.strip()



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

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

        _snake_case						=							[1] * len(self.prefix_tokens		)
        _snake_case						=							[1] * len(self.suffix_tokens		)
        if token_ids_a is None:
          return prefix_ones + ([0] * len(A__		)) + suffix_ones
        return prefix_ones + ([0] * len(A__		)) + ([0] * len(A__		)) + suffix_ones



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							{self.convert_ids_to_tokens(A__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab



      def __getstate__(      self				:    str		)   ->					Dict:
        _snake_case						=							self.__dict__.copy()
        _snake_case						=							None
        return state



      def __setstate__(      self				:    Union[str, Any]      , A__				:    Dict		)   ->					None:
        _snake_case						=							d

        # for backward compatibility
        if not hasattr(self      , '''sp_model_kwargs'''		):
          _snake_case						=							{}

        _snake_case						=							load_spm(self.spm_file      , self.sp_model_kwargs		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							Path(A__		)
        if not save_dir.is_dir():
          raise OSError(f"""{save_directory} should be a directory"""		)
        _snake_case						=							save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
        )
        _snake_case						=							save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
        )

        save_json(self.encoder      , A__		)

        if os.path.abspath(self.spm_file		) != os.path.abspath(A__		) and os.path.isfile(self.spm_file		):
          copyfile(self.spm_file      , A__		)
        elif not os.path.isfile(self.spm_file		):
          with open(A__      , '''wb'''		) as fi:
            _snake_case						=							self.sp_model.serialized_model_proto()
            fi.write(A__		)

        return (str(A__		), str(A__		))



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    List[str]      , A__				:    str = "en"      , A__				:    Optional[List[str]] = None      , A__				:    str = "ro"      , **A__				:    List[Any]      , )   ->					BatchEncoding:
        _snake_case						=							src_lang
        _snake_case						=							tgt_lang
        self.set_src_lang_special_tokens(self.src_lang		)
        return super().prepare_seqaseq_batch(A__      , A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    int      , A__				:    Optional[str]      , A__				:    Optional[str]      , **A__				:    Union[str, Any]		)   ->					Tuple:
        if src_lang is None or tgt_lang is None:
          raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
        _snake_case						=							src_lang
        _snake_case						=							self(A__      , add_special_tokens=A__      , **A__		)
        _snake_case						=							self.get_lang_id(A__		)
        _snake_case						=							tgt_lang_id
        return inputs



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        self.set_src_lang_special_tokens(self.src_lang		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Dict:
        self.set_tgt_lang_special_tokens(self.tgt_lang		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    str		)   ->					None:
        _snake_case						=							self.get_lang_token(A__		)
        _snake_case						=							self.lang_token_to_id[lang_token]
        _snake_case						=							[self.cur_lang_id]
        _snake_case						=							[self.eos_token_id]



      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    str		)   ->					None:
        _snake_case						=							self.get_lang_token(A__		)
        _snake_case						=							self.lang_token_to_id[lang_token]
        _snake_case						=							[self.cur_lang_id]
        _snake_case						=							[self.eos_token_id]



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    str		)   ->					str:
        return self.lang_code_to_token[lang]







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    str		)   ->					int:
        _snake_case						=							self.get_lang_token(A__		)
        return self.lang_token_to_id[lang_token]



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     sentencepiece.SentencePieceProcessor:



  """simple docstring"""






  _snake_case						=							sentencepiece.SentencePieceProcessor(**_UpperCamelCase					)
  spm.Load(str(_UpperCamelCase					)					)
  return spm



def 				snake_case_(_UpperCamelCase					)							->     Union[Dict, List]:



  """simple docstring"""






  with open(_UpperCamelCase			,	'''r'''					) as f:
    return json.load(_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     None:



  """simple docstring"""






  with open(_UpperCamelCase			,	'''w'''					) as f:
    json.dump(_UpperCamelCase			,	_UpperCamelCase			,	indent=2					)

