





'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a   	=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
a   	=    "cuda" if torch.cuda.is_available() else "cpu"

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							text.split(__UpperCAmelCase   )
   return [character.join(text[i : i + n]   ).strip() for i in range(0				,						len(__UpperCAmelCase   )				,						__UpperCAmelCase   )]

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   for title, text in zip(documents["""title"""]				,						documents["""text"""]   ):
      if text is not None:
         for passage in split_text(__UpperCAmelCase   ):
            titles.append(title if title is not None else """"""   )
            texts.append(__UpperCAmelCase   )
   return {"title": titles, "text": texts}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ctx_tokenizer(
       documents["""title"""]				,						documents["""text"""]				,						truncation=__UpperCAmelCase				,						padding="""longest"""				,						return_tensors="""pt"""   )["""input_ids"""]
   __SCREAMING_SNAKE_CASE   				=							ctx_encoder(input_ids.to(device=__UpperCAmelCase   )				,						return_dict=__UpperCAmelCase   ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''simple docstring'''







   logger.info("""Step 1 - Create the dataset"""   )
   ######################################

   # The dataset needed for RAG must have three columns:
   # - title (string): title of the document
   # - text (string): text of a passage of the document
   # - embeddings (array of dimension d): DPR representation of the passage
   # Let's say you have documents in tab-separated csv files with columns "title" and "text"
   assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

   # You can load a Dataset object this way
   __SCREAMING_SNAKE_CASE   				=							load_dataset(
       """csv"""				,						data_files=[rag_example_args.csv_path]				,						split="""train"""				,						delimiter="""\t"""				,						column_names=["""title""", """text"""]   )

   # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

   # Then split the documents into passages of 100 words
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

   # And compute the embeddings
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
   __SCREAMING_SNAKE_CASE   				=							Features(
       {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
   __SCREAMING_SNAKE_CASE   				=							dataset.map(
       partial(__UpperCAmelCase				,						ctx_encoder=__UpperCAmelCase				,						ctx_tokenizer=__UpperCAmelCase   )				,						batched=__UpperCAmelCase				,						batch_size=processing_args.batch_size				,						features=__UpperCAmelCase				,						)

   # And finally save your dataset
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset"""   )
   dataset.save_to_disk(__UpperCAmelCase   )
   # from datasets import load_from_disk
   # dataset = load_from_disk(passages_path)  # to reload the dataset

   ######################################
   logger.info("""Step 2 - Index the dataset"""   )
   ######################################

   # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
   __SCREAMING_SNAKE_CASE   				=							faiss.IndexHNSWFlat(index_hnsw_args.d				,						index_hnsw_args.m				,						faiss.METRIC_INNER_PRODUCT   )
   dataset.add_faiss_index("""embeddings"""				,						custom_index=__UpperCAmelCase   )

   # And save the index
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset_hnsw_index.faiss"""   )
   dataset.get_index("""embeddings"""   ).save(__UpperCAmelCase   )
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      ), metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/dpr-ctx_encoder-multiset-base', metadata={
           'help': (
               'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
               ' \'facebook/dpr-ctx_encoder-multiset-base\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb'      ), metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}, )







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The number of bi-directional links created for every new element during the HNSW index construction.'
           )
       }, )


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    a   	=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    a	, a	, a   	=    parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        a   	=    rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)












'''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


a   	=    logging.get_logger(__name__)

a   	=    {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

a   	=    {
    "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",
    },
}

a   	=    {
    "abeja/gpt-neox-japanese-2.7b": 2048,
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







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

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

   return vocab, raw_vocab, ids_to_tokens, emoji







class __a			( _snake_case      ):
   __UpperCamelCase :			Tuple						   =						VOCAB_FILES_NAMES
   __UpperCamelCase :			Tuple						   =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase :			Any						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase :			Union[str, Any]						   =						['input_ids', 'attention_mask']





   def __init__(       self					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      str="<|endoftext|>"					,lowerCamelCase					:      List[Any]="<|endoftext|>"					,lowerCamelCase					:      str="<|startoftext|>"					,lowerCamelCase					:      Dict="<|endoftext|>"					,lowerCamelCase					:      int=False					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      super().__init__(
          unk_token=lowerCamelCase					,pad_token=lowerCamelCase					,bos_token=lowerCamelCase					,eos_token=lowerCamelCase					,do_clean_text=lowerCamelCase					,**lowerCamelCase					,)
      if not os.path.isfile(lowerCamelCase     ):
         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(lowerCamelCase     ):
         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)`"""     )
      __SCREAMING_SNAKE_CASE   				=							do_clean_text
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							load_vocab_and_emoji(lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							SubWordJapaneseTokenizer(
          vocab=self.vocab					,ids_to_tokens=self.ids_to_tokens					,emoji=self.emoji     )





   @property
   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.raw_vocab     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      return self.subword_tokenizer.tokenize(lowerCamelCase					,clean=self.do_clean_text     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      return self.vocab.get(lowerCamelCase					,self.vocab.get(self.unk_token     )     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      return self.subword_tokenizer.convert_id_to_token(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""""".join(lowerCamelCase     ).strip()
      return out_string





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      "Conversation"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCamelCase					,add_special_tokens=lowerCamelCase     ) + [self.eos_token_id]     )

      if len(lowerCamelCase     ) > self.model_max_length:
         __SCREAMING_SNAKE_CASE   				=							input_ids[-self.model_max_length :]
      return input_ids





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[str] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0
      if os.path.isdir(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							os.path.join(
             lowerCamelCase					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(
             lowerCamelCase					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]     )
      else:
         __SCREAMING_SNAKE_CASE   				=							(
             (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
         )
         __SCREAMING_SNAKE_CASE   				=							(
             (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
         )
      with open(lowerCamelCase					,"""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!"""     )
               __SCREAMING_SNAKE_CASE   				=							token_index
            writer.write(""",""".join(lowerCamelCase     ) + """\n"""     )
            index += 1
      with open(lowerCamelCase					,"""w"""					,encoding="""utf-8"""     ) as writer:
         json.dump(self.emoji					,lowerCamelCase     )
      return vocab_file, emoji_file







class __a			( _snake_case      ):





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

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





   def __len__(       self					:      List[str]     ):

      '''simple docstring'''

      return len(self.ids_to_tokens     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<URL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<EMAIL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<TEL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<DATE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<DATE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<PRICE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							content.translate(self.content_transa     )
      while "<BLOCK><BLOCK>" in content:
         __SCREAMING_SNAKE_CASE   				=							content.replace("""<BLOCK><BLOCK>"""					,"""<BLOCK>"""     )
      return content





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							text.replace(""" """					,"""<SP>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""　"""					,"""<SP>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\r\n"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\n"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\r"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\t"""					,"""<TAB>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""—"""					,"""ー"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""−"""					,"""ー"""     )
      for k, v in self.emoji["emoji"].items():
         if k in text:
            __SCREAMING_SNAKE_CASE   				=							text.replace(lowerCamelCase					,lowerCamelCase     )
      if clean:
         __SCREAMING_SNAKE_CASE   				=							self.clean_text(lowerCamelCase     )

      def check_simbol(lowerCamelCase					:      List[str]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 2:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 8) + int(e[1]     )
            if (
                (c >= 0xC_2A1 and c <= 0xC_2BF)
                or (c >= 0xC_780 and c <= 0xC_783)
                or (c >= 0xC_AB9 and c <= 0xC_BBF)
                or (c >= 0xC_C80 and c <= 0xC_DA2)
            ):
               return True
         return False

      def checkuae(lowerCamelCase					:      Union[str, Any]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 3:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
            if c >= 0xE28_080 and c <= 0xE2B_07F:
               return True
         return False

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]="\n"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							self.ids_to_tokens[index][0]
      if word[:6] == "<|byte" and word[-2:] == "|>":
         byte_tokens.append(int(word[6:-2]     )     )
      else:
         if len(lowerCamelCase     ) > 0:
            words.append(bytearray(lowerCamelCase     ).decode("""utf-8"""					,errors="""replace"""     )     )
            __SCREAMING_SNAKE_CASE   				=							[]
         if word[:7] == "<|emoji" and word[-2:] == "|>":
            words.append(self.emoji["""emoji_inv"""][word]     )
         elif word == "<SP>":
            words.append(""" """     )
         elif word == "<BR>":
            words.append(lowerCamelCase     )
         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(lowerCamelCase     )
      if len(lowerCamelCase     ) > 0:
         words.append(bytearray(lowerCamelCase     ).decode("""utf-8"""					,errors="""replace"""     )     )
      __SCREAMING_SNAKE_CASE   				=							"""""".join(lowerCamelCase     )
      return text





