'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE_:					Tuple  =8





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Optional[Any]=BITS      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       x.device

     UpperCAmelCase_	   =       (x * 2_55).int().clamp(0 ,   2_55      )

     UpperCAmelCase_	   =       2 ** torch.arange(bits - 1 ,   -1 ,   -1 ,   device=snake_case_      )
     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "d -> d 1 1"      )
     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "b c h w -> b c 1 h w"      )

     UpperCAmelCase_	   =       ((x & mask) != 0).float()
     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "b c d h w -> b (c d) h w"      )
     UpperCAmelCase_	   =       bits * 2 - 1
     return bits





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			Dict=BITS      )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       x.device

     UpperCAmelCase_	   =       (x > 0).int()
     UpperCAmelCase_	   =       2 ** torch.arange(bits - 1 ,   -1 ,   -1 ,   device=snake_case_ ,   dtype=torch.intaa      )

     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "d -> d 1 1"      )
     UpperCAmelCase_	   =       rearrange(snake_case_ ,   "b (c d) h w -> b c d h w" ,   d=8      )
     UpperCAmelCase_	   =       reduce(x * mask ,   "b c d h w -> b c h w" ,   "sum"      )
     return (dec / 2_55).clamp(0.0 ,   1.0      )





def      lowerCAmelCase_    ( self	:			int ,   snake_case_	:			torch.FloatTensor ,   snake_case_	:			int ,   snake_case_	:			torch.FloatTensor ,   snake_case_	:			float = 0.0 ,   snake_case_	:			bool = True ,   snake_case_	:			List[str]=None ,   snake_case_	:			bool = True ,   )   ->				Union[DDIMSchedulerOutput, Tuple]:


     '''simple docstring'''




     if self.num_inference_steps is None:
          raise ValueError(
              "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"      )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     UpperCAmelCase_	   =       timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     UpperCAmelCase_	   =       self.alphas_cumprod[timestep]
     UpperCAmelCase_	   =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     UpperCAmelCase_	   =       1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_	   =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     UpperCAmelCase_	   =       self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_	   =       torch.clamp(snake_case_ ,   -scale ,   snake_case_      )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     UpperCAmelCase_	   =       self._get_variance(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          UpperCAmelCase_	   =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_	   =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_	   =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          UpperCAmelCase_	   =       model_output.device if torch.is_tensor(snake_case_      ) else "cpu"
          UpperCAmelCase_	   =       torch.randn(model_output.shape ,   dtype=model_output.dtype ,   generator=snake_case_      ).to(snake_case_      )
          UpperCAmelCase_	   =       self._get_variance(snake_case_ ,   snake_case_      ) ** 0.5 * eta * noise

          UpperCAmelCase_	   =       prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=snake_case_ ,   pred_original_sample=snake_case_      )





def      lowerCAmelCase_    ( self	:			Union[str, Any] ,   snake_case_	:			torch.FloatTensor ,   snake_case_	:			int ,   snake_case_	:			torch.FloatTensor ,   snake_case_	:			Dict="epsilon" ,   snake_case_	:			List[Any]=None ,   snake_case_	:			bool = True ,   )   ->				Union[DDPMSchedulerOutput, Tuple]:


     '''simple docstring'''




     UpperCAmelCase_	   =       timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          UpperCAmelCase_   ,			UpperCAmelCase_	   =       torch.split(snake_case_ ,   sample.shape[1] ,   dim=1      )
     else:
          UpperCAmelCase_	   =       None

     # 1. compute alphas, betas
     UpperCAmelCase_	   =       self.alphas_cumprod[t]
     UpperCAmelCase_	   =       self.alphas_cumprod[t - 1] if t > 0 else self.one
     UpperCAmelCase_	   =       1 - alpha_prod_t
     UpperCAmelCase_	   =       1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          UpperCAmelCase_	   =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          UpperCAmelCase_	   =       model_output
     else:
          raise ValueError(f"""Unsupported prediction_type {prediction_type}."""      )

     # 3. Clip "predicted x_0"
     UpperCAmelCase_	   =       self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_	   =       torch.clamp(snake_case_ ,   -scale ,   snake_case_      )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase_	   =       (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     UpperCAmelCase_	   =       self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase_	   =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     UpperCAmelCase_	   =       0
     if t > 0:
          UpperCAmelCase_	   =       torch.randn(
              model_output.size() ,   dtype=model_output.dtype ,   layout=model_output.layout ,   generator=snake_case_      ).to(model_output.device      )
          UpperCAmelCase_	   =       (self._get_variance(snake_case_ ,   predicted_variance=snake_case_      ) ** 0.5) * noise

     UpperCAmelCase_	   =       pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=snake_case_ ,   pred_original_sample=snake_case_      )






class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[str]       ,      __a   :   UNetaDConditionModel       ,      __a   :   Union[DDIMScheduler, DDPMScheduler]       ,      __a   :   Optional[float] = 1.0       ,      ):
         super().__init__()
         UpperCAmelCase_	   =       bit_scale
         UpperCAmelCase_	   =       (
             ddim_bit_scheduler_step if isinstance(__a       ,      __a			) else ddpm_bit_scheduler_step
         )

         self.register_modules(unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   List[Any]       ,      __a   :   Optional[int] = 256       ,      __a   :   Optional[int] = 256       ,      __a   :   Optional[int] = 50       ,      __a   :   Optional[torch.Generator] = None       ,      __a   :   Optional[int] = 1       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Union[str, Any]       ,      ):
         UpperCAmelCase_	   =       torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       decimal_to_bits(__a			) * self.bit_scale
         UpperCAmelCase_	   =       latents.to(self.device			)

         self.scheduler.set_timesteps(__a			)

         for t in self.progress_bar(self.scheduler.timesteps			):
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a			).prev_sample

         UpperCAmelCase_	   =       bits_to_decimal(__a			)

         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)





'''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




