






'''simple docstring'''

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
     raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)

UpperCAmelCase__						:List[str]          =			"""Hello world! cécé herlolip"""
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Any:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				FairseqRobertaModel.from_pretrained(_lowercase     )
     roberta.eval()  # disable dropout
     __lowerCamelCase						:       Dict				   =				roberta.model.encoder.sentence_encoder
     __lowerCamelCase						:       str				   =				XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,						hidden_size=roberta.cfg.model.encoder_embed_dim,						num_hidden_layers=roberta.cfg.model.encoder_layers,						num_attention_heads=roberta.cfg.model.encoder_attention_heads,						intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim,						max_position_embeddings=514,						type_vocab_size=1,						layer_norm_eps=1e-5,						)
     if classification_head:
          __lowerCamelCase						:       List[str]				   =				roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our RoBERTa config:""",						_lowercase     )

     __lowerCamelCase						:       Optional[Any]				   =				XLMRobertaXLForSequenceClassification(_lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(_lowercase     )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __lowerCamelCase						:       List[Any]				   =				roberta_sent_encoder.embed_tokens.weight
     __lowerCamelCase						:       List[str]				   =				roberta_sent_encoder.embed_positions.weight
     __lowerCamelCase						:       Optional[Any]				   =				torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

     __lowerCamelCase						:       Optional[Any]				   =				roberta_sent_encoder.layer_norm.weight
     __lowerCamelCase						:       Optional[Any]				   =				roberta_sent_encoder.layer_norm.bias

     for i in range(config.num_hidden_layers     ):
          # Encoder: start of layer
          __lowerCamelCase						:       BertLayer				   =				model.roberta.encoder.layer[i]
          __lowerCamelCase						:       TransformerSentenceEncoderLayer				   =				roberta_sent_encoder.layers[i]

          __lowerCamelCase						:       RobertaAttention				   =				layer.attention
          __lowerCamelCase						:       Optional[int]				   =				roberta_layer.self_attn_layer_norm.weight
          __lowerCamelCase						:       List[str]				   =				roberta_layer.self_attn_layer_norm.bias

          # self attention
          __lowerCamelCase						:       BertSelfAttention				   =				layer.attention.self
          assert (
              roberta_layer.self_attn.k_proj.weight.data.shape
              == roberta_layer.self_attn.q_proj.weight.data.shape
              == roberta_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)     )
          )

          __lowerCamelCase						:       str				   =				roberta_layer.self_attn.q_proj.weight
          __lowerCamelCase						:       Tuple				   =				roberta_layer.self_attn.q_proj.bias
          __lowerCamelCase						:       Dict				   =				roberta_layer.self_attn.k_proj.weight
          __lowerCamelCase						:       int				   =				roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase						:       List[Any]				   =				roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase						:       Optional[Any]				   =				roberta_layer.self_attn.v_proj.bias

          # self-attention output
          __lowerCamelCase						:       BertSelfOutput				   =				layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          __lowerCamelCase						:       str				   =				roberta_layer.self_attn.out_proj.weight
          __lowerCamelCase						:       Tuple				   =				roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          __lowerCamelCase						:       Any				   =				roberta_layer.final_layer_norm.weight
          __lowerCamelCase						:       Union[str, Any]				   =				roberta_layer.final_layer_norm.bias

          # intermediate
          __lowerCamelCase						:       BertIntermediate				   =				layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase						:       List[Any]				   =				roberta_layer.fca.weight
          __lowerCamelCase						:       Optional[int]				   =				roberta_layer.fca.bias

          # output
          __lowerCamelCase						:       BertOutput				   =				layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase						:       Union[str, Any]				   =				roberta_layer.fca.weight
          __lowerCamelCase						:       int				   =				roberta_layer.fca.bias
          # end of layer

     if classification_head:
          __lowerCamelCase						:       Optional[int]				   =				roberta.model.classification_heads["""mnli"""].dense.weight
          __lowerCamelCase						:       Any				   =				roberta.model.classification_heads["""mnli"""].dense.bias
          __lowerCamelCase						:       Optional[Any]				   =				roberta.model.classification_heads["""mnli"""].out_proj.weight
          __lowerCamelCase						:       Optional[Any]				   =				roberta.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __lowerCamelCase						:       Union[str, Any]				   =				roberta.model.encoder.lm_head.dense.weight
          __lowerCamelCase						:       Tuple				   =				roberta.model.encoder.lm_head.dense.bias
          __lowerCamelCase						:       Optional[int]				   =				roberta.model.encoder.lm_head.layer_norm.weight
          __lowerCamelCase						:       str				   =				roberta.model.encoder.lm_head.layer_norm.bias
          __lowerCamelCase						:       Optional[int]				   =				roberta.model.encoder.lm_head.weight
          __lowerCamelCase						:       int				   =				roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __lowerCamelCase						:       torch.Tensor				   =				roberta.encode(_lowercase     ).unsqueeze(0     )  # batch of size 1

     __lowerCamelCase						:       List[str]				   =				model(_lowercase     )[0]
     if classification_head:
          __lowerCamelCase						:       int				   =				roberta.model.classification_heads["""mnli"""](roberta.extract_features(_lowercase     )     )
     else:
          __lowerCamelCase						:       List[str]				   =				roberta.model(_lowercase     )[0]
     print(our_output.shape,						their_output.shape     )
     __lowerCamelCase						:       Optional[int]				   =				torch.max(torch.abs(our_output - their_output     )     ).item()
     print(f"max_absolute_diff = {max_absolute_diff}"     )  # ~ 1e-7
     __lowerCamelCase						:       Dict				   =				torch.allclose(_lowercase,						_lowercase,						atol=1e-3     )
     print("""Do both models output the same tensors?""",						"""🔥""" if success else """💩"""     )
     if not success:
          raise Exception("""Something went wRoNg"""     )

     pathlib.Path(_lowercase     ).mkdir(parents=_lowercase,						exist_ok=_lowercase     )
     print(f"Saving model to {pytorch_dump_folder_path}"     )
     model.save_pretrained(_lowercase     )


if __name__ == "__main__":
     UpperCAmelCase__						:int          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
     )
     UpperCAmelCase__						:List[Any]          =			parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )











'''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


UpperCAmelCase__						:Union[str, Any]          =			logging.get_logger(__name__)

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

UpperCAmelCase__						:List[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""",
    },
}

UpperCAmelCase__						:Optional[Any]          =			{
    """abeja/gpt-neox-japanese-2.7b""": 2_048,
}
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Dict				   =				json.loads(f.read()     )

     __lowerCamelCase						:       Dict				   =				collections.OrderedDict()
     __lowerCamelCase						:       int				   =				collections.OrderedDict()
     __lowerCamelCase						:       List[Any]				   =				collections.OrderedDict()
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Union[str, Any]				   =				f.readlines()
     __lowerCamelCase						:       Any				   =				[[t.rstrip("""\n"""     )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""     ).split(""","""     ) for t in token]
     for idx, b in enumerate(_lowercase     ):
          __lowerCamelCase						:       str				   =				b
          __lowerCamelCase						:       List[str]				   =				idx
          for wd in b:
               __lowerCamelCase						:       Optional[Any]				   =				idx

     return vocab, raw_vocab, ids_to_tokens, emoji







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Optional[Any]   		=			VOCAB_FILES_NAMES
       snake_case__    :     List[str]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Union[str, Any]   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Union[str, Any]   		=			['input_ids', 'attention_mask']




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


            """simple docstring"""







            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)`"""					)
            __lowerCamelCase						:       Tuple				   =				do_clean_text
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				load_vocab_and_emoji(A__	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				SubWordJapaneseTokenizer(
                vocab=self.vocab	,    ids_to_tokens=self.ids_to_tokens	,    emoji=self.emoji					)




       @property
       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            return len(self.raw_vocab					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : List[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[Any]	,    A__      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            return self.subword_tokenizer.convert_id_to_token(A__					)




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""""".join(A__					).strip()
            return out_string




       def 					a_					(					self      : str	,    A__      : "Conversation"					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				[]
            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:
                 __lowerCamelCase						:       str				   =				input_ids[-self.model_max_length :]
            return input_ids




       def 					a_					(					self      : str	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				0
            if os.path.isdir(A__					):
                 __lowerCamelCase						:       Union[str, Any]				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
                 __lowerCamelCase						:       int				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]					)
            else:
                 __lowerCamelCase						:       str				   =				(
                     (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
                 )
                 __lowerCamelCase						:       int				   =				(
                     (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!"""					)
                           __lowerCamelCase						:       Union[str, Any]				   =				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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Union[str, Any]	,    A__      : Any	,    A__      : str	,    A__      : Optional[Any]					):


            """simple docstring"""







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




       def __len__(					self      : Tuple					):


            """simple docstring"""







            return len(self.ids_to_tokens					)




       def 					a_					(					self      : Dict	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<URL>"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<EMAIL>"""	,    A__					)
            __lowerCamelCase						:       str				   =				self.content_repattera.sub("""<TEL>"""	,    A__					)
            __lowerCamelCase						:       Optional[int]				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       List[str]				   =				self.content_repattera.sub("""<PRICE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				content.translate(self.content_transa					)
            while "<BLOCK><BLOCK>" in content:
                 __lowerCamelCase						:       Dict				   =				content.replace("""<BLOCK><BLOCK>"""	,    """<BLOCK>"""					)
            return content




       def 					a_					(					self      : List[Any]	,    A__      : Dict	,    A__      : Optional[Any]=False					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				text.replace(""" """	,    """<SP>"""					)
            __lowerCamelCase						:       Optional[int]				   =				text.replace("""　"""	,    """<SP>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r"""	,    """<BR>"""					)
            __lowerCamelCase						:       List[str]				   =				text.replace("""\t"""	,    """<TAB>"""					)
            __lowerCamelCase						:       List[Any]				   =				text.replace("""—"""	,    """ー"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""−"""	,    """ー"""					)
            for k, v in self.emoji["emoji"].items():
                 if k in text:
                      __lowerCamelCase						:       Dict				   =				text.replace(A__	,    A__					)
            if clean:
                 __lowerCamelCase						:       Optional[int]				   =				self.clean_text(A__					)

            def check_simbol(A__      : Union[str, Any]					):
                 __lowerCamelCase						:       str				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 2:
                      __lowerCamelCase						:       Optional[int]				   =				(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					):
                 __lowerCamelCase						:       List[Any]				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 3:
                      __lowerCamelCase						:       List[Any]				   =				(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

            __lowerCamelCase						:       List[Any]				   =				0
            __lowerCamelCase						:       Optional[Any]				   =				[]
            while pos < len(A__					):
                 __lowerCamelCase						:       Tuple				   =				min(len(A__					)	,    pos + self.maxlen + 1					) if text[pos] == """<""" else pos + 3
                 __lowerCamelCase						:       Optional[int]				   =				[]  # (token_id, token, pos)
                 for e in range(A__	,    A__	,    -1					):
                      __lowerCamelCase						:       Dict				   =				text[pos:e]
                      if wd in self.vocab:
                           if wd[0] == "<" and len(A__					) > 2:
                                __lowerCamelCase						:       Any				   =				[(self.vocab[wd], wd, e)]
                                break
                           else:
                                candidates.append((self.vocab[wd], wd, e)					)
                 if len(A__					) > 0:
                      # the smallest token_id is adopted
                      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				sorted(A__	,    key=lambda A__					: x[0]					)[0]
                      result.append(A__					)
                      __lowerCamelCase						:       Optional[Any]				   =				e
                 else:
                      __lowerCamelCase						:       Any				   =				pos + 1
                      __lowerCamelCase						:       Any				   =				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					)
                      __lowerCamelCase						:       Optional[int]				   =				end
            return result




       def 					a_					(					self      : Optional[Any]	,    A__      : Optional[int]	,    A__      : Any="\n"					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				[]
            __lowerCamelCase						:       List[Any]				   =				[]
            __lowerCamelCase						:       Optional[int]				   =				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"""					)					)
                      __lowerCamelCase						:       Tuple				   =				[]
                 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"""					)					)
            __lowerCamelCase						:       str				   =				"""""".join(A__					)
            return text



