'''simple docstring'''





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_    ( snake_case_	:			Tuple      )   ->				Any:


     '''simple docstring'''




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




class 	__A				(			nn.Module     ):



    def __init__(self   :   List[str]       ,      __a   :   Tuple			):
         super().__init__()
         UpperCAmelCase_	   =       config.output_attentions
         UpperCAmelCase_	   =       config.output_hidden_states
         UpperCAmelCase_	   =       nn.ModuleList([BertLayer(__a			) for _ in range(config.num_hidden_layers			)]			)
         UpperCAmelCase_	   =       nn.ModuleList([BertHighway(__a			) for _ in range(config.num_hidden_layers			)]			)

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



    def  _lowercase						(self   :   Any       ,      __a   :   Dict			):
         if (type(__a			) is float) or (type(__a			) is int):
              for i in range(len(self.early_exit_entropy			)			):
                   UpperCAmelCase_	   =       x
         else:
              UpperCAmelCase_	   =       x



    def  _lowercase						(self   :   str       ,      __a   :   Dict			):
         UpperCAmelCase_	   =       pooler.state_dict()
         for highway in self.highway:
              for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name]			)






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Any       ,      __a   :   Optional[int]=None       ,      __a   :   int=None       ,      __a   :   Dict=None       ,      __a   :   Any=None       ,      ):
         UpperCAmelCase_	   =       ()
         UpperCAmelCase_	   =       ()
         UpperCAmelCase_	   =       ()
         for i, layer_module in enumerate(self.layer			):
              if self.output_hidden_states:
                   UpperCAmelCase_	   =       all_hidden_states + (hidden_states,)

              UpperCAmelCase_	   =       layer_module(
                  __a       ,      __a       ,      head_mask[i]       ,      __a       ,      __a			)
              UpperCAmelCase_	   =       layer_outputs[0]

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

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

              UpperCAmelCase_	   =       self.highway[i](__a			)
              # logits, pooled_output

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

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

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

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

         UpperCAmelCase_	   =       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__     ):



    def __init__(self   :   List[str]       ,      __a   :   str			):
         super().__init__(__a			)
         UpperCAmelCase_	   =       config

         UpperCAmelCase_	   =       BertEmbeddings(__a			)
         UpperCAmelCase_	   =       DeeBertEncoder(__a			)
         UpperCAmelCase_	   =       BertPooler(__a			)

         self.init_weights()



    def  _lowercase						(self   :   Optional[int]			):
         self.encoder.init_highway_pooler(self.pooler			)



    def  _lowercase						(self   :   Optional[Any]			):
         return self.embeddings.word_embeddings



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Optional[Any]			):
         UpperCAmelCase_	   =       value



    def  _lowercase						(self   :   str       ,      __a   :   List[str]			):
         for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(__a			)






    @add_start_docstrings_to_model_forward(__a			)
    def  _lowercase						(self   :   Dict       ,      __a   :   List[str]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   int=None       ,      __a   :   Optional[Any]=None       ,      __a   :   List[str]=None       ,      __a   :   Any=None       ,      __a   :   Optional[Any]=None       ,      __a   :   List[Any]=None       ,      ):
         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:
              UpperCAmelCase_	   =       input_ids.size()
         elif inputs_embeds is not None:
              UpperCAmelCase_	   =       inputs_embeds.size()[:-1]
         else:
              raise ValueError("You have to specify either input_ids or inputs_embeds"			)

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

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

         UpperCAmelCase_	   =       encoder_extended_attention_mask.to(
             dtype=next(self.parameters()			).dtype			)  # fp16 compatibility
         UpperCAmelCase_	   =       (1.0 - encoder_extended_attention_mask) * -1_00_00.0

         # 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]
         UpperCAmelCase_	   =       self.get_head_mask(__a       ,      self.config.num_hidden_layers			)

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

         UpperCAmelCase_	   =       (
             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__     ):



    def __init__(self   :   List[str]       ,      __a   :   Optional[Any]       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       message
         UpperCAmelCase_	   =       exit_layer  # start from 1!




class 	__A				(			nn.Module     ):



    def __init__(self   :   List[Any]       ,      __a   :   List[str]			):
         super().__init__()
         UpperCAmelCase_	   =       BertPooler(__a			)
         UpperCAmelCase_	   =       nn.Dropout(config.hidden_dropout_prob			)
         UpperCAmelCase_	   =       nn.Linear(config.hidden_size       ,      config.num_labels			)






    def  _lowercase						(self   :   Dict       ,      __a   :   Optional[Any]			):
         # Pooler
         UpperCAmelCase_	   =       encoder_outputs[0]
         UpperCAmelCase_	   =       self.pooler(__a			)
         # "return" pooler_output

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

         # Dropout and classification
         UpperCAmelCase_	   =       bmodel_output[1]

         UpperCAmelCase_	   =       self.dropout(__a			)
         UpperCAmelCase_	   =       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__     ):



    def __init__(self   :   Any       ,      __a   :   Tuple			):
         super().__init__(__a			)
         UpperCAmelCase_	   =       config.num_labels
         UpperCAmelCase_	   =       config.num_hidden_layers

         UpperCAmelCase_	   =       DeeBertModel(__a			)
         UpperCAmelCase_	   =       nn.Dropout(config.hidden_dropout_prob			)
         UpperCAmelCase_	   =       nn.Linear(config.hidden_size       ,      self.config.num_labels			)

         self.init_weights()






    @add_start_docstrings_to_model_forward(__a			)
    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   str=None       ,      __a   :   Dict=None       ,      __a   :   List[Any]=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   List[str]=None       ,      __a   :   Optional[Any]=None       ,      __a   :   str=-1       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       self.num_layers
         try:
              UpperCAmelCase_	   =       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

              UpperCAmelCase_	   =       outputs[1]

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

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

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

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





'''simple docstring'''





import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_:					Tuple  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[str]  ={'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

SCREAMING_SNAKE_CASE_:					Optional[int]  ={
    'abeja/gpt-neox-japanese-2.7b': 20_48,
}





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Tuple      )   ->				Union[str, Any]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       json.loads(f.read()      )

     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
     UpperCAmelCase_	   =       [[t.rstrip("\n"      )] if (t == "," or "," not in t) else t.rstrip("\n"      ).split(","      ) for t in token]
     for idx, b in enumerate(snake_case_      ):
          UpperCAmelCase_	   =       b
          UpperCAmelCase_	   =       idx
          for wd in b:
               UpperCAmelCase_	   =       idx

     return vocab, raw_vocab, ids_to_tokens, emoji




class 	__A				(			UpperCamelCase__     ):
    a__	:						List[str]	       =  VOCAB_FILES_NAMES
    a__	:						Tuple	       =  PRETRAINED_VOCAB_FILES_MAP
    a__	:						Union[str, Any]	       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]	       =  ["""input_ids""", """attention_mask"""]



    def __init__(self   :   Any       ,      __a   :   List[Any]       ,      __a   :   Dict       ,      __a   :   int="<|endoftext|>"       ,      __a   :   Union[str, Any]="<|endoftext|>"       ,      __a   :   int="<|startoftext|>"       ,      __a   :   Tuple="<|endoftext|>"       ,      __a   :   Optional[int]=False       ,      **__a   :   int       ,      ):
         super().__init__(
             unk_token=__a       ,      pad_token=__a       ,      bos_token=__a       ,      eos_token=__a       ,      do_clean_text=__a       ,      **__a       ,      )
         if not os.path.isfile(__a			):
              raise ValueError(
                  f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                  " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
         if not os.path.isfile(__a			):
              raise ValueError(
                  f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                  " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"			)
         UpperCAmelCase_	   =       do_clean_text
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       load_vocab_and_emoji(__a       ,      __a			)
         UpperCAmelCase_	   =       SubWordJapaneseTokenizer(
             vocab=self.vocab       ,      ids_to_tokens=self.ids_to_tokens       ,      emoji=self.emoji			)



    @property
    def  _lowercase						(self   :   Optional[Any]			):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab			)



    def  _lowercase						(self   :   List[Any]			):
         return dict(self.raw_vocab       ,      **self.added_tokens_encoder			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   int			):
         return self.subword_tokenizer.tokenize(__a       ,      clean=self.do_clean_text			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   List[str]			):
         return self.vocab.get(__a       ,      self.vocab.get(self.unk_token			)			)



    def  _lowercase						(self   :   int       ,      __a   :   List[Any]			):
         return self.subword_tokenizer.convert_id_to_token(__a			)



    def  _lowercase						(self   :   Dict       ,      __a   :   str			):
         UpperCAmelCase_	   =       "".join(__a			).strip()
         return out_string



    def  _lowercase						(self   :   int       ,      __a   :   "Conversation"			):
         UpperCAmelCase_	   =       []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a       ,      add_special_tokens=__a			) + [self.eos_token_id]			)

         if len(__a			) > self.model_max_length:
              UpperCAmelCase_	   =       input_ids[-self.model_max_length :]
         return input_ids






    def  _lowercase						(self   :   int       ,      __a   :   str       ,      __a   :   Optional[str] = None			):
         UpperCAmelCase_	   =       0
         if os.path.isdir(__a			):
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]			)
         else:
              UpperCAmelCase_	   =       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
              )
              UpperCAmelCase_	   =       (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
              )
         with open(__a       ,      "w"       ,      encoding="utf-8"			) as writer:
              for token_index, token in self.ids_to_tokens.items():
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                            " Please check that the vocabulary is not corrupted!"			)
                        UpperCAmelCase_	   =       token_index
                   writer.write(",".join(__a			) + "\n"			)
                   index += 1
         with open(__a       ,      "w"       ,      encoding="utf-8"			) as writer:
              json.dump(self.emoji       ,      __a			)
         return vocab_file, emoji_file




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[Any]       ,      __a   :   Dict       ,      __a   :   Any       ,      __a   :   int			):
         UpperCAmelCase_	   =       vocab  # same as swe
         UpperCAmelCase_	   =       ids_to_tokens  # same as bpe
         UpperCAmelCase_	   =       emoji
         UpperCAmelCase_	   =       np.max([len(__a			) for w in self.vocab.keys()]			)
         UpperCAmelCase_	   =       re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"			)
         UpperCAmelCase_	   =       re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"			)
         UpperCAmelCase_	   =       re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"			)
         UpperCAmelCase_	   =       re.compile(
             r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
         UpperCAmelCase_	   =       re.compile(
             r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"			)
         UpperCAmelCase_	   =       re.compile(
             r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"			)
         UpperCAmelCase_	   =       "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
         UpperCAmelCase_	   =       "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
         UpperCAmelCase_	   =       str.maketrans({k: "<BLOCK>" for k in keisen + blocks}			)



    def __len__(self   :   Dict			):
         return len(self.ids_to_tokens			)



    def  _lowercase						(self   :   str       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.content_repattera.sub("<URL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<EMAIL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<TEL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<PRICE>"       ,      __a			)
         UpperCAmelCase_	   =       content.translate(self.content_transa			)
         while "<BLOCK><BLOCK>" in content:
              UpperCAmelCase_	   =       content.replace("<BLOCK><BLOCK>"       ,      "<BLOCK>"			)
         return content



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Union[str, Any]       ,      __a   :   str=False			):
         UpperCAmelCase_	   =       text.replace(" "       ,      "<SP>"			)
         UpperCAmelCase_	   =       text.replace("　"       ,      "<SP>"			)
         UpperCAmelCase_	   =       text.replace("\r\n"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\n"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\r"       ,      "<BR>"			)
         UpperCAmelCase_	   =       text.replace("\t"       ,      "<TAB>"			)
         UpperCAmelCase_	   =       text.replace("—"       ,      "ー"			)
         UpperCAmelCase_	   =       text.replace("−"       ,      "ー"			)
         for k, v in self.emoji["emoji"].items():
              if k in text:
                   UpperCAmelCase_	   =       text.replace(__a       ,      __a			)
         if clean:
              UpperCAmelCase_	   =       self.clean_text(__a			)

         def check_simbol(__a   :   List[Any]			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 2:
                   UpperCAmelCase_	   =       (int(e[0]			) << 8) + int(e[1]			)
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(__a   :   Tuple			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 3:
                   UpperCAmelCase_	   =       (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        return True
              return False

         UpperCAmelCase_	   =       0
         UpperCAmelCase_	   =       []
         while pos < len(__a			):
              UpperCAmelCase_	   =       min(len(__a			)       ,      pos + self.maxlen + 1			) if text[pos] == "<" else pos + 3
              UpperCAmelCase_	   =       []  # (token_id, token, pos)
              for e in range(__a       ,      __a       ,      -1			):
                   UpperCAmelCase_	   =       text[pos:e]
                   if wd in self.vocab:
                        if wd[0] == "<" and len(__a			) > 2:
                             UpperCAmelCase_	   =       [(self.vocab[wd], wd, e)]
                             break
                        else:
                             candidates.append((self.vocab[wd], wd, e)			)
              if len(__a			) > 0:
                   # the smallest token_id is adopted
                   UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       sorted(__a       ,      key=lambda __a			: x[0]			)[0]
                   result.append(__a			)
                   UpperCAmelCase_	   =       e
              else:
                   UpperCAmelCase_	   =       pos + 1
                   UpperCAmelCase_	   =       text[pos:end]
                   if check_simbol(__a			):
                        result.append("<KIGOU>"			)
                   elif checkuae(__a			):
                        result.append("<U2000U2BFF>"			)
                   else:
                        for i in wd.encode("utf-8"			):
                             result.append("<|byte%d|>" % i			)
                   UpperCAmelCase_	   =       end
         return result






    def  _lowercase						(self   :   int       ,      __a   :   Optional[Any]       ,      __a   :   Optional[int]="\n"			):
         UpperCAmelCase_	   =       []
         UpperCAmelCase_	   =       []
         UpperCAmelCase_	   =       self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
              byte_tokens.append(int(word[6:-2]			)			)
         else:
              if len(__a			) > 0:
                   words.append(bytearray(__a			).decode("utf-8"       ,      errors="replace"			)			)
                   UpperCAmelCase_	   =       []
              if word[:7] == "<|emoji" and word[-2:] == "|>":
                   words.append(self.emoji["emoji_inv"][word]			)
              elif word == "<SP>":
                   words.append(" "			)
              elif word == "<BR>":
                   words.append(__a			)
              elif word == "<TAB>":
                   words.append("\t"			)
              elif word == "<BLOCK>":
                   words.append("▀"			)
              elif word == "<KIGOU>":
                   words.append("ǀ"			)
              elif word == "<U2000U2BFF>":
                   words.append("‖"			)
              else:
                   words.append(__a			)
         if len(__a			) > 0:
              words.append(bytearray(__a			).decode("utf-8"       ,      errors="replace"			)			)
         UpperCAmelCase_	   =       "".join(__a			)
         return text




